System

Abstract

An object of a system according to an embodiment is to easily diagnose a growth condition and a disease of a plant and provide appropriate advice.SOLUTION: A system includes a reception unit, an analysis unit, an advice unit, and a registration unit. The reception unit inputs an image of a plant. The analysis unit analyzes the image input by the reception unit and diagnoses a growth condition and the presence or absence of a disease. The advice unit provides advice on watering, additional fertilization, replanting, and medication based on the diagnosis result obtained by the analysis unit. The registration unit registers season and weather information of an area and a growth environment of a home.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that diagnosing plant growth conditions and diseases requires specialized knowledge, making it difficult for general users.

[0005] The system according to the embodiment aims to easily diagnose the growth status and diseases of plants and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an advice unit, and a registration unit. The reception unit inputs an image of a plant. The analysis unit analyzes the image input by the reception unit and diagnoses the plant's growth status and the presence or absence of disease. The advice unit provides advice on watering, fertilizing, repotting, and medication based on the diagnosis results obtained by the analysis unit. The registration unit registers local seasonal and weather information, and the home growth environment. [Effects of the Invention]

[0007] The system according to the embodiment can easily diagnose the growth status and diseases of plants and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A plant diagnostic system according to an embodiment of the present invention uses images of plants to diagnose their growth status and disease and provide appropriate advice. In this system, a user takes an image of a plant and inputs it into the system. A generating AI analyzes the image and diagnoses the plant's growth status and the presence or absence of disease. Based on the diagnosis results, the system provides advice on watering, fertilizing, repotting, and medication. The system also registers patterns based on local seasonal and weather information and the home's growing environment, providing optimal advice. Furthermore, if disease is suspected, the system suggests the dosage and frequency of commercially available fertilizers and chemicals, and modifies the prescription based on follow-up images. For example, in a plant diagnostic system, a user uploads an image taken with a smartphone to the system. The image is then input into the generating AI, which then analyzes the input image. The generating AI analyzes the plant's flower color, leaf color, size, and other factors to diagnose the plant's growth status and the presence or absence of disease. For example, if the leaves turn yellow, it may diagnose possible nutrient deficiency or disease. Based on the diagnosis results, the system provides advice on watering, fertilizing, repotting, and medication. For example, if the leaves turn yellow, it may recommend adding a specific fertilizer. The system also registers patterns based on local seasons, weather information, and the home's growing environment, and provides optimal advice based on those patterns. For example, if local temperatures are forecast to be high, the system will advise users to increase the frequency of watering. Furthermore, if disease is suspected, the system will suggest the combination and frequency of commercially available fertilizers and chemicals. For example, if a specific disease is suspected, the system will suggest a chemical that is effective against that disease. The system also modifies prescriptions based on progress images. For example, it re-analyzes images of the plant after treatment and modifies the prescription as necessary. This allows the plant diagnosis system to efficiently diagnose plant growth conditions and diseases and provide appropriate advice. This allows the plant diagnosis system to easily manage plant health, even without specialized knowledge. For example, the system can quickly and accurately diagnose plant growth conditions and diseases and provide appropriate advice, thereby maintaining plant health. Users can also receive optimal advice based on local seasons, weather information, and the home's growing environment.Furthermore, if disease is suspected, the system will suggest the appropriate combination and frequency of commercially available fertilizers and chemicals, and the prescription can be revised based on follow-up images, enabling more effective treatment.

[0029] A plant diagnostic system according to an embodiment includes a reception unit, an analysis unit, an advice unit, and a registration unit. The reception unit inputs an image of a plant. The image of a plant may include, but is not limited to, flower color, leaf color, and size. For example, the reception unit uploads an image taken by a user with a smartphone to the system. The reception unit can also input images taken with a digital camera. The reception unit can automatically recognize the format of the image and pass it to the analysis unit. For example, the reception unit can accept images in JPEG, PNG, TIFF, and other formats. The analysis unit uses a generation AI to analyze the image input by the reception unit and diagnose the plant's growth status and the presence or absence of disease. The analysis unit analyzes, for example, the plant's flower color, leaf color, and size. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the plant image and diagnose the plant's growth status and the presence or absence of disease. The analysis unit can also analyze the plant image using a multimodal generation AI. For example, if the color of a plant's flowers turns yellow, the generation AI will diagnose the possibility of nutrient deficiency or disease. The analysis unit can also use the generation AI to diagnose the possibility of a specific disease if the color of the plant's leaves changes. The advice unit provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results obtained by the analysis unit. For example, if the leaves turn yellow, the advice unit will advise adding a specific fertilizer. The advice unit can also register patterns based on local seasons, weather information, and the home's growing environment, and provide optimal advice based on these. For example, if high temperatures are forecast in the area, the advice unit will advise increasing the frequency of watering. Furthermore, if disease is suspected, the advice unit will suggest the combination and frequency of commercially available fertilizers and chemicals. For example, if a specific disease is suspected, the advice unit will suggest a chemical that is effective against that disease. The advice unit can also modify prescriptions based on follow-up images. For example, it re-analyzes images of the plant after treatment and modifies the prescription as necessary. The registration unit registers patterns based on local seasons, weather information, and the home's growing environment. The registration unit registers meteorological information such as local temperature, precipitation, and sunshine hours.The registration unit can also register the temperature, humidity, light intensity, etc. of the home growing environment (e.g., a garden, a balcony, etc.). This allows the plant diagnosis system according to the embodiment to efficiently diagnose the growth condition and disease of the plant and provide appropriate advice. For example, by quickly and accurately diagnosing the growth condition and disease of the plant and providing appropriate advice, the health of the plant can be maintained. The user can also receive optimal advice based on local seasons, weather information, and the home growing environment. Furthermore, if a disease is suspected, the system can suggest the combination and frequency of commercially available fertilizers and chemicals, and modify the prescription based on follow-up images, enabling more effective treatment.

[0030] The analysis unit can analyze the flower color, leaf color, and size of a plant to diagnose its growth status and the presence or absence of disease. The analysis unit, for example, analyzes the flower color of a plant. For example, the analysis unit analyzes the RGB values ​​of the flower color to detect color changes. The analysis unit can also analyze the leaf color. For example, the analysis unit analyzes the RGB values ​​of the leaf color to detect color changes. The analysis unit can also analyze the size of a plant. For example, the analysis unit measures the length and width of leaves and the diameter of flowers to evaluate the growth progress. This enables accurate diagnosis by analyzing detailed plant characteristics. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs an image of a plant into a generation AI, which analyzes the flower color, leaf color, and size to diagnose its growth status and the presence or absence of disease.

[0031] The advice unit can provide advice on watering, fertilizing, repotting, and medication based on the diagnosis results. The advice unit provides, for example, watering advice based on the diagnosis results. For example, if the leaves of a plant are turning yellow, the advice unit advises adding a specific fertilizer. The advice unit can also provide advice on fertilizing. For example, if the plant's growth is slowing, the advice unit advises adding a specific fertilizer. The advice unit can also provide advice on repotting. For example, if the roots of a plant are crowded, the advice unit advises repotting. The advice unit can also provide advice on medication. For example, if the advice unit is suspected of having a disease, the advice unit advises using a specific medicine. This makes it easier to manage the health of plants by providing specific advice based on the diagnosis results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can generate advice based on the diagnosis results using the generation AI and provide it to the user.

[0032] The registration unit can register local seasons and weather information, and the home's growing environment. The registration unit, for example, registers local seasons and weather information. For example, the registration unit registers weather information such as the local temperature, precipitation, and sunshine hours. The registration unit can also register the home's growing environment. For example, the registration unit registers the temperature, humidity, and light intensity of the garden or balcony. This makes it possible to provide advice that takes into account local environmental information. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input local weather information into the generation AI, which then analyzes and registers the weather information.

[0033] The advice unit can suggest the formulation and frequency of application of commercially available fertilizers or chemicals when a disease is suspected. For example, when a disease is suspected, the advice unit can suggest the formulation and frequency of application of commercially available fertilizers. For example, when a specific disease is suspected, the advice unit can suggest a fertilizer that is effective against that disease. The advice unit can also suggest the formulation and frequency of application of commercially available chemicals. For example, when a specific disease is suspected, the advice unit can suggest a chemical that is effective against that disease. This makes it possible to maintain the health of the plant by suggesting appropriate measures when a disease is suspected. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can suggest the formulation and frequency of application of fertilizers or chemicals based on the disease diagnosis results of the generation AI.

[0034] The advice unit can modify the treatment method based on the follow-up images. The advice unit modifies the treatment method based on the follow-up images, for example. For example, the advice unit re-analyzes the images of the plant after treatment and modifies the prescription as necessary. The advice unit can also evaluate the progress of the treatment based on the follow-up images. For example, the advice unit analyzes the images of the plant after treatment and evaluates the effectiveness of the treatment. This enables more effective treatment by modifying the prescription based on the follow-up. Some or all of the above-mentioned prescription in the advice unit may be performed using, or without, the generation AI, for example. For example, the advice unit inputs the follow-up images into the generation AI, which evaluates the progress of the treatment and modifies the prescription as necessary.

[0035] The reception unit can analyze the user's past image submission history and select an appropriate reception method. The reception unit can, for example, suggest an optimal reception method based on the types of images the user has frequently submitted in the past. For example, the reception unit can analyze the user's past submission history and select an optimal reception method for a specific time period. The reception unit can also customize the reception method by referring to the user's past feedback. For example, the reception unit can suggest an optimal reception method based on the user's past feedback. This improves user convenience by providing an optimal reception method based on the past history. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past image submission history into the generation AI, which can select the optimal reception method.

[0036] When receiving an image, the reception unit can perform filtering based on the user's current plant type and condition. The reception unit, for example, automatically recognizes the plant type in the image submitted by the user and filters related information. For example, the reception unit analyzes the plant type and provides related information. The reception unit can also analyze the condition (health, illness, etc.) of the plant in the image submitted by the user and perform appropriate filtering. For example, the reception unit analyzes the condition of the plant and provides related information. The reception unit can also perform filtering based on the growth stage of the plant in the image submitted by the user. For example, the reception unit analyzes the growth stage of the plant and provides related information. This makes it possible to provide appropriate information according to the plant type and condition. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the plant type and condition in the image submitted by the user to the generation AI, which can then perform appropriate filtering.

[0037] When receiving an image, the reception unit can select an appropriate reception means depending on the user's input method. For example, when a user describes the condition of a plant by voice, the reception unit prioritizes receiving the voice input. For example, the reception unit records the user's voice and converts it into text data using voice recognition technology. Furthermore, when a user provides detailed information in text, the reception unit can also prioritize receiving the text input. For example, the reception unit analyzes the user's text input and selects an appropriate reception means. Furthermore, when a user uploads an image, the reception unit can prioritize receiving the image input. For example, the reception unit analyzes the image uploaded by the user and selects an appropriate reception means. This enables flexible response depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's voice data, text data, and image data into a generation AI, which can then select an appropriate reception means.

[0038] When receiving images, the reception unit can prioritize receiving images that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving images related to plants in that area. For example, the reception unit provides information related to plants in the area based on the user's geographical location information. The reception unit can also prioritize receiving images related to weather conditions in the area based on the user's location information. For example, the reception unit provides information related to weather conditions in the area based on the user's location information. The reception unit can also prioritize receiving images related to plant disease information in the area based on the user's location information. For example, the reception unit provides information related to plant disease information in the area based on the user's location information. This enables appropriate information to be provided taking geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which then prioritizes receiving images that are highly relevant.

[0039] When receiving an image, the reception unit can analyze the user's online activity and receive related images. The reception unit, for example, automatically receives images of plants posted by the user on social media. For example, the reception unit analyzes the user's social media activity and preferentially receives images of related plants. The reception unit can also receive images of related plants by referring to posts by the user's friends on social media. For example, the reception unit analyzes posts by the user's friends on social media and provides related information. This enables flexible responses that take social media activity into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI, which then receives related images.

[0040] When receiving an image, the reception unit can adjust the reception method based on the user's past feedback. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit simplifies the reception procedure by referring to the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's past feedback. For example, the reception unit suggests an optimal reception method based on the user's past feedback. This enables customization based on past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which can select the optimal reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the plant. For example, in the case of an important plant, the analysis unit performs a detailed analysis and provides detailed information. For example, the analysis unit adjusts the level of detail of the analysis based on the type and growth stage of the plant. The analysis unit can also perform a basic analysis of a common plant and provide necessary information. For example, the analysis unit adjusts the level of detail of the analysis based on the health status of the plant. The analysis unit can also perform a detailed analysis of a plant of particular interest to the user and provide additional information. For example, the analysis unit adjusts the level of detail of the analysis based on the user's past analysis results. This enables detailed analysis according to the importance of the plant. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input plant priority data into the generation AI, which can then adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the type of plant. For example, the analysis unit applies a color analysis algorithm to plants with distinctive flower colors. For example, the analysis unit analyzes the color of the plant's flowers and detects color changes. The analysis unit can also apply a shape analysis algorithm to plants with distinctive leaf shapes. For example, the analysis unit analyzes the shape of the plant's leaves and detects changes in shape. The analysis unit can also apply a size analysis algorithm to plants for which size is important. For example, the analysis unit analyzes the size of the plant and evaluates the progress of growth. This enables appropriate analysis depending on the type of plant. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input plant type data into the generation AI, which then applies an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and learns specific patterns. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. This makes it possible to improve accuracy based on past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can set analysis priorities based on the time the plant was photographed. For example, the analysis unit prioritizes analysis of plants in their growing season. For example, the analysis unit prioritizes analysis of plants in their growing season based on the time the plant was photographed. The analysis unit can also quickly analyze plants suspected of being sick. For example, the analysis unit prioritizes analysis of plants suspected of being sick based on the time the plant was photographed. The analysis unit can also prioritize analysis of plants in which the user is particularly interested. For example, the analysis unit prioritizes analysis of plants in which the user is particularly interested based on the time the plant was photographed. This enables prioritization according to the time the plant was photographed. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input plant photography time data to the generation AI, which can then set the analysis priorities.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the plants. The analysis unit, for example, analyzes plants of the same species together to efficiently provide results. For example, the analysis unit analyzes plants of the same species together based on the relevance of the plants. The analysis unit can also prioritize analyzing plants suspected of being sick and provide results quickly. For example, the analysis unit prioritizes analyzing plants suspected of being sick based on the relevance of the plants. The analysis unit can also prioritize analyzing plants in which the user is particularly interested and provide detailed results. For example, the analysis unit prioritizes analyzing plants in which the user is particularly interested based on the relevance of the plants. This enables efficient analysis based on relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input plant relevance data to the generation AI, which can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's expertise. For example, if the user is a beginner, the analysis unit provides analysis results in simple language, avoiding technical terminology. For example, the analysis unit adjusts the use of technical terminology based on the user's expertise level. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terminology. For example, the analysis unit uses appropriate technical terminology based on the user's expertise level. Furthermore, if the user is an expert, the analysis unit can provide analysis results using detailed technical terminology. For example, the analysis unit uses detailed technical terminology based on the user's expertise level. This makes it possible to provide appropriate analysis results according to the user's expertise level. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's expertise level data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0047] When providing advice, the advice unit can adjust the level of detail of the advice based on the priority of the plant. For example, the advice unit provides detailed advice for an important plant. For example, the advice unit adjusts the level of detail of the advice based on the type and growth stage of the plant. The advice unit can also provide basic advice for common plants. For example, the advice unit adjusts the level of detail of the advice based on the health condition of the plant. The advice unit can also provide detailed advice for a plant in which the user is particularly interested. For example, the advice unit adjusts the level of detail of the advice by referring to past advice results provided to the user. This makes it possible to provide detailed advice according to the importance of the plant. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input plant priority data into the generation AI, which can then adjust the level of detail of the advice.

[0048] When providing advice, the advice unit can apply an appropriate advice algorithm depending on the type of plant. For example, for a plant with distinctive flower color, the advice unit provides advice based on color. For example, the advice unit analyzes the color of the plant's flowers and provides appropriate advice. The advice unit can also provide advice based on shape for a plant with distinctive leaf shape. For example, the advice unit analyzes the shape of the plant's leaves and provides appropriate advice. The advice unit can also provide advice based on size for a plant for which size is important. For example, the advice unit analyzes the size of the plant and provides appropriate advice. This makes it possible to provide appropriate advice depending on the type of plant. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input plant type data into the generation AI, which then applies an appropriate advice algorithm.

[0049] When providing advice, the advice unit can improve the accuracy of the advice based on the user's past advice results. The advice unit, for example, optimizes the advice algorithm based on the user's past advice results. For example, the advice unit analyzes the user's past advice results and learns specific patterns. The advice unit can also adjust the level of detail of the advice based on the user's past advice results. For example, the advice unit improves the accuracy of the advice by referring to the user's past advice results. This makes it possible to improve accuracy based on the past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit inputs the user's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0050] When providing advice, the advice unit can set a priority of advice based on the time when the plant was photographed. For example, the advice unit prioritizes providing advice for plants in their growing season. For example, the advice unit prioritizes advice for plants in their growing season based on the time when the plant was photographed. The advice unit can also quickly provide advice for plants suspected of being sick. For example, the advice unit prioritizes advice for plants suspected of being sick based on the time when the plant was photographed. The advice unit can also prioritize providing advice for plants in which the user is particularly interested. For example, the advice unit prioritizes advice for plants in which the user is particularly interested based on the time when the plant was photographed. This enables prioritization according to the time when the plant was photographed. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input data on the time when the plant was photographed into the generation AI, which can then set the priority of advice.

[0051] When providing advice, the advice unit can adjust the order of advice based on the relevance of the plants. For example, the advice unit can group plants of the same type together to provide results efficiently. For example, the advice unit can group plants of the same type together based on the relevance of the plants. The advice unit can also prioritize advice on plants suspected of being sick and provide results quickly. For example, the advice unit can prioritize advice on plants suspected of being sick based on the relevance of the plants. The advice unit can also prioritize advice on plants in which the user is particularly interested and provide detailed results. For example, the advice unit can prioritize advice on plants in which the user is particularly interested based on the relevance of the plants. This enables efficient advice based on relevance. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input plant relevance data into the generation AI, which can adjust the order of advice.

[0052] When providing advice, the advice unit can adjust the use of technical terminology in the advice depending on the user's expertise. For example, if the user is a beginner, the advice unit provides advice in simple language, avoiding technical terminology. For example, the advice unit adjusts the use of technical terminology based on the user's expertise level. Furthermore, if the user is an intermediate user, the advice unit can provide advice using appropriate technical terminology. For example, the advice unit uses appropriate technical terminology based on the user's expertise level. Furthermore, if the user is an expert, the advice unit can provide advice using detailed technical terminology. For example, the advice unit uses detailed technical terminology based on the user's expertise level. This makes it possible to provide appropriate advice according to the user's expertise level. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input the user's expertise level data into the generation AI, which can adjust the use of technical terminology in the advice.

[0053] During registration, the registration unit can optimize the registration algorithm by referring to past registration data. The registration unit, for example, selects an optimal registration algorithm based on the past registration data. For example, the registration unit analyzes the past registration data and learns specific patterns. The registration unit can also adjust the level of detail of registration from the past registration data. For example, the registration unit improves the accuracy of registration by referring to the past registration data. This enables optimal registration based on past data. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input the past registration data into the generation AI, which then optimizes the registration algorithm.

[0054] The registration unit can update the registration data based on user feedback during registration. The registration unit, for example, modifies the registration data based on user feedback. For example, the registration unit analyzes the user feedback and learns specific patterns. The registration unit can also optimize the registration algorithm by referring to the user feedback. For example, the registration unit adjusts the level of detail of the registration data based on the user feedback. This enables flexible data updating based on user feedback. Some or all of the above-described processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input user feedback data into the generation AI, which then updates the registration data.

[0055] The registration unit can set the priority of registered data based on the time when the plant was photographed during registration. For example, in the case of a plant in its growing stage, the registration unit weights the data as important. For example, the registration unit preferentially registers data of plants in their growing stage based on the time when the plant was photographed. The registration unit can also quickly weight the registered data of plants suspected of being sick. For example, the registration unit preferentially registers data of plants suspected of being sick based on the time when the plant was photographed. The registration unit can also weight the registered data as detailed data in the case of plants in which the user is particularly interested. For example, the registration unit preferentially registers data of plants in which the user is particularly interested based on the time when the plant was photographed. This enables appropriate data weighting according to the time when the plant was photographed. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input data on the time when the plant was photographed into the generation AI, which can then set the priority of the registered data.

[0056] The registration unit can integrate information from multiple data sources to expand the registered data during registration. For example, the registration unit integrates and registers image data provided by the user with weather information. For example, the registration unit integrates the user's image data with weather data to expand the registered data. The registration unit can also integrate and register user feedback with past registered data. For example, the registration unit integrates the user's feedback data with past registered data to expand the registered data. The registration unit can also integrate and register the user's social media activity data with plant growth data. For example, the registration unit integrates the user's social media activity data with plant growth data to expand the registered data. This enables the registration of richer data by integrating different data sources. Some or all of the above-described processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input multiple data sources into the generation AI, which then integrates the information to expand the registered data.

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

[0058] When analyzing the image of a plant, the analysis unit can also predict the plant's growth. For example, the analysis unit can predict the plant's future growth based on its current condition and notify the user. The analysis unit can also suggest appropriate cultivation methods based on the predicted plant growth. For example, the analysis unit can predict the amount of nutrients and water the plant will need as it grows and provide advice to the user. Furthermore, the analysis unit can suggest the appropriate time to repot the plant based on the predicted plant growth. This allows the user to manage the plant appropriately in anticipation of its future growth.

[0059] The registration unit can record the growth history of the plant when registering the image of the plant. For example, the registration unit can record the growth process of the plant in chronological order, allowing the user to check the past growth status. The registration unit can also analyze the growth trend based on the growth history of the plant. For example, the registration unit can analyze changes in the growth rate and health condition of the plant and notify the user. Furthermore, the registration unit can predict future growth based on the growth history of the plant. This allows the user to manage the growth of the plant in detail.

[0060] When analyzing images of plants, the analysis unit can evaluate the stress level of the plant. For example, the analysis unit can analyze changes in the color and shape of the plant's leaves to detect signs of stress. The analysis unit can also suggest appropriate measures based on the plant's stress level. For example, if the plant is feeling stressed, the analysis unit can suggest appropriate watering or adding fertilizer. Furthermore, the analysis unit can continuously monitor the plant's stress level and notify the user. This allows the user to always be aware of the plant's health condition.

[0061] When analyzing plant images, the analysis unit can predict the progression of a plant disease. For example, the analysis unit can predict the future progression of the disease based on the current state of the disease and notify the user. The analysis unit can also suggest appropriate treatment methods based on the predicted progression of the disease. For example, the analysis unit can suggest appropriate medicines or treatment methods to slow the progression of the disease. Furthermore, the analysis unit can continuously monitor the progression of the disease and notify the user. This allows the user to detect plant diseases early and take appropriate measures.

[0062] The registration unit can record the environmental conditions of the plant when registering the image of the plant. For example, the registration unit can record the temperature, humidity, light intensity, etc. of the environment in which the plant grows, allowing the user to check the environmental conditions. The registration unit can also suggest an environment suitable for plant growth based on the environmental conditions. For example, the registration unit can suggest how to adjust the temperature and humidity so that the plant grows in the optimal environment. Furthermore, the registration unit can continuously monitor changes in the environmental conditions and notify the user. This allows the user to optimize the growing environment for the plant.

[0063] When analyzing plant images, the analysis unit can evaluate the plant's nutritional status. For example, the analysis unit can analyze changes in the color and shape of the plant's leaves to detect signs of nutrient deficiency. The analysis unit can also suggest the appropriate type and amount of fertilizer based on the plant's nutritional status. For example, if the plant needs a specific nutrient, the analysis unit can suggest a fertilizer containing that nutrient. Furthermore, the analysis unit can continuously monitor the plant's nutritional status and notify the user. This allows the user to constantly understand the plant's nutritional status and manage it appropriately.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit inputs an image of a plant. The plant image includes information such as flower color, leaf color, and size. Users can upload images taken with their smartphones or digital cameras to the system. The reception unit accepts images in JPEG, PNG, TIFF, and other formats, automatically recognizes the image format, and passes it to the analysis unit. Step 2: The analysis unit analyzes the images input by the reception unit and diagnoses the growth status and presence or absence of disease. Using the generation AI, it analyzes the color and size of the plant's flowers and leaves to diagnose the possibility of nutritional deficiency or disease. For example, if the plant's flowers are yellowing, the generation AI will diagnose the possibility of nutritional deficiency or disease. Also, if the leaves are discolored, it will diagnose the possibility of a specific disease. Step 3: The advice unit provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results obtained by the analysis unit. For example, if the leaves are turning yellow, it will advise adding a specific fertilizer. It also registers patterns based on local seasons, weather information, and the home's growing environment, and provides optimal advice based on these. For example, if high temperatures are forecast in the area, it will advise increasing the frequency of watering. Furthermore, if disease is suspected, it will suggest the combination and frequency of commercially available fertilizers and chemicals, and modify the prescription based on follow-up images. Step 4: The registration unit registers patterns of local seasons, meteorological information, and the home's growing environment. For example, it registers meteorological information such as the local temperature, precipitation, and sunshine hours. It can also register the temperature, humidity, and light intensity of the home's growing environment (for example, a garden or balcony).

[0066] (Example 2) A plant diagnostic system according to an embodiment of the present invention uses images of plants to diagnose their growth status and disease and provide appropriate advice. In this system, a user takes an image of a plant and inputs it into the system. A generating AI analyzes the image and diagnoses the plant's growth status and the presence or absence of disease. Based on the diagnosis results, the system provides advice on watering, fertilizing, repotting, and medication. The system also registers patterns based on local seasonal and weather information and the home's growing environment, providing optimal advice. Furthermore, if disease is suspected, the system suggests the dosage and frequency of commercially available fertilizers and chemicals, and modifies the prescription based on follow-up images. For example, in a plant diagnostic system, a user uploads an image taken with a smartphone to the system. The image is then input into the generating AI, which then analyzes the input image. The generating AI analyzes the plant's flower color, leaf color, size, and other factors to diagnose the plant's growth status and the presence or absence of disease. For example, if the leaves turn yellow, it may diagnose possible nutrient deficiency or disease. Based on the diagnosis results, the system provides advice on watering, fertilizing, repotting, and medication. For example, if the leaves turn yellow, it may recommend adding a specific fertilizer. The system also registers patterns based on local seasons, weather information, and the home's growing environment, and provides optimal advice based on those patterns. For example, if local temperatures are forecast to be high, the system will advise users to increase the frequency of watering. Furthermore, if disease is suspected, the system will suggest the combination and frequency of commercially available fertilizers and chemicals. For example, if a specific disease is suspected, the system will suggest a chemical that is effective against that disease. The system also modifies prescriptions based on progress images. For example, it re-analyzes images of the plant after treatment and modifies the prescription as necessary. This allows the plant diagnosis system to efficiently diagnose plant growth conditions and diseases and provide appropriate advice. This allows the plant diagnosis system to easily manage plant health, even without specialized knowledge. For example, the system can quickly and accurately diagnose plant growth conditions and diseases and provide appropriate advice, thereby maintaining plant health. Users can also receive optimal advice based on local seasons, weather information, and the home's growing environment.Furthermore, if disease is suspected, the system will suggest the appropriate combination and frequency of commercially available fertilizers and chemicals, and the prescription can be revised based on follow-up images, enabling more effective treatment.

[0067] A plant diagnostic system according to an embodiment includes a reception unit, an analysis unit, an advice unit, and a registration unit. The reception unit inputs an image of a plant. The image of a plant may include, but is not limited to, flower color, leaf color, and size. For example, the reception unit uploads an image taken by a user with a smartphone to the system. The reception unit can also input images taken with a digital camera. The reception unit can automatically recognize the format of the image and pass it to the analysis unit. For example, the reception unit can accept images in JPEG, PNG, TIFF, and other formats. The analysis unit uses a generation AI to analyze the image input by the reception unit and diagnose the plant's growth status and the presence or absence of disease. The analysis unit analyzes, for example, the plant's flower color, leaf color, and size. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the plant image and diagnose the plant's growth status and the presence or absence of disease. The analysis unit can also analyze the plant image using a multimodal generation AI. For example, if the color of a plant's flowers turns yellow, the generation AI will diagnose the possibility of nutrient deficiency or disease. The analysis unit can also use the generation AI to diagnose the possibility of a specific disease if the color of the plant's leaves changes. The advice unit provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results obtained by the analysis unit. For example, if the leaves turn yellow, the advice unit will advise adding a specific fertilizer. The advice unit can also register patterns based on local seasons, weather information, and the home's growing environment, and provide optimal advice based on these. For example, if high temperatures are forecast in the area, the advice unit will advise increasing the frequency of watering. Furthermore, if disease is suspected, the advice unit will suggest the combination and frequency of commercially available fertilizers and chemicals. For example, if a specific disease is suspected, the advice unit will suggest a chemical that is effective against that disease. The advice unit can also modify prescriptions based on follow-up images. For example, it re-analyzes images of the plant after treatment and modifies the prescription as necessary. The registration unit registers patterns based on local seasons, weather information, and the home's growing environment. The registration unit registers meteorological information such as local temperature, precipitation, and sunshine hours.The registration unit can also register the temperature, humidity, light intensity, etc. of the home growing environment (e.g., a garden, a balcony, etc.). This allows the plant diagnosis system according to the embodiment to efficiently diagnose the growth condition and disease of the plant and provide appropriate advice. For example, by quickly and accurately diagnosing the growth condition and disease of the plant and providing appropriate advice, the health of the plant can be maintained. The user can also receive optimal advice based on local seasons, weather information, and the home growing environment. Furthermore, if a disease is suspected, the system can suggest the combination and frequency of commercially available fertilizers and chemicals, and modify the prescription based on follow-up images, enabling more effective treatment.

[0068] The analysis unit can analyze the flower color, leaf color, and size of a plant to diagnose its growth status and the presence or absence of disease. The analysis unit, for example, analyzes the flower color of a plant. For example, the analysis unit analyzes the RGB values ​​of the flower color to detect color changes. The analysis unit can also analyze the leaf color. For example, the analysis unit analyzes the RGB values ​​of the leaf color to detect color changes. The analysis unit can also analyze the size of a plant. For example, the analysis unit measures the length and width of leaves and the diameter of flowers to evaluate the growth progress. This enables accurate diagnosis by analyzing detailed plant characteristics. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs an image of a plant into a generation AI, which analyzes the flower color, leaf color, and size to diagnose its growth status and the presence or absence of disease.

[0069] The advice unit can provide advice on watering, fertilizing, repotting, and medication based on the diagnosis results. The advice unit provides, for example, watering advice based on the diagnosis results. For example, if the leaves of a plant are turning yellow, the advice unit advises adding a specific fertilizer. The advice unit can also provide advice on fertilizing. For example, if the plant's growth is slowing, the advice unit advises adding a specific fertilizer. The advice unit can also provide advice on repotting. For example, if the roots of a plant are crowded, the advice unit advises repotting. The advice unit can also provide advice on medication. For example, if the advice unit is suspected of having a disease, the advice unit advises using a specific medicine. This makes it easier to manage the health of plants by providing specific advice based on the diagnosis results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can generate advice based on the diagnosis results using the generation AI and provide it to the user.

[0070] The registration unit can register local seasons and weather information, and the home's growing environment. The registration unit, for example, registers local seasons and weather information. For example, the registration unit registers weather information such as the local temperature, precipitation, and sunshine hours. The registration unit can also register the home's growing environment. For example, the registration unit registers the temperature, humidity, and light intensity of the garden or balcony. This makes it possible to provide advice that takes into account local environmental information. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input local weather information into the generation AI, which then analyzes and registers the weather information.

[0071] The advice unit can suggest the formulation and frequency of application of commercially available fertilizers or chemicals when a disease is suspected. For example, when a disease is suspected, the advice unit can suggest the formulation and frequency of application of commercially available fertilizers. For example, when a specific disease is suspected, the advice unit can suggest a fertilizer that is effective against that disease. The advice unit can also suggest the formulation and frequency of application of commercially available chemicals. For example, when a specific disease is suspected, the advice unit can suggest a chemical that is effective against that disease. This makes it possible to maintain the health of the plant by suggesting appropriate measures when a disease is suspected. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can suggest the formulation and frequency of application of fertilizers or chemicals based on the disease diagnosis results of the generation AI.

[0072] The advice unit can modify the treatment method based on the follow-up images. The advice unit modifies the treatment method based on the follow-up images, for example. For example, the advice unit re-analyzes the images of the plant after treatment and modifies the prescription as necessary. The advice unit can also evaluate the progress of the treatment based on the follow-up images. For example, the advice unit analyzes the images of the plant after treatment and evaluates the effectiveness of the treatment. This enables more effective treatment by modifying the prescription based on the follow-up. Some or all of the above-mentioned prescription in the advice unit may be performed using, or without, the generation AI, for example. For example, the advice unit inputs the follow-up images into the generation AI, which evaluates the progress of the treatment and modifies the prescription as necessary.

[0073] The reception unit can analyze the user's emotions and adjust the timing of image reception based on the analyzed user emotions. For example, when the user is feeling stressed, the reception unit can quickly receive images to reduce the user's burden. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, when the user is relaxed, the reception unit can provide detailed guidance and carefully receive images. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly receive images. For example, the reception unit can analyze the user's text input and estimate the user's emotions. This enables flexible responses according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the timing of receiving the image.

[0074] The reception unit can analyze the user's past image submission history and select an appropriate reception method. The reception unit can, for example, suggest an optimal reception method based on the types of images the user has frequently submitted in the past. For example, the reception unit can analyze the user's past submission history and select an optimal reception method for a specific time period. The reception unit can also customize the reception method by referring to the user's past feedback. For example, the reception unit can suggest an optimal reception method based on the user's past feedback. This improves user convenience by providing an optimal reception method based on the past history. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past image submission history into the generation AI, which can select the optimal reception method.

[0075] When receiving an image, the reception unit can perform filtering based on the user's current plant type and condition. The reception unit, for example, automatically recognizes the plant type in the image submitted by the user and filters related information. For example, the reception unit analyzes the plant type and provides related information. The reception unit can also analyze the condition (health, illness, etc.) of the plant in the image submitted by the user and perform appropriate filtering. For example, the reception unit analyzes the condition of the plant and provides related information. The reception unit can also perform filtering based on the growth stage of the plant in the image submitted by the user. For example, the reception unit analyzes the growth stage of the plant and provides related information. This makes it possible to provide appropriate information according to the plant type and condition. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the plant type and condition in the image submitted by the user to the generation AI, which can then perform appropriate filtering.

[0076] When receiving an image, the reception unit can select an appropriate reception means depending on the user's input method. For example, when a user describes the condition of a plant by voice, the reception unit prioritizes receiving the voice input. For example, the reception unit records the user's voice and converts it into text data using voice recognition technology. Furthermore, when a user provides detailed information in text, the reception unit can also prioritize receiving the text input. For example, the reception unit analyzes the user's text input and selects an appropriate reception means. Furthermore, when a user uploads an image, the reception unit can prioritize receiving the image input. For example, the reception unit analyzes the image uploaded by the user and selects an appropriate reception means. This enables flexible response depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's voice data, text data, and image data into a generation AI, which can then select an appropriate reception means.

[0077] The reception unit can analyze the user's emotions and determine the priority of images to be received based on the analyzed user emotions. For example, if the user feels anxious, the reception unit prioritizes receiving the images and analyzes them quickly. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Alternatively, if the user feels relaxed, the reception unit can receive images with normal priority. For example, the reception unit records the user's voice and estimates the emotion using voice analysis technology. Alternatively, if the user is in a hurry, the reception unit can prioritize receiving the images and start analyzing them immediately. For example, the reception unit analyzes the user's text input and estimates the emotion. This enables prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and determine the priority of the images.

[0078] When receiving images, the reception unit can prioritize receiving images that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving images related to plants in that area. For example, the reception unit provides information related to plants in the area based on the user's geographical location information. The reception unit can also prioritize receiving images related to weather conditions in the area based on the user's location information. For example, the reception unit provides information related to weather conditions in the area based on the user's location information. The reception unit can also prioritize receiving images related to plant disease information in the area based on the user's location information. For example, the reception unit provides information related to plant disease information in the area based on the user's location information. This enables appropriate information to be provided taking geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which then prioritizes receiving images that are highly relevant.

[0079] When receiving an image, the reception unit can analyze the user's online activity and receive related images. The reception unit, for example, automatically receives images of plants posted by the user on social media. For example, the reception unit analyzes the user's social media activity and preferentially receives images of related plants. The reception unit can also receive images of related plants by referring to posts by the user's friends on social media. For example, the reception unit analyzes posts by the user's friends on social media and provides related information. This enables flexible responses that take social media activity into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI, which then receives related images.

[0080] When receiving an image, the reception unit can adjust the reception method based on the user's past feedback. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit simplifies the reception procedure by referring to the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's past feedback. For example, the reception unit suggests an optimal reception method based on the user's past feedback. This enables customization based on past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which can select the optimal reception method.

[0081] The analysis unit can analyze the user's emotions and adjust the presentation method of the analysis based on the analyzed user's emotions. For example, if the user is feeling anxious, the analysis unit displays the analysis results concisely and clearly. For example, the analysis unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. The analysis unit can also quickly display key analysis results if the user is in a hurry. For example, the analysis unit analyzes the user's text input and estimates the user's emotions. This makes it possible to provide analysis results that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then infer the emotion and adjust the way the analysis is expressed.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the plant. For example, in the case of an important plant, the analysis unit performs a detailed analysis and provides detailed information. For example, the analysis unit adjusts the level of detail of the analysis based on the type and growth stage of the plant. The analysis unit can also perform a basic analysis of a common plant and provide necessary information. For example, the analysis unit adjusts the level of detail of the analysis based on the health status of the plant. The analysis unit can also perform a detailed analysis of a plant of particular interest to the user and provide additional information. For example, the analysis unit adjusts the level of detail of the analysis based on the user's past analysis results. This enables detailed analysis according to the importance of the plant. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input plant priority data into the generation AI, which can then adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the type of plant. For example, the analysis unit applies a color analysis algorithm to plants with distinctive flower colors. For example, the analysis unit analyzes the color of the plant's flowers and detects color changes. The analysis unit can also apply a shape analysis algorithm to plants with distinctive leaf shapes. For example, the analysis unit analyzes the shape of the plant's leaves and detects changes in shape. The analysis unit can also apply a size analysis algorithm to plants for which size is important. For example, the analysis unit analyzes the size of the plant and evaluates the progress of growth. This enables appropriate analysis depending on the type of plant. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input plant type data into the generation AI, which then applies an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and learns specific patterns. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. This makes it possible to improve accuracy based on past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0085] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. The analysis unit can also provide a concise and clear analysis result if the user is feeling anxious. For example, the analysis unit analyzes the user's text input and estimates the emotion. This makes it possible to provide an analysis result that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then infer the emotion and adjust the length of the analysis.

[0086] During analysis, the analysis unit can set analysis priorities based on the time the plant was photographed. For example, the analysis unit prioritizes analysis of plants in their growing season. For example, the analysis unit prioritizes analysis of plants in their growing season based on the time the plant was photographed. The analysis unit can also quickly analyze plants suspected of being sick. For example, the analysis unit prioritizes analysis of plants suspected of being sick based on the time the plant was photographed. The analysis unit can also prioritize analysis of plants in which the user is particularly interested. For example, the analysis unit prioritizes analysis of plants in which the user is particularly interested based on the time the plant was photographed. This enables prioritization according to the time the plant was photographed. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input plant photography time data to the generation AI, which can then set the analysis priorities.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the plants. The analysis unit, for example, analyzes plants of the same species together to efficiently provide results. For example, the analysis unit analyzes plants of the same species together based on the relevance of the plants. The analysis unit can also prioritize analyzing plants suspected of being sick and provide results quickly. For example, the analysis unit prioritizes analyzing plants suspected of being sick based on the relevance of the plants. The analysis unit can also prioritize analyzing plants in which the user is particularly interested and provide detailed results. For example, the analysis unit prioritizes analyzing plants in which the user is particularly interested based on the relevance of the plants. This enables efficient analysis based on relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input plant relevance data to the generation AI, which can adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's expertise. For example, if the user is a beginner, the analysis unit provides analysis results in simple language, avoiding technical terminology. For example, the analysis unit adjusts the use of technical terminology based on the user's expertise level. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terminology. For example, the analysis unit uses appropriate technical terminology based on the user's expertise level. Furthermore, if the user is an expert, the analysis unit can provide analysis results using detailed technical terminology. For example, the analysis unit uses detailed technical terminology based on the user's expertise level. This makes it possible to provide appropriate analysis results according to the user's expertise level. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's expertise level data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0089] The advice unit can analyze the user's emotions and adjust the way the advice is presented based on the analyzed user's emotions. For example, if the user is feeling anxious, the advice unit provides concise and clear advice. For example, the advice unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, the advice unit can provide detailed advice if the user is relaxed. For example, the advice unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the advice unit can quickly provide advice that focuses on the key points. For example, the advice unit analyzes the user's text input and estimates the user's emotions. This enables flexible advice to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit can be performed using, for example, the generation AI, or without the generation AI. For example, the advice unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the way the advice is expressed.

[0090] When providing advice, the advice unit can adjust the level of detail of the advice based on the priority of the plant. For example, the advice unit provides detailed advice for an important plant. For example, the advice unit adjusts the level of detail of the advice based on the type and growth stage of the plant. The advice unit can also provide basic advice for common plants. For example, the advice unit adjusts the level of detail of the advice based on the health condition of the plant. The advice unit can also provide detailed advice for a plant in which the user is particularly interested. For example, the advice unit adjusts the level of detail of the advice by referring to past advice results provided to the user. This makes it possible to provide detailed advice according to the importance of the plant. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input plant priority data into the generation AI, which can then adjust the level of detail of the advice.

[0091] When providing advice, the advice unit can apply an appropriate advice algorithm depending on the type of plant. For example, for a plant with distinctive flower color, the advice unit provides advice based on color. For example, the advice unit analyzes the color of the plant's flowers and provides appropriate advice. The advice unit can also provide advice based on shape for a plant with distinctive leaf shape. For example, the advice unit analyzes the shape of the plant's leaves and provides appropriate advice. The advice unit can also provide advice based on size for a plant for which size is important. For example, the advice unit analyzes the size of the plant and provides appropriate advice. This makes it possible to provide appropriate advice depending on the type of plant. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input plant type data into the generation AI, which then applies an appropriate advice algorithm.

[0092] When providing advice, the advice unit can improve the accuracy of the advice based on the user's past advice results. The advice unit, for example, optimizes the advice algorithm based on the user's past advice results. For example, the advice unit analyzes the user's past advice results and learns specific patterns. The advice unit can also adjust the level of detail of the advice based on the user's past advice results. For example, the advice unit improves the accuracy of the advice by referring to the user's past advice results. This makes it possible to improve accuracy based on the past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit inputs the user's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0093] The advice unit can analyze the user's emotions and adjust the length of the advice based on the analyzed user's emotions. For example, if the user is in a hurry, the advice unit provides short and to-the-point advice. For example, the advice unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, the advice unit can provide detailed advice if the user is relaxed. For example, the advice unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, the advice unit can provide concise and clear advice if the user is feeling anxious. For example, the advice unit analyzes the user's text input and estimates the user's emotions. This enables flexible advice to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit can be performed using, for example, the generation AI, or without the generation AI. For example, the advice unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the length of the advice.

[0094] When providing advice, the advice unit can set a priority of advice based on the time when the plant was photographed. For example, the advice unit prioritizes providing advice for plants in their growing season. For example, the advice unit prioritizes advice for plants in their growing season based on the time when the plant was photographed. The advice unit can also quickly provide advice for plants suspected of being sick. For example, the advice unit prioritizes advice for plants suspected of being sick based on the time when the plant was photographed. The advice unit can also prioritize providing advice for plants in which the user is particularly interested. For example, the advice unit prioritizes advice for plants in which the user is particularly interested based on the time when the plant was photographed. This enables prioritization according to the time when the plant was photographed. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input data on the time when the plant was photographed into the generation AI, which can then set the priority of advice.

[0095] When providing advice, the advice unit can adjust the order of advice based on the relevance of the plants. For example, the advice unit can group plants of the same type together to provide results efficiently. For example, the advice unit can group plants of the same type together based on the relevance of the plants. The advice unit can also prioritize advice on plants suspected of being sick and provide results quickly. For example, the advice unit can prioritize advice on plants suspected of being sick based on the relevance of the plants. The advice unit can also prioritize advice on plants in which the user is particularly interested and provide detailed results. For example, the advice unit can prioritize advice on plants in which the user is particularly interested based on the relevance of the plants. This enables efficient advice based on relevance. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input plant relevance data into the generation AI, which can adjust the order of advice.

[0096] When providing advice, the advice unit can adjust the use of technical terminology in the advice depending on the user's expertise. For example, if the user is a beginner, the advice unit provides advice in simple language, avoiding technical terminology. For example, the advice unit adjusts the use of technical terminology based on the user's expertise level. Furthermore, if the user is an intermediate user, the advice unit can provide advice using appropriate technical terminology. For example, the advice unit uses appropriate technical terminology based on the user's expertise level. Furthermore, if the user is an expert, the advice unit can provide advice using detailed technical terminology. For example, the advice unit uses detailed technical terminology based on the user's expertise level. This makes it possible to provide appropriate advice according to the user's expertise level. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input the user's expertise level data into the generation AI, which can adjust the use of technical terminology in the advice.

[0097] The registration unit can analyze the user's emotions and select registration data based on the analyzed user emotions. For example, when the user is feeling anxious, the registration unit prioritizes registering important data. For example, the registration unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, when the user is relaxed, the registration unit can register detailed data. For example, the registration unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, when the user is in a hurry, the registration unit can quickly register data that focuses on the key points. For example, the registration unit analyzes the user's text input and estimates the emotion. This enables flexible data registration according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, the generation AI, or without the generation AI. For example, the registration unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and select the registration data.

[0098] During registration, the registration unit can optimize the registration algorithm by referring to past registration data. The registration unit, for example, selects an optimal registration algorithm based on the past registration data. For example, the registration unit analyzes the past registration data and learns specific patterns. The registration unit can also adjust the level of detail of registration from the past registration data. For example, the registration unit improves the accuracy of registration by referring to the past registration data. This enables optimal registration based on past data. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input the past registration data into the generation AI, which then optimizes the registration algorithm.

[0099] The registration unit can update the registration data based on user feedback during registration. The registration unit, for example, modifies the registration data based on user feedback. For example, the registration unit analyzes the user feedback and learns specific patterns. The registration unit can also optimize the registration algorithm by referring to the user feedback. For example, the registration unit adjusts the level of detail of the registration data based on the user feedback. This enables flexible data updating based on user feedback. Some or all of the above-described processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input user feedback data into the generation AI, which then updates the registration data.

[0100] The registration unit can analyze the user's emotions and adjust the registration frequency based on the analyzed user emotions. For example, if the user is feeling anxious, the registration unit can register data frequently to provide a sense of security. For example, the registration unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the registration unit can register data at a normal frequency. For example, the registration unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the registration unit can quickly register the minimum necessary data. For example, the registration unit can analyze the user's text input and estimate the user's emotions. This enables flexible data registration according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, the generation AI, or without the generation AI. For example, the registration unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the frequency of registration.

[0101] The registration unit can set the priority of registered data based on the time when the plant was photographed during registration. For example, in the case of a plant in its growing stage, the registration unit weights the data as important. For example, the registration unit preferentially registers data of plants in their growing stage based on the time when the plant was photographed. The registration unit can also quickly weight the registered data of plants suspected of being sick. For example, the registration unit preferentially registers data of plants suspected of being sick based on the time when the plant was photographed. The registration unit can also weight the registered data as detailed data in the case of plants in which the user is particularly interested. For example, the registration unit preferentially registers data of plants in which the user is particularly interested based on the time when the plant was photographed. This enables appropriate data weighting according to the time when the plant was photographed. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input data on the time when the plant was photographed into the generation AI, which can then set the priority of the registered data.

[0102] The registration unit can integrate information from multiple data sources to expand the registered data during registration. For example, the registration unit integrates and registers image data provided by the user with weather information. For example, the registration unit integrates the user's image data with weather data to expand the registered data. The registration unit can also integrate and register user feedback with past registered data. For example, the registration unit integrates the user's feedback data with past registered data to expand the registered data. The registration unit can also integrate and register the user's social media activity data with plant growth data. For example, the registration unit integrates the user's social media activity data with plant growth data to expand the registered data. This enables the registration of richer data by integrating different data sources. Some or all of the above-described processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input multiple data sources into the generation AI, which then integrates the information to expand the registered data. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, advice unit, and registration unit, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows a user to upload an image of a plant taken with a smartphone to the system. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the image of the plant using a generative AI to diagnose the plant's growth status and the presence or absence of disease. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results. The registration unit is realized by the specific processing unit 290 of the data processing device 12, and registers patterns of local seasons, weather information, and the home growth environment. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, advice unit, and registration unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to upload images of plants taken with the smart glasses 214 to the system. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes images of plants using a generative AI to diagnose the plant's growth status and the presence or absence of disease. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results. The registration unit is realized by the specific processing unit 290 of the data processing device 12, and registers patterns of local seasons, weather information, and the home's growth environment. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, advice unit, and registration unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314, and allows a user to upload an image of a plant taken with the headset terminal 314 to the system. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the image of the plant using a generative AI to diagnose the plant's growth status and the presence or absence of disease. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results. The registration unit is realized by the specific processing unit 290 of the data processing device 12, and registers patterns of local seasons, weather information, and the home growth environment. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, advice unit, and registration unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to upload images of plants photographed by the robot 414 to the system. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the images of the plants using a generative AI to diagnose the plant's growth status and the presence or absence of disease. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results. The registration unit is realized by the specific processing unit 290 of the data processing device 12, and registers patterns of local seasons, weather information, and the home's growth environment.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] When analyzing the image of a plant, the analysis unit can also predict the plant's growth. For example, the analysis unit can predict the plant's future growth based on its current condition and notify the user. The analysis unit can also suggest appropriate cultivation methods based on the predicted plant growth. For example, the analysis unit can predict the amount of nutrients and water the plant will need as it grows and provide advice to the user. Furthermore, the analysis unit can suggest the appropriate time to repot the plant based on the predicted plant growth. This allows the user to manage the plant appropriately in anticipation of its future growth.

[0105] The advice unit can analyze the user's emotions and customize the content of the advice based on the analyzed user's emotions. For example, if the user is feeling stressed, the advice unit can provide simple and easy-to-follow advice. If the user is relaxed, the advice unit can also provide detailed and specialized advice. Furthermore, if the user is in a hurry, the advice unit can also provide short advice that hits the main points. This makes it possible to provide flexible advice according to the user's emotions.

[0106] The registration unit can record the growth history of the plant when registering the image of the plant. For example, the registration unit can record the growth process of the plant in chronological order, allowing the user to check the past growth status. The registration unit can also analyze the growth trend based on the growth history of the plant. For example, the registration unit can analyze changes in the growth rate and health condition of the plant and notify the user. Furthermore, the registration unit can predict future growth based on the growth history of the plant. This allows the user to manage the growth of the plant in detail.

[0107] When analyzing images of plants, the analysis unit can evaluate the stress level of the plant. For example, the analysis unit can analyze changes in the color and shape of the plant's leaves to detect signs of stress. The analysis unit can also suggest appropriate measures based on the plant's stress level. For example, if the plant is feeling stressed, the analysis unit can suggest appropriate watering or adding fertilizer. Furthermore, the analysis unit can continuously monitor the plant's stress level and notify the user. This allows the user to always be aware of the plant's health condition.

[0108] The advice unit can analyze the user's emotions and adjust the timing of advice based on the analyzed user's emotions. For example, if the user is feeling stressed, the advice unit can quickly provide advice to reduce the user's burden. If the user is relaxed, the advice unit can provide detailed guidance to ensure the user's full understanding. Furthermore, if the user is in a hurry, the advice unit can provide short advice that focuses on the main points. This allows for flexible advice that corresponds to the user's emotions.

[0109] When analyzing plant images, the analysis unit can predict the progression of a plant disease. For example, the analysis unit can predict the future progression of the disease based on the current state of the disease and notify the user. The analysis unit can also suggest appropriate treatment methods based on the predicted progression of the disease. For example, the analysis unit can suggest appropriate medicines or treatment methods to slow the progression of the disease. Furthermore, the analysis unit can continuously monitor the progression of the disease and notify the user. This allows the user to detect plant diseases early and take appropriate measures.

[0110] The advice unit can analyze the user's emotions and adjust the format of the advice based on the analyzed user's emotions. For example, if the user is feeling stressed, the advice unit can provide concise, visual advice. If the user is relaxed, the advice unit can provide detailed text-format advice. Furthermore, if the user is in a hurry, the advice unit can provide audio-format advice. This allows for flexible advice according to the user's emotions.

[0111] The registration unit can record the environmental conditions of the plant when registering the image of the plant. For example, the registration unit can record the temperature, humidity, light intensity, etc. of the environment in which the plant grows, allowing the user to check the environmental conditions. The registration unit can also suggest an environment suitable for plant growth based on the environmental conditions. For example, the registration unit can suggest how to adjust the temperature and humidity so that the plant grows in the optimal environment. Furthermore, the registration unit can continuously monitor changes in the environmental conditions and notify the user. This allows the user to optimize the growing environment for the plant.

[0112] When analyzing plant images, the analysis unit can evaluate the plant's nutritional status. For example, the analysis unit can analyze changes in the color and shape of the plant's leaves to detect signs of nutrient deficiency. The analysis unit can also suggest the appropriate type and amount of fertilizer based on the plant's nutritional status. For example, if the plant needs a specific nutrient, the analysis unit can suggest a fertilizer containing that nutrient. Furthermore, the analysis unit can continuously monitor the plant's nutritional status and notify the user. This allows the user to constantly understand the plant's nutritional status and manage it appropriately.

[0113] The advice unit can analyze the user's emotions and adjust the frequency of advice based on the analyzed user's emotions. For example, if the user is feeling stressed, the advice unit can provide advice frequently to reduce the user's anxiety. If the user is relaxed, the advice unit can also provide advice at a normal frequency. Furthermore, if the user is in a hurry, the advice unit can also quickly provide the minimum amount of advice necessary. This makes it possible to provide flexible advice according to the user's emotions.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit inputs an image of a plant. The plant image includes information such as flower color, leaf color, and size. Users can upload images taken with their smartphones or digital cameras to the system. The reception unit accepts images in JPEG, PNG, TIFF, and other formats, automatically recognizes the image format, and passes it to the analysis unit. Step 2: The analysis unit analyzes the images input by the reception unit and diagnoses the growth status and presence or absence of disease. Using the generation AI, it analyzes the color and size of the plant's flowers and leaves to diagnose the possibility of nutritional deficiency or disease. For example, if the plant's flowers are yellowing, the generation AI will diagnose the possibility of nutritional deficiency or disease. Also, if the leaves are discolored, it will diagnose the possibility of a specific disease. Step 3: The advice unit provides advice on watering, fertilizing, repotting, medication, etc. based on the diagnosis results obtained by the analysis unit. For example, if the leaves are turning yellow, it will advise adding a specific fertilizer. It also registers patterns based on local seasons, weather information, and the home's growing environment, and provides optimal advice based on these. For example, if high temperatures are forecast in the area, it will advise increasing the frequency of watering. Furthermore, if disease is suspected, it will suggest the combination and frequency of commercially available fertilizers and chemicals, and modify the prescription based on follow-up images. Step 4: The registration unit registers patterns of local seasons, meteorological information, and the home's growing environment. For example, it registers meteorological information such as the local temperature, precipitation, and sunshine hours. It can also register the temperature, humidity, and light intensity of the home's growing environment (for example, a garden or balcony).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting an image of a plant; an analysis unit that analyzes the image input by the reception unit and diagnoses the growth status and the presence or absence of disease; an advice unit that provides advice on watering, fertilizing, replanting, and medication based on the diagnosis results obtained by the analysis unit; A registration unit for registering local seasons and weather information, and the home growing environment. A system characterized by:

2. The analysis unit Analyze the color and size of the flowers and leaves of plants to diagnose their growth status and the presence or absence of disease.

2. The system of claim 1.

3. The advice unit Based on the diagnostic results, advice is provided on watering, fertilizing, repotting, and medication.

2. The system of claim 1.

4. The registration unit Register local seasonal and weather information, and your home's growing environment 2. The system of claim 1.

5. The advice unit If disease is suspected, we will suggest the mix and frequency of commercial fertilizer or chemical applications.

2. The system of claim 1.

6. The advice unit Modify treatment methods through follow-up images 2. The system of claim 1.

7. The reception unit Analyzes user emotions and adjusts the timing of image reception based on the analyzed user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past image submission history and select the appropriate reception method 2. The system of claim 1.

Images