System

Abstract

An object of the system according to the embodiment is to specify a type and a state of a plant and provide appropriate advice.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, an information reception unit, and a provision unit. The reception unit receives an image of a plant from a user. The analysis unit analyzes the image received by the reception unit and specifies the type and state of the plant. The information receiving unit receives information from a user. The providing unit provides advice based on the information obtained by the analyzing unit and the information receiving unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to identify the type and condition of plants and provide appropriate advice.

[0005] The system according to the embodiment aims to identify the type and condition of a plant and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, an information receiving unit, and a providing unit. The receiving unit receives an image of a plant from a user. The analysis unit analyzes the image received by the receiving unit and identifies the type and condition of the plant. The information receiving unit receives information from the user. The providing unit provides advice based on the information obtained by the analysis unit and the information receiving unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the type and condition of a plant 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 gardening support system according to an embodiment of the present invention allows a user to send images of plants, which are analyzed by a generation AI to provide appropriate advice. In the gardening support system, a user sends an image of a plant, which the generation AI analyzes to identify the plant's type and condition, and provides appropriate advice based on the information provided by the user. For example, the gardening support system allows a user to take a photo of the plant they want to consult about. For example, the system recommends taking a photo of the entire plant or any problematic areas. This image is then sent to the generation AI. The gardening support system then uses the generation AI to analyze the received image. The generation AI identifies the plant's type and condition using image recognition technology. For example, the generation AI analyzes the leaf shape, color, and stem thickness to identify the plant's type. The system also detects changes in leaf color and disease spots to determine the plant's health. Furthermore, the gardening support system allows a user to communicate the plant's condition to the generation AI. For example, the user inputs information such as "I may have watered it too much recently" or "It's in a shady spot." This information is then sent to the generation AI. The generation AI provides appropriate advice based on the received image and the information provided by the user. For example, it provides advice such as, "You may be watering too much, so reduce the frequency of watering" or "Move the plant to a sunny spot." This advice is displayed on the user's smartphone or computer. This allows the gardening support system to easily teach even beginners how to care for their plants. The gardening support system allows users to send images of their plants, which the generating AI analyzes and provides appropriate advice, making it easy for even beginners to learn how to care for their plants. For example, if the leaves of a houseplant turn yellow, the generating AI can identify the cause and suggest appropriate measures to maintain the plant's health. In vegetable cultivation, the generating AI can also provide appropriate advice to increase harvest yields.

[0029] A gardening support system according to an embodiment includes a receiving unit, an analysis unit, an information receiving unit, and a providing unit. The receiving unit receives plant images from a user. Plant images from a user may be in, for example, JPEG or PNG format, but are not limited to these. The receiving unit receives plant images taken by the user from, for example, a smartphone or PC. The receiving unit can also automatically adjust the image resolution and convert the image into a format suitable for analysis. For example, the receiving unit may convert low-resolution images to high resolution to improve analysis accuracy. The analysis unit uses generative AI to analyze the images received by the receiving unit and identify the type and condition of the plant. The analysis may be performed using, for example, an image analysis algorithm or deep learning technology, but is not limited to these examples. For example, the analysis unit may analyze the shape and color of the leaves, the thickness of the stem, and the like to identify the type of plant. The analysis unit can also detect changes in leaf color and disease spots to determine the health of the plant. For example, the analysis unit may detect changes in leaf color to determine the presence or absence of pests. The information receiving unit receives information regarding the condition of the plant from the user. The information may include, but is not limited to, text information and numerical data. For example, the information receiving unit may receive information input by the user, such as "I think I've watered it too much recently" or "It's in a place with poor sunlight." The information receiving unit may also automatically analyze the information input by the user and send it to the analysis unit. The providing unit provides advice based on the information obtained by the analysis unit and the information receiving unit. The advice may be provided in the form of, for example, a text message or an image guide, but is not limited to, such examples. For example, the providing unit may provide advice such as "You may be watering it too much, so please water it less frequently" or "Move it to a sunny location." The providing unit may also display the advice on the user's smartphone or computer. In this way, the gardening support system according to the embodiment allows users to send images of plants, and the generation AI analyzes and provides appropriate advice, making it easy for even beginner gardeners to learn how to care for their plants.

[0030] The analysis unit can identify the type and condition of a plant using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and computer vision technology. The analysis unit can identify the type of plant using, for example, deep learning technology. For example, the analysis unit can analyze the shape and color of leaves, stem thickness, etc. to identify the type of plant. The analysis unit can also identify the condition of a plant using computer vision technology. For example, the analysis unit can detect changes in leaf color, disease spots, etc. to determine the health of the plant. The analysis unit can also identify the growth stage of a plant using image recognition technology. For example, the analysis unit can analyze the height and number of leaves of a plant to determine the growth stage. In this way, the type and condition of a plant can be accurately identified using image recognition technology. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input an image of a plant into the generation AI and cause the generation AI to identify the type and condition of the plant.

[0031] The information receiving unit can receive information about the condition of the plant from the user. Information about the condition of the plant includes, but is not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. The information receiving unit can receive information input by the user, such as, "I may have watered it too much recently" or "It's in a place with poor sunlight." The information receiving unit can also automatically analyze the information input by the user and transmit it to the analysis unit. For example, the information receiving unit can analyze the information input by the user using text analysis technology and transmit it to the analysis unit. Furthermore, the information receiving unit can determine the condition of the plant based on the information input by the user. For example, the information receiving unit can determine the frequency of watering and the amount of sunlight based on the information input by the user. By receiving detailed condition information from the user, more appropriate advice can be provided. Some or all of the above-described processing by the information receiving unit can be performed using, for example, AI, or without AI. For example, the information receiving unit can input the information input by the user to a generation AI and have the generation AI analyze the information.

[0032] The providing unit can provide advice based on the health condition of the plant. Examples of the plant's health condition include, but are not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. The providing unit can provide advice based on the plant's health condition, such as "You may be overwatering the plant, so reduce the frequency of watering" or "Move the plant to a sunny location." The providing unit can also provide advice on how to select fertilizer and how to deal with pests and diseases based on the plant's health condition. For example, the providing unit can suggest an appropriate type of fertilizer and the timing of fertilization based on the plant's health condition. Furthermore, the providing unit can suggest methods for preventing and exterminating pests and diseases based on the plant's health condition. For example, the providing unit can suggest an appropriate type of pesticide and its application method based on the plant's health condition. This makes plant care easier by providing advice based on the plant's health condition. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input data on the plant's health condition into a generating AI and cause the generating AI to generate advice.

[0033] The providing unit includes an identification unit that identifies pests and diseases. The identification unit identifies pests and diseases using, for example, image analysis technology. For example, the identification unit analyzes images of plants to identify the type of pest or disease. The identification unit can also identify pests and diseases using symptom pattern matching technology. For example, the identification unit analyzes patterns of lesions and discoloration that appear on plant leaves and stems to identify the type of pest or disease. Furthermore, the identification unit can suggest appropriate countermeasures based on the results of pest and disease identification. For example, the identification unit suggests appropriate types and application methods of pesticides for identified pests and diseases. In this way, appropriate countermeasures can be taken by identifying pests and diseases. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input images of plants to the generation AI and cause the generation AI to identify pests and diseases.

[0034] The providing unit includes a suggestion unit that suggests a method for selecting a fertilizer. The suggestion unit suggests an appropriate method for selecting a fertilizer based on, for example, the type of plant and the condition of the soil. For example, the suggestion unit suggests an appropriate type of fertilizer and the timing of fertilization depending on the type of plant. The suggestion unit can also suggest an appropriate method for selecting a fertilizer based on the condition of the soil. For example, the suggestion unit suggests an appropriate type of fertilizer based on the pH value and nutrient content of the soil. Furthermore, the suggestion unit can also suggest an appropriate method for selecting a fertilizer depending on the growth stage of the plant. For example, the suggestion unit suggests nitrogen fertilizer in the early growth stage of the plant and phosphate fertilizer in the later growth stage. This allows the suggestion of an appropriate fertilizer selection method to promote plant growth. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data regarding the type of plant and the condition of the soil into the generation AI and cause the generation AI to execute a suggestion on how to select a fertilizer.

[0035] The reception unit can analyze the user's past image submission history and select an appropriate reception method. For example, the reception unit can analyze the time periods during which the user frequently submitted images in the past and encourage submission during those time periods. The reception unit can also prioritize submission methods (smartphone, computer, etc.) that the user has used in the past. Furthermore, the reception unit can encourage submission during specific days of the week or time periods based on the user's past submission history. For example, the reception unit can analyze the submission dates and times of images the user has previously submitted and suggest the optimal reception time. The reception unit can also analyze the devices the user has previously used and suggest the optimal submission method. In this way, by analyzing the past history, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0036] When receiving images, the reception unit can filter the images based on the user's current gardening situation and areas of interest. For example, the reception unit can receive only relevant images based on the type of plant the user is currently growing. The reception unit can also receive only relevant images based on the user's areas of interest (e.g., ornamental plants, vegetables). The reception unit can also receive appropriate images based on the user's gardening situation (beginner, intermediate, advanced). For example, the reception unit filters relevant images based on the type of plant the user is currently growing. The reception unit can also filter relevant images based on the user's areas of interest. This enables efficient support by receiving appropriate images based on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input data regarding the user's gardening situation and areas of interest to the generation AI and have the generation AI perform image 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 the user issues instructions by voice, the reception unit prioritizes reception of the voice input. Furthermore, when the user issues instructions by text, the reception unit can also prioritize reception of the text input. Furthermore, when the user sends an image, the reception unit can also prioritize reception of the image input. For example, when the user issues instructions by voice, the reception unit converts the voice into text using voice recognition technology and receives the text. Furthermore, when the user issues instructions by text, the reception unit analyzes the content of the instructions using text analysis technology and receives the text. This improves user convenience by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input data regarding the user's input method to a generation AI and cause the generation AI to select the optimal reception means.

[0038] When receiving images, the reception unit can prioritize receiving highly relevant images by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving images of plants related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to plants at the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving images related to plants around the user's home. For example, the reception unit can acquire the user's geographical location information from GPS data or an IP address and prioritize receiving highly relevant images based on that information. Furthermore, the reception unit can filter images of plants related to a specific area based on the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant images can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant images.

[0039] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive images of plants posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related plant images. Furthermore, the reception unit can also receive related plant images based on the activity of the user's friends on social media. For example, the reception unit can analyze the user's social media account and filter related plant images. The reception unit can also preferentially receive related plant images based on the user's social media posts. This allows for efficient reception of related images by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input data regarding the user's social media activity into the generation AI and cause the generation AI to receive related images.

[0040] When receiving an image, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. For example, the reception unit suggests the optimal reception time and submission method based on the user's past feedback. The reception unit also encourages reception on a specific day of the week or time period based on the user's past feedback. In this way, by reflecting the past feedback, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the plant. For example, the analysis unit performs a detailed analysis for an important plant. The analysis unit can also perform a normal analysis for a common plant. Furthermore, the analysis unit can perform a simplified analysis for a less important plant. For example, the analysis unit adjusts the level of detail of the analysis based on the rarity or growth stage of the plant. The analysis unit can also adjust the level of detail of the analysis based on the user's level of interest. In this way, by adjusting the level of detail of the analysis according to the importance of the plant, more detailed analysis can be performed for important plants. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the plant to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the plant category. For example, in the case of a houseplant, the analysis unit can apply an analysis algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the analysis unit can also apply an analysis algorithm dedicated to vegetables. Furthermore, in the case of a flower, the analysis unit can also apply an analysis algorithm dedicated to flowers. For example, the analysis unit selects an appropriate analysis algorithm depending on the plant category and performs the analysis. The analysis unit can also improve the accuracy of the analysis based on the plant category. In this way, by applying an analysis algorithm according to the plant category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the plant category to the generation AI and cause the generation AI to select an analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs a current analysis by referring to the analysis results of images previously submitted by the user. The analysis unit can also find specific patterns from the user's past analysis results and improve the accuracy of the analysis. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the current analysis algorithm based on the analysis results of images previously submitted by the user. The analysis unit can also find specific patterns based on the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the plants. For example, the analysis unit prioritizes analysis of recently submitted plants. The analysis unit can also postpone analysis of plants that were submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit determines the analysis priority based on the date and time of submission of the plants. The analysis unit can also determine the analysis priority based on the growth stage or season of the plants. In this way, by determining the analysis priority based on the time of submission, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the time of submission of the plants to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the plants. For example, the analysis unit prioritizes analysis of highly related plants. The analysis unit can also postpone analysis of less related plants. Furthermore, the analysis unit can adjust the order of analysis based on the relevance. For example, the analysis unit prioritizes analysis of plants of the same species or plants with the same risk of pests. The analysis unit can also prioritize analysis of plants in the same cultivation environment. In this way, by adjusting the order of analysis based on the relevance of the plants, highly related plants can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of the plants to the generation AI and cause the generation AI to 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 level of expertise. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results that use technical terminology appropriately. Furthermore, if the user is an advanced user, the analysis unit can provide analysis results that use a lot of technical terminology. For example, the analysis unit can adjust the way the analysis results are presented based on the user's level of expertise. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0047] The information accepting unit can analyze the user's past information provision history and select the optimal reception method when accepting information. For example, the information accepting unit can automatically display information that the user has frequently provided in the past as candidates. The information accepting unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the information accepting unit can predict and suggest information that will be used during a specific time period based on the user's past information provision history. For example, the information accepting unit can analyze the date and time of information provided by the user in the past and suggest the optimal reception time. The information accepting unit can also analyze input methods used by the user in the past and suggest the optimal input method. In this way, the optimal reception method can be provided to the user by analyzing the past history. Some or all of the above-mentioned processing in the information accepting unit can be performed using, for example, AI, or can be performed without using AI. For example, the information accepting unit can input the user's past information provision history data into a generation AI and cause the generation AI to select the optimal reception method.

[0048] When receiving information, the information receiving unit can filter the information based on the user's current gardening situation and areas of interest. For example, the information receiving unit can receive only relevant information based on the type of plant the user is currently growing. The information receiving unit can also receive only relevant information based on the user's areas of interest (e.g., ornamental plants, vegetables). Furthermore, the information receiving unit can receive appropriate information based on the user's gardening situation (beginner, intermediate, advanced). For example, the information receiving unit filters relevant information based on the type of plant the user is currently growing. The information receiving unit can also filter relevant information based on the user's areas of interest. This enables efficient support by receiving appropriate information based on the user's situation and areas of interest. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or without AI. For example, the information receiving unit can input data related to the user's gardening situation and areas of interest to the generation AI and have the generation AI perform information filtering.

[0049] When receiving information, the information receiving unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information receiving unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the information receiving unit can prioritize receiving information related to plants at the user's travel destination. Furthermore, when the user is at home, the information receiving unit can prioritize receiving information related to plants around the user's home. For example, the information receiving unit can acquire the user's geographical location information from GPS data or an IP address and prioritize receiving highly relevant information based on that information. Furthermore, the information receiving unit can filter information related to a specific area based on the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information receiving unit may be performed using, or without, AI. For example, the information receiving unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0050] The information receiving unit can analyze the user's social media activity and receive related information when receiving information. For example, the information receiving unit can prioritize receiving information about plants posted by the user on social media. The information receiving unit can also analyze the user's social media activity and receive related plant information. Furthermore, the information receiving unit can also receive related plant information based on the activity of the user's friends on social media. For example, the information receiving unit can analyze the user's social media account and filter related plant information. The information receiving unit can also prioritize receiving related plant information based on the user's social media posts. This allows for efficient reception of related information by analyzing the user's social media activity. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or without AI. For example, the information receiving unit can input data about the user's social media activity to the generation AI and cause the generation AI to receive related information.

[0051] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the plant. For example, the providing unit provides detailed advice for an important plant. The providing unit can also provide normal advice for a common plant. Furthermore, the providing unit can provide simplified advice for a less important plant. For example, the providing unit adjusts the level of detail of the advice based on the rarity or growth stage of the plant. The providing unit can also adjust the level of detail of the advice based on the user's level of interest. In this way, by adjusting the level of detail of the advice according to the importance of the plant, more detailed advice can be provided for important plants. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the importance of the plant to the generating AI and cause the generating AI to adjust the level of detail of the advice.

[0052] When providing advice, the providing unit can apply different advice algorithms depending on the plant category. For example, in the case of a houseplant, the providing unit can apply an advice algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the providing unit can also apply an advice algorithm dedicated to vegetables. Furthermore, in the case of a flower, the providing unit can also apply an advice algorithm dedicated to flowers. For example, the providing unit selects an appropriate advice algorithm depending on the plant category and provides advice. Furthermore, the providing unit can improve the accuracy of advice based on the plant category. In this way, more accurate advice can be provided by applying an advice algorithm according to the plant category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the plant category to the generating AI and cause the generating AI to select an advice algorithm.

[0053] When providing advice, the providing unit can improve the accuracy of the advice by referring to past advice results for the user. For example, the providing unit provides current advice by referring to the results of advice received by the user in the past. The providing unit can also find a specific pattern from the user's past advice results and improve the accuracy of the advice. Furthermore, the providing unit can adjust the advice algorithm based on the user's past advice results. For example, the providing unit adjusts the current advice algorithm based on the results of advice received by the user in the past. The providing unit can also find a specific pattern based on the user's past advice results and improve the accuracy of the advice. In this way, the accuracy of the advice can be improved by referring to the past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0054] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the plant. For example, the providing unit can prioritize advice for recently submitted plants. The providing unit can also postpone advice for plants that were submitted earlier. Furthermore, the providing unit can adjust the order of advice based on the time of submission. For example, the providing unit can determine the priority of advice based on the date and time of submission of the plant. The providing unit can also determine the priority of advice based on the growth stage or season of the plant. In this way, by determining the priority of advice based on the time of submission, it is possible to give priority to advice based on the latest information. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the time of submission of the plant to the generating AI and cause the generating AI to determine the priority of advice.

[0055] When providing advice, the providing unit can adjust the order of advice based on the relevance of the plants. For example, the providing unit prioritizes advice for highly relevant plants. The providing unit can also postpone advice for less relevant plants. Furthermore, the providing unit can adjust the order of advice based on the relevance. For example, the providing unit prioritizes advice for plants of the same species or plants with the same risk of pests. The providing unit can also prioritize advice for plants in the same cultivation environment. In this way, by adjusting the order of advice based on the relevance of the plants, it is possible to prioritize advice for highly relevant plants. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data regarding the relevance of plants to the generating AI and cause the generating AI to adjust the order of advice.

[0056] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. For example, if the user is a beginner, the providing unit can provide advice that avoids technical terms. Furthermore, if the user is an intermediate expert, the providing unit can provide advice that uses technical terms appropriately. Furthermore, if the user is an advanced expert, the providing unit can provide advice that uses a lot of technical terms. For example, the providing unit adjusts the way the advice is expressed based on the user's level of expertise. Furthermore, the providing unit can adjust the level of detail of the advice according to the user's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0057] When identifying pests and diseases, the identification unit can adjust the level of detail of the identification based on the importance of the plant. For example, in the case of an important plant, the identification unit performs detailed identification of pests and diseases. In addition, in the case of a common plant, the identification unit can also perform normal identification of pests and diseases. Furthermore, in the case of a less important plant, the identification unit can perform simplified identification of pests and diseases. For example, the identification unit adjusts the level of detail of the identification based on the rarity or growth stage of the plant. In addition, the identification unit can adjust the level of detail of the identification based on the user's level of interest. In this way, by adjusting the level of detail of the identification according to the importance of the plant, more detailed identification of important plants can be performed. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data regarding the importance of the plant to the generation AI and cause the generation AI to adjust the level of detail of the identification.

[0058] When identifying pests or diseases, the identification unit can apply different identification algorithms depending on the plant category. For example, in the case of a houseplant, the identification unit can apply an identification algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the identification unit can also apply an identification algorithm dedicated to vegetables. Furthermore, in the case of a flower, the identification unit can also apply an identification algorithm dedicated to flowers. For example, the identification unit selects an appropriate identification algorithm depending on the plant category and performs identification. The identification unit can also improve the accuracy of identification based on the plant category. In this way, by applying an identification algorithm according to the plant category, more accurate identification results can be provided. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data regarding the plant category to the generation AI and cause the generation AI to select an identification algorithm.

[0059] When identifying pests and diseases, the identification unit can improve the accuracy of the identification by referring to the user's past identification results. For example, the identification unit performs current identification by referring to the results of pests and diseases identified by the user in the past. The identification unit can also find specific patterns from the user's past identification results and improve the accuracy of the identification. Furthermore, the identification unit can adjust the identification algorithm based on the user's past identification results. For example, the identification unit adjusts the current identification algorithm based on the results of pests and diseases identified by the user in the past. The identification unit can also find specific patterns based on the user's past identification results and improve the accuracy of the identification. In this way, the accuracy of the identification can be improved by referring to the past identification results. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past identification result data into the generation AI and cause the generation AI to improve the accuracy of the identification.

[0060] When identifying pests and diseases, the identification unit can determine a specific priority based on the time of submission of the plants. For example, the identification unit can prioritize the identification of recently submitted plants. The identification unit can also postpone the identification of older submitted plants. Furthermore, the identification unit can adjust the identification order based on the time of submission. For example, the identification unit can determine a specific priority based on the date and time of submission of the plants. The identification unit can also determine a specific priority based on the growth stage or season of the plants. In this way, by determining a specific priority based on the time of submission, the latest information can be preferentially identified. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data regarding the time of submission of the plants to the generation AI and cause the generation AI to determine the specific priority.

[0061] When identifying pests and diseases, the identification unit can adjust the identification order based on the relevance of the plants. For example, the identification unit prioritizes identifying highly related plants. The identification unit can also postpone identifying less related plants. Furthermore, the identification unit can adjust the identification order based on the relevance. For example, the identification unit prioritizes identifying plants of the same species or plants with the same risk of pests and diseases. The identification unit can also prioritize identifying plants in the same cultivation environment. In this way, by adjusting the identification order based on the relevance of the plants, highly related plants can be prioritized. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input data regarding the relevance of the plants to the generation AI and cause the generation AI to adjust the identification order.

[0062] When identifying pests and diseases, the identification unit can adjust the use of specific technical terms according to the user's level of expertise. For example, if the user is a beginner, the identification unit can provide identification results that avoid technical terms. Furthermore, if the user is an intermediate user, the identification unit can provide identification results that use a moderate amount of technical terms. Furthermore, if the user is an advanced user, the identification unit can provide identification results that use a lot of technical terms. For example, the identification unit can adjust the way the identification results are presented based on the user's level of expertise. Furthermore, the identification unit can adjust the level of detail of the identification results according to the user's level of expertise. By adjusting the use of specific technical terms according to the user's level of expertise, identification results that are easy for the user to understand can be provided. Some or all of the above-described processing in the identification unit may be performed, for example, using AI or without AI. For example, the identification unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the identification results are presented.

[0063] When suggesting fertilizer selection, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the plant. For example, the suggestion unit can suggest detailed fertilizer selection methods for important plants. The suggestion unit can also suggest regular fertilizer selection methods for common plants. The suggestion unit can also suggest simplified fertilizer selection methods for less important plants. For example, the suggestion unit can adjust the level of detail of the suggestion based on the rarity or growth stage of the plant. The suggestion unit can also adjust the level of detail of the suggestion based on the user's level of interest. In this way, by adjusting the level of detail of the suggestion according to the importance of the plant, more detailed suggestions can be made for important plants. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the importance of the plant to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0064] When suggesting fertilizer selection, the suggestion unit can apply different suggestion algorithms depending on the plant category. For example, in the case of a houseplant, the suggestion unit can apply a suggestion algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the suggestion unit can apply a suggestion algorithm dedicated to vegetables. Furthermore, in the case of a flower, the suggestion unit can apply a suggestion algorithm dedicated to flowers. For example, the suggestion unit selects an appropriate suggestion algorithm depending on the plant category and makes a suggestion. Furthermore, the suggestion unit can improve the accuracy of the suggestion based on the plant category. In this way, by applying a suggestion algorithm according to the plant category, more accurate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the plant category to the generation AI and cause the generation AI to select a suggestion algorithm.

[0065] When making a suggestion for selecting a fertilizer, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit provides a current suggestion by referring to the user's past suggestion results. The suggestion unit can also find specific patterns from the user's past suggestion results and improve the accuracy of the suggestion. Furthermore, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit adjusts the current suggestion algorithm based on the user's past suggestion results. The suggestion unit can also find specific patterns based on the user's past suggestion results and improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0066] When suggesting fertilizer selection, the suggestion unit can determine the priority of suggestions based on the time of plant submission. For example, the suggestion unit can prioritize recently submitted plants. The suggestion unit can also postpone plants that have been submitted recently. Furthermore, the suggestion unit can adjust the order of suggestions based on the time of submission. For example, the suggestion unit determines the priority of suggestions based on the date and time of plant submission. The suggestion unit can also determine the priority of suggestions based on the growth stage or season of the plant. In this way, by determining the priority of suggestions based on the time of submission, the latest information can be preferentially suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the time of plant submission to the generation AI and cause the generation AI to determine the priority of suggestions.

[0067] When suggesting fertilizer selection, the suggestion unit can adjust the order of suggestions based on the relevance of the plants. For example, the suggestion unit prioritizes suggesting highly relevant plants. The suggestion unit can also postpone suggesting less relevant plants. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance. For example, the suggestion unit prioritizes suggesting plants of the same species or plants with the same risk of pests. The suggestion unit can also prioritize suggesting plants in the same cultivation environment. In this way, by adjusting the order of suggestions based on the relevance of the plants, highly relevant plants can be prioritized for suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data regarding the relevance of plants to the generation AI and cause the generation AI to adjust the order of suggestions.

[0068] When suggesting a fertilizer selection, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can provide a suggestion that avoids technical terminology. Furthermore, if the user is an intermediate user, the suggestion unit can provide a suggestion that uses technical terminology appropriately. Furthermore, if the user is an advanced user, the suggestion unit can provide a suggestion that uses a lot of technical terminology. For example, the suggestion unit can adjust the way the suggestion is expressed based on the user's level of expertise. Furthermore, the suggestion unit can adjust the level of detail of the suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the suggestion according to the user's level of expertise, it is possible to provide a suggestion that is easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the suggestion is expressed.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The reception unit can analyze the user's past gardening activity history and provide optimal advice. For example, the reception unit can record the types of plants the user has grown in the past and their growth status, and suggest current plant care methods based on that data. The reception unit can also analyze problems the user has encountered in the past and how they have been resolved, allowing for quick response if a similar problem occurs. Furthermore, the reception unit can provide individually customized advice taking into account the user's gardening habits and preferences. This allows for more accurate advice to be provided by utilizing the user's past gardening activity history.

[0071] The analysis unit can predict the growth of plants. For example, the analysis unit predicts future growth based on the current state of the plant and past growth data. The analysis unit can also consider weather data and seasonal changes to suggest optimal environmental conditions for plant growth. Furthermore, the analysis unit can provide an appropriate care schedule based on the plant growth prediction. This makes it possible to maintain the health of plants by predicting their growth and providing appropriate care.

[0072] The information receiving unit can evaluate the user's gardening skill level and provide appropriate advice. For example, the information receiving unit can evaluate the user's skill level based on information input by the user and past gardening activities. The information receiving unit can also provide basic advice for beginners and specialized advice for advanced gardeners depending on the user's skill level. Furthermore, the information receiving unit can also suggest how to select appropriate gardening tools and materials depending on the user's skill level. This can increase the success rate of gardening by providing advice according to the user's skill level.

[0073] The providing unit can provide advice according to the growth stage of the plant. For example, when a plant is in different growth stages such as the germination stage, growth stage, flowering stage, and harvest stage, the providing unit can suggest a care method according to each stage. The providing unit can also suggest an appropriate type of fertilizer and the timing of fertilization according to the growth stage of the plant. Furthermore, the providing unit can also suggest an appropriate frequency and amount of watering according to the growth stage of the plant. In this way, by providing advice according to the growth stage of the plant, it is possible to maintain the health of the plant and promote its growth.

[0074] The providing unit can suggest methods for preventing pests and diseases of plants. For example, the providing unit can suggest appropriate pest and disease prevention methods depending on the type and growth stage of the plant. The providing unit can also predict the risk of pest and disease outbreaks depending on changes in the season and weather and suggest preventive measures. Furthermore, the providing unit can suggest preventive measures for quickly dealing with similar problems if they occur, based on information about pests and diseases that the user has encountered in the past. This makes it possible to maintain the health of plants by preventing the outbreak of pests and diseases before they occur.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The reception unit receives an image of a plant from a user. The image of a plant from a user may be in, but is not limited to, a JPEG format or a PNG format. The reception unit receives, for example, an image of a plant taken by a user from a smartphone or a computer. The reception unit can also automatically adjust the resolution of the image and convert it into a format suitable for analysis. For example, the reception unit can convert a low-resolution image into a high-resolution image to improve the accuracy of the analysis. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit and identify the type and condition of the plant. The analysis is performed using, for example, an image analysis algorithm or deep learning technology, but is not limited to these examples. For example, the analysis unit analyzes the shape and color of the leaves, the thickness of the stem, etc., to identify the type of plant. The analysis unit can also detect changes in leaf color and disease spots, etc., to determine the health of the plant. For example, the analysis unit detects changes in leaf color and determines the presence or absence of pests. Step 3: The information receiving unit receives information about the plant's condition from the user. The information includes, but is not limited to, text information and numerical data. For example, the information receiving unit receives information input by the user such as "I may have watered it too much recently" or "It's in a place with poor sunlight." The information receiving unit can also automatically analyze the information input by the user and send it to the analysis unit. Step 4: The providing unit provides advice based on the information obtained by the analysis unit and the information receiving unit. The advice may be provided in the form of, for example, a text message or an image guide, but is not limited to these examples. For example, the providing unit may provide advice such as "You may be watering the plant too much, so reduce the frequency of watering" or "Move the plant to a sunny location." The providing unit may also display the advice on the user's smartphone or computer.

[0077] (Example 2) A gardening support system according to an embodiment of the present invention allows a user to send images of plants, which are analyzed by a generation AI to provide appropriate advice. In the gardening support system, a user sends an image of a plant, which the generation AI analyzes to identify the plant's type and condition, and provides appropriate advice based on the information provided by the user. For example, the gardening support system allows a user to take a photo of the plant they want to consult about. For example, the system recommends taking a photo of the entire plant or any problematic areas. This image is then sent to the generation AI. The gardening support system then uses the generation AI to analyze the received image. The generation AI identifies the plant's type and condition using image recognition technology. For example, the generation AI analyzes the leaf shape, color, and stem thickness to identify the plant's type. The system also detects changes in leaf color and disease spots to determine the plant's health. Furthermore, the gardening support system allows a user to communicate the plant's condition to the generation AI. For example, the user inputs information such as "I may have watered it too much recently" or "It's in a shady spot." This information is then sent to the generation AI. The generation AI provides appropriate advice based on the received image and the information provided by the user. For example, it provides advice such as, "You may be watering too much, so reduce the frequency of watering" or "Move the plant to a sunny spot." This advice is displayed on the user's smartphone or computer. This allows the gardening support system to easily teach even beginners how to care for their plants. The gardening support system allows users to send images of their plants, which the generating AI analyzes and provides appropriate advice, making it easy for even beginners to learn how to care for their plants. For example, if the leaves of a houseplant turn yellow, the generating AI can identify the cause and suggest appropriate measures to maintain the plant's health. In vegetable cultivation, the generating AI can also provide appropriate advice to increase harvest yields.

[0078] A gardening support system according to an embodiment includes a receiving unit, an analysis unit, an information receiving unit, and a providing unit. The receiving unit receives plant images from a user. Plant images from a user may be in, for example, JPEG or PNG format, but are not limited to these. The receiving unit receives plant images taken by the user from, for example, a smartphone or PC. The receiving unit can also automatically adjust the image resolution and convert the image into a format suitable for analysis. For example, the receiving unit may convert low-resolution images to high resolution to improve analysis accuracy. The analysis unit uses generative AI to analyze the images received by the receiving unit and identify the type and condition of the plant. The analysis may be performed using, for example, an image analysis algorithm or deep learning technology, but is not limited to these examples. For example, the analysis unit may analyze the shape and color of the leaves, the thickness of the stem, and the like to identify the type of plant. The analysis unit can also detect changes in leaf color and disease spots to determine the health of the plant. For example, the analysis unit may detect changes in leaf color to determine the presence or absence of pests. The information receiving unit receives information regarding the condition of the plant from the user. The information may include, but is not limited to, text information and numerical data. For example, the information receiving unit may receive information input by the user, such as "I think I've watered it too much recently" or "It's in a place with poor sunlight." The information receiving unit may also automatically analyze the information input by the user and send it to the analysis unit. The providing unit provides advice based on the information obtained by the analysis unit and the information receiving unit. The advice may be provided in the form of, for example, a text message or an image guide, but is not limited to, such examples. For example, the providing unit may provide advice such as "You may be watering it too much, so please water it less frequently" or "Move it to a sunny location." The providing unit may also display the advice on the user's smartphone or computer. In this way, the gardening support system according to the embodiment allows users to send images of plants, and the generation AI analyzes and provides appropriate advice, making it easy for even beginner gardeners to learn how to care for their plants.

[0079] The analysis unit can identify the type and condition of a plant using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and computer vision technology. The analysis unit can identify the type of plant using, for example, deep learning technology. For example, the analysis unit can analyze the shape and color of leaves, stem thickness, etc. to identify the type of plant. The analysis unit can also identify the condition of a plant using computer vision technology. For example, the analysis unit can detect changes in leaf color, disease spots, etc. to determine the health of the plant. The analysis unit can also identify the growth stage of a plant using image recognition technology. For example, the analysis unit can analyze the height and number of leaves of a plant to determine the growth stage. In this way, the type and condition of a plant can be accurately identified using image recognition technology. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input an image of a plant into the generation AI and cause the generation AI to identify the type and condition of the plant.

[0080] The information receiving unit can receive information about the condition of the plant from the user. Information about the condition of the plant includes, but is not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. The information receiving unit can receive information input by the user, such as, "I may have watered it too much recently" or "It's in a place with poor sunlight." The information receiving unit can also automatically analyze the information input by the user and transmit it to the analysis unit. For example, the information receiving unit can analyze the information input by the user using text analysis technology and transmit it to the analysis unit. Furthermore, the information receiving unit can determine the condition of the plant based on the information input by the user. For example, the information receiving unit can determine the frequency of watering and the amount of sunlight based on the information input by the user. By receiving detailed condition information from the user, more appropriate advice can be provided. Some or all of the above-described processing by the information receiving unit can be performed using, for example, AI, or without AI. For example, the information receiving unit can input the information input by the user to a generation AI and have the generation AI analyze the information.

[0081] The providing unit can provide advice based on the health condition of the plant. Examples of the plant's health condition include, but are not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. The providing unit can provide advice based on the plant's health condition, such as "You may be overwatering the plant, so reduce the frequency of watering" or "Move the plant to a sunny location." The providing unit can also provide advice on how to select fertilizer and how to deal with pests and diseases based on the plant's health condition. For example, the providing unit can suggest an appropriate type of fertilizer and the timing of fertilization based on the plant's health condition. Furthermore, the providing unit can suggest methods for preventing and exterminating pests and diseases based on the plant's health condition. For example, the providing unit can suggest an appropriate type of pesticide and its application method based on the plant's health condition. This makes plant care easier by providing advice based on the plant's health condition. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input data on the plant's health condition into a generating AI and cause the generating AI to generate advice.

[0082] The providing unit includes an identification unit that identifies pests and diseases. The identification unit identifies pests and diseases using, for example, image analysis technology. For example, the identification unit analyzes images of plants to identify the type of pest or disease. The identification unit can also identify pests and diseases using symptom pattern matching technology. For example, the identification unit analyzes patterns of lesions and discoloration that appear on plant leaves and stems to identify the type of pest or disease. Furthermore, the identification unit can suggest appropriate countermeasures based on the results of pest and disease identification. For example, the identification unit suggests appropriate types and application methods of pesticides for identified pests and diseases. In this way, appropriate countermeasures can be taken by identifying pests and diseases. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input images of plants to the generation AI and cause the generation AI to identify pests and diseases.

[0083] The providing unit includes a suggestion unit that suggests a method for selecting a fertilizer. The suggestion unit suggests an appropriate method for selecting a fertilizer based on, for example, the type of plant and the condition of the soil. For example, the suggestion unit suggests an appropriate type of fertilizer and the timing of fertilization depending on the type of plant. The suggestion unit can also suggest an appropriate method for selecting a fertilizer based on the condition of the soil. For example, the suggestion unit suggests an appropriate type of fertilizer based on the pH value and nutrient content of the soil. Furthermore, the suggestion unit can also suggest an appropriate method for selecting a fertilizer depending on the growth stage of the plant. For example, the suggestion unit suggests nitrogen fertilizer in the early growth stage of the plant and phosphate fertilizer in the later growth stage. This allows the suggestion of an appropriate fertilizer selection method to promote plant growth. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data regarding the type of plant and the condition of the soil into the generation AI and cause the generation AI to execute a suggestion on how to select a fertilizer.

[0084] The reception unit can identify the user's emotions and adjust the timing of image reception based on the identified user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to accept images at a time when the AI ​​can relax. The reception unit can also be configured to accept images immediately if the user is relaxed. Furthermore, the reception unit can also be configured to accept images quickly if the user is in a hurry. 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. For example, the reception unit can calculate an emotion score based on changes in facial expression and adjust the timing of image reception. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of image reception. This allows the user's stress to be reduced by adjusting the timing of image reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The reception unit can analyze the user's past image submission history and select an appropriate reception method. For example, the reception unit can analyze the time periods during which the user frequently submitted images in the past and encourage submission during those time periods. The reception unit can also prioritize submission methods (smartphone, computer, etc.) that the user has used in the past. Furthermore, the reception unit can encourage submission during specific days of the week or time periods based on the user's past submission history. For example, the reception unit can analyze the submission dates and times of images the user has previously submitted and suggest the optimal reception time. The reception unit can also analyze the devices the user has previously used and suggest the optimal submission method. In this way, by analyzing the past history, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0086] When receiving images, the reception unit can filter the images based on the user's current gardening situation and areas of interest. For example, the reception unit can receive only relevant images based on the type of plant the user is currently growing. The reception unit can also receive only relevant images based on the user's areas of interest (e.g., ornamental plants, vegetables). The reception unit can also receive appropriate images based on the user's gardening situation (beginner, intermediate, advanced). For example, the reception unit filters relevant images based on the type of plant the user is currently growing. The reception unit can also filter relevant images based on the user's areas of interest. This enables efficient support by receiving appropriate images based on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input data regarding the user's gardening situation and areas of interest to the generation AI and have the generation AI perform image filtering.

[0087] When receiving an image, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user issues instructions by voice, the reception unit prioritizes reception of the voice input. Furthermore, when the user issues instructions by text, the reception unit can also prioritize reception of the text input. Furthermore, when the user sends an image, the reception unit can also prioritize reception of the image input. For example, when the user issues instructions by voice, the reception unit converts the voice into text using voice recognition technology and receives the text. Furthermore, when the user issues instructions by text, the reception unit analyzes the content of the instructions using text analysis technology and receives the text. This improves user convenience by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input data regarding the user's input method to a generation AI and cause the generation AI to select the optimal reception means.

[0088] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize important images. Furthermore, when the user is relaxed, the reception unit can also prioritize images with a normal priority. Furthermore, when the user is in a hurry, the reception unit can prioritize images with a high level of urgency. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expression and determine the priority of images. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of images. Thus, by prioritizing images according to the user's emotions, important images can be prioritized. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] When receiving images, the reception unit can prioritize receiving highly relevant images by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving images of plants related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to plants at the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving images related to plants around the user's home. For example, the reception unit can acquire the user's geographical location information from GPS data or an IP address and prioritize receiving highly relevant images based on that information. Furthermore, the reception unit can filter images of plants related to a specific area based on the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant images can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant images.

[0090] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive images of plants posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related plant images. Furthermore, the reception unit can also receive related plant images based on the activity of the user's friends on social media. For example, the reception unit can analyze the user's social media account and filter related plant images. The reception unit can also preferentially receive related plant images based on the user's social media posts. This allows for efficient reception of related images by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input data regarding the user's social media activity into the generation AI and cause the generation AI to receive related images.

[0091] When receiving an image, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. For example, the reception unit suggests the optimal reception time and submission method based on the user's past feedback. The reception unit also encourages reception on a specific day of the week or time period based on the user's past feedback. In this way, by reflecting the past feedback, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. Furthermore, the analysis unit can provide visually easy-to-understand analysis results when the user is stressed. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expression and adjust the presentation method of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the presentation method of the analysis. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the plant. For example, the analysis unit performs a detailed analysis for an important plant. The analysis unit can also perform a normal analysis for a common plant. Furthermore, the analysis unit can perform a simplified analysis for a less important plant. For example, the analysis unit adjusts the level of detail of the analysis based on the rarity or growth stage of the plant. The analysis unit can also adjust the level of detail of the analysis based on the user's level of interest. In this way, by adjusting the level of detail of the analysis according to the importance of the plant, more detailed analysis can be performed for important plants. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the plant to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0094] During analysis, the analysis unit can apply different analysis algorithms depending on the plant category. For example, in the case of a houseplant, the analysis unit can apply an analysis algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the analysis unit can also apply an analysis algorithm dedicated to vegetables. Furthermore, in the case of a flower, the analysis unit can also apply an analysis algorithm dedicated to flowers. For example, the analysis unit selects an appropriate analysis algorithm depending on the plant category and performs the analysis. The analysis unit can also improve the accuracy of the analysis based on the plant category. In this way, by applying an analysis algorithm according to the plant category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the plant category to the generation AI and cause the generation AI to select an analysis algorithm.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs a current analysis by referring to the analysis results of images previously submitted by the user. The analysis unit can also find specific patterns from the user's past analysis results and improve the accuracy of the analysis. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the current analysis algorithm based on the analysis results of images previously submitted by the user. The analysis unit can also find specific patterns based on the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expression and adjust the length of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0097] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the plants. For example, the analysis unit prioritizes analysis of recently submitted plants. The analysis unit can also postpone analysis of plants that were submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit determines the analysis priority based on the date and time of submission of the plants. The analysis unit can also determine the analysis priority based on the growth stage or season of the plants. In this way, by determining the analysis priority based on the time of submission, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the time of submission of the plants to the generation AI and have the generation AI determine the analysis priority.

[0098] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the plants. For example, the analysis unit prioritizes analysis of highly related plants. The analysis unit can also postpone analysis of less related plants. Furthermore, the analysis unit can adjust the order of analysis based on the relevance. For example, the analysis unit prioritizes analysis of plants of the same species or plants with the same risk of pests. The analysis unit can also prioritize analysis of plants in the same cultivation environment. In this way, by adjusting the order of analysis based on the relevance of the plants, highly related plants can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of the plants to the generation AI and cause the generation AI to adjust the order of analysis.

[0099] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results that use technical terminology appropriately. Furthermore, if the user is an advanced user, the analysis unit can provide analysis results that use a lot of technical terminology. For example, the analysis unit can adjust the way the analysis results are presented based on the user's level of expertise. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0100] The information receiving unit can estimate the user's emotions and adjust the information receiving method based on the estimated user emotions. For example, if the user is feeling stressed, the information receiving unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the information receiving unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the information receiving unit can prioritize voice input and quickly receive information. For example, the information receiving unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, the information receiving unit can calculate an emotion score based on changes in facial expressions and adjust the information receiving method. The information receiving unit can also record the user's voice and estimate the user's emotions using voice analysis technology. For example, the information receiving unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the information receiving method. This allows the user's stress to be reduced by adjusting the information receiving method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 information receiving unit may be performed using AI, or may be performed without using AI. For example, the information receiving unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0101] The information accepting unit can analyze the user's past information provision history and select the optimal reception method when accepting information. For example, the information accepting unit can automatically display information that the user has frequently provided in the past as candidates. The information accepting unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the information accepting unit can predict and suggest information that will be used during a specific time period based on the user's past information provision history. For example, the information accepting unit can analyze the date and time of information provided by the user in the past and suggest the optimal reception time. The information accepting unit can also analyze input methods used by the user in the past and suggest the optimal input method. In this way, the optimal reception method can be provided to the user by analyzing the past history. Some or all of the above-mentioned processing in the information accepting unit can be performed using, for example, AI, or can be performed without using AI. For example, the information accepting unit can input the user's past information provision history data into a generation AI and cause the generation AI to select the optimal reception method.

[0102] When receiving information, the information receiving unit can filter the information based on the user's current gardening situation and areas of interest. For example, the information receiving unit can receive only relevant information based on the type of plant the user is currently growing. The information receiving unit can also receive only relevant information based on the user's areas of interest (e.g., ornamental plants, vegetables). Furthermore, the information receiving unit can receive appropriate information based on the user's gardening situation (beginner, intermediate, advanced). For example, the information receiving unit filters relevant information based on the type of plant the user is currently growing. The information receiving unit can also filter relevant information based on the user's areas of interest. This enables efficient support by receiving appropriate information based on the user's situation and areas of interest. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or without AI. For example, the information receiving unit can input data related to the user's gardening situation and areas of interest to the generation AI and have the generation AI perform information filtering.

[0103] The information receiving unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, when the user is stressed, the information receiving unit can prioritize important information. Furthermore, when the user is relaxed, the information receiving unit can also prioritize information with a normal priority. Furthermore, when the user is in a hurry, the information receiving unit can prioritize information with a high level of urgency. For example, the information receiving unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the information receiving unit can calculate an emotion score based on changes in facial expression and determine the priority of information. The information receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the information receiving unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of information. By prioritizing information according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the information receiving unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0104] When receiving information, the information receiving unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information receiving unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the information receiving unit can prioritize receiving information related to plants at the user's travel destination. Furthermore, when the user is at home, the information receiving unit can prioritize receiving information related to plants around the user's home. For example, the information receiving unit can acquire the user's geographical location information from GPS data or an IP address and prioritize receiving highly relevant information based on that information. Furthermore, the information receiving unit can filter information related to a specific area based on the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information receiving unit may be performed using, or without, AI. For example, the information receiving unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0105] The information receiving unit can analyze the user's social media activity and receive related information when receiving information. For example, the information receiving unit can prioritize receiving information about plants posted by the user on social media. The information receiving unit can also analyze the user's social media activity and receive related plant information. Furthermore, the information receiving unit can also receive related plant information based on the activity of the user's friends on social media. For example, the information receiving unit can analyze the user's social media account and filter related plant information. The information receiving unit can also prioritize receiving related plant information based on the user's social media posts. This allows for efficient reception of related information by analyzing the user's social media activity. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or without AI. For example, the information receiving unit can input data about the user's social media activity to the generation AI and cause the generation AI to receive related information.

[0106] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, the providing unit can provide detailed advice when the user is relaxed. The providing unit can also provide concise advice when the user is in a hurry. Furthermore, the providing unit can provide visually easy-to-understand advice when the user is stressed. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expression and adjust the way the advice is presented. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way the advice is presented. This allows the user to be provided with advice that is easy to understand by adjusting the way the advice is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0107] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the plant. For example, the providing unit provides detailed advice for an important plant. The providing unit can also provide normal advice for a common plant. Furthermore, the providing unit can provide simplified advice for a less important plant. For example, the providing unit adjusts the level of detail of the advice based on the rarity or growth stage of the plant. The providing unit can also adjust the level of detail of the advice based on the user's level of interest. In this way, by adjusting the level of detail of the advice according to the importance of the plant, more detailed advice can be provided for important plants. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the importance of the plant to the generating AI and cause the generating AI to adjust the level of detail of the advice.

[0108] When providing advice, the providing unit can apply different advice algorithms depending on the plant category. For example, in the case of a houseplant, the providing unit can apply an advice algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the providing unit can also apply an advice algorithm dedicated to vegetables. Furthermore, in the case of a flower, the providing unit can also apply an advice algorithm dedicated to flowers. For example, the providing unit selects an appropriate advice algorithm depending on the plant category and provides advice. Furthermore, the providing unit can improve the accuracy of advice based on the plant category. In this way, more accurate advice can be provided by applying an advice algorithm according to the plant category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the plant category to the generating AI and cause the generating AI to select an advice algorithm.

[0109] When providing advice, the providing unit can improve the accuracy of the advice by referring to past advice results for the user. For example, the providing unit provides current advice by referring to the results of advice received by the user in the past. The providing unit can also find a specific pattern from the user's past advice results and improve the accuracy of the advice. Furthermore, the providing unit can adjust the advice algorithm based on the user's past advice results. For example, the providing unit adjusts the current advice algorithm based on the results of advice received by the user in the past. The providing unit can also find a specific pattern based on the user's past advice results and improve the accuracy of the advice. In this way, the accuracy of the advice can be improved by referring to the past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0110] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is stressed, the providing unit can provide visually easy-to-understand advice. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expression and adjust the length of the advice. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the advice. By adjusting the length of the advice according to the user's emotions, optimal advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0111] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the plant. For example, the providing unit can prioritize advice for recently submitted plants. The providing unit can also postpone advice for plants that were submitted earlier. Furthermore, the providing unit can adjust the order of advice based on the time of submission. For example, the providing unit can determine the priority of advice based on the date and time of submission of the plant. The providing unit can also determine the priority of advice based on the growth stage or season of the plant. In this way, by determining the priority of advice based on the time of submission, it is possible to give priority to advice based on the latest information. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the time of submission of the plant to the generating AI and cause the generating AI to determine the priority of advice.

[0112] When providing advice, the providing unit can adjust the order of advice based on the relevance of the plants. For example, the providing unit prioritizes advice for highly relevant plants. The providing unit can also postpone advice for less relevant plants. Furthermore, the providing unit can adjust the order of advice based on the relevance. For example, the providing unit prioritizes advice for plants of the same species or plants with the same risk of pests. The providing unit can also prioritize advice for plants in the same cultivation environment. In this way, by adjusting the order of advice based on the relevance of the plants, it is possible to prioritize advice for highly relevant plants. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data regarding the relevance of plants to the generating AI and cause the generating AI to adjust the order of advice.

[0113] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. For example, if the user is a beginner, the providing unit can provide advice that avoids technical terms. Furthermore, if the user is an intermediate expert, the providing unit can provide advice that uses technical terms appropriately. Furthermore, if the user is an advanced expert, the providing unit can provide advice that uses a lot of technical terms. For example, the providing unit adjusts the way the advice is expressed based on the user's level of expertise. Furthermore, the providing unit can adjust the level of detail of the advice according to the user's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0114] The identification unit can estimate the user's emotions and adjust the pest identification method based on the estimated user emotions. For example, if the user is relaxed, the identification unit can provide a detailed pest identification method. Furthermore, if the user is in a hurry, the identification unit can provide a concise pest identification method. Furthermore, if the user is stressed, the identification unit can provide a visually easy-to-understand pest identification method. For example, the identification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the identification unit can calculate an emotion score based on changes in facial expression and adjust the pest identification method. The identification unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the identification unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the pest identification method. This allows the pest identification method to be adjusted according to the user's emotions, thereby providing identification results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0115] When identifying pests and diseases, the identification unit can adjust the level of detail of the identification based on the importance of the plant. For example, in the case of an important plant, the identification unit performs detailed identification of pests and diseases. In addition, in the case of a common plant, the identification unit can also perform normal identification of pests and diseases. Furthermore, in the case of a less important plant, the identification unit can perform simplified identification of pests and diseases. For example, the identification unit adjusts the level of detail of the identification based on the rarity or growth stage of the plant. In addition, the identification unit can adjust the level of detail of the identification based on the user's level of interest. In this way, by adjusting the level of detail of the identification according to the importance of the plant, more detailed identification of important plants can be performed. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data regarding the importance of the plant to the generation AI and cause the generation AI to adjust the level of detail of the identification.

[0116] When identifying pests or diseases, the identification unit can apply different identification algorithms depending on the plant category. For example, in the case of a houseplant, the identification unit can apply an identification algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the identification unit can also apply an identification algorithm dedicated to vegetables. Furthermore, in the case of a flower, the identification unit can also apply an identification algorithm dedicated to flowers. For example, the identification unit selects an appropriate identification algorithm depending on the plant category and performs identification. The identification unit can also improve the accuracy of identification based on the plant category. In this way, by applying an identification algorithm according to the plant category, more accurate identification results can be provided. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data regarding the plant category to the generation AI and cause the generation AI to select an identification algorithm.

[0117] When identifying pests and diseases, the identification unit can improve the accuracy of the identification by referring to the user's past identification results. For example, the identification unit performs current identification by referring to the results of pests and diseases identified by the user in the past. The identification unit can also find specific patterns from the user's past identification results and improve the accuracy of the identification. Furthermore, the identification unit can adjust the identification algorithm based on the user's past identification results. For example, the identification unit adjusts the current identification algorithm based on the results of pests and diseases identified by the user in the past. The identification unit can also find specific patterns based on the user's past identification results and improve the accuracy of the identification. In this way, the accuracy of the identification can be improved by referring to the past identification results. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past identification result data into the generation AI and cause the generation AI to improve the accuracy of the identification.

[0118] The identification unit can estimate the user's emotions and adjust the specific length based on the estimated user emotions. For example, if the user is in a hurry, the identification unit can provide a short and concise identification result. Furthermore, if the user is relaxed, the identification unit can provide a detailed identification result. Furthermore, if the user is stressed, the identification unit can provide a visually easy-to-understand identification result. For example, the identification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the identification unit can calculate an emotion score based on changes in facial expression and adjust the specific length. The identification unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the identification unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the specific length. By adjusting the specific length according to the user's emotions, the system can provide an optimal identification result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0119] When identifying pests and diseases, the identification unit can determine a specific priority based on the time of submission of the plants. For example, the identification unit can prioritize the identification of recently submitted plants. The identification unit can also postpone the identification of older submitted plants. Furthermore, the identification unit can adjust the identification order based on the time of submission. For example, the identification unit can determine a specific priority based on the date and time of submission of the plants. The identification unit can also determine a specific priority based on the growth stage or season of the plants. In this way, by determining a specific priority based on the time of submission, the latest information can be preferentially identified. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data regarding the time of submission of the plants to the generation AI and cause the generation AI to determine the specific priority.

[0120] When identifying pests and diseases, the identification unit can adjust the identification order based on the relevance of the plants. For example, the identification unit prioritizes identifying highly related plants. The identification unit can also postpone identifying less related plants. Furthermore, the identification unit can adjust the identification order based on the relevance. For example, the identification unit prioritizes identifying plants of the same species or plants with the same risk of pests and diseases. The identification unit can also prioritize identifying plants in the same cultivation environment. In this way, by adjusting the identification order based on the relevance of the plants, highly related plants can be prioritized. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input data regarding the relevance of the plants to the generation AI and cause the generation AI to adjust the identification order.

[0121] When identifying pests and diseases, the identification unit can adjust the use of specific technical terms according to the user's level of expertise. For example, if the user is a beginner, the identification unit can provide identification results that avoid technical terms. Furthermore, if the user is an intermediate user, the identification unit can provide identification results that use a moderate amount of technical terms. Furthermore, if the user is an advanced user, the identification unit can provide identification results that use a lot of technical terms. For example, the identification unit can adjust the way the identification results are presented based on the user's level of expertise. Furthermore, the identification unit can adjust the level of detail of the identification results according to the user's level of expertise. By adjusting the use of specific technical terms according to the user's level of expertise, identification results that are easy for the user to understand can be provided. Some or all of the above-described processing in the identification unit may be performed, for example, using AI or without AI. For example, the identification unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the identification results are presented.

[0122] The suggestion unit can estimate the user's emotions and adjust the method of suggesting how to select fertilizers based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest detailed instructions for selecting fertilizers. Furthermore, if the user is in a hurry, the suggestion unit can suggest simple instructions for selecting fertilizers. Furthermore, if the user is stressed, the suggestion unit can suggest visually easy-to-understand instructions for selecting fertilizers. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expression and adjust the method of suggesting how to select fertilizers. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the method of suggesting how to select fertilizers. This allows the method of suggesting how to select fertilizers to be adjusted according to the user's emotions, thereby providing suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0123] When suggesting fertilizer selection, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the plant. For example, the suggestion unit can suggest detailed fertilizer selection methods for important plants. The suggestion unit can also suggest regular fertilizer selection methods for common plants. The suggestion unit can also suggest simplified fertilizer selection methods for less important plants. For example, the suggestion unit can adjust the level of detail of the suggestion based on the rarity or growth stage of the plant. The suggestion unit can also adjust the level of detail of the suggestion based on the user's level of interest. In this way, by adjusting the level of detail of the suggestion according to the importance of the plant, more detailed suggestions can be made for important plants. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the importance of the plant to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0124] When suggesting fertilizer selection, the suggestion unit can apply different suggestion algorithms depending on the plant category. For example, in the case of a houseplant, the suggestion unit can apply a suggestion algorithm dedicated to houseplants. Furthermore, in the case of a vegetable, the suggestion unit can apply a suggestion algorithm dedicated to vegetables. Furthermore, in the case of a flower, the suggestion unit can apply a suggestion algorithm dedicated to flowers. For example, the suggestion unit selects an appropriate suggestion algorithm depending on the plant category and makes a suggestion. Furthermore, the suggestion unit can improve the accuracy of the suggestion based on the plant category. In this way, by applying a suggestion algorithm according to the plant category, more accurate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the plant category to the generation AI and cause the generation AI to select a suggestion algorithm.

[0125] When making a suggestion for selecting a fertilizer, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit provides a current suggestion by referring to the user's past suggestion results. The suggestion unit can also find specific patterns from the user's past suggestion results and improve the accuracy of the suggestion. Furthermore, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit adjusts the current suggestion algorithm based on the user's past suggestion results. The suggestion unit can also find specific patterns based on the user's past suggestion results and improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0126] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expression and adjust the length of the suggestions. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the suggestions. By adjusting the length of the suggestions according to the user's emotions, the optimal suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0127] When suggesting fertilizer selection, the suggestion unit can determine the priority of suggestions based on the time of plant submission. For example, the suggestion unit can prioritize recently submitted plants. The suggestion unit can also postpone plants that have been submitted recently. Furthermore, the suggestion unit can adjust the order of suggestions based on the time of submission. For example, the suggestion unit determines the priority of suggestions based on the date and time of plant submission. The suggestion unit can also determine the priority of suggestions based on the growth stage or season of the plant. In this way, by determining the priority of suggestions based on the time of submission, the latest information can be preferentially suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the time of plant submission to the generation AI and cause the generation AI to determine the priority of suggestions.

[0128] When suggesting fertilizer selection, the suggestion unit can adjust the order of suggestions based on the relevance of the plants. For example, the suggestion unit prioritizes suggesting highly relevant plants. The suggestion unit can also postpone suggesting less relevant plants. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance. For example, the suggestion unit prioritizes suggesting plants of the same species or plants with the same risk of pests. The suggestion unit can also prioritize suggesting plants in the same cultivation environment. In this way, by adjusting the order of suggestions based on the relevance of the plants, highly relevant plants can be prioritized for suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data regarding the relevance of plants to the generation AI and cause the generation AI to adjust the order of suggestions.

[0129] When suggesting a fertilizer selection, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can provide a suggestion that avoids technical terminology. Furthermore, if the user is an intermediate user, the suggestion unit can provide a suggestion that uses technical terminology appropriately. Furthermore, if the user is an advanced user, the suggestion unit can provide a suggestion that uses a lot of technical terminology. For example, the suggestion unit can adjust the way the suggestion is expressed based on the user's level of expertise. Furthermore, the suggestion unit can adjust the level of detail of the suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the suggestion according to the user's level of expertise, it is possible to provide a suggestion that is easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the way the suggestion is expressed. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, information reception unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives an image of a plant photographed by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the type and condition of the plant using a generation AI. The information reception unit is realized, for example, by the control unit 46A of the smart device 14 and receives information on the condition of the plant from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the analysis results. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies pests and diseases. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a fertilizer selection method. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, information reception unit, and provision 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 receives an image of a plant photographed by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the type and condition of the plant using a generation AI. The information reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives information on the condition of the plant from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the analysis results. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies pests and diseases. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a fertilizer selection method. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, information reception unit, and provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and receives an image of a plant photographed by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the type and condition of the plant using a generation AI. The information reception unit is realized by the control unit 46A of the headset terminal 314 and receives information on the condition of the plant from the user. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the analysis results. The identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies pests and diseases. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a fertilizer selection method. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, information reception unit, and provision unit, 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 receives images of plants photographed by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the type and condition of the plant using a generation AI. The information reception unit is realized, for example, by the control unit 46A of the robot 414 and receives information on the condition of the plant from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the analysis results. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies pests and diseases. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a fertilizer selection method.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The reception unit can analyze the user's past gardening activity history and provide optimal advice. For example, the reception unit can record the types of plants the user has grown in the past and their growth status, and suggest current plant care methods based on that data. The reception unit can also analyze problems the user has encountered in the past and how they have been resolved, allowing for quick response if a similar problem occurs. Furthermore, the reception unit can provide individually customized advice taking into account the user's gardening habits and preferences. This allows for more accurate advice to be provided by utilizing the user's past gardening activity history.

[0132] The analysis unit can predict the growth of plants. For example, the analysis unit predicts future growth based on the current state of the plant and past growth data. The analysis unit can also consider weather data and seasonal changes to suggest optimal environmental conditions for plant growth. Furthermore, the analysis unit can provide an appropriate care schedule based on the plant growth prediction. This makes it possible to maintain the health of plants by predicting their growth and providing appropriate care.

[0133] The information receiving unit can evaluate the user's gardening skill level and provide appropriate advice. For example, the information receiving unit can evaluate the user's skill level based on information input by the user and past gardening activities. The information receiving unit can also provide basic advice for beginners and specialized advice for advanced gardeners depending on the user's skill level. Furthermore, the information receiving unit can also suggest how to select appropriate gardening tools and materials depending on the user's skill level. This can increase the success rate of gardening by providing advice according to the user's skill level.

[0134] The providing unit can provide advice according to the growth stage of the plant. For example, when a plant is in different growth stages such as the germination stage, growth stage, flowering stage, and harvest stage, the providing unit can suggest a care method according to each stage. The providing unit can also suggest an appropriate type of fertilizer and the timing of fertilization according to the growth stage of the plant. Furthermore, the providing unit can also suggest an appropriate frequency and amount of watering according to the growth stage of the plant. In this way, by providing advice according to the growth stage of the plant, it is possible to maintain the health of the plant and promote its growth.

[0135] The providing unit can suggest methods for preventing pests and diseases of plants. For example, the providing unit can suggest appropriate pest and disease prevention methods depending on the type and growth stage of the plant. The providing unit can also predict the risk of pest and disease outbreaks depending on changes in the season and weather and suggest preventive measures. Furthermore, the providing unit can suggest preventive measures for quickly dealing with similar problems if they occur, based on information about pests and diseases that the user has encountered in the past. This makes it possible to maintain the health of plants by preventing the outbreak of pests and diseases before they occur.

[0136] The reception unit can estimate the user's emotions and adjust the content of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide concise and easy-to-follow advice. If the user is relaxed, the reception unit can also provide detailed advice. Furthermore, if the user is in a hurry, the reception unit can also provide advice that can be quickly implemented. In this way, by adjusting the content of advice according to the user's emotions, it is possible to reduce the user's stress and increase the rate at which advice is implemented.

[0137] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is in a hurry. Furthermore, the analysis unit can display analysis results that are visually easy to understand when the user is feeling stressed. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0138] The information accepting unit can estimate the user's emotions and adjust the information input method based on the estimated user's emotions. For example, the information accepting unit can provide a simple input form when the user is feeling stressed. The information accepting unit can also provide detailed input options when the user is relaxed. Furthermore, the information accepting unit can prioritize voice input when the user is in a hurry. In this way, adjusting the information input method according to the user's emotions can reduce the user's stress and allow the user to input information smoothly.

[0139] The providing unit can estimate the user's emotions and adjust the timing of advice based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide advice immediately. Also, if the user is feeling stressed, the providing unit can provide advice at an appropriate timing. Furthermore, if the user is in a hurry, the providing unit can provide advice quickly. In this way, by adjusting the timing of advice according to the user's emotions, it is possible to reduce the user's stress and improve the ease with which the advice is accepted.

[0140] The providing unit can estimate the user's emotions and adjust the format of the advice based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed advice in text format. If the user is in a hurry, the providing unit can also provide concise advice in bullet point format. Furthermore, if the user is feeling stressed, the providing unit can also provide advice in the form of visually easy-to-understand diagrams or videos. In this way, by adjusting the format of advice according to the user's emotions, it is possible to provide advice that is easy for the user to understand.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The reception unit receives an image of a plant from a user. The image of a plant from a user may be in, but is not limited to, a JPEG format or a PNG format. The reception unit receives, for example, an image of a plant taken by a user from a smartphone or a computer. The reception unit can also automatically adjust the resolution of the image and convert it into a format suitable for analysis. For example, the reception unit can convert a low-resolution image into a high-resolution image to improve the accuracy of the analysis. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit and identify the type and condition of the plant. The analysis is performed using, for example, an image analysis algorithm or deep learning technology, but is not limited to these examples. For example, the analysis unit analyzes the shape and color of the leaves, the thickness of the stem, etc., to identify the type of plant. The analysis unit can also detect changes in leaf color and disease spots, etc., to determine the health of the plant. For example, the analysis unit detects changes in leaf color and determines the presence or absence of pests. Step 3: The information receiving unit receives information about the plant's condition from the user. The information includes, but is not limited to, text information and numerical data. For example, the information receiving unit receives information input by the user such as "I may have watered it too much recently" or "It's in a place with poor sunlight." The information receiving unit can also automatically analyze the information input by the user and send it to the analysis unit. Step 4: The providing unit provides advice based on the information obtained by the analysis unit and the information receiving unit. The advice may be provided in the form of, for example, a text message or an image guide, but is not limited to these examples. For example, the providing unit may provide advice such as "You may be watering the plant too much, so reduce the frequency of watering" or "Move the plant to a sunny location." The providing unit may also display the advice on the user's smartphone or computer.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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 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.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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 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.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image of a plant from a user; an analysis unit that analyzes the image received by the reception unit and identifies the type and state of the plant; an information receiving unit that receives information from a user; a providing unit that provides advice based on the information obtained by the analyzing unit and the information receiving unit. A system characterized by:

2. The analysis unit Identifying plant species and conditions using image recognition technology 2. The system of claim 1.

3. The information receiving unit Accepts information about the plant's condition from the user 2. The system of claim 1.

4. The providing unit Providing advice based on plant health 2. The system of claim 1.

5. The providing unit Equipped with an identification unit that identifies pests 2. The system of claim 1.

6. The providing unit Equipped with a proposal department that proposes fertilizer selection methods 2. The system of claim 1.

7. The reception unit Identifying user emotions and adjusting the timing of image reception based on the identified 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.

9. The reception unit As images are received, they are filtered based on the user's current gardening situation and interests.

2. The system of claim 1.

Images