System

Abstract

An object of a system according to an embodiment is to evaluate a state of a plant in a home garden and propose an optimal growing method.SOLUTION: A system includes a photograph acquisition unit, an analysis unit, and a proposal unit. The photograph acquisition unit acquires a photograph of a home garden from a user. The analysis unit analyzes the photograph acquired by the photograph acquisition unit, and evaluates the state of the plant. The proposal unit proposes an optimum growing method based on the state of the plant evaluated by the analysis 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 the problem that it is difficult to accurately evaluate the condition of plants in home gardens and propose optimal cultivation methods.

[0005] The system according to the embodiment aims to evaluate the condition of plants in a home garden and propose optimal cultivation methods. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, an analysis unit, and a proposal unit. The photo acquisition unit acquires photos of the home vegetable garden from a user. The analysis unit analyzes the photos acquired by the photo acquisition unit and evaluates the condition of the plants. The proposal unit proposes an optimal cultivation method based on the condition of the plants evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the condition of plants in a home garden and suggest optimal cultivation methods. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The cultivation suggestion system according to an embodiment of the present invention is a system that acquires cultivation information for a home vegetable garden from a photograph and suggests the optimal cultivation method based on that information. In this system, a user takes a photograph of the home vegetable garden and inputs the photograph into a generating AI, which then analyzes the condition of the plants and suggests the optimal cultivation method. This allows the cultivation suggestion system to enable the user to efficiently cultivate plants in their home vegetable garden.

[0029] A plant cultivation suggestion system according to an embodiment includes a photo acquisition unit, an analysis unit, and a suggestion unit. The photo acquisition unit acquires photos of a home vegetable garden from a user. For example, the user uploads photos taken with a smartphone to the system. The photo acquisition unit can also acquire photos taken with a digital camera via a USB cable. The photo acquisition unit can also acquire photos by downloading them from cloud storage. For example, the photo acquisition unit acquires photos stored by the user on Google Drive or Dropbox. The analysis unit analyzes the photos acquired by the photo acquisition unit and evaluates the condition of the plant. For example, the analysis unit can use a generation AI to analyze the color and shape of leaves to determine whether the plant is healthy. The analysis unit can also use the generation AI to analyze the thickness of stems to evaluate the plant's growth condition. The analysis unit can also use the generation AI to analyze the blooming condition of flowers to evaluate the plant's flowering status. For example, the generation AI uses a fine-tuned model to accurately analyze the condition of the plant. The suggestion unit suggests an optimal plant cultivation method based on the condition of the plant evaluated by the analysis unit. For example, the suggestion unit suggests the frequency and amount of watering. The suggestion unit can also suggest the type of fertilizer and the timing of fertilization. The suggestion unit can also suggest how to adjust the hours of sunlight. For example, the suggestion unit provides advice to the user in the form of, "This plant needs to be watered once a week. Use a fertilizer with a high nitrogen content once a month." This allows the cultivation suggestion system according to the embodiment to enable users to efficiently cultivate plants in their home gardens. For example, even beginners can easily understand the health condition of plants and practice appropriate cultivation methods. Furthermore, continuous monitoring allows users to adjust cultivation methods while watching the growth of the plants.

[0030] The photo acquisition unit allows the generation AI to automatically suggest optimal shooting angles and lighting conditions for photos taken by the user and encourage re-shooting. For example, when a user takes a photo of their home vegetable garden, the photo acquisition unit allows the generation AI to analyze the photo and suggest optimal shooting angles and lighting conditions. For example, if the leaves of a plant are in shadow, the generation AI may instruct the generation AI to "make sure the light is coming from a little further to the right." The photo acquisition unit can also instruct the generation AI to adjust the shooting angle to capture the entire plant. For example, it provides specific instructions such as "point the camera slightly upward." The photo acquisition unit can also instruct the generation AI to adjust the lighting conditions. For example, it provides instructions such as "take the photo using natural light." This allows the user to take photos under optimal conditions.

[0031] The photo acquisition unit allows the generating AI to enlarge a portion of the plant in real time when taking a photo and instruct the user to capture the details. For example, when a user takes a photo of a plant, the photo acquisition unit allows the generating AI to enlarge a portion of the plant in real time and instruct the user to capture the details. For example, the photo acquisition unit provides specific instructions such as, "Please enlarge and take a photo of this leaf." The photo acquisition unit can also instruct the generating AI to enlarge a specific portion to detect signs of plant disease. For example, the photo acquisition unit provides instructions such as, "There are signs of disease in this portion. Please take a detailed photo." The photo acquisition unit can also instruct the generating AI to enlarge a specific portion to evaluate the plant's growth status in detail. For example, the photo acquisition unit provides instructions such as, "Please enlarge and take a photo of this stem." This allows the user to accurately capture the details.

[0032] A drone can automatically take photos of the entire home garden and input them into the generative AI. For example, a drone can automatically fly and take photos of the home garden from multiple angles. A drone can also focus on taking photos of a specific area of ​​the home garden. For example, it can take photos in response to instructions such as, "Take detailed photos of this area." A drone can also adjust its altitude to capture an overall picture of the home garden. For example, it can take photos in response to instructions such as, "Increase your altitude and take a photo of the entire area." In this way, photos of the entire home garden can be automatically taken and input into the generative AI.

[0033] Voice memos can be added to photos, and the generation AI can use that voice information for analysis. For example, the photo acquisition unit adds voice memos to photos taken by the user, and the generation AI can use that voice information for analysis. For example, a voice memo such as "This plant hasn't been doing well lately" can be added. The photo acquisition unit can also allow the user to add voice memos containing detailed descriptions of the plant's condition. For example, a voice memo such as "The color of these leaves is changing." The photo acquisition unit can also add voice memos containing questions about plant cultivation methods. For example, a voice memo such as "What kind of fertilizer is suitable for this plant?" can be added. This allows the generation AI to analyze more detailed information.

[0034] Generative AI can analyze a plant's growth history and compare it with past data to detect abnormalities. For example, generative AI can analyze a plant's growth history and compare it with past data to detect abnormalities. For example, it can analyze changes in leaf color and shape and display an alert if an abnormality is found. Generative AI can also analyze a plant's growth rate and compare it with past data to detect abnormalities. For example, it can display an alert if growth is slowing. Generative AI can also analyze signs of plant disease and compare it with past data to detect abnormalities. For example, it can display an alert if signs of disease are found. This makes it possible to analyze a plant's growth history and detect abnormalities early.

[0035] When analyzing the condition of a plant, the generative AI can simultaneously analyze environmental data such as soil quality, humidity, and temperature. For example, when analyzing the condition of a plant, the generative AI can simultaneously analyze environmental data such as soil quality, humidity, and temperature. For example, it can analyze the soil pH value and humidity to evaluate the health of the plant. The generative AI can also analyze temperature data to evaluate the environmental conditions suitable for plant growth. For example, it can analyze air temperature and soil temperature to evaluate the temperature conditions suitable for plant growth. The generative AI can also analyze humidity data to evaluate the humidity conditions suitable for plant growth. For example, it can analyze relative humidity and absolute humidity to evaluate the humidity conditions suitable for plant growth. This allows environmental data to be simultaneously analyzed when analyzing the condition of a plant.

[0036] The generating AI can compare the data of other users in analyzing the condition of plants and provide a benchmark. For example, the generating AI can compare the data of other users in analyzing the condition of plants and provide a benchmark. For example, it can compare growth data of the same type of plant to evaluate the growth status of the user's plant. The generating AI can also evaluate the effectiveness of the user's cultivation method by comparing it with that of other users. For example, it can compare the type of fertilizer used by other users and the timing of fertilization to evaluate the user's cultivation method. The generating AI can also develop algorithms to benchmark the growth of the user's plants based on the data of other users. For example, it can provide a benchmark based on growth rate or changes in leaf color. This allows it to provide a benchmark by comparing it with the data of other users.

[0037] Generative AI can visualize the condition of a plant as a 3D model, allowing users to intuitively understand it. For example, generative AI can visualize the condition of a plant as a 3D model, allowing users to intuitively understand it. For example, it can display the plant's growth process in a 3D model, allowing users to check the growth status. Generative AI can also display the plant's health condition in a 3D model, allowing users to intuitively understand it. For example, it can display changes in leaf color and shape in a 3D model. Generative AI can also display signs of plant disease in a 3D model, allowing users to intuitively understand it. For example, it can highlight areas showing signs of disease in the 3D model. This allows the plant's condition to be visualized as a 3D model, allowing users to intuitively understand it.

[0038] The generation AI can reflect the user's past cultivation history in the cultivation methods it proposes and customize them individually. For example, the generation AI can analyze the user's past cultivation history and propose the optimal cultivation method based on that data. For example, it can refer to cultivation methods that have been successful in the past and propose a method that is suitable for the current plant. The generation AI can also propose individually customized cultivation methods based on the user's past cultivation history. For example, it can provide advice on how to avoid cultivation methods that have failed in the past. The generation AI can also develop algorithms for adjusting cultivation methods based on the user's past cultivation history. For example, it can propose the optimal cultivation conditions for the current plant based on past data. This makes it possible to reflect the user's past cultivation history and propose individually customized cultivation methods.

[0039] The generation AI can collect user feedback on the proposed training methods and improve the proposals based on that feedback. For example, the generation AI can collect user feedback on the proposed training methods and improve the proposals based on that data. For example, if a user provides feedback that "this method was ineffective," the generation AI can suggest a different method. The generation AI can also develop an algorithm to adjust the training methods based on user feedback. For example, it can adjust the algorithm based on the feedback and suggest a more effective training method. The generation AI can also update the proposals based on user feedback. For example, it can improve the proposals based on the feedback and reflect that in the next proposal. This allows the proposals to be improved based on user feedback.

[0040] The generative AI can share the proposed cultivation method with other users and promote the exchange of opinions within the community. For example, the generative AI can share the proposed cultivation method with other users and promote the exchange of opinions within the community. For example, the generative AI can post the cultivation method on an online forum and collect feedback from other users. The generative AI can also share the proposed cultivation method on a social media group and exchange opinions with other users. For example, the generative AI can share the cultivation method on a Facebook group or Twitter. The generative AI can also share the proposed cultivation method with a local gardening club and exchange opinions with other members. For example, the generative AI can introduce the cultivation method at a club meeting. This allows the cultivation method to be shared with other users and promote the exchange of opinions within the community.

[0041] The generation AI can provide the proposed cultivation method in video format, making it easier for the user to understand visually. For example, the generation AI can provide the proposed cultivation method in video format, making it easier for the user to understand visually. For example, it can explain how to water and how to apply fertilizer in a video. The generation AI can also show the steps of the cultivation method in a video, making it easier for the user to understand intuitively. For example, it can show how to prune and how to repot in a video. The generation AI can also emphasize the key points of the cultivation method in the video, making it easier for the user to understand important points. For example, it can emphasize the key points of how to apply fertilizer in a video. In this way, the cultivation method is provided in video format, making it easier for the user to understand visually.

[0042] Generative AI can analyze plant growth data over the long term and suggest the optimal cultivation method for each season. Generative AI can, for example, analyze plant growth data over the long term and suggest the optimal cultivation method for each season. For example, it can suggest using a specific fertilizer in the spring and increasing the frequency of watering in the summer. Generative AI can also adjust cultivation methods for each season based on plant growth data. For example, it can suggest pruning in the fall and taking measures to keep the plant warm in the winter. Generative AI can also develop algorithms to optimize cultivation methods for each season based on plant growth data. For example, it can suggest the optimal cultivation conditions for the current season based on past data. This makes it possible to analyze plant growth data over the long term and suggest the optimal cultivation method for each season.

[0043] Generative AI can predict plant growth based on monitoring data and suggest future cultivation methods. Generative AI can, for example, predict plant growth based on monitoring data and suggest future cultivation methods. For example, it can predict how a plant will grow and suggest appropriate cultivation methods. Generative AI can also suggest future cultivation plans based on plant growth predictions. For example, it can suggest cultivation plans for the next season. Generative AI can also suggest long-term cultivation strategies based on plant growth predictions. For example, it can predict growth several months into the future and adjust cultivation methods based on that. This makes it possible to predict plant growth based on monitoring data and suggest future cultivation methods.

[0044] The generative AI can store monitoring data in the cloud and make it accessible from multiple devices. For example, the generative AI can store monitoring data in the cloud and allow users to access it from multiple devices. For example, the data can be checked from a smartphone or tablet. The generative AI can also allow users to access the data anytime, anywhere based on the data stored in the cloud. For example, the data can be accessed from any device with an internet connection. The generative AI can also back up the data based on the data stored in the cloud. For example, the generative AI can back up the data regularly to prevent data loss. This allows the monitoring data to be stored in the cloud and accessible from multiple devices.

[0045] The generating AI can share the monitoring data with other users and jointly improve the training methods. The generating AI, for example, can share the monitoring data with other users and jointly improve the training methods. For example, the data can be shared in an online community and advice can be received from other users. The generating AI can also make specific proposals for improving the training methods jointly with other users based on the monitoring data. For example, the data can be shared and training methods can be jointly considered. The generating AI can also develop algorithms for improving the training methods jointly with other users based on the monitoring data. For example, the optimal training method can be proposed based on data from multiple users. This allows the monitoring data to be shared with other users and training methods to be jointly improved.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The cultivation suggestion system may further include a plant identification unit. The plant identification unit automatically identifies the type of plant from a photo taken by the user. For example, it analyzes the shape and color of the leaves and the characteristics of the stem to identify the plant type. The plant identification unit can also suggest the optimal cultivation method based on the identified plant type. For example, it provides specific advice such as, "This plant is a tomato. Tomatoes need to be watered once a week." The plant identification unit can also evaluate the risk of specific pests and diseases depending on the type of plant the user is growing and suggest preventive measures. For example, it provides advice such as, "This plant is susceptible to aphids, so check it regularly." This allows the user to practice the optimal cultivation method for the type of plant they are growing.

[0048] The plant cultivation suggestion system may further include a communication unit. The communication unit provides a platform for users to share cultivation information and exchange advice. For example, a user can post photos of the plants they are growing and their cultivation methods, and receive feedback from other users. The communication unit also allows users to post questions about cultivation and receive answers from other users and experts. For example, a user might post a question such as, "The leaves of this plant are turning yellow. What should I do?" The communication unit also allows users to share their successes and failures in cultivation and exchange information with other users. For example, a user might share a success story such as, "Using this method, I was able to grow big tomatoes." This allows users to share information with each other and obtain hints for improving their cultivation methods.

[0049] The cultivation suggestion system can also visualize plant growth as a 3D model, allowing users to intuitively understand it. For example, the plant's growth process can be displayed in a 3D model, allowing users to check the growth status. The plant's health condition can also be displayed in a 3D model, allowing users to intuitively understand it. For example, changes in leaf color and shape can be displayed in a 3D model. Signs of plant disease can also be displayed in a 3D model, allowing users to intuitively understand it. For example, areas showing signs of disease can be highlighted in the 3D model. In this way, the plant's condition can be visualized in a 3D model, allowing users to intuitively understand it.

[0050] The cultivation suggestion system can also analyze plant growth data over the long term and suggest optimal cultivation methods for each season. For example, it can suggest using a specific fertilizer in the spring and increasing the frequency of watering in the summer. It can also adjust cultivation methods for each season based on plant growth data. For example, it can suggest pruning in the fall and taking measures to keep plants warm in the winter. It can also develop algorithms to optimize cultivation methods for each season based on plant growth data. For example, it can suggest the optimal cultivation conditions for the current season based on past data. This makes it possible to analyze plant growth data over the long term and suggest optimal cultivation methods for each season.

[0051] The cultivation suggestion system can also predict plant growth based on the monitoring data and suggest future cultivation methods. For example, it can predict how a plant will grow and suggest appropriate cultivation methods. It can also suggest future cultivation plans based on the plant growth predictions. For example, it can suggest cultivation plans for the next season. It can also suggest long-term cultivation strategies based on the plant growth predictions. For example, it can predict growth several months into the future and adjust cultivation methods based on that. This makes it possible to predict plant growth based on monitoring data and suggest future cultivation methods.

[0052] The cultivation suggestion system can further provide the proposed cultivation methods in video format, making them visually easier to understand. For example, watering methods and fertilizing methods can be explained in video. The cultivation method steps can also be shown in video, allowing the user to intuitively understand. For example, pruning methods and repotting procedures can be shown in video. The cultivation method can also highlight key points in the video, making it easier for the user to understand the important points. For example, the key points of fertilizing can be highlighted in video. In this way, the cultivation method can be provided in video format, making it visually easier to understand.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The photo acquisition unit acquires photos of the home garden from the user. For example, the user uploads photos taken with a smartphone to the system. The photo acquisition unit can also acquire photos taken with a digital camera via a USB cable. Furthermore, the photo acquisition unit can also acquire photos by downloading them from cloud storage. For example, the photo acquisition unit acquires photos saved by the user on Google Drive or Dropbox. Step 2: The analysis unit analyzes the photos acquired by the photo acquisition unit and evaluates the condition of the plant. For example, the analysis unit uses the generation AI to analyze the color and shape of the leaves and determine whether the plant is healthy. The analysis unit can also use the generation AI to analyze the thickness of the stem and evaluate the growth condition of the plant. The analysis unit can also use the generation AI to analyze the blooming condition of the flowers and evaluate the flowering status of the plant. For example, the generation AI uses a pre-finished model to analyze the condition of the plant with high accuracy. Step 3: The suggestion unit suggests the optimal cultivation method based on the plant's condition evaluated by the analysis unit. For example, the suggestion unit suggests the frequency and amount of watering. The suggestion unit can also suggest the type of fertilizer and the timing of fertilization. The suggestion unit can also suggest how to adjust the hours of sunlight. For example, the suggestion unit provides advice to the user in the form of, "This plant needs to be watered once a week. Use fertilizer with a high nitrogen content once a month."

[0055] (Example 2) The cultivation suggestion system according to an embodiment of the present invention is a system that acquires cultivation information for a home vegetable garden from a photograph and suggests the optimal cultivation method based on that information. In this system, a user takes a photograph of the home vegetable garden and inputs the photograph into a generating AI, which then analyzes the condition of the plants and suggests the optimal cultivation method. This allows the cultivation suggestion system to enable the user to efficiently cultivate plants in their home vegetable garden.

[0056] A plant cultivation suggestion system according to an embodiment includes a photo acquisition unit, an analysis unit, and a suggestion unit. The photo acquisition unit acquires photos of a home vegetable garden from a user. For example, the user uploads photos taken with a smartphone to the system. The photo acquisition unit can also acquire photos taken with a digital camera via a USB cable. The photo acquisition unit can also acquire photos by downloading them from cloud storage. For example, the photo acquisition unit acquires photos stored by the user on Google Drive or Dropbox. The analysis unit analyzes the photos acquired by the photo acquisition unit and evaluates the condition of the plant. For example, the analysis unit can use a generation AI to analyze the color and shape of leaves to determine whether the plant is healthy. The analysis unit can also use the generation AI to analyze the thickness of stems to evaluate the plant's growth condition. The analysis unit can also use the generation AI to analyze the blooming condition of flowers to evaluate the plant's flowering status. For example, the generation AI uses a fine-tuned model to accurately analyze the condition of the plant. The suggestion unit suggests an optimal plant cultivation method based on the condition of the plant evaluated by the analysis unit. For example, the suggestion unit suggests the frequency and amount of watering. The suggestion unit can also suggest the type of fertilizer and the timing of fertilization. The suggestion unit can also suggest how to adjust the hours of sunlight. For example, the suggestion unit provides advice to the user in the form of, "This plant needs to be watered once a week. Use a fertilizer with a high nitrogen content once a month." This allows the cultivation suggestion system according to the embodiment to enable users to efficiently cultivate plants in their home gardens. For example, even beginners can easily understand the health condition of plants and practice appropriate cultivation methods. Furthermore, continuous monitoring allows users to adjust cultivation methods while watching the growth of the plants.

[0057] The photo acquisition unit allows the generation AI to automatically suggest optimal shooting angles and lighting conditions for photos taken by the user and encourage re-shooting. For example, when a user takes a photo of their home vegetable garden, the photo acquisition unit allows the generation AI to analyze the photo and suggest optimal shooting angles and lighting conditions. For example, if the leaves of a plant are in shadow, the generation AI may instruct the generation AI to "make sure the light is coming from a little further to the right." The photo acquisition unit can also instruct the generation AI to adjust the shooting angle to capture the entire plant. For example, it provides specific instructions such as "point the camera slightly upward." The photo acquisition unit can also instruct the generation AI to adjust the lighting conditions. For example, it provides instructions such as "take the photo using natural light." This allows the user to take photos under optimal conditions.

[0058] The photo acquisition unit allows the generating AI to enlarge a portion of the plant in real time when taking a photo and instruct the user to capture the details. For example, when a user takes a photo of a plant, the photo acquisition unit allows the generating AI to enlarge a portion of the plant in real time and instruct the user to capture the details. For example, the photo acquisition unit provides specific instructions such as, "Please enlarge and take a photo of this leaf." The photo acquisition unit can also instruct the generating AI to enlarge a specific portion to detect signs of plant disease. For example, the photo acquisition unit provides instructions such as, "There are signs of disease in this portion. Please take a detailed photo." The photo acquisition unit can also instruct the generating AI to enlarge a specific portion to evaluate the plant's growth status in detail. For example, the photo acquisition unit provides instructions such as, "Please enlarge and take a photo of this stem." This allows the user to accurately capture the details.

[0059] The photo acquisition unit can use the emotion estimation function to analyze the user's emotions when taking a photo and provide photography advice to reduce stress. For example, when a user takes a photo of a plant, the photo acquisition unit's generation AI can use the emotion estimation function to analyze the user's emotions and provide advice to reduce stress. For example, it can display a message such as "Please relax and take the photo." Furthermore, if the user feels anxious about taking a photo, the photo acquisition unit can provide specific advice to reduce that anxiety. For example, it can display a message such as "We'll teach you some photography tips." Furthermore, if the user has questions about photography, the photo acquisition unit can provide information to resolve those questions. For example, it can provide specific advice such as "You'll get better results if you take the photo at this angle." This reduces the user's stress and allows them to take photos in a relaxed manner.

[0060] A drone can automatically take photos of the entire home garden and input them into the generative AI. For example, a drone can automatically fly and take photos of the home garden from multiple angles. A drone can also focus on taking photos of a specific area of ​​the home garden. For example, it can take photos in response to instructions such as, "Take detailed photos of this area." A drone can also adjust its altitude to capture an overall picture of the home garden. For example, it can take photos in response to instructions such as, "Increase your altitude and take a photo of the entire area." In this way, photos of the entire home garden can be automatically taken and input into the generative AI.

[0061] Voice memos can be added to photos, and the generation AI can use that voice information for analysis. For example, the photo acquisition unit adds voice memos to photos taken by the user, and the generation AI can use that voice information for analysis. For example, a voice memo such as "This plant hasn't been doing well lately" can be added. The photo acquisition unit can also allow the user to add voice memos containing detailed descriptions of the plant's condition. For example, a voice memo such as "The color of these leaves is changing." The photo acquisition unit can also add voice memos containing questions about plant cultivation methods. For example, a voice memo such as "What kind of fertilizer is suitable for this plant?" can be added. This allows the generation AI to analyze more detailed information.

[0062] A camera app equipped with an emotion estimation function can alleviate any anxiety or doubts a user may have when taking a photo in real time. For example, if a user is feeling anxious, the app can display a message such as "Please relax and take the photo." Furthermore, if a user has doubts about taking a photo, the generative AI can provide information to resolve the doubt. For example, the app can provide specific advice such as "You will get better results if you take the photo from this angle." Furthermore, if a user is feeling stressed about taking a photo, the generative AI can provide advice to reduce the stress. For example, the app can display a message such as "We'll teach you some photography tips." This allows the app to alleviate any anxiety or doubt a user may have when taking a photo in real time.

[0063] Generative AI can analyze a plant's growth history and compare it with past data to detect abnormalities. For example, generative AI can analyze a plant's growth history and compare it with past data to detect abnormalities. For example, it can analyze changes in leaf color and shape and display an alert if an abnormality is found. Generative AI can also analyze a plant's growth rate and compare it with past data to detect abnormalities. For example, it can display an alert if growth is slowing. Generative AI can also analyze signs of plant disease and compare it with past data to detect abnormalities. For example, it can display an alert if signs of disease are found. This makes it possible to analyze a plant's growth history and detect abnormalities early.

[0064] When analyzing the condition of a plant, the generative AI can simultaneously analyze environmental data such as soil quality, humidity, and temperature. For example, when analyzing the condition of a plant, the generative AI can simultaneously analyze environmental data such as soil quality, humidity, and temperature. For example, it can analyze the soil pH value and humidity to evaluate the health of the plant. The generative AI can also analyze temperature data to evaluate the environmental conditions suitable for plant growth. For example, it can analyze air temperature and soil temperature to evaluate the temperature conditions suitable for plant growth. The generative AI can also analyze humidity data to evaluate the humidity conditions suitable for plant growth. For example, it can analyze relative humidity and absolute humidity to evaluate the humidity conditions suitable for plant growth. This allows environmental data to be simultaneously analyzed when analyzing the condition of a plant.

[0065] The generative AI uses the emotion estimation function to generate summaries that capture the emotional nuances of plants, allowing the emotional elements to be reflected in the evaluation. For example, when summarizing, the generative AI uses the emotion estimation function to capture the emotional nuances of plants. For example, it generates summaries based on emotion scores. The generative AI also uses the emotion estimation function to build a system that reflects the emotional elements of plants in evaluations. For example, it performs evaluations based on emotion scores. The generative AI also uses the emotion estimation function to develop an algorithm for generating summaries that capture the emotional nuances of plants. For example, it generates summaries based on emotion scores and reflects these in the evaluations. In this way, by generating summaries that capture emotional nuances, the emotional elements can also be reflected in the evaluations.

[0066] The generating AI can compare the data of other users in analyzing the condition of plants and provide a benchmark. For example, the generating AI can compare the data of other users in analyzing the condition of plants and provide a benchmark. For example, it can compare growth data of the same type of plant to evaluate the growth status of the user's plant. The generating AI can also evaluate the effectiveness of the user's cultivation method by comparing it with that of other users. For example, it can compare the type of fertilizer used by other users and the timing of fertilization to evaluate the user's cultivation method. The generating AI can also develop algorithms to benchmark the growth of the user's plants based on the data of other users. For example, it can provide a benchmark based on growth rate or changes in leaf color. This allows it to provide a benchmark by comparing it with the data of other users.

[0067] Generative AI can visualize the condition of a plant as a 3D model, allowing users to intuitively understand it. For example, generative AI can visualize the condition of a plant as a 3D model, allowing users to intuitively understand it. For example, it can display the plant's growth process in a 3D model, allowing users to check the growth status. Generative AI can also display the plant's health condition in a 3D model, allowing users to intuitively understand it. For example, it can display changes in leaf color and shape in a 3D model. Generative AI can also display signs of plant disease in a 3D model, allowing users to intuitively understand it. For example, it can highlight areas showing signs of disease in the 3D model. This allows the plant's condition to be visualized as a 3D model, allowing users to intuitively understand it.

[0068] The generation AI can use the emotion estimation function to provide advice to reduce the anxiety the user feels about the condition of the plant. For example, the generation AI can use the emotion estimation function to provide advice to reduce the anxiety the user feels about the condition of the plant. For example, it can display a message such as, "This plant is healthy. Don't worry." Furthermore, if the user is anxious about how to grow the plant, the generation AI can provide specific advice to reduce that anxiety. For example, it can display a message such as, "Try this method." Furthermore, if the user is anxious about the health of the plant, the generation AI can provide information to reduce that anxiety. For example, it can display a message such as, "This plant is growing well." This makes it possible to provide advice to reduce the anxiety the user feels about the condition of the plant.

[0069] The generation AI can reflect the user's past cultivation history in the cultivation methods it proposes and customize them individually. For example, the generation AI can analyze the user's past cultivation history and propose the optimal cultivation method based on that data. For example, it can refer to cultivation methods that have been successful in the past and propose a method that is suitable for the current plant. The generation AI can also propose individually customized cultivation methods based on the user's past cultivation history. For example, it can provide advice on how to avoid cultivation methods that have failed in the past. The generation AI can also develop algorithms for adjusting cultivation methods based on the user's past cultivation history. For example, it can propose the optimal cultivation conditions for the current plant based on past data. This makes it possible to reflect the user's past cultivation history and propose individually customized cultivation methods.

[0070] The generation AI can collect user feedback on the proposed training methods and improve the proposals based on that feedback. For example, the generation AI can collect user feedback on the proposed training methods and improve the proposals based on that data. For example, if a user provides feedback that "this method was ineffective," the generation AI can suggest a different method. The generation AI can also develop an algorithm to adjust the training methods based on user feedback. For example, it can adjust the algorithm based on the feedback and suggest a more effective training method. The generation AI can also update the proposals based on user feedback. For example, it can improve the proposals based on the feedback and reflect that in the next proposal. This allows the proposals to be improved based on user feedback.

[0071] The generation AI can use the emotion estimation function to analyze the emotions the user feels about the proposed training method and make suggestions that will elicit positive emotions. For example, the generation AI can use the emotion estimation function to analyze the emotions the user feels about the proposed training method and make suggestions that will elicit positive emotions. For example, it can display a message such as, "This method is easy and effective." The generation AI can also make specific suggestions to elicit positive emotions based on the emotions the user feels about the proposed training method. For example, it can display a message such as, "Try this method." The generation AI can also adjust the content of the suggestions based on the emotions the user feels about the proposed training method. For example, if the user feels anxious, it can provide advice to alleviate that anxiety. This allows the user to have positive emotions about the proposed training method.

[0072] The generative AI can share the proposed cultivation method with other users and promote the exchange of opinions within the community. For example, the generative AI can share the proposed cultivation method with other users and promote the exchange of opinions within the community. For example, the generative AI can post the cultivation method on an online forum and collect feedback from other users. The generative AI can also share the proposed cultivation method on a social media group and exchange opinions with other users. For example, the generative AI can share the cultivation method on a Facebook group or Twitter. The generative AI can also share the proposed cultivation method with a local gardening club and exchange opinions with other members. For example, the generative AI can introduce the cultivation method at a club meeting. This allows the cultivation method to be shared with other users and promote the exchange of opinions within the community.

[0073] The generation AI can provide the proposed cultivation method in video format, making it easier for the user to understand visually. For example, the generation AI can provide the proposed cultivation method in video format, making it easier for the user to understand visually. For example, it can explain how to water and how to apply fertilizer in a video. The generation AI can also show the steps of the cultivation method in a video, making it easier for the user to understand intuitively. For example, it can show how to prune and how to repot in a video. The generation AI can also emphasize the key points of the cultivation method in the video, making it easier for the user to understand important points. For example, it can emphasize the key points of how to apply fertilizer in a video. In this way, the cultivation method is provided in video format, making it easier for the user to understand visually.

[0074] The generation AI can use the emotion estimation function to provide additional information to resolve any doubts or concerns the user may have about the proposed cultivation method. For example, the generation AI can use the emotion estimation function to provide additional information to resolve any doubts or concerns the user may have about the proposed cultivation method. For example, it can display a message such as, "Even beginners can easily practice this method." The generation AI can also provide specific information to resolve any doubts the user may have about the cultivation method. For example, it can provide a detailed explanation in response to a question such as, "How do I use this fertilizer?" The generation AI can also provide information to alleviate any doubts the user may have about the cultivation method. For example, it can display a message such as, "This method is effective. Don't worry." This makes it possible to provide additional information to resolve any doubts or concerns the user may have about the proposed cultivation method.

[0075] Generative AI can analyze plant growth data over the long term and suggest the optimal cultivation method for each season. Generative AI can, for example, analyze plant growth data over the long term and suggest the optimal cultivation method for each season. For example, it can suggest using a specific fertilizer in the spring and increasing the frequency of watering in the summer. Generative AI can also adjust cultivation methods for each season based on plant growth data. For example, it can suggest pruning in the fall and taking measures to keep the plant warm in the winter. Generative AI can also develop algorithms to optimize cultivation methods for each season based on plant growth data. For example, it can suggest the optimal cultivation conditions for the current season based on past data. This makes it possible to analyze plant growth data over the long term and suggest the optimal cultivation method for each season.

[0076] Generative AI can predict plant growth based on monitoring data and suggest future cultivation methods. Generative AI can, for example, predict plant growth based on monitoring data and suggest future cultivation methods. For example, it can predict how a plant will grow and suggest appropriate cultivation methods. Generative AI can also suggest future cultivation plans based on plant growth predictions. For example, it can suggest cultivation plans for the next season. Generative AI can also suggest long-term cultivation strategies based on plant growth predictions. For example, it can predict growth several months into the future and adjust cultivation methods based on that. This makes it possible to predict plant growth based on monitoring data and suggest future cultivation methods.

[0077] The generation AI can use the emotion estimation function to analyze the user's level of satisfaction with the plant's growth and provide positive feedback. For example, the generation AI can use the emotion estimation function to analyze the user's level of satisfaction with the plant's growth and provide positive feedback. For example, it can display a message such as, "The plant is growing well. That's wonderful." The generation AI can also make specific suggestions for providing positive feedback based on the user's level of satisfaction with the plant's growth. For example, it can display a message such as, "Please continue using this method." The generation AI can also develop an algorithm to adjust feedback based on the user's level of satisfaction with the plant's growth. For example, if satisfaction is high, it can suggest continuing with that method, and if satisfaction is low, it can suggest areas for improvement. In this way, the generation AI can analyze the user's level of satisfaction with the plant's growth and provide positive feedback.

[0078] The generative AI can store monitoring data in the cloud and make it accessible from multiple devices. For example, the generative AI can store monitoring data in the cloud and allow users to access it from multiple devices. For example, the data can be checked from a smartphone or tablet. The generative AI can also allow users to access the data anytime, anywhere based on the data stored in the cloud. For example, the data can be accessed from any device with an internet connection. The generative AI can also back up the data based on the data stored in the cloud. For example, the generative AI can back up the data regularly to prevent data loss. This allows the monitoring data to be stored in the cloud and accessible from multiple devices.

[0079] The generating AI can share the monitoring data with other users and jointly improve the training methods. The generating AI, for example, can share the monitoring data with other users and jointly improve the training methods. For example, the data can be shared in an online community and advice can be received from other users. The generating AI can also make specific proposals for improving the training methods jointly with other users based on the monitoring data. For example, the data can be shared and training methods can be jointly considered. The generating AI can also develop algorithms for improving the training methods jointly with other users based on the monitoring data. For example, the optimal training method can be proposed based on data from multiple users. This allows the monitoring data to be shared with other users and training methods to be jointly improved.

[0080] The generation AI can use the emotion estimation function to provide advice to reduce the anxiety the user feels about the growth of the plant. For example, the generation AI can use the emotion estimation function to provide advice to reduce the anxiety the user feels about the growth of the plant. For example, it can display a message such as, "This plant is healthy. Don't worry." Furthermore, if the user is anxious about how to grow the plant, the generation AI can provide specific advice to reduce the anxiety. For example, it can display a message such as, "Try this method." Furthermore, if the user is anxious about the health of the plant, the generation AI can provide information to reduce the anxiety. For example, it can display a message such as, "This plant is growing well." In this way, it is possible to provide advice to reduce the anxiety the user feels about the growth of the plant.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The cultivation suggestion system may further include an audio guide unit. The audio guide unit provides audio advice when the user takes a photo of the home vegetable garden. For example, it provides specific audio instructions such as "Move the camera a little more to the right." The audio guide unit can also provide audio information about the condition of the plants. For example, it can provide audio information such as "This plant is healthy." The audio guide unit can also provide audio advice to help the user relax if they are feeling anxious about taking a photo. For example, it can provide an audio message such as "Take a deep breath and relax." This allows the user to relax when taking a photo and take a photo under optimal conditions.

[0083] The cultivation suggestion system may further include a plant identification unit. The plant identification unit automatically identifies the type of plant from a photo taken by the user. For example, it analyzes the shape and color of the leaves and the characteristics of the stem to identify the plant type. The plant identification unit can also suggest the optimal cultivation method based on the identified plant type. For example, it provides specific advice such as, "This plant is a tomato. Tomatoes need to be watered once a week." The plant identification unit can also evaluate the risk of specific pests and diseases depending on the type of plant the user is growing and suggest preventive measures. For example, it provides advice such as, "This plant is susceptible to aphids, so check it regularly." This allows the user to practice the optimal cultivation method for the type of plant they are growing.

[0084] The plant cultivation suggestion system may further include a communication unit. The communication unit provides a platform for users to share cultivation information and exchange advice. For example, a user can post photos of the plants they are growing and their cultivation methods, and receive feedback from other users. The communication unit also allows users to post questions about cultivation and receive answers from other users and experts. For example, a user might post a question such as, "The leaves of this plant are turning yellow. What should I do?" The communication unit also allows users to share their successes and failures in cultivation and exchange information with other users. For example, a user might share a success story such as, "Using this method, I was able to grow big tomatoes." This allows users to share information with each other and obtain hints for improving their cultivation methods.

[0085] The cultivation suggestion system can further use the emotion estimation function to analyze the user's level of satisfaction with the plant's growth and provide positive feedback. For example, it can display a message such as "The plant is growing well. That's wonderful." The emotion estimation function can also be used to make specific suggestions for providing positive feedback based on the user's level of satisfaction with the plant's growth. For example, it can display a message such as "Please continue using this method." The emotion estimation function can also be used to develop an algorithm for adjusting feedback based on the user's level of satisfaction with the plant's growth. For example, if satisfaction is high, it can suggest continuing the method, and if satisfaction is low, it can suggest areas for improvement. In this way, the system can analyze the user's level of satisfaction with the plant's growth and provide positive feedback.

[0086] The cultivation suggestion system can also visualize plant growth as a 3D model, allowing users to intuitively understand it. For example, the plant's growth process can be displayed in a 3D model, allowing users to check the growth status. The plant's health condition can also be displayed in a 3D model, allowing users to intuitively understand it. For example, changes in leaf color and shape can be displayed in a 3D model. Signs of plant disease can also be displayed in a 3D model, allowing users to intuitively understand it. For example, areas showing signs of disease can be highlighted in the 3D model. In this way, the plant's condition can be visualized in a 3D model, allowing users to intuitively understand it.

[0087] The plant cultivation suggestion system can further use the emotion estimation function to provide additional information to resolve any doubts or concerns the user may have about the proposed cultivation method. For example, it can display a message such as, "Even beginners can easily put this method into practice." The emotion estimation function can also be used to provide specific information to resolve any doubts the user may have about the cultivation method. For example, it can provide a detailed explanation in response to a question such as, "How do I use this fertilizer?" The emotion estimation function can also be used to provide information to alleviate any concerns the user may have about the cultivation method. For example, it can display a message such as, "This method is effective. Don't worry." This makes it possible to provide additional information to resolve any doubts or concerns the user may have about the proposed cultivation method.

[0088] The cultivation suggestion system can also analyze plant growth data over the long term and suggest optimal cultivation methods for each season. For example, it can suggest using a specific fertilizer in the spring and increasing the frequency of watering in the summer. It can also adjust cultivation methods for each season based on plant growth data. For example, it can suggest pruning in the fall and taking measures to keep plants warm in the winter. It can also develop algorithms to optimize cultivation methods for each season based on plant growth data. For example, it can suggest the optimal cultivation conditions for the current season based on past data. This makes it possible to analyze plant growth data over the long term and suggest optimal cultivation methods for each season.

[0089] The plant cultivation suggestion system can further use the emotion estimation function to provide advice to alleviate any anxiety the user may feel about the plant's growth. For example, a message such as "This plant is healthy. Don't worry" can be displayed. Furthermore, if the user is feeling anxious about how to cultivate the plant, the emotion estimation function can also be used to provide specific advice to alleviate that anxiety. For example, a message such as "Try this method" can be displayed. Furthermore, if the user is feeling anxious about the plant's health, the emotion estimation function can also be used to provide information to alleviate that anxiety. For example, a message such as "This plant is growing well" can be displayed. In this way, advice to alleviate any anxiety the user may feel about the plant's growth can be provided.

[0090] The cultivation suggestion system can also predict plant growth based on the monitoring data and suggest future cultivation methods. For example, it can predict how a plant will grow and suggest appropriate cultivation methods. It can also suggest future cultivation plans based on the plant growth predictions. For example, it can suggest cultivation plans for the next season. It can also suggest long-term cultivation strategies based on the plant growth predictions. For example, it can predict growth several months into the future and adjust cultivation methods based on that. This makes it possible to predict plant growth based on monitoring data and suggest future cultivation methods.

[0091] The cultivation suggestion system can further provide the proposed cultivation methods in video format, making them visually easier to understand. For example, watering methods and fertilizing methods can be explained in video. The cultivation method steps can also be shown in video, allowing the user to intuitively understand. For example, pruning methods and repotting procedures can be shown in video. The cultivation method can also highlight key points in the video, making it easier for the user to understand the important points. For example, the key points of fertilizing can be highlighted in video. In this way, the cultivation method can be provided in video format, making it visually easier to understand.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The photo acquisition unit acquires photos of the home garden from the user. For example, the user uploads photos taken with a smartphone to the system. The photo acquisition unit can also acquire photos taken with a digital camera via a USB cable. Furthermore, the photo acquisition unit can also acquire photos by downloading them from cloud storage. For example, the photo acquisition unit acquires photos saved by the user on Google Drive or Dropbox. Step 2: The analysis unit analyzes the photos acquired by the photo acquisition unit and evaluates the condition of the plant. For example, the analysis unit uses the generation AI to analyze the color and shape of the leaves and determine whether the plant is healthy. The analysis unit can also use the generation AI to analyze the thickness of the stem and evaluate the growth condition of the plant. The analysis unit can also use the generation AI to analyze the blooming condition of the flowers and evaluate the flowering status of the plant. For example, the generation AI uses a pre-finished model to analyze the condition of the plant with high accuracy. Step 3: The suggestion unit suggests the optimal cultivation method based on the plant's condition evaluated by the analysis unit. For example, the suggestion unit suggests the frequency and amount of watering. The suggestion unit can also suggest the type of fertilizer and the timing of fertilization. The suggestion unit can also suggest how to adjust the hours of sunlight. For example, the suggestion unit provides advice to the user in the form of, "This plant needs to be watered once a week. Use fertilizer with a high nitrogen content once a month."

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. [Explanation of symbols]

[0161] 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 photo acquisition unit that acquires photos of the home vegetable garden from a user; an analysis unit that analyzes the photograph acquired by the photograph acquisition unit and evaluates the state of the plant; a proposal unit that proposes an optimal cultivation method based on the state of the plant evaluated by the analysis unit. A system characterized by:

2. The photo acquisition unit For photos taken by users, the AI ​​automatically suggests the best shooting angle and lighting conditions, prompting users to take the photo again.

2. The system of claim 1.

3. Drones are Automatically take photos of the entire home vegetable garden and input them into the generation AI 2. The system of claim 1.

4. The generating AI is The growth history of the plant is analyzed and compared with past data to detect abnormalities.

2. The system of claim 1.

5. The generating AI is The training method is individually customized by reflecting the user's past training history.

2. The system of claim 1.

6. The photo acquisition unit Using an emotion estimation function, the emotion of the user when taking a photo is analyzed and photography advice is provided to reduce stress.

2. The system of claim 1.

7. The generating AI is Using an emotion estimation function, the emotion the user feels about the state of the plant is analyzed and positive feedback is provided.

2. The system of claim 1.

8. The generating AI is Using an emotion estimation function, the emotion felt by the user regarding the proposed raising method is analyzed, and the proposal that elicits positive emotions is made.

2. The system of claim 1.

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