System

Abstract

An object of a system according to an embodiment is to make it possible to easily obtain necessary knowledge and advice when starting a home garden.SOLUTION: A system includes an image acquisition unit, an analysis unit, and an advice provision unit. The image acquisition unit acquires a photograph of a plant from a user. The analysis unit analyzes the photograph of the plant acquired by the image acquisition unit. The advice providing unit provides advice on the basis of a result analyzed 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] With conventional technology, it was difficult to obtain the knowledge and advice needed to start a home vegetable garden, making it a high hurdle for beginners.

[0005] The system according to the embodiment aims to make it easy to obtain the knowledge and advice needed when starting a home vegetable garden. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, and an advice provision unit. The image acquisition unit acquires a photo of a plant from a user. The analysis unit analyzes the photo of the plant acquired by the image acquisition unit. The advice provision unit provides advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily obtain the knowledge and advice necessary when starting a home vegetable garden. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The home gardening support system according to an embodiment of the present invention uses a generative AI to analyze the condition of plants based on photos of the plants taken by the user and provides optimal advice. This allows the home gardening support system to help users easily start a home garden and provide specific advice for maintaining the health of the plants.

[0029] The home gardening support system according to the embodiment includes an image acquisition unit, an analysis unit, and an advice provision unit. The image acquisition unit acquires plant photos from a user. For example, a user can take a photo of a plant using a smartphone app and send it to the image acquisition unit. Alternatively, a webcam can be used to acquire plant photos in real time. The image acquisition unit can also receive image files uploaded by the user. The analysis unit analyzes the plant photos acquired by the image acquisition unit. For example, the generation AI can use image recognition technology to analyze the color and shape of a plant's leaves to determine its health. The generation AI can also predict the plant's growth stage using a machine learning algorithm. The generation AI can also detect signs of plant pests and diseases. For example, the generation AI can detect discoloration or spots on leaves and identify the type of pest or disease. The advice provision unit provides advice based on the results of the analysis by the analysis unit. For example, the generation AI can provide specific advice, such as "This plant needs more sunlight" or "Please water it more frequently," depending on the plant's health. The generation AI can also suggest a future care schedule based on plant growth predictions. The generating AI can also suggest preventive measures against pests and diseases. For example, the generating AI suggests preventive care during periods when the risk of a particular pest or disease is high. This allows the home gardening support system according to the embodiment to support users in easily starting a home garden and provide specific advice for maintaining the health of their plants. For example, users can take photos of their plants through a smartphone app and receive advice from the generating AI. They can also monitor the condition of their plants in real time using a webcam and receive advice from the generating AI. Furthermore, users can have uploaded image files analyzed and receive advice from the generating AI.

[0030] The analysis unit can predict plant growth and suggest a future care schedule. In the analysis unit, for example, the generation AI predicts plant growth based on photos of the plant taken by the user. For example, it predicts the period from the current growth stage to the next growth stage and suggests a care schedule required during that time. The generation AI also predicts plant growth and provides the user with a specific care schedule. For example, it notifies the user of the next watering or additional fertilizer application timing. The generation AI also suggests a future care schedule based on the plant growth prediction. For example, it predicts care required at seasonal changes and the risk of specific pests and diseases and suggests countermeasures. This makes it possible to predict plant growth and suggest a future care schedule.

[0031] The analysis unit can perform early detection of plant pests and diseases and suggest preventive measures. In the analysis unit, for example, the generation AI performs early detection of pests and diseases based on photos of plants taken by the user. For example, it detects discoloration or spots on leaves, identifies the type of pest or disease, and suggests preventive measures. The generation AI also performs image analysis of plants to support early detection of pests and diseases. For example, it detects early symptoms of specific pests and diseases and suggests early countermeasures to the user. The generation AI also performs image analysis aimed at early detection of plant pests and diseases and suggests preventive measures. For example, it suggests preventive care during periods when the risk of specific pests and diseases is high. This makes it possible to perform early detection of plant pests and diseases and suggest preventive measures.

[0032] The analysis unit can visualize the growth process of a plant in a 3D model and provide visual feedback to the user. The analysis unit, for example, uses generative AI to visualize the growth process of a plant in a 3D model and provide visual feedback to the user. For example, the current growth stage and future growth predictions are displayed in the 3D model. The analysis unit also visualizes the growth process of a plant in a 3D model, allowing the user to visually check the progress of growth. For example, the growth of the plant's leaves and stems is reflected in the 3D model in real time. The analysis unit also uses generative AI to visualize the growth process of a plant in a 3D model and provide visual feedback to the user. For example, the changes that occur as the plant grows are simulated in the 3D model and presented to the user. This makes it possible to visualize the growth process of a plant in a 3D model and provide visual feedback to the user.

[0033] In addition to understanding the plant's condition, the analysis unit can analyze the soil's nutritional state and pH value and recommend the optimal fertilizer or amendment. For example, using a generation AI, the analysis unit can use a generation AI to analyze the soil's nutritional state and pH value in addition to understanding the plant's condition and recommend the optimal fertilizer or amendment. For example, it can detect a nutrient deficiency in the soil and recommend a specific fertilizer. The analysis unit can also analyze the soil's nutritional state and pH value, and the generation AI can recommend the optimal fertilizer or amendment. For example, if the soil's pH value is on the acidic side, it can recommend an alkaline amendment. In addition, the analysis unit can use a generation AI to analyze the soil's nutritional state and pH value in addition to understanding the plant's condition and recommend the optimal fertilizer or amendment. For example, if a specific nutrient is lacking, it can recommend a fertilizer that supplements that nutrient. This makes it possible to analyze the soil's nutritional state and pH value and recommend the optimal fertilizer or amendment.

[0034] The selection of seeds and seedlings included in the kit can be customized to suit the climate and season of the user's region. The selection of seeds and seedlings included in the kit can be customized to suit, for example, the climate and season of the user's region. For example, plants with high cold tolerance can be selected for cold regions. The seeds and seedlings included in the kit can also be optimized according to the climate and season of the user's region. For example, plants with high drought tolerance can be selected for dry regions. The seeds and seedlings included in the kit can also be customized based on the climate and season of the user's region. For example, plants that are optimal for each season can be selected and included in the kit. This allows the seeds and seedlings to be customized to suit the climate and season of the user's region.

[0035] By incorporating smart sensors into the kit, it is possible to monitor the humidity and temperature of the soil in real time and encourage appropriate care. By incorporating smart sensors into the kit, it is possible to monitor the humidity and temperature of the soil in real time. For example, if the humidity drops, it will automatically send a notification to encourage watering. The smart sensors can also be used to monitor the humidity and temperature of the soil in real time and encourage appropriate care. For example, if the temperature is too high, it will suggest providing shade. The smart sensors built into the kit can also monitor the humidity and temperature of the soil in real time and encourage the user to take appropriate care. For example, if the humidity is too high, it will send a notification to encourage drainage. This makes it possible to monitor the humidity and temperature of the soil in real time and encourage appropriate care.

[0036] By incorporating AR technology into the kit, it is possible to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. By incorporating AR technology into the kit, it is possible to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. For example, the growth of a plant can be displayed in real time using AR. In addition, AR technology can be used to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. For example, the future appearance of the plant can be displayed using AR. In addition, by incorporating AR technology into the kit, it is possible to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. For example, the changes that occur as the plant grows can be simulated using AR. This allows users to virtually experience the growth process of a plant.

[0037] A community function can be added to the kit to provide a platform where users can exchange information and share advice with other home gardening users. A community function can be added to the kit to provide a platform where users can exchange information and share advice with other home gardening users, for example. For example, a forum can be set up where users can post questions and advice. A platform can also be provided using the community function where users can exchange information and share advice with other home gardening users, for example, a function can be added to share success stories and failure stories. A community function can also be added to the kit to provide a platform where users can exchange information and share advice with other home gardening users, for example, a function can be provided that allows users to chat with each other in real time. This allows users to exchange information and share advice with other home gardening users.

[0038] It is possible to monitor the usage of rental fields and suggest optimal usage methods and areas for improvement. A system can be built that monitors the usage of rental fields and, for example, suggests optimal usage methods and areas for improvement. For example, it can analyze the frequency of field use and the growth status of crops to suggest efficient usage methods. It can also monitor the usage of rental fields in real time and suggest specific improvements to users. For example, if part of the field is unused, it can suggest planting other crops. It can also monitor the usage of rental fields and suggest optimal usage methods and areas for improvement. For example, it can notify the user of the timing and method of care according to the growth of the crops. This makes it possible to monitor the usage of rental fields and suggest optimal usage methods and areas for improvement.

[0039] By introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering. By introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering, for example. For example, a system is provided that uses soil humidity sensors to automatically water when needed. Furthermore, a smart irrigation system is used to automate watering of a rental field, watering at the optimal time. For example, a watering schedule is adjusted based on weather data. Furthermore, by introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering. For example, a system is provided that adjusts the optimal amount of water according to the type of crop and its growth stage. In this way, by introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering.

[0040] By adding a community garden function to a rental field, it is possible to provide a system that allows multiple users to use the field jointly. ... For example, a system can be built in which users cooperate to manage the field. Furthermore, by using the community garden function, it is possible to provide a system that allows multiple users to use the rental field jointly. For example, it is possible to provide a platform for dividing up work and sharing harvests. Furthermore, by adding a community garden function to a rental field, it is possible to provide a system that allows multiple users to use the field jointly. For example, it is possible to provide a function that allows users to communicate with each other in real time. This makes it possible to provide a system in which multiple users can use the rental field jointly.

[0041] A marketplace can be provided for users of rental fields where they can sell and exchange their harvested products. A marketplace can be provided for users of rental fields where they can sell and exchange their harvested products, for example. For example, a function for selling harvested products through an online platform can be provided. A system can also be provided using the marketplace where users of rental fields can sell and exchange their harvested products. For example, a function for users to exchange harvested products with each other can be provided. A marketplace can also be provided for users of rental fields where they can sell and exchange their harvested products. For example, a system can be provided for evaluating the quality and price of harvested products. This makes it possible to provide a marketplace where users of rental fields can sell and exchange their harvested products.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The home gardening support system may further include a voice recognition unit. The voice recognition unit allows a user to report the status of a plant by voice. For example, if the user says, "The leaves are turning yellow," the voice recognition unit sends the information to the analysis unit, which then determines the health status of the plant based on the information. The voice recognition unit also allows a user to ask questions by voice. For example, if the user asks, "How often should I water this plant?", the advice providing unit provides an appropriate answer. The voice recognition unit also allows a user to receive advice by voice. For example, the analysis unit can analyze the health status of a plant, and the advice providing unit can notify the user by voice, "This plant needs more sunlight." This allows a user to report the status of a plant and receive advice by voice without using their hands.

[0044] The home gardening support system may further include a weather forecasting unit. The weather forecasting unit acquires local weather data and provides it to the analysis unit. For example, the weather forecasting unit enables the analysis unit to adjust plant care schedules based on future weather forecasts. For example, if rain is expected, the analysis unit may suggest reducing the frequency of watering. The weather forecasting unit may also suggest care required as the seasons change. For example, as winter approaches, the analysis unit may suggest care methods for cold-resistant plants. Furthermore, the weather forecasting unit may suggest preventive measures for specific weather conditions. For example, if strong winds are expected, the analysis unit may suggest measures to protect plants. This allows the user to provide optimal care according to the weather.

[0045] The home gardening support system can further include a plant identification unit. The plant identification unit identifies the type of plant based on a photo of the plant taken by the user. For example, when a user purchases a new plant, the user can take a photo of the plant and send it to the plant identification unit, which then identifies the type of plant and suggests the best care method for that plant. The plant identification unit can also identify the type of plant the user finds outdoors. For example, the user can take a photo of a plant they find while taking a walk and send it to the plant identification unit, which then identifies the type of plant and provides information about it. The plant identification unit can also identify the type of plant the user is growing and suggest preventive measures for pests and diseases specific to that plant. This allows the user to provide the best care for the plant they are growing.

[0046] The home gardening support system can also record the plant growth process as a time-lapse video and provide it to the user. For example, the analysis unit periodically takes photos of the plant and generates a time-lapse video based on the photos. The time-lapse video also allows the user to visually check the growth of the plant. For example, the user can look back on the plant's growth process and check how it has grown. Furthermore, the time-lapse video also allows the user to share the plant's growth with others. For example, the user can post the time-lapse video on social media and share the plant's growth with friends and family. This allows the user to visually enjoy the growth of the plant.

[0047] The home gardening support system can also store plant growth data in the cloud and make it accessible from other devices. For example, users can access plant growth data from their smartphones, tablets, or PCs to check care history and growth progress. The data stored in the cloud can also be used as reference data for the analysis unit to predict plant growth. For example, future growth predictions can be made based on past growth data and care schedules can be proposed. Furthermore, data stored in the cloud can also be shared with other users. For example, data can be shared within a home gardening community, allowing users to receive advice from other users. This allows users to access plant growth data from multiple devices and provide efficient care.

[0048] The home gardening support system can further provide users with quizzes and trivia about plant growth based on the data about plant growth. For example, the analysis unit analyzes plant growth data and generates quizzes and trivia based on that data. For example, a quiz such as "How much will this plant grow in how many days?" is provided. The quizzes and trivia are also intended to allow users to learn about plant growth. For example, by answering the quiz, users can deepen their knowledge about plant growth. Furthermore, the quizzes and trivia are also intended to allow users to enjoy plant growth. For example, by answering the quiz, users can maintain their interest in plant growth. This allows users to have fun while learning about plant growth.

[0049] The home gardening support system can further provide the user with a report on the growth of the plant based on the data on the growth of the plant. For example, the analysis unit analyzes the plant growth data and generates a report based on the data. For example, a weekly or monthly growth report is provided. The report is also intended to allow the user to look back on the growth of the plant. For example, the user can understand the progress of the plant's growth by checking the report. Furthermore, the report is also intended to allow the user to share the growth of the plant with others. For example, the user can post the report on a social networking site and share the growth of the plant with friends and family. This allows the user to visually check the growth of the plant and share it with others.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The image acquisition unit acquires a photo of a plant from a user. For example, a photo of the plant can be taken using a smartphone app and sent to the image acquisition unit. Alternatively, a webcam can be used to acquire plant photos in real time. Furthermore, the image acquisition unit can receive image files uploaded by the user. Step 2: The analysis unit analyzes the photos of the plants acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to analyze the color and shape of the plant's leaves and determine its health condition. The generation AI can also predict the plant's growth stage using machine learning algorithms. The generation AI can also detect signs of pests and diseases in plants. For example, the generation AI can detect discoloration and spots on the leaves and identify the type of pest or disease. Step 3: The advice provider provides advice based on the results of the analysis by the analyzer. For example, the generator AI can provide specific advice such as "This plant needs more sunlight" or "Please water it more frequently" based on the plant's health condition. The generator AI can also suggest future care schedules based on predicted plant growth. The generator AI can also suggest preventive measures for pests and diseases. For example, the generator AI can suggest preventive care during periods when the risk of a particular pest or disease outbreak is high.

[0052] (Example 2) The home gardening support system according to an embodiment of the present invention uses a generative AI to analyze the condition of plants based on photos of the plants taken by the user and provides optimal advice. This allows the home gardening support system to help users easily start a home garden and provide specific advice for maintaining the health of the plants.

[0053] The home gardening support system according to the embodiment includes an image acquisition unit, an analysis unit, and an advice provision unit. The image acquisition unit acquires plant photos from a user. For example, a user can take a photo of a plant using a smartphone app and send it to the image acquisition unit. Alternatively, a webcam can be used to acquire plant photos in real time. The image acquisition unit can also receive image files uploaded by the user. The analysis unit analyzes the plant photos acquired by the image acquisition unit. For example, the generation AI can use image recognition technology to analyze the color and shape of a plant's leaves to determine its health. The generation AI can also predict the plant's growth stage using a machine learning algorithm. The generation AI can also detect signs of plant pests and diseases. For example, the generation AI can detect discoloration or spots on leaves and identify the type of pest or disease. The advice provision unit provides advice based on the results of the analysis by the analysis unit. For example, the generation AI can provide specific advice, such as "This plant needs more sunlight" or "Please water it more frequently," depending on the plant's health. The generation AI can also suggest a future care schedule based on plant growth predictions. The generating AI can also suggest preventive measures against pests and diseases. For example, the generating AI suggests preventive care during periods when the risk of a particular pest or disease is high. This allows the home gardening support system according to the embodiment to support users in easily starting a home garden and provide specific advice for maintaining the health of their plants. For example, users can take photos of their plants through a smartphone app and receive advice from the generating AI. They can also monitor the condition of their plants in real time using a webcam and receive advice from the generating AI. Furthermore, users can have uploaded image files analyzed and receive advice from the generating AI.

[0054] The analysis unit can predict plant growth and suggest a future care schedule. In the analysis unit, for example, the generation AI predicts plant growth based on photos of the plant taken by the user. For example, it predicts the period from the current growth stage to the next growth stage and suggests a care schedule required during that time. The generation AI also predicts plant growth and provides the user with a specific care schedule. For example, it notifies the user of the next watering or additional fertilizer application timing. The generation AI also suggests a future care schedule based on the plant growth prediction. For example, it predicts care required at seasonal changes and the risk of specific pests and diseases and suggests countermeasures. This makes it possible to predict plant growth and suggest a future care schedule.

[0055] The analysis unit can perform early detection of plant pests and diseases and suggest preventive measures. In the analysis unit, for example, the generation AI performs early detection of pests and diseases based on photos of plants taken by the user. For example, it detects discoloration or spots on leaves, identifies the type of pest or disease, and suggests preventive measures. The generation AI also performs image analysis of plants to support early detection of pests and diseases. For example, it detects early symptoms of specific pests and diseases and suggests early countermeasures to the user. The generation AI also performs image analysis aimed at early detection of plant pests and diseases and suggests preventive measures. For example, it suggests preventive care during periods when the risk of specific pests and diseases is high. This makes it possible to perform early detection of plant pests and diseases and suggest preventive measures.

[0056] The analysis unit can use the emotion estimation function to analyze the user's emotional state and provide advice to increase motivation for plant care. For example, the analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and provide advice to increase motivation for plant care. For example, if the user is feeling stressed, the analysis unit can suggest plant care that has a relaxing effect. The analysis unit can also analyze the user's emotional state and provide specific advice to increase motivation for plant care. For example, if the user is feeling positive, the analysis unit can provide advice encouraging the user to take on more challenges. The analysis unit can also use the emotion estimation function to analyze the user's emotional state and provide personalized advice to increase motivation for plant care. For example, the analysis unit can present encouraging messages or success stories that correspond to the user's emotions. In this way, the analysis unit can analyze the user's emotional state and provide advice to increase motivation for plant care.

[0057] The analysis unit can visualize the growth process of a plant in a 3D model and provide visual feedback to the user. The analysis unit, for example, uses generative AI to visualize the growth process of a plant in a 3D model and provide visual feedback to the user. For example, the current growth stage and future growth predictions are displayed in the 3D model. The analysis unit also visualizes the growth process of a plant in a 3D model, allowing the user to visually check the progress of growth. For example, the growth of the plant's leaves and stems is reflected in the 3D model in real time. The analysis unit also uses generative AI to visualize the growth process of a plant in a 3D model and provide visual feedback to the user. For example, the changes that occur as the plant grows are simulated in the 3D model and presented to the user. This makes it possible to visualize the growth process of a plant in a 3D model and provide visual feedback to the user.

[0058] In addition to understanding the plant's condition, the analysis unit can analyze the soil's nutritional state and pH value and recommend the optimal fertilizer or amendment. For example, using a generation AI, the analysis unit can use a generation AI to analyze the soil's nutritional state and pH value in addition to understanding the plant's condition and recommend the optimal fertilizer or amendment. For example, it can detect a nutrient deficiency in the soil and recommend a specific fertilizer. The analysis unit can also analyze the soil's nutritional state and pH value, and the generation AI can recommend the optimal fertilizer or amendment. For example, if the soil's pH value is on the acidic side, it can recommend an alkaline amendment. In addition, the analysis unit can use a generation AI to analyze the soil's nutritional state and pH value in addition to understanding the plant's condition and recommend the optimal fertilizer or amendment. For example, if a specific nutrient is lacking, it can recommend a fertilizer that supplements that nutrient. This makes it possible to analyze the soil's nutritional state and pH value and recommend the optimal fertilizer or amendment.

[0059] The analysis unit can use the emotion estimation function to provide an interactive guide for amplifying the joy and satisfaction the user feels as the plant grows. For example, the analysis unit can use the emotion estimation function to provide an interactive guide for amplifying the joy and satisfaction the user feels as the plant grows. For example, the analysis unit can display a message that gives the user a sense of accomplishment as the plant grows. The analysis unit can also analyze the user's emotional state and provide specific guidance for amplifying the joy and satisfaction the user feels as the plant grows. For example, the analysis unit can record the plant's growth process and provide feedback that makes the user feel a sense of accomplishment. The analysis unit can also use the emotion estimation function to provide an interactive guide for amplifying the joy and satisfaction the user feels as the plant grows. For example, the analysis unit can visually display changes that occur as the plant grows, making the user feel a sense of accomplishment. This makes it possible to provide an interactive guide for amplifying the joy and satisfaction the user feels as the plant grows.

[0060] The selection of seeds and seedlings included in the kit can be customized to suit the climate and season of the user's region. The selection of seeds and seedlings included in the kit can be customized to suit, for example, the climate and season of the user's region. For example, plants with high cold tolerance can be selected for cold regions. The seeds and seedlings included in the kit can also be optimized according to the climate and season of the user's region. For example, plants with high drought tolerance can be selected for dry regions. The seeds and seedlings included in the kit can also be customized based on the climate and season of the user's region. For example, plants that are optimal for each season can be selected and included in the kit. This allows the seeds and seedlings to be customized to suit the climate and season of the user's region.

[0061] By incorporating smart sensors into the kit, it is possible to monitor the humidity and temperature of the soil in real time and encourage appropriate care. By incorporating smart sensors into the kit, it is possible to monitor the humidity and temperature of the soil in real time. For example, if the humidity drops, it will automatically send a notification to encourage watering. The smart sensors can also be used to monitor the humidity and temperature of the soil in real time and encourage appropriate care. For example, if the temperature is too high, it will suggest providing shade. The smart sensors built into the kit can also monitor the humidity and temperature of the soil in real time and encourage the user to take appropriate care. For example, if the humidity is too high, it will send a notification to encourage drainage. This makes it possible to monitor the humidity and temperature of the soil in real time and encourage appropriate care.

[0062] The emotion estimation function can be used to include in the kit encouraging messages and advice that correspond to the user's emotional state. The emotion estimation function can be used, for example, to include in the kit encouraging messages and advice that correspond to the user's emotional state. For example, if the user is feeling stressed, a message with a relaxing effect can be provided. The emotion estimation function can also be used to analyze the user's emotional state and include in the kit encouraging messages and advice that correspond to the emotions. For example, if the user is feeling positive, a message that encourages further challenges can be provided. The emotion estimation function can also be used to include in the kit encouraging messages and advice that correspond to the user's emotional state. For example, a personalized message that corresponds to the user's emotions can be provided. This makes it possible to provide encouraging messages and advice that correspond to the user's emotional state.

[0063] By incorporating AR technology into the kit, it is possible to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. By incorporating AR technology into the kit, it is possible to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. For example, the growth of a plant can be displayed in real time using AR. In addition, AR technology can be used to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. For example, the future appearance of the plant can be displayed using AR. In addition, by incorporating AR technology into the kit, it is possible to provide a function that allows users to virtually experience the growth process of a plant through a smartphone. For example, the changes that occur as the plant grows can be simulated using AR. This allows users to virtually experience the growth process of a plant.

[0064] A community function can be added to the kit to provide a platform where users can exchange information and share advice with other home gardening users. A community function can be added to the kit to provide a platform where users can exchange information and share advice with other home gardening users, for example. For example, a forum can be set up where users can post questions and advice. A platform can also be provided using the community function where users can exchange information and share advice with other home gardening users, for example, a function can be added to share success stories and failure stories. A community function can also be added to the kit to provide a platform where users can exchange information and share advice with other home gardening users, for example, a function can be provided that allows users to chat with each other in real time. This allows users to exchange information and share advice with other home gardening users.

[0065] The emotion estimation function can be used to incorporate game elements into the kit to enhance the sense of accomplishment the user feels as the plant grows. The emotion estimation function can be used to incorporate game elements into the kit to enhance the sense of accomplishment the user feels as the plant grows. For example, a system is provided that allows points to be earned according to the growth of a plant. The emotion estimation function can also be used to analyze the user's emotional state and incorporate game elements into the kit to enhance the sense of accomplishment. For example, a system is provided that allows rewards to be earned by completing missions associated with the growth of a plant. The emotion estimation function can also be used to incorporate game elements into the kit to enhance the sense of accomplishment the user feels as the plant grows. For example, a system is provided that allows badges and titles to be earned according to the growth of a plant. This makes it possible to provide game elements that enhance the sense of accomplishment the user feels as the plant grows.

[0066] It is possible to monitor the usage of rental fields and suggest optimal usage methods and areas for improvement. A system can be built that monitors the usage of rental fields and, for example, suggests optimal usage methods and areas for improvement. For example, it can analyze the frequency of field use and the growth status of crops to suggest efficient usage methods. It can also monitor the usage of rental fields in real time and suggest specific improvements to users. For example, if part of the field is unused, it can suggest planting other crops. It can also monitor the usage of rental fields and suggest optimal usage methods and areas for improvement. For example, it can notify the user of the timing and method of care according to the growth of the crops. This makes it possible to monitor the usage of rental fields and suggest optimal usage methods and areas for improvement.

[0067] By introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering. By introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering, for example. For example, a system is provided that uses soil humidity sensors to automatically water when needed. Furthermore, a smart irrigation system is used to automate watering of a rental field, watering at the optimal time. For example, a watering schedule is adjusted based on weather data. Furthermore, by introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering. For example, a system is provided that adjusts the optimal amount of water according to the type of crop and its growth stage. In this way, by introducing a smart irrigation system to a rental field, it is possible to automate and optimize watering.

[0068] The emotion estimation function can be used to suggest farm work schedules and activities that correspond to the user's emotional state. The emotion estimation function can be used to suggest farm work schedules and activities that correspond to the user's emotional state. For example, if the user is feeling stressed, tasks that have a relaxing effect can be suggested. The emotion estimation function can also be used to analyze the user's emotional state and suggest farm work schedules and activities that correspond to the emotions. For example, if the user is feeling positive, challenging tasks can be suggested. The emotion estimation function can also be used to suggest farm work schedules and activities that correspond to the user's emotional state. For example, a personalized work schedule can be provided that corresponds to the user's emotions. This makes it possible to suggest farm work schedules and activities that correspond to the user's emotional state.

[0069] By adding a community garden function to a rental field, it is possible to provide a system that allows multiple users to use the field jointly. ... For example, a system can be built in which users cooperate to manage the field. Furthermore, by using the community garden function, it is possible to provide a system that allows multiple users to use the rental field jointly. For example, it is possible to provide a platform for dividing up work and sharing harvests. Furthermore, by adding a community garden function to a rental field, it is possible to provide a system that allows multiple users to use the field jointly. For example, it is possible to provide a function that allows users to communicate with each other in real time. This makes it possible to provide a system in which multiple users can use the rental field jointly.

[0070] A marketplace can be provided for users of rental fields where they can sell and exchange their harvested products. A marketplace can be provided for users of rental fields where they can sell and exchange their harvested products, for example. For example, a function for selling harvested products through an online platform can be provided. A system can also be provided using the marketplace where users of rental fields can sell and exchange their harvested products. For example, a function for users to exchange harvested products with each other can be provided. A marketplace can also be provided for users of rental fields where they can sell and exchange their harvested products. For example, a system can be provided for evaluating the quality and price of harvested products. This makes it possible to provide a marketplace where users of rental fields can sell and exchange their harvested products.

[0071] The emotion estimation function can be used to provide a relaxation program that enhances the relaxation effect that a user feels while working in the fields. The emotion estimation function can be used to provide, for example, a relaxation program that enhances the relaxation effect that a user feels while working in the fields. For example, relaxing music or a meditation guide can be provided. The emotion estimation function can also be used to analyze the user's emotional state and provide a specific program that enhances the relaxation effect. For example, activities that allow the user to relax between work in the fields can be suggested. The emotion estimation function can also be used to provide a relaxation program that enhances the relaxation effect that a user feels while working in the fields. For example, a relaxation menu can be provided that corresponds to the user's emotions. In this way, a relaxation program can be provided that enhances the relaxation effect that a user feels while working in the fields.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The home gardening support system may further include a voice recognition unit. The voice recognition unit allows a user to report the status of a plant by voice. For example, if the user says, "The leaves are turning yellow," the voice recognition unit sends the information to the analysis unit, which then determines the health status of the plant based on the information. The voice recognition unit also allows a user to ask questions by voice. For example, if the user asks, "How often should I water this plant?", the advice providing unit provides an appropriate answer. The voice recognition unit also allows a user to receive advice by voice. For example, the analysis unit can analyze the health status of a plant, and the advice providing unit can notify the user by voice, "This plant needs more sunlight." This allows a user to report the status of a plant and receive advice by voice without using their hands.

[0074] The home gardening support system may further include a weather forecasting unit. The weather forecasting unit acquires local weather data and provides it to the analysis unit. For example, the weather forecasting unit enables the analysis unit to adjust plant care schedules based on future weather forecasts. For example, if rain is expected, the analysis unit may suggest reducing the frequency of watering. The weather forecasting unit may also suggest care required as the seasons change. For example, as winter approaches, the analysis unit may suggest care methods for cold-resistant plants. Furthermore, the weather forecasting unit may suggest preventive measures for specific weather conditions. For example, if strong winds are expected, the analysis unit may suggest measures to protect plants. This allows the user to provide optimal care according to the weather.

[0075] The home gardening support system can further include a plant identification unit. The plant identification unit identifies the type of plant based on a photo of the plant taken by the user. For example, when a user purchases a new plant, the user can take a photo of the plant and send it to the plant identification unit, which then identifies the type of plant and suggests the best care method for that plant. The plant identification unit can also identify the type of plant the user finds outdoors. For example, the user can take a photo of a plant they find while taking a walk and send it to the plant identification unit, which then identifies the type of plant and provides information about it. The plant identification unit can also identify the type of plant the user is growing and suggest preventive measures for pests and diseases specific to that plant. This allows the user to provide the best care for the plant they are growing.

[0076] The home gardening support system can also use the emotion estimation function to adjust the difficulty of plant care based on the user's emotional state. For example, if the user is feeling stressed, the system can suggest an easy care method to help the user relax. On the other hand, if the user is feeling positive, the system can suggest a slightly more difficult care method to provide a fun challenge. Furthermore, the emotion estimation function can also be used to adjust the plant care schedule according to the user's emotional state. For example, if the user is busy, the system can suggest reducing the frequency of care, and if the user has more free time, the system can suggest increasing the frequency of care. This allows the user to optimally care for their plants according to their emotional state.

[0077] The home gardening support system can also record the plant growth process as a time-lapse video and provide it to the user. For example, the analysis unit periodically takes photos of the plant and generates a time-lapse video based on the photos. The time-lapse video also allows the user to visually check the growth of the plant. For example, the user can look back on the plant's growth process and check how it has grown. Furthermore, the time-lapse video also allows the user to share the plant's growth with others. For example, the user can post the time-lapse video on social media and share the plant's growth with friends and family. This allows the user to visually enjoy the growth of the plant.

[0078] The home gardening support system can also store plant growth data in the cloud and make it accessible from other devices. For example, users can access plant growth data from their smartphones, tablets, or PCs to check care history and growth progress. The data stored in the cloud can also be used as reference data for the analysis unit to predict plant growth. For example, future growth predictions can be made based on past growth data and care schedules can be proposed. Furthermore, data stored in the cloud can also be shared with other users. For example, data can be shared within a home gardening community, allowing users to receive advice from other users. This allows users to access plant growth data from multiple devices and provide efficient care.

[0079] The home gardening support system can further use an emotion estimation function to provide plant care reminders based on the user's emotional state. For example, if the user is feeling stressed, the system can reduce the frequency of reminders to help the user relax. Alternatively, if the user is feeling positive, the system can increase the frequency of reminders to encourage the user to actively care for their plants. Furthermore, the emotion estimation function can be used to adjust the content of reminders according to the user's emotional state. For example, when the user is busy, the system can suggest simple care methods, and when the user has more time, the system can suggest detailed care methods. This allows the user to receive optimal reminders according to their emotional state and efficiently care for their plants.

[0080] The home gardening support system can further provide users with quizzes and trivia about plant growth based on the data about plant growth. For example, the analysis unit analyzes plant growth data and generates quizzes and trivia based on that data. For example, a quiz such as "How much will this plant grow in how many days?" is provided. The quizzes and trivia are also intended to allow users to learn about plant growth. For example, by answering the quiz, users can deepen their knowledge about plant growth. Furthermore, the quizzes and trivia are also intended to allow users to enjoy plant growth. For example, by answering the quiz, users can maintain their interest in plant growth. This allows users to have fun while learning about plant growth.

[0081] The home gardening support system can further provide the user with a report on the growth of the plant based on the data on the growth of the plant. For example, the analysis unit analyzes the plant growth data and generates a report based on the data. For example, a weekly or monthly growth report is provided. The report is also intended to allow the user to look back on the growth of the plant. For example, the user can understand the progress of the plant's growth by checking the report. Furthermore, the report is also intended to allow the user to share the growth of the plant with others. For example, the user can post the report on a social networking site and share the growth of the plant with friends and family. This allows the user to visually check the growth of the plant and share it with others.

[0082] The home gardening support system can further use the emotion estimation function to personalize plant care advice based on the user's emotional state. For example, if the user is feeling stressed, the system can suggest relaxing plant care methods, allowing the user to relax. If the user is feeling positive, the system can suggest challenging care methods, allowing the user to enjoy plant care. Furthermore, the emotion estimation function can also be used to provide plant care advice according to the user's emotional state. For example, when the user is busy, the system can suggest simple care methods, and when the user has more time, the system can suggest detailed care methods. This allows the user to provide optimal plant care according to their emotional state.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The image acquisition unit acquires a photo of a plant from a user. For example, a photo of the plant can be taken using a smartphone app and sent to the image acquisition unit. Alternatively, a webcam can be used to acquire plant photos in real time. Furthermore, the image acquisition unit can receive image files uploaded by the user. Step 2: The analysis unit analyzes the photos of the plants acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to analyze the color and shape of the plant's leaves and determine its health condition. The generation AI can also predict the plant's growth stage using machine learning algorithms. The generation AI can also detect signs of pests and diseases in plants. For example, the generation AI can detect discoloration and spots on the leaves and identify the type of pest or disease. Step 3: The advice provider provides advice based on the results of the analysis by the analyzer. For example, the generator AI can provide specific advice such as "This plant needs more sunlight" or "Please water it more frequently" based on the plant's health condition. The generator AI can also suggest future care schedules based on predicted plant growth. The generator AI can also suggest preventive measures for pests and diseases. For example, the generator AI can suggest preventive care during periods when the risk of a particular pest or disease outbreak is high.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. an image acquisition unit that acquires a photo of a plant from a user; an analysis unit that analyzes the photograph of the plant acquired by the image acquisition unit; an advice providing unit that provides advice based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Predicts plant growth and suggests future care schedules 2. The system of claim 1.

3. The analysis unit Early detection of plant pests and diseases and suggesting preventative measures 2. The system of claim 1.

4. The analysis unit Analyzes the user's emotional state and provides advice to increase motivation for plant care 2. The system of claim 1.

5. The analysis unit Visualize the plant growth process in 3D models and provide visual feedback to users 2. The system of claim 1.

Images