system

Abstract

The system according to this embodiment aims to facilitate plant care and suggest appropriate timing and methods for maintenance. [Solution] The system according to the embodiment comprises a pot equipped with a sensor, an analysis unit, a suggestion unit, a feedback unit, a learning unit, and a recommendation unit. The pot equipped with a sensor detects temperature, humidity, mass, etc. The analysis unit analyzes the data collected by the sensor. The suggestion unit suggests appropriate maintenance timing and methods based on the data analyzed by the analysis unit. The feedback unit provides visual feedback on the maintenance methods suggested by the suggestion unit. The learning unit learns from user feedback and improves the accuracy of its suggestions. The recommendation unit recommends maintenance tools and items as needed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to take care of plants and it was difficult to determine the appropriate timing and method.

[0005] The system according to the embodiment aims to facilitate the care of plants and propose appropriate timing and methods.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a pot equipped with a sensor, an analysis unit, a suggestion unit, a feedback unit, a learning unit, and a recommendation unit. The pot equipped with the sensor detects temperature, humidity, mass, etc. The analysis unit analyzes the data collected by the sensor. The suggestion unit suggests appropriate maintenance timing and methods based on the data analyzed by the analysis unit. The feedback unit provides visual feedback on the maintenance methods suggested by the suggestion unit. The learning unit learns from user feedback and improves the accuracy of its suggestions. The recommendation unit recommends maintenance tools and items as needed. [Effects of the Invention]

[0007] The system according to this embodiment can facilitate plant care and suggest appropriate timing and methods. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The plant cultivation support system according to an embodiment of the present invention is a system for supporting plant cultivation. This plant cultivation support system comprises a pot equipped with sensors that detect temperature, humidity, mass, etc., an analysis unit that analyzes data collected by the sensors, a suggestion unit that proposes appropriate care timing and methods based on the data analyzed by the analysis unit, a feedback unit that provides visual feedback on the care methods proposed by the suggestion unit, a learning unit that learns from user feedback and improves the accuracy of suggestions, and a recommendation unit that recommends care tools and items as needed. For example, the plant cultivation support system installs sensors that detect temperature, humidity, mass, etc. in the pot in which the plant is grown and collects this data. Next, the generated AI analyzes the collected data to determine the growth rate and dryness of the plant. Furthermore, it proposes appropriate care timing and methods, taking into account information such as the user's schedule, the current season, weather, and the weather forecast for the coming days. In addition, the visual feedback function allows the user to understand the condition of the plant in real time on the app. For example, by taking pictures of changes in the color and shape of the plant's leaves with a camera and having the generated AI analyze the images, the plant's health can be diagnosed. Furthermore, if problems arise with plant growth, the generating AI diagnoses the symptoms and suggests solutions. The generating AI learns from user feedback and improves the accuracy of its suggestions. For example, by inputting feedback on the results of actual care performed by the user, the generating AI can learn from those results and incorporate them into future suggestions. Additionally, it can integrate with online stores to recommend care tools and items as needed, leading to purchases. For instance, it can suggest fertilizers and watering tools necessary for plant growth, making them easily accessible to users. This system creates a world where everyone can properly cultivate plants and find solace in their beauty and presence. Moreover, even difficult-to-care-for plants can be properly cared for with the support of the generating AI, enabling a society where people can easily enjoy a rich life surrounded by plants. In this way, the plant cultivation support system assists plant growth and suggests appropriate timing and methods for care, enabling anyone to properly cultivate plants.

[0029] The plant cultivation support system according to this embodiment comprises a sensor, an analysis unit, a suggestion unit, a feedback unit, a learning unit, and a recommendation unit. The sensor detects temperature, humidity, mass, etc. The sensor is installed, for example, in a pot to monitor the plant's growth environment. The sensor includes a temperature sensor, a humidity sensor, a mass sensor, etc. The temperature sensor measures, for example, the internal temperature of the pot. The humidity sensor measures, for example, the internal humidity of the pot. The mass sensor measures, for example, the mass of the pot and estimates the amount of moisture. The analysis unit analyzes the data collected by the sensor. The analysis unit analyzes the data using, for example, a generative AI to determine the plant's growth stage and dryness. The analysis unit takes, for example, temperature data, humidity data, and mass data as input and outputs the plant's growth status. The suggestion unit proposes appropriate care timing and methods based on the data analyzed by the analysis unit. The suggestion unit makes suggestions using, for example, a generative AI. The suggestion unit makes suggestions considering information such as the user's schedule, the current season, weather, and the weather forecast for the coming days. The suggestion unit takes user schedule data, seasonal data, weather data, and weather forecast data as input and outputs appropriate care methods. The feedback unit provides visual feedback on the care methods suggested by the suggestion unit. The feedback unit uses generative AI to provide feedback, for example. The feedback unit takes pictures of changes in the color and shape of plant leaves with a camera and analyzes the images. The feedback unit uses camera data as input and outputs the plant's health status, for example. The learning unit learns from user feedback to improve the accuracy of suggestions. The learning unit uses generative AI for learning, for example. The learning unit learns the results of the care actually performed by the user and reflects them in the next suggestion. The learning unit uses user feedback data as input and improves the accuracy of suggestions, for example. The recommendation unit recommends care tools and items as needed. The recommendation unit uses generative AI for recommendations, for example. The recommendation unit works with online stores to suggest necessary tools and items. The recommendation unit uses online store data as input and outputs recommended items, for example.This means that the plant cultivation support system assists in plant growth and suggests appropriate timing and methods for care, enabling anyone to properly grow plants.

[0030] Sensors detect temperature, humidity, mass, and other parameters. For example, sensors are installed in pots to monitor the plant's growing environment. Sensors include temperature sensors, humidity sensors, and mass sensors. A temperature sensor measures, for example, the internal temperature of the pot. A humidity sensor measures, for example, the internal humidity of the pot. A mass sensor measures, for example, the mass of the pot and estimates the water content. These sensors have wireless communication capabilities and can transmit collected data to a central database in real time. The temperature sensor can measure not only the internal temperature of the pot but also the external ambient temperature, allowing for a comprehensive understanding of temperature changes in the plant's environment. The humidity sensor can measure not only soil humidity but also the humidity of the surrounding air, enabling accurate determination of the plant's water needs. The mass sensor accurately measures changes in the pot's mass, allowing for estimation of water absorption and evaporation. This allows the sensor to comprehensively monitor the plant's growing environment and provide detailed data. Furthermore, the sensor has a periodic calibration function, enabling it to provide highly accurate data over long periods. This allows the sensor to accurately understand the plant's growth environment and provide fundamental data for appropriate cultivation support.

[0031] The analysis unit analyzes data collected by sensors. For example, the analysis unit uses generative AI to analyze the data and determine the growth rate and dryness of plants. For example, the analysis unit takes temperature data, humidity data, and mass data as input and outputs the growth status of plants. The generative AI has learned from past data and growth patterns for each plant species, which enables it to determine the growth status of plants with high accuracy. Specifically, it determines whether plants are growing within an appropriate temperature range from temperature data and evaluates the dryness of the soil from humidity data. From mass data, it estimates the moisture content of the pot and confirms whether the plants are receiving sufficient moisture. Furthermore, the analysis unit can detect unusual data patterns using an anomaly detection algorithm and detect problems early. For example, if a sudden temperature change or a drop in humidity is detected, the analysis unit will immediately issue a warning and propose appropriate countermeasures. This allows the analysis unit to grasp the growth status of plants in real time and provide information for appropriate cultivation support. In addition, the analysis unit can perform trend analysis based on long-term data and predict plant growth patterns and environmental changes. This allows the analysis unit to play a crucial role in maintaining the health of plants and providing an optimal growing environment.

[0032] The suggestion unit proposes appropriate care timings and methods based on data analyzed by the analysis unit. The suggestion unit makes suggestions using, for example, generative AI. The suggestion unit makes suggestions considering information such as the user's schedule, the current season, weather, and upcoming weather forecasts. For example, the suggestion unit takes user schedule data, seasonal data, weather data, and weather forecast data as input and outputs appropriate care methods. The generative AI comprehensively analyzes this data and proposes the optimal care timings and methods. For example, it considers the user's schedule and proposes care timings that avoid busy times, and suggests appropriate watering and fertilizing amounts according to the current season and weather. Furthermore, based on upcoming weather forecasts, it can suggest refraining from watering if rain is expected. This allows the suggestion unit to make flexible suggestions tailored to the user's lifestyle and environment. The suggestion unit can learn from the user's past care history and plant growth data, continuously improving the accuracy of its suggestions. This allows the suggestion unit to provide optimal cultivation support for the user and play an important role in maintaining the health of the plants.

[0033] The feedback unit provides visual feedback on the care methods proposed by the suggestion unit. The feedback unit provides feedback using, for example, generative AI. The feedback unit takes pictures of changes in the color and shape of the plant's leaves with a camera and analyzes the images. The feedback unit takes camera data as input and outputs the plant's health status. The generative AI uses image recognition technology to analyze changes in the color and shape of the plant's leaves and determines whether the plant is healthy. For example, if the leaves are turning yellow, it may be due to nutrient deficiency or overwatering, so appropriate measures are suggested. If the shape of the leaves is deformed, it may be due to pest or disease damage, so early countermeasures can be taken. In this way, the feedback unit can visually grasp the plant's health status and provide specific feedback to the user. Furthermore, the feedback unit can record the results of the care actually performed by the user and reflect them in the next suggestion. In this way, the feedback unit can play an important role in improving the user's plant care skills.

[0034] The learning unit learns from user feedback to improve the accuracy of its suggestions. For example, the learning unit uses generative AI for learning. The learning unit learns the results of actual care performed by users and reflects this in future suggestions. For example, the learning unit uses user feedback data as input to improve the accuracy of its suggestions. The generative AI analyzes the results of care performed by users and plant growth data, learning successful and unsuccessful care methods. This allows the learning unit to provide optimal suggestions tailored to each user's characteristics and plant type. For example, it learns how a particular plant grows under specific environmental conditions and reflects this in future suggestions. This allows the learning unit to continuously improve the accuracy of its suggestions and provide optimal cultivation support for users. Furthermore, the learning unit can comprehensively analyze feedback from multiple users to find common patterns and trends. This allows the learning unit to provide highly accurate suggestions to a wider range of users.

[0035] The recommendation system recommends care tools and items as needed. For example, the recommendation system uses generative AI to make recommendations. It collaborates with online stores to suggest necessary tools and items. For example, it takes online store data as input and outputs recommended items. The generative AI analyzes the user's cultivation history and the current state of the plants to suggest the most suitable tools and items. For example, if a plant is nutrient-deficient, it suggests appropriate fertilizer; if there is pest or disease damage, it suggests effective pesticides. Furthermore, it can also suggest items appropriate for the season and weather. For example, in winter, it suggests insulating covers and heaters; in summer, it suggests shade nets and automatic watering systems. This allows the recommendation system to provide users with the tools and items they need at the right time, supporting the maintenance of plant health. Additionally, the recommendation system can provide more accurate recommendations based on the user's purchase history and ratings. This allows the recommendation system to provide users with optimal cultivation support and play a crucial role in maintaining the health of their plants.

[0036] The analysis unit can determine the growth rate and dryness of plants. For example, the analysis unit evaluates the growth rate based on factors such as the size of the plant's leaves, the thickness of its stems, and the number of flowers. For example, the analysis unit evaluates the dryness based on factors such as soil moisture and the degree of leaf wilting. For example, the analysis unit takes temperature data, humidity data, and mass data as input and outputs the plant's growth status. This allows for accurate determination of the plant's growth rate and dryness, enabling the suggestion of appropriate care methods.

[0037] The suggestion unit can make suggestions considering information such as the user's schedule, the current season, weather, and upcoming weather forecasts. For example, the suggestion unit takes user schedule data, seasonal data, weather data, and weather forecast data as input and outputs appropriate care methods. For example, the suggestion unit can suggest the timing of watering based on the user's schedule. For example, the suggestion unit can suggest the type of fertilizer based on the current season. For example, the suggestion unit can suggest care methods that take into account the amount of sunlight based on weather data. This allows the system to suggest the optimal care method tailored to the user's schedule and environment.

[0038] The feedback unit can capture images of changes in the color and shape of plant leaves using a camera and analyze those images. For example, the feedback unit takes camera data as input and outputs the plant's health status. For example, the feedback unit can analyze changes in leaf color to evaluate the plant's health status. For example, the feedback unit can analyze changes in leaf shape to evaluate the plant's health status. For example, the feedback unit can diagnose the plant's health status based on changes in leaf color and shape. This allows for a visual understanding of the plant's health status and provides appropriate feedback.

[0039] The learning unit can learn from the results of the user's actual care and reflect them in future suggestions. For example, the learning unit can take user feedback data as input to improve the accuracy of its suggestions. For example, the learning unit can learn from the user's watering results and suggest the next watering timing. For example, the learning unit can learn from the user's fertilizer usage results and suggest the next type of fertilizer to use. For example, the learning unit can learn from the user's pruning results and suggest the next pruning method. In this way, the accuracy of suggestions improves by learning from user feedback.

[0040] The recommendation function can integrate with online stores to suggest necessary tools and items. For example, it can take online store data as input and output recommended items. For instance, it can suggest fertilizers needed for plant growth, watering tools, or pruning tools. This makes it easy for users to purchase necessary tools and items.

[0041] The sensor can be fitted with additional sensors to detect specific components depending on the type of plant. For example, a sensor can be added to detect the soil's pH value to maintain appropriate acidity. For example, sensors can be added to detect water clarity and oxygen concentration to provide an optimal environment. For example, sensors can be added to detect soil nutrients (e.g., nitrogen, phosphorus, potassium) to suggest appropriate fertilizers. This allows for the provision of an optimal environment tailored to the type of plant.

[0042] The sensor can transmit data to the cloud in real time, allowing for remote monitoring of plant conditions. For example, the sensor can monitor plant conditions in real time via a smartphone app. The sensor can be shared with family and friends, allowing multiple people to monitor plant conditions and provide necessary care. The sensor can also transmit data to experts, allowing for remote advice. This enables real-time monitoring of plant conditions even from a distance.

[0043] The sensor can integrate data with other smart home devices to optimize the overall indoor environment. For example, it can work with a smart thermostat to optimize room temperature. For example, it can work with a smart humidifier to optimize humidity. For example, it can work with smart lighting to provide the optimal light environment for plants. This optimizes the overall indoor environment and promotes plant growth.

[0044] The sensor can use the data to monitor external factors that affect plant growth. For example, the sensor can monitor indoor CO2 concentration and issue an alert to encourage ventilation. For example, if indoor CO2 concentration is low, the sensor can suggest a device to supply the CO2 necessary for plant growth. For example, the sensor can monitor indoor CO2 concentration and maintain an optimal environment for plant growth. This allows for the maintenance of an optimal environment by monitoring external factors that affect plant growth.

[0045] The analysis unit can analyze plant growth patterns over the long term and predict future growth. For example, the analysis unit can predict plant growth rates based on past data and estimate future growth. For example, the analysis unit can analyze seasonal growth patterns and predict the optimal growth period. For example, the analysis unit can analyze different growth patterns for each plant species and make individual growth predictions. In this way, by analyzing plant growth patterns, future growth can be predicted and appropriate care can be provided.

[0046] The analysis unit can evaluate the health of plants based on the analysis results and issue alerts if abnormalities are detected. For example, the analysis unit can analyze changes in leaf color and shape and issue alerts if abnormalities are detected. For example, the analysis unit can detect abnormalities in soil humidity and pH values ​​and issue alerts. For example, the analysis unit can detect rapid changes in temperature and humidity and issue alerts before they affect the health of plants. This allows for evaluation of plant health and rapid response if abnormalities are detected.

[0047] The analysis unit can compare analysis results with other users' data and provide benchmarks. For example, the analysis unit can compare the growth status of your plants with the growth data of other users to evaluate your own plant's growth. For example, the analysis unit can suggest optimal growth conditions based on other users' data. For example, the analysis unit can refer to the success stories of other users and suggest areas for improvement. This allows you to evaluate the growth status of your plants by comparing them with other users' data.

[0048] The analysis unit can use the analysis results to propose optimal environmental conditions for plant growth. For example, the analysis unit can suggest the optimal range of temperature and humidity to promote plant growth. For example, the analysis unit can suggest the optimal pH value and nutrient balance of the soil. For example, the analysis unit can suggest the optimal conditions for light intensity and irradiation time. In this way, by proposing the optimal environmental conditions for plant growth, it promotes growth.

[0049] The proposal department can suggest different care methods depending on the type of plant and its growth stage. For example, for succulents, the proposal department might suggest reducing the frequency of watering. For flowering plants, the proposal department might suggest fertilizers appropriate for the flowering season. For young plants, the proposal department might suggest special care to promote growth. This allows the department to suggest the optimal care method for each type of plant and its growth stage.

[0050] The proposal department can compare the proposed content with the user's past behavioral history to make the most appropriate suggestions. For example, the proposal department can make the most appropriate suggestions based on the user's past skincare routines. For example, the proposal department can suggest successful skincare routines based on the user's past behavioral history. For example, the proposal department can analyze the user's past behavioral history and suggest areas for improvement. In this way, the proposal department can make the most appropriate suggestions based on the user's past behavioral history.

[0051] The proposal department can compare the proposed content with the success stories of other users and provide reference information. For example, the proposal department can make optimal proposals based on the success stories of other users. For example, the proposal department can refer to the data of other users and propose areas for improvement. For example, the proposal department can analyze the success stories of other users and propose the optimal care methods. In this way, it is possible to make optimal proposals based on the success stories of other users.

[0052] The proposal department can customize the recommendations according to the season and provide optimal care methods. For example, in spring, the proposal department will suggest fertilizers to promote growth. In summer, for example, the proposal department will suggest watering frequency to prevent drying out. In winter, for example, the proposal department will suggest measures to protect plants from the cold. In this way, by providing optimal care methods for each season, the department promotes plant growth.

[0053] The feedback unit can visually display the plant's growth history using graphs and charts during feedback. For example, the feedback unit can display the plant's growth rate in a graph, allowing for a visual understanding of past growth patterns. For example, the feedback unit can display the plant's health status in a chart, identifying when abnormalities occurred. For example, the feedback unit can display the plant's watering and fertilization history in a graph, helping to determine the appropriate timing for care. This makes it easier to understand the plant's growth status by visually displaying its growth history.

[0054] The feedback unit can compare the feedback content with the user's past behavior and suggest areas for improvement. For example, the feedback unit can compare the user's past skincare methods with their current methods and suggest areas for improvement. For example, the feedback unit can suggest successful skincare methods based on the user's past behavioral history. For example, the feedback unit can analyze the user's past behavioral history and suggest the optimal care method. By comparing with the user's past behavior, it can suggest areas for improvement and provide better skincare methods.

[0055] The feedback unit can compare feedback content with other users' data and provide benchmarks. For example, the feedback unit can evaluate the growth status of your plants by comparing them with the growth data of other users. For example, the feedback unit can suggest optimal growth conditions based on other users' data. For example, the feedback unit can suggest areas for improvement by referring to the success stories of other users. In this way, you can evaluate the growth status of your plants by comparing them with the data of other users.

[0056] The feedback unit can use the feedback data to suggest optimal environmental conditions for plant growth. For example, the feedback unit can suggest the optimal range of temperature and humidity to promote plant growth. For example, the feedback unit can suggest the optimal pH value and nutrient balance of the soil. For example, the feedback unit can suggest the optimal conditions for light intensity and irradiation time. In this way, by suggesting the optimal environmental conditions for plant growth, it promotes growth.

[0057] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data.

[0058] The learning unit can use training data to predict plant growth patterns and incorporate these predictions into future proposals. For example, the learning unit can predict plant growth rates based on training data and incorporate these predictions into future proposals. For example, the learning unit can analyze training data to predict seasonal growth patterns. For example, the learning unit can use training data to predict different growth patterns for each plant species and incorporate these predictions into proposals. In this way, predicting plant growth patterns allows for the incorporation of these predictions into future proposals.

[0059] The learning unit can optimize its learning algorithm by referencing data from other users during the learning process. For example, the learning unit selects the optimal learning algorithm based on data from other users. For example, the learning unit analyzes data from other users and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referencing data from other users. Thus, by referencing data from other users, the accuracy of the learning algorithm is improved.

[0060] The learning unit can use training data to propose optimal environmental conditions for plant growth. For example, the learning unit can propose the optimal range of temperature and humidity based on training data. For example, the learning unit can analyze training data to propose the optimal pH value and nutrient balance for soil. For example, the learning unit can use training data to propose the optimal conditions for light intensity and irradiation time. In this way, by proposing the optimal environmental conditions for plant growth, it promotes growth.

[0061] The recommendation function can suggest the most suitable items based on the type and growth stage of the plant. For example, for succulents, it might suggest an automatic watering system to reduce the frequency of watering. For flowering plants, it might suggest a fertilizer suited to the flowering season. For young plants, it might suggest a special fertilizer to promote growth. This allows the system to suggest the most suitable items based on the type and growth stage of the plant.

[0062] The recommendation system can suggest the most suitable items by comparing recommendations with the user's past purchase history. For example, the recommendation system can suggest the most suitable items based on the items the user has purchased in the past. For example, the recommendation system can suggest successful items based on the user's past purchase history. For example, the recommendation system can analyze the user's past purchase history and suggest areas for improvement. This allows the system to suggest the most suitable items based on the user's past purchase history.

[0063] The recommendation system can compare recommendations with success stories from other users and provide reference information. For example, the recommendation system can make optimal recommendations based on the success stories of other users. For example, the recommendation system can suggest improvements based on data from other users. For example, the recommendation system can analyze the success stories of other users and suggest the most suitable items. This allows for optimal recommendations based on the success stories of other users.

[0064] The recommendation department can customize its recommendations seasonally and suggest the most suitable items. For example, in spring, it might suggest fertilizers to promote growth. In summer, it might suggest automatic watering devices to prevent drying out. In winter, it might suggest heat-retaining items to protect plants from the cold. By suggesting the most suitable items for each season, it promotes plant growth.

[0065] The recommendation system can customize its recommendations to match the characteristics of the user's region. For example, it can suggest plant cultivation items suited to the user's local climate, fertilizers suited to the user's local soil characteristics, or watering devices suited to the user's local weather patterns. This allows the system to suggest the most suitable items for each user's region.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The plant cultivation support system may also include a growth recording unit that records plant growth and notifies the user of its progress. The growth recording unit can periodically record and notify the user of, for example, the plant's height, the number of leaves, and the flowering status. The growth recording unit can also visually display plant growth using graphs or charts, allowing the user to grasp the progress at a glance. Furthermore, the growth recording unit can set alerts related to plant growth and notify the user if an abnormality is detected. This allows the user to understand plant growth in detail and provide appropriate care.

[0068] The plant growth support system may further include an external factor monitoring unit that monitors external factors affecting plant growth. For example, the external factor monitoring unit might monitor the indoor CO2 concentration and issue an alert to encourage ventilation. For example, if the indoor CO2 concentration is low, the external factor monitoring unit might suggest a device to supply the CO2 necessary for plant growth. The external factor monitoring unit might also monitor the indoor CO2 concentration to maintain an optimal environment for plant growth. This allows for the maintenance of an optimal environment by monitoring external factors that affect plant growth.

[0069] The plant cultivation support system can also incorporate community features related to plant growth. These community features could, for example, allow users to share information and receive advice from other plant enthusiasts. They could also allow users to post updates on their plants' growth and receive feedback from other users. Furthermore, they could provide information on plant cultivation events and workshops, enabling users to participate. This would allow users to interact with other plant enthusiasts and deepen their knowledge of plant cultivation.

[0070] The plant cultivation support system can also include a cloud integration unit that stores plant growth data in the cloud, allowing access from multiple devices. This cloud integration unit enables access to plant growth data from devices such as smartphones, tablets, and personal computers. For example, data can be shared with family and friends, allowing multiple people to monitor plant growth. The cloud integration unit can also send data to experts, allowing them to receive advice remotely. This enables users to access plant growth data and provide appropriate care from anywhere.

[0071] The plant growth support system can also include a smart home integration unit that integrates plant growth data with other smart home devices to optimize the overall indoor environment. For example, the smart home integration unit can integrate with a smart thermostat to optimize room temperature. It can also integrate with a smart humidifier to optimize humidity. Furthermore, it can integrate with smart lighting to provide the optimal light environment for plants. This optimizes the overall indoor environment, thereby promoting plant growth.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: Sensors detect temperature, humidity, mass, etc. Sensors are installed, for example, in pots to monitor the plant's growing environment. A temperature sensor measures the internal temperature of the pot, a humidity sensor measures the internal humidity of the pot, and a mass sensor measures the mass of the pot to estimate the moisture content. Step 2: The analysis unit analyzes the data collected by the sensors. The analysis unit uses a generating AI to analyze the data and determine the growth rate and dryness of the plants. It takes temperature data, humidity data, and mass data as input and outputs the plant growth status. Step 3: The suggestion unit proposes appropriate maintenance timing and methods based on the data analyzed by the analysis unit. The suggestion unit uses a generation AI to make suggestions, taking into account information such as the user's schedule, current season, weather, and upcoming weather forecast. It takes the user's schedule data, season data, weather data, and weather forecast data as input and outputs appropriate maintenance methods. Step 4: The feedback unit provides visual feedback on the care methods proposed by the suggestion unit. The feedback unit uses a generative AI to provide feedback, taking pictures of changes in the color and shape of the plant's leaves with a camera and analyzing the images. The camera data is used as input, and the plant's health status is output. Step 5: The learning unit learns from user feedback and improves the accuracy of its suggestions. The learning unit uses generative AI to learn from the results of the user's actual skincare routine and incorporates this into future suggestions. User feedback data is used as input to improve the accuracy of the suggestions. Step 6: The recommendation unit recommends care tools and items as needed. The recommendation unit uses AI to make recommendations, and in conjunction with the online store, it suggests the necessary tools and items. It takes online store data as input and outputs recommended items.

[0074] (Example of form 2) The plant cultivation support system according to an embodiment of the present invention is a system for supporting plant cultivation. This plant cultivation support system comprises a pot equipped with sensors that detect temperature, humidity, mass, etc., an analysis unit that analyzes data collected by the sensors, a suggestion unit that proposes appropriate care timing and methods based on the data analyzed by the analysis unit, a feedback unit that provides visual feedback on the care methods proposed by the suggestion unit, a learning unit that learns from user feedback and improves the accuracy of suggestions, and a recommendation unit that recommends care tools and items as needed. For example, the plant cultivation support system installs sensors that detect temperature, humidity, mass, etc. in the pot in which the plant is grown and collects this data. Next, the generated AI analyzes the collected data to determine the growth rate and dryness of the plant. Furthermore, it proposes appropriate care timing and methods, taking into account information such as the user's schedule, the current season, weather, and the weather forecast for the coming days. In addition, the visual feedback function allows the user to understand the condition of the plant in real time on the app. For example, by taking pictures of changes in the color and shape of the plant's leaves with a camera and having the generated AI analyze the images, the plant's health can be diagnosed. Furthermore, if problems arise with plant growth, the generating AI diagnoses the symptoms and suggests solutions. The generating AI learns from user feedback and improves the accuracy of its suggestions. For example, by inputting feedback on the results of actual care performed by the user, the generating AI can learn from those results and incorporate them into future suggestions. Additionally, it can integrate with online stores to recommend care tools and items as needed, leading to purchases. For instance, it can suggest fertilizers and watering tools necessary for plant growth, making them easily accessible to users. This system creates a world where everyone can properly cultivate plants and find solace in their beauty and presence. Moreover, even difficult-to-care-for plants can be properly cared for with the support of the generating AI, enabling a society where people can easily enjoy a rich life surrounded by plants. In this way, the plant cultivation support system assists plant growth and suggests appropriate timing and methods for care, enabling anyone to properly cultivate plants.

[0075] The plant cultivation support system according to this embodiment comprises a sensor, an analysis unit, a suggestion unit, a feedback unit, a learning unit, and a recommendation unit. The sensor detects temperature, humidity, mass, etc. The sensor is installed, for example, in a pot to monitor the plant's growth environment. The sensor includes a temperature sensor, a humidity sensor, a mass sensor, etc. The temperature sensor measures, for example, the internal temperature of the pot. The humidity sensor measures, for example, the internal humidity of the pot. The mass sensor measures, for example, the mass of the pot and estimates the amount of moisture. The analysis unit analyzes the data collected by the sensor. The analysis unit analyzes the data using, for example, a generative AI to determine the plant's growth stage and dryness. The analysis unit takes, for example, temperature data, humidity data, and mass data as input and outputs the plant's growth status. The suggestion unit proposes appropriate care timing and methods based on the data analyzed by the analysis unit. The suggestion unit makes suggestions using, for example, a generative AI. The suggestion unit makes suggestions considering information such as the user's schedule, the current season, weather, and the weather forecast for the coming days. The suggestion unit takes user schedule data, seasonal data, weather data, and weather forecast data as input and outputs appropriate care methods. The feedback unit provides visual feedback on the care methods suggested by the suggestion unit. The feedback unit uses generative AI to provide feedback, for example. The feedback unit takes pictures of changes in the color and shape of plant leaves with a camera and analyzes the images. The feedback unit uses camera data as input and outputs the plant's health status, for example. The learning unit learns from user feedback to improve the accuracy of suggestions. The learning unit uses generative AI for learning, for example. The learning unit learns the results of the care actually performed by the user and reflects them in the next suggestion. The learning unit uses user feedback data as input and improves the accuracy of suggestions, for example. The recommendation unit recommends care tools and items as needed. The recommendation unit uses generative AI for recommendations, for example. The recommendation unit works with online stores to suggest necessary tools and items. The recommendation unit uses online store data as input and outputs recommended items, for example.This means that the plant cultivation support system assists in plant growth and suggests appropriate timing and methods for care, enabling anyone to properly grow plants.

[0076] Sensors detect temperature, humidity, mass, and other parameters. For example, sensors are installed in pots to monitor the plant's growing environment. Sensors include temperature sensors, humidity sensors, and mass sensors. A temperature sensor measures, for example, the internal temperature of the pot. A humidity sensor measures, for example, the internal humidity of the pot. A mass sensor measures, for example, the mass of the pot and estimates the water content. These sensors have wireless communication capabilities and can transmit collected data to a central database in real time. The temperature sensor can measure not only the internal temperature of the pot but also the external ambient temperature, allowing for a comprehensive understanding of temperature changes in the plant's environment. The humidity sensor can measure not only soil humidity but also the humidity of the surrounding air, enabling accurate determination of the plant's water needs. The mass sensor accurately measures changes in the pot's mass, allowing for estimation of water absorption and evaporation. This allows the sensor to comprehensively monitor the plant's growing environment and provide detailed data. Furthermore, the sensor has a periodic calibration function, enabling it to provide highly accurate data over long periods. This allows the sensor to accurately understand the plant's growth environment and provide fundamental data for appropriate cultivation support.

[0077] The analysis unit analyzes data collected by sensors. For example, the analysis unit uses generative AI to analyze the data and determine the growth rate and dryness of plants. For example, the analysis unit takes temperature data, humidity data, and mass data as input and outputs the growth status of plants. The generative AI has learned from past data and growth patterns for each plant species, which enables it to determine the growth status of plants with high accuracy. Specifically, it determines whether plants are growing within an appropriate temperature range from temperature data and evaluates the dryness of the soil from humidity data. From mass data, it estimates the moisture content of the pot and confirms whether the plants are receiving sufficient moisture. Furthermore, the analysis unit can detect unusual data patterns using an anomaly detection algorithm and detect problems early. For example, if a sudden temperature change or a drop in humidity is detected, the analysis unit will immediately issue a warning and propose appropriate countermeasures. This allows the analysis unit to grasp the growth status of plants in real time and provide information for appropriate cultivation support. In addition, the analysis unit can perform trend analysis based on long-term data and predict plant growth patterns and environmental changes. This allows the analysis unit to play a crucial role in maintaining the health of plants and providing an optimal growing environment.

[0078] The suggestion unit proposes appropriate care timings and methods based on data analyzed by the analysis unit. The suggestion unit makes suggestions using, for example, generative AI. The suggestion unit makes suggestions considering information such as the user's schedule, the current season, weather, and upcoming weather forecasts. For example, the suggestion unit takes user schedule data, seasonal data, weather data, and weather forecast data as input and outputs appropriate care methods. The generative AI comprehensively analyzes this data and proposes the optimal care timings and methods. For example, it considers the user's schedule and proposes care timings that avoid busy times, and suggests appropriate watering and fertilizing amounts according to the current season and weather. Furthermore, based on upcoming weather forecasts, it can suggest refraining from watering if rain is expected. This allows the suggestion unit to make flexible suggestions tailored to the user's lifestyle and environment. The suggestion unit can learn from the user's past care history and plant growth data, continuously improving the accuracy of its suggestions. This allows the suggestion unit to provide optimal cultivation support for the user and play an important role in maintaining the health of the plants.

[0079] The feedback unit provides visual feedback on the care methods proposed by the suggestion unit. The feedback unit provides feedback using, for example, generative AI. The feedback unit takes pictures of changes in the color and shape of the plant's leaves with a camera and analyzes the images. The feedback unit takes camera data as input and outputs the plant's health status. The generative AI uses image recognition technology to analyze changes in the color and shape of the plant's leaves and determines whether the plant is healthy. For example, if the leaves are turning yellow, it may be due to nutrient deficiency or overwatering, so appropriate measures are suggested. If the shape of the leaves is deformed, it may be due to pest or disease damage, so early countermeasures can be taken. In this way, the feedback unit can visually grasp the plant's health status and provide specific feedback to the user. Furthermore, the feedback unit can record the results of the care actually performed by the user and reflect them in the next suggestion. In this way, the feedback unit can play an important role in improving the user's plant care skills.

[0080] The learning unit learns from user feedback to improve the accuracy of its suggestions. For example, the learning unit uses generative AI for learning. The learning unit learns the results of actual care performed by users and reflects this in future suggestions. For example, the learning unit uses user feedback data as input to improve the accuracy of its suggestions. The generative AI analyzes the results of care performed by users and plant growth data, learning successful and unsuccessful care methods. This allows the learning unit to provide optimal suggestions tailored to each user's characteristics and plant type. For example, it learns how a particular plant grows under specific environmental conditions and reflects this in future suggestions. This allows the learning unit to continuously improve the accuracy of its suggestions and provide optimal cultivation support for users. Furthermore, the learning unit can comprehensively analyze feedback from multiple users to find common patterns and trends. This allows the learning unit to provide highly accurate suggestions to a wider range of users.

[0081] The recommendation system recommends care tools and items as needed. For example, the recommendation system uses generative AI to make recommendations. It collaborates with online stores to suggest necessary tools and items. For example, it takes online store data as input and outputs recommended items. The generative AI analyzes the user's cultivation history and the current state of the plants to suggest the most suitable tools and items. For example, if a plant is nutrient-deficient, it suggests appropriate fertilizer; if there is pest or disease damage, it suggests effective pesticides. Furthermore, it can also suggest items appropriate for the season and weather. For example, in winter, it suggests insulating covers and heaters; in summer, it suggests shade nets and automatic watering systems. This allows the recommendation system to provide users with the tools and items they need at the right time, supporting the maintenance of plant health. Additionally, the recommendation system can provide more accurate recommendations based on the user's purchase history and ratings. This allows the recommendation system to provide users with optimal cultivation support and play a crucial role in maintaining the health of their plants.

[0082] The analysis unit can determine the growth rate and dryness of plants. For example, the analysis unit evaluates the growth rate based on factors such as the size of the plant's leaves, the thickness of its stems, and the number of flowers. For example, the analysis unit evaluates the dryness based on factors such as soil moisture and the degree of leaf wilting. For example, the analysis unit takes temperature data, humidity data, and mass data as input and outputs the plant's growth status. This allows for accurate determination of the plant's growth rate and dryness, enabling the suggestion of appropriate care methods.

[0083] The suggestion unit can make suggestions considering information such as the user's schedule, the current season, weather, and upcoming weather forecasts. For example, the suggestion unit takes user schedule data, seasonal data, weather data, and weather forecast data as input and outputs appropriate care methods. For example, the suggestion unit can suggest the timing of watering based on the user's schedule. For example, the suggestion unit can suggest the type of fertilizer based on the current season. For example, the suggestion unit can suggest care methods that take into account the amount of sunlight based on weather data. This allows the system to suggest the optimal care method tailored to the user's schedule and environment.

[0084] The feedback unit can capture images of changes in the color and shape of plant leaves using a camera and analyze those images. For example, the feedback unit takes camera data as input and outputs the plant's health status. For example, the feedback unit can analyze changes in leaf color to evaluate the plant's health status. For example, the feedback unit can analyze changes in leaf shape to evaluate the plant's health status. For example, the feedback unit can diagnose the plant's health status based on changes in leaf color and shape. This allows for a visual understanding of the plant's health status and provides appropriate feedback.

[0085] The learning unit can learn from the results of the user's actual care and reflect them in future suggestions. For example, the learning unit can take user feedback data as input to improve the accuracy of its suggestions. For example, the learning unit can learn from the user's watering results and suggest the next watering timing. For example, the learning unit can learn from the user's fertilizer usage results and suggest the next type of fertilizer to use. For example, the learning unit can learn from the user's pruning results and suggest the next pruning method. In this way, the accuracy of suggestions improves by learning from user feedback.

[0086] The recommendation function can integrate with online stores to suggest necessary tools and items. For example, it can take online store data as input and output recommended items. For instance, it can suggest fertilizers needed for plant growth, watering tools, or pruning tools. This makes it easy for users to purchase necessary tools and items.

[0087] The sensor can estimate the user's emotions and adjust the data acquisition frequency based on the estimated emotions. For example, the sensor can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the sensor can record the user's voice and estimate the emotions using voice analysis technology. For example, the sensor can collect the user's biometric data (heart rate or skin electrical activity) and estimate the emotions using an emotion estimation algorithm. For example, if the user is stressed, the sensor can increase the data acquisition frequency to provide more detailed information. For example, if the user is relaxed, the sensor can decrease the data acquisition frequency to provide only the minimum necessary information. For example, if the user is busy, the sensor can set the data acquisition frequency to a moderate level and provide only important information. In this way, by adjusting the data acquisition frequency according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The sensor can be fitted with additional sensors to detect specific components depending on the type of plant. For example, a sensor can be added to detect the soil's pH value to maintain appropriate acidity. For example, sensors can be added to detect water clarity and oxygen concentration to provide an optimal environment. For example, sensors can be added to detect soil nutrients (e.g., nitrogen, phosphorus, potassium) to suggest appropriate fertilizers. This allows for the provision of an optimal environment tailored to the type of plant.

[0089] The sensor can transmit data to the cloud in real time, allowing for remote monitoring of plant conditions. For example, the sensor can monitor plant conditions in real time via a smartphone app. The sensor can be shared with family and friends, allowing multiple people to monitor plant conditions and provide necessary care. The sensor can also transmit data to experts, allowing for remote advice. This enables real-time monitoring of plant conditions even from a distance.

[0090] The sensor can estimate the user's emotions and adjust the data display method based on the estimated emotions. For example, the sensor can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the sensor can record the user's voice and estimate the emotions using voice analysis technology. For example, the sensor can collect the user's biometric data (heart rate or skin electrical activity) and estimate the emotions using an emotion estimation algorithm. For example, if the user is stressed, the sensor can provide a simple and highly visible display method. For example, if the user is relaxed, the sensor can provide a display method that includes detailed data. For example, if the user is busy, the sensor can provide a concise display method that gets straight to the point. This allows for the provision of more appropriate information by adjusting the data display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The sensor can integrate data with other smart home devices to optimize the overall indoor environment. For example, it can work with a smart thermostat to optimize room temperature. For example, it can work with a smart humidifier to optimize humidity. For example, it can work with smart lighting to provide the optimal light environment for plants. This optimizes the overall indoor environment and promotes plant growth.

[0092] The sensor can use the data to monitor external factors that affect plant growth. For example, the sensor can monitor indoor CO2 concentration and issue an alert to encourage ventilation. For example, if indoor CO2 concentration is low, the sensor can suggest a device to supply the CO2 necessary for plant growth. For example, the sensor can monitor indoor CO2 concentration and maintain an optimal environment for plant growth. This allows for the maintenance of an optimal environment by monitoring external factors that affect plant growth.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed data. For example, if the user is busy, the analysis unit provides a concise display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analysis unit can analyze plant growth patterns over the long term and predict future growth. For example, the analysis unit can predict plant growth rates based on past data and estimate future growth. For example, the analysis unit can analyze seasonal growth patterns and predict the optimal growth period. For example, the analysis unit can analyze different growth patterns for each plant species and make individual growth predictions. In this way, by analyzing plant growth patterns, future growth can be predicted and appropriate care can be provided.

[0095] The analysis unit can evaluate the health of plants based on the analysis results and issue alerts if abnormalities are detected. For example, the analysis unit can analyze changes in leaf color and shape and issue alerts if abnormalities are detected. For example, the analysis unit can detect abnormalities in soil humidity and pH values ​​and issue alerts. For example, the analysis unit can detect rapid changes in temperature and humidity and issue alerts before they affect the health of plants. This allows for evaluation of plant health and rapid response if abnormalities are detected.

[0096] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will display detailed analysis results. For example, if the user is busy, the analysis unit will prioritize displaying concise analysis results. In this way, by determining the priority of analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The analysis unit can compare analysis results with other users' data and provide benchmarks. For example, the analysis unit can compare the growth status of your plants with the growth data of other users to evaluate your own plant's growth. For example, the analysis unit can suggest optimal growth conditions based on other users' data. For example, the analysis unit can refer to the success stories of other users and suggest areas for improvement. This allows you to evaluate the growth status of your plants by comparing them with other users' data.

[0098] The analysis unit can use the analysis results to propose optimal environmental conditions for plant growth. For example, the analysis unit can suggest the optimal range of temperature and humidity to promote plant growth. For example, the analysis unit can suggest the optimal pH value and nutrient balance of the soil. For example, the analysis unit can suggest the optimal conditions for light intensity and irradiation time. In this way, by proposing the optimal environmental conditions for plant growth, it promotes growth.

[0099] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is stressed, the suggestion unit will provide simple and highly visible suggestions. For example, if the user is relaxed, the suggestion unit will provide suggestions that include detailed information. For example, if the user is busy, the suggestion unit will provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The proposal department can suggest different care methods depending on the type of plant and its growth stage. For example, for succulents, the proposal department might suggest reducing the frequency of watering. For flowering plants, the proposal department might suggest fertilizers appropriate for the flowering season. For young plants, the proposal department might suggest special care to promote growth. This allows the department to suggest the optimal care method for each type of plant and its growth stage.

[0101] The proposal department can compare the proposed content with the user's past behavioral history to make the most appropriate suggestions. For example, the proposal department can make the most appropriate suggestions based on the user's past skincare routines. For example, the proposal department can suggest successful skincare routines based on the user's past behavioral history. For example, the proposal department can analyze the user's past behavioral history and suggest areas for improvement. In this way, the proposal department can make the most appropriate suggestions based on the user's past behavioral history.

[0102] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can record the user's voice and estimate emotions using voice analysis technology. For example, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the suggestion unit will prioritize displaying important suggestions. For example, if the user is relaxed, the suggestion unit will display detailed suggestions. For example, if the user is busy, the suggestion unit will prioritize displaying concise suggestions. In this way, by determining the priority of suggestions according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The proposal department can compare the proposed content with the success stories of other users and provide reference information. For example, the proposal department can make optimal proposals based on the success stories of other users. For example, the proposal department can refer to the data of other users and propose areas for improvement. For example, the proposal department can analyze the success stories of other users and propose the optimal care methods. In this way, it is possible to make optimal proposals based on the success stories of other users.

[0104] The proposal department can customize the recommendations according to the season and provide optimal care methods. For example, in spring, the proposal department will suggest fertilizers to promote growth. In summer, for example, the proposal department will suggest watering frequency to prevent drying out. In winter, for example, the proposal department will suggest measures to protect plants from the cold. In this way, by providing optimal care methods for each season, the department promotes plant growth.

[0105] The feedback unit can estimate the user's emotions and adjust the way feedback is displayed based on the estimated emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the feedback unit can record the user's voice and estimate emotions using voice analysis technology. For example, the feedback unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the user is stressed, the feedback unit provides simple and highly visible feedback. For example, if the user is relaxed, the feedback unit provides feedback that includes detailed information. For example, if the user is busy, the feedback unit provides concise feedback that gets straight to the point. This allows for the provision of more appropriate information by adjusting the way feedback is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The feedback unit can visually display the plant's growth history using graphs and charts during feedback. For example, the feedback unit can display the plant's growth rate in a graph, allowing for a visual understanding of past growth patterns. For example, the feedback unit can display the plant's health status in a chart, identifying when abnormalities occurred. For example, the feedback unit can display the plant's watering and fertilization history in a graph, helping to determine the appropriate timing for care. This makes it easier to understand the plant's growth status by visually displaying its growth history.

[0107] The feedback unit can compare the feedback content with the user's past behavior and suggest areas for improvement. For example, the feedback unit can compare the user's past skincare methods with their current methods and suggest areas for improvement. For example, the feedback unit can suggest successful skincare methods based on the user's past behavioral history. For example, the feedback unit can analyze the user's past behavioral history and suggest the optimal care method. By comparing with the user's past behavior, it can suggest areas for improvement and provide better skincare methods.

[0108] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the feedback unit can record the user's voice and estimate emotions using voice analysis technology. For example, the feedback unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the feedback unit will prioritize displaying important feedback. For example, if the user is relaxed, the feedback unit will display detailed feedback. For example, if the user is busy, the feedback unit will prioritize displaying concise feedback. In this way, by determining the priority of feedback according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The feedback unit can compare feedback content with other users' data and provide benchmarks. For example, the feedback unit can evaluate the growth status of your plants by comparing them with the growth data of other users. For example, the feedback unit can suggest optimal growth conditions based on other users' data. For example, the feedback unit can suggest areas for improvement by referring to the success stories of other users. In this way, you can evaluate the growth status of your plants by comparing them with the data of other users.

[0110] The feedback unit can use the feedback data to suggest optimal environmental conditions for plant growth. For example, the feedback unit can suggest the optimal range of temperature and humidity to promote plant growth. For example, the feedback unit can suggest the optimal pH value and nutrient balance of the soil. For example, the feedback unit can suggest the optimal conditions for light intensity and irradiation time. In this way, by suggesting the optimal environmental conditions for plant growth, it promotes growth.

[0111] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the learning unit can record the user's voice and estimate the emotions using voice analysis technology. For example, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the user is stressed, the learning unit will prioritize learning important data. For example, if the user is relaxed, the learning unit will prioritize learning detailed data. For example, if the user is busy, the learning unit will prioritize learning concise data. This allows for the provision of more appropriate information by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data.

[0113] The learning unit can use training data to predict plant growth patterns and incorporate these predictions into future proposals. For example, the learning unit can predict plant growth rates based on training data and incorporate these predictions into future proposals. For example, the learning unit can analyze training data to predict seasonal growth patterns. For example, the learning unit can use training data to predict different growth patterns for each plant species and incorporate these predictions into proposals. In this way, predicting plant growth patterns allows for the incorporation of these predictions into future proposals.

[0114] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the learning unit can record the user's voice and estimate emotions using voice analysis technology. For example, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the user is stressed, the learning unit increases the learning frequency and makes suggestions quickly. For example, if the user is relaxed, the learning unit decreases the learning frequency and learns detailed data. For example, if the user is busy, the learning unit sets the learning frequency to a moderate level and prioritizes learning important data. This allows for the provision of more appropriate information by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0115] The learning unit can optimize its learning algorithm by referencing data from other users during the learning process. For example, the learning unit selects the optimal learning algorithm based on data from other users. For example, the learning unit analyzes data from other users and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referencing data from other users. Thus, by referencing data from other users, the accuracy of the learning algorithm is improved.

[0116] The learning unit can use training data to propose optimal environmental conditions for plant growth. For example, the learning unit can propose the optimal range of temperature and humidity based on training data. For example, the learning unit can analyze training data to propose the optimal pH value and nutrient balance for soil. For example, the learning unit can use training data to propose the optimal conditions for light intensity and irradiation time. In this way, by proposing the optimal environmental conditions for plant growth, it promotes growth.

[0117] The recommendation unit can estimate the user's emotions and adjust the recommendation content based on the estimated emotions. For example, the recommendation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the recommendation unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the recommendation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the recommendation unit will provide simple and highly visual recommendations. For example, if the user is relaxed, the recommendation unit will provide recommendations that include detailed information. For example, if the user is busy, the recommendation unit will provide concise recommendations that get straight to the point. In this way, by adjusting the recommendation content according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0118] The recommendation function can suggest the most suitable items based on the type and growth stage of the plant. For example, for succulents, it might suggest an automatic watering system to reduce the frequency of watering. For flowering plants, it might suggest a fertilizer suited to the flowering season. For young plants, it might suggest a special fertilizer to promote growth. This allows the system to suggest the most suitable items based on the type and growth stage of the plant.

[0119] The recommendation system can suggest the most suitable items by comparing recommendations with the user's past purchase history. For example, the recommendation system can suggest the most suitable items based on the items the user has purchased in the past. For example, the recommendation system can suggest successful items based on the user's past purchase history. For example, the recommendation system can analyze the user's past purchase history and suggest areas for improvement. This allows the system to suggest the most suitable items based on the user's past purchase history.

[0120] The recommendation system can estimate the user's emotions and determine the priority of recommendations based on those emotions. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the recommendation system can record the user's voice and estimate their emotions using voice analysis technology. For example, the recommendation system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the recommendation system will prioritize displaying important recommendations. For example, if the user is relaxed, the recommendation system will display detailed recommendations. For example, if the user is busy, the recommendation system will prioritize displaying concise recommendations. By prioritizing recommendations according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0121] The recommendation system can compare recommendations with success stories from other users and provide reference information. For example, the recommendation system can make optimal recommendations based on the success stories of other users. For example, the recommendation system can suggest improvements based on data from other users. For example, the recommendation system can analyze the success stories of other users and suggest the most suitable items. This allows for optimal recommendations based on the success stories of other users.

[0122] The recommendation department can customize its recommendations seasonally and suggest the most suitable items. For example, in spring, it might suggest fertilizers to promote growth. In summer, it might suggest automatic watering devices to prevent drying out. In winter, it might suggest heat-retaining items to protect plants from the cold. By suggesting the most suitable items for each season, it promotes plant growth.

[0123] The recommendation system can customize its recommendations to match the characteristics of the user's region. For example, it can suggest plant cultivation items suited to the user's local climate, fertilizers suited to the user's local soil characteristics, or watering devices suited to the user's local weather patterns. This allows the system to suggest the most suitable items for each user's region.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The plant cultivation support system may also include a growth recording unit that records plant growth and notifies the user of its progress. The growth recording unit can periodically record and notify the user of, for example, the plant's height, the number of leaves, and the flowering status. The growth recording unit can also visually display plant growth using graphs or charts, allowing the user to grasp the progress at a glance. Furthermore, the growth recording unit can set alerts related to plant growth and notify the user if an abnormality is detected. This allows the user to understand plant growth in detail and provide appropriate care.

[0126] The plant growth support system may further include an external factor monitoring unit that monitors external factors affecting plant growth. For example, the external factor monitoring unit might monitor the indoor CO2 concentration and issue an alert to encourage ventilation. For example, if the indoor CO2 concentration is low, the external factor monitoring unit might suggest a device to supply the CO2 necessary for plant growth. The external factor monitoring unit might also monitor the indoor CO2 concentration to maintain an optimal environment for plant growth. This allows for the maintenance of an optimal environment by monitoring external factors that affect plant growth.

[0127] The plant cultivation support system can also incorporate community features related to plant growth. These community features could, for example, allow users to share information and receive advice from other plant enthusiasts. They could also allow users to post updates on their plants' growth and receive feedback from other users. Furthermore, they could provide information on plant cultivation events and workshops, enabling users to participate. This would allow users to interact with other plant enthusiasts and deepen their knowledge of plant cultivation.

[0128] The plant cultivation support system can also include a cloud integration unit that stores plant growth data in the cloud, allowing access from multiple devices. This cloud integration unit enables access to plant growth data from devices such as smartphones, tablets, and personal computers. For example, data can be shared with family and friends, allowing multiple people to monitor plant growth. The cloud integration unit can also send data to experts, allowing them to receive advice remotely. This enables users to access plant growth data and provide appropriate care from anywhere.

[0129] The plant growth support system can also include a smart home integration unit that integrates plant growth data with other smart home devices to optimize the overall indoor environment. For example, the smart home integration unit can integrate with a smart thermostat to optimize room temperature. It can also integrate with a smart humidifier to optimize humidity. Furthermore, it can integrate with smart lighting to provide the optimal light environment for plants. This optimizes the overall indoor environment, thereby promoting plant growth.

[0130] The plant cultivation support system may further include an emotion advice unit that estimates the user's emotions and provides advice on plant growth based on those emotions. The emotion advice unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate their emotions using voice analysis technology. It can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is stressed, the emotion advice unit might suggest a plant with a relaxing effect. If the user is relaxed, it might suggest a plant that is enjoyable to grow. If the user is busy, it might suggest a low-maintenance plant. This allows the system to provide more appropriate information by suggesting the optimal plant according to the user's emotions.

[0131] The plant growth support system may further include an emotion feedback unit that estimates the user's emotions and provides feedback on plant growth based on the estimated emotions. The emotion feedback unit can, for example, capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate emotions using voice analysis technology. Another method is to collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the user is stressed, the emotion feedback unit provides simple, easily understandable feedback. If the user is relaxed, it provides detailed feedback. If the user is busy, it provides concise, to-the-point feedback. This allows for more appropriate information to be provided by adjusting the feedback display method according to the user's emotions.

[0132] The plant cultivation support system may further include an emotion suggestion unit that estimates the user's emotions and provides suggestions regarding plant growth based on those emotions. The emotion suggestion unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate their emotions using voice analysis technology. It can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is stressed, the emotion suggestion unit can suggest plant care methods that promote relaxation. If the user is relaxed, it can suggest plant care methods that promote enjoyable growth. If the user is busy, it can suggest low-maintenance plant care methods. This allows the system to provide more appropriate information by suggesting the optimal care method according to the user's emotions.

[0133] The plant cultivation support system may further include an emotion learning unit that estimates the user's emotions and learns about plant growth based on the estimated emotions. The emotion learning unit can, for example, capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The emotion learning unit can, for example, record the user's voice and estimate emotions using voice analysis technology. The emotion learning unit can, for example, collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. The emotion learning unit can, for example, prioritize learning important data when the user is stressed. The emotion learning unit can, for example, prioritize learning detailed data when the user is relaxed. The emotion learning unit can, for example, prioritize learning concise data when the user is busy. This allows the system to provide more appropriate information by selecting learning data according to the user's emotions.

[0134] The plant cultivation support system may further include an emotion recommendation unit that estimates the user's emotions and makes recommendations regarding plant growth based on those emotions. The emotion recommendation unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate their emotions using voice analysis technology. Another possible method is to collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is stressed, the emotion recommendation unit can provide simple and easily understandable recommendations. If the user is relaxed, it can provide detailed recommendations. If the user is busy, it can provide concise and to-the-point recommendations. By adjusting the recommendations according to the user's emotions, the system can provide more appropriate information.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: Sensors detect temperature, humidity, mass, etc. Sensors are installed, for example, in pots to monitor the plant's growing environment. A temperature sensor measures the internal temperature of the pot, a humidity sensor measures the internal humidity of the pot, and a mass sensor measures the mass of the pot to estimate the moisture content. Step 2: The analysis unit analyzes the data collected by the sensors. The analysis unit uses a generating AI to analyze the data and determine the growth rate and dryness of the plants. It takes temperature data, humidity data, and mass data as input and outputs the plant growth status. Step 3: The suggestion unit proposes appropriate maintenance timing and methods based on the data analyzed by the analysis unit. The suggestion unit uses a generation AI to make suggestions, taking into account information such as the user's schedule, current season, weather, and upcoming weather forecast. It takes the user's schedule data, season data, weather data, and weather forecast data as input and outputs appropriate maintenance methods. Step 4: The feedback unit provides visual feedback on the care methods proposed by the suggestion unit. The feedback unit uses a generative AI to provide feedback, taking pictures of changes in the color and shape of the plant's leaves with a camera and analyzing the images. The camera data is used as input, and the plant's health status is output. Step 5: The learning unit learns from user feedback and improves the accuracy of its suggestions. The learning unit uses generative AI to learn from the results of the user's actual skincare routine and incorporates this into future suggestions. User feedback data is used as input to improve the accuracy of the suggestions. Step 6: The recommendation unit recommends care tools and items as needed. The recommendation unit uses AI to make recommendations, and in conjunction with the online store, it suggests the necessary tools and items. It takes online store data as input and outputs recommended items.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the sensor, analysis unit, proposal unit, feedback unit, learning unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sensor is installed in the smart device 14 and detects temperature, humidity, mass, etc. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor. The proposal unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and proposes appropriate maintenance timing and methods based on the analyzed data. The feedback unit is implemented in, for example, the control unit 46A of the smart device 14 and provides visual feedback on the proposed maintenance method. The learning unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and learns feedback from the user to improve the accuracy of the proposal. The recommendation unit is implemented in, for example, the control unit 46A of the smart device 14 and recommends maintenance tools and items as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the sensor, analysis unit, proposal unit, feedback unit, learning unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor is installed in the smart glasses 214 and detects temperature, humidity, mass, etc. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes appropriate cleaning timing and methods based on the analyzed data. The feedback unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides visual feedback on the proposed cleaning method. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns user feedback to improve the accuracy of the proposals. The recommendation unit is implemented in the specific processing unit 46A of the smart glasses 214 and recommends cleaning tools and items as needed. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the sensor, analysis unit, proposal unit, feedback unit, learning unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor is installed in the headset terminal 314 and detects temperature, humidity, mass, etc. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes appropriate maintenance timing and methods based on the analyzed data. The feedback unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides visual feedback on the proposed maintenance method. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns user feedback to improve the accuracy of the proposals. The recommendation unit is implemented in the specific processing unit 46A of the headset terminal 314 and recommends maintenance tools and items as needed. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the sensor, analysis unit, proposal unit, feedback unit, learning unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the sensor is installed on the robot 414 and detects temperature, humidity, mass, etc. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor. The proposal unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and proposes appropriate maintenance timing and methods based on the analyzed data. The feedback unit is implemented in, for example, the control unit 46A of the robot 414 and provides visual feedback on the proposed maintenance method. The learning unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and learns feedback from the user to improve the accuracy of the proposal. The recommendation unit is implemented in, for example, the control unit 46A of the robot 414 and recommends maintenance tools and items as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] 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.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A pot equipped with sensors that detect temperature, humidity, mass, etc. An analysis unit that analyzes the data collected by the aforementioned sensor, Based on the data analyzed by the aforementioned analysis unit, a proposal unit proposes appropriate maintenance timing and methods. A feedback unit provides visual feedback on the maintenance method proposed by the aforementioned proposal unit, A learning unit that learns from user feedback and improves the accuracy of its suggestions, It includes a recommendation section that recommends maintenance tools and items as needed. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Determining the growth stage and dryness of plants The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The system makes suggestions while taking into account the user's schedule, the current season, weather, and upcoming weather forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is The system uses cameras to photograph changes in the color and shape of plant leaves and then analyzes those images. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, Learn from the results of the user's actual skincare routine and incorporate them into future suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recommendation unit is, We collaborate with online stores to suggest necessary tools and items. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor is The system estimates the user's emotions and adjusts the frequency of sensor data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sensor is Depending on the type of plant, additional sensors will be installed to detect specific components. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sensor is The data is sent to the cloud in real time, allowing you to monitor the condition of the plants remotely. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sensor is The system estimates the user's emotions and adjusts the sensor data display method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sensor is By integrating data with other smart home devices, the overall indoor environment can be optimized. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned sensor is Using data to monitor external factors that affect plant growth The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We analyze plant growth patterns over the long term and predict future growth. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Based on the analysis results, the system evaluates the health of the plants and issues an alert if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We compare the analysis results with other users' data and provide benchmarks. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Using the analysis results, we propose the optimal environmental conditions for plant growth. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we suggest different care methods depending on the type of plant and its growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, We compare the proposed content with the user's past behavior history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We will compare the proposed content with the success stories of other users and provide reference information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We customize our suggestions seasonally to provide the most suitable care methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is During feedback, the plant's growth history is visually displayed using graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is We compare the feedback with the user's past behavior and suggest areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is We compare feedback content with data from other users and provide benchmarks. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is Based on the feedback, we will propose the optimal environmental conditions for plant growth. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, Using training data, we predict plant growth patterns and incorporate this into future proposals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the learning algorithm is optimized by referencing data from other users. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, Using training data, we propose the optimal environmental conditions for plant growth. The system described in Appendix 1, characterized by the features described herein. (Note 37) The recommendation unit is, It estimates the user's emotions and adjusts recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The recommendation unit is, When making recommendations, the system suggests the most suitable items based on the type of plant and its growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 39) The recommendation unit is, The recommendation system compares the user's past purchase history with the recommended items to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 40) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The recommendation unit is, The recommendation content is compared with success stories from other users to provide reference information. The system described in Appendix 1, characterized by the features described herein. (Note 42) The recommendation unit is, We customize our recommendations seasonally to suggest the most suitable items. The system described in Appendix 1, characterized by the features described herein. (Note 43) The recommendation unit is, Customize recommendation content to match the user's regional characteristics. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A pot equipped with sensors that detect temperature, humidity, mass, etc. An analysis unit that analyzes the data collected by the aforementioned sensor, Based on the data analyzed by the aforementioned analysis unit, a proposal unit proposes appropriate maintenance timing and methods. A feedback unit provides visual feedback on the maintenance method proposed by the aforementioned proposal unit, A learning unit that learns from user feedback and improves the accuracy of its suggestions, It includes a recommendation section that recommends maintenance tools and items as needed. A system characterized by the following features.

2. The aforementioned analysis unit, Determining the growth stage and dryness of plants The system according to feature 1.

3. The aforementioned proposal section is, The system makes suggestions based on information such as the user's schedule, the current season, weather, and upcoming weather forecasts. The system according to feature 1.

4. The aforementioned feedback unit is The system uses cameras to photograph changes in the color and shape of plant leaves and then analyzes those images. The system according to feature 1.

5. The aforementioned learning unit, Learn from the results of the user's actual skincare routine and incorporate them into future suggestions. The system according to feature 1.

6. The recommendation unit is, We collaborate with online stores to suggest necessary tools and items. The system according to feature 1.

7. The aforementioned sensor is The system estimates the user's emotions and adjusts the frequency of sensor data acquisition based on the estimated emotions. The system according to feature 1.

8. The aforementioned sensor is Depending on the type of plant, additional sensors will be installed to detect specific components. The system according to feature 1.

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