system

Abstract

The system according to this embodiment aims to select the most suitable plants for the user's indoor environment and to provide management methods and interior design suggestions. [Solution] The system according to the embodiment comprises a measurement unit, an analysis unit, a selection unit, a schedule creation unit, a support unit, and an interior design proposal unit. The measurement unit automatically measures the user's floor plan and indoor environment. The analysis unit analyzes the data measured by the measurement unit. The selection unit selects the optimal plants based on the data analyzed by the analysis unit. The schedule creation unit creates a management schedule for the plants selected by the selection unit. The support unit provides interactive support to the user based on the schedule created by the schedule creation unit. The interior design proposal unit makes interior design proposals based on the support provided by the support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, it cannot be said that sufficient selection of plants optimal for the user's indoor environment, management methods, and interior proposals have been carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to select plants optimal for the user's indoor environment and provide management methods and interior proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a measurement unit, an analysis unit, a selection unit, a schedule creation unit, a support unit, and an interior design proposal unit. The measurement unit automatically measures the user's floor plan and indoor environment. The analysis unit analyzes the data measured by the measurement unit. The selection unit selects the optimal plants based on the data analyzed by the analysis unit. The schedule creation unit creates a management schedule for the plants selected by the selection unit. The support unit provides interactive support to the user based on the schedule created by the schedule creation unit. The interior design proposal unit makes interior design proposals based on the support provided by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can select plants that are optimal for the user's indoor environment and provide management methods and interior design suggestions. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 gardening support system according to an embodiment of the present invention is a system that proposes the optimal plants and their management methods based on the region and indoor environment, and provides a total proposal that includes interior design. This gardening support system automatically measures the user's floor plan and indoor environment (temperature, light intensity, etc.) and analyzes the user's behavior patterns. Next, it proposes the selection and placement of the optimal plants based on this data. Furthermore, it creates and proposes a management schedule for each individual plant. For example, it adjusts the timing of watering and fertilizing based on photos of the plants and their placement environment, and provides pruning advice based on photos of the tree's shape. It also provides interactive support through an AI chatbot to answer the user's questions. Furthermore, it accumulates the user's reactions to past advice and adjusts their preferences. Finally, it provides interior design suggestions based on the indoor environment (room and pot color scheme, etc.) and guides the user to a purchase site. Through this mechanism, the user is supported in making gardening decisions, tasks are automated, and a personalized experience is provided. As a result, the gardening support system can propose the optimal plants and management methods based on the user's floor plan and indoor environment, and provide a total proposal that includes interior design.

[0029] The gardening support system according to this embodiment comprises a measurement unit, an analysis unit, a selection unit, a schedule creation unit, a support unit, and an interior design proposal unit. The measurement unit automatically measures the user's floor plan and indoor environment. The measurement unit can automatically measure indoor environmental factors such as temperature and light intensity. The measurement unit measures indoor temperature and light intensity in real time using a temperature sensor and a light sensor. For example, the measurement unit can measure indoor temperature using a temperature sensor and measure indoor light intensity using a light sensor. The measurement unit can also collect data for analyzing the user's behavior patterns. The analysis unit analyzes the data measured by the measurement unit. For example, the analysis unit can analyze the user's behavior patterns using an algorithm based on the collected data. For example, the analysis unit can perform analysis using a machine learning algorithm based on the collected data to analyze the user's behavior patterns. The selection unit selects the optimal plants based on the data analyzed by the analysis unit. For example, the selection unit can propose the optimal plant selection and its placement. The selection unit selects the most suitable plants and proposes their placement based on the user's indoor environment and behavioral patterns. The scheduling unit creates a management schedule for the plants selected by the selection unit. The scheduling unit can, for example, adjust watering and fertilizing timing based on photos of the plants and their surrounding environment. The scheduling unit analyzes photos of the plants and their surrounding environment to adjust watering and fertilizing timing. The scheduling unit analyzes photos of the plants and their surrounding environment to adjust watering and fertilizing timing. The support unit provides interactive support to the user based on the schedule created by the scheduling unit. The support unit can, for example, provide Q&A via an AI chatbot. The support unit uses an AI chatbot to answer user questions in real time.The Interior Design Department makes interior design proposals based on the support provided by the Support Department. The Interior Design Department can make interior design proposals based on the indoor environment (such as the color scheme of the room and pots) and guide users to the purchase site. The Interior Design Department can analyze the indoor environment and make the most suitable interior design proposals for the user. The Interior Design Department can analyze the indoor environment and make the most suitable interior design proposals for the user. As a result, the gardening support system according to the embodiment can propose the most suitable plants and care methods based on the user's floor plan and indoor environment, and can make total proposals that include interior design.

[0030] The measurement unit automatically measures the user's room layout and indoor environment. For example, the measurement unit can automatically measure indoor environmental factors such as temperature and light intensity. The measurement unit uses temperature sensors and light sensors to measure indoor temperature and light intensity in real time. Specifically, temperature sensors are installed in various locations throughout the room, allowing for detailed temperature measurements at different locations. This makes it possible to understand temperature differences between rooms and temperature changes over time. Light sensors are installed near windows or in the center of rooms to measure the amount of natural light during the day and the influence of artificial lighting. This allows for the collection of data to provide a suitable light environment for plants. The measurement unit can also collect data to analyze the user's behavior patterns. For example, sensors can detect the time of day the user is in the room and their movement routes to understand their behavior patterns. This allows for the provision of information to optimize plant placement and management methods. Furthermore, by adding humidity sensors and carbon dioxide sensors, the measurement unit can also measure indoor humidity and air quality. This allows for a comprehensive understanding of environmental factors affecting plant growth, enabling more accurate gardening support. The measurement department sends this data to a cloud server and uses it for analysis and proposals in collaboration with other departments.

[0031] The analysis unit analyzes the data measured by the measurement unit. For example, the analysis unit can analyze user behavior patterns. To analyze user behavior patterns, the analysis unit uses algorithms based on the collected data. Specifically, it uses machine learning algorithms to analyze user behavior patterns and optimize plant placement and management methods. For example, if a user is often in the room at a specific time, it can suggest providing the optimal amount of light and temperature for that time. It can also suggest placing plants along paths that the user frequently travels to make plant care easier. Furthermore, the analysis unit can perform trend analysis based on past data to predict seasonal environmental changes and changes in user behavior patterns. This allows it to provide information for long-term gardening planning. The analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a sudden change in temperature or light intensity is detected, it can suggest measures to minimize the impact on plants. In this way, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The selection unit selects the most suitable plants based on data analyzed by the analysis unit. For example, the selection unit can propose the optimal plant selection and placement. Specifically, it determines the type and location of plants based on the user's indoor environment and behavioral patterns. For instance, it might place sunlight-loving plants near windows where sufficient light is available, and temperature-sensitive plants in areas with stable temperatures. It also suggests placing plants in frequently seen and easily accessible locations based on the user's behavioral patterns, making plant care easier. The selection unit utilizes a database of plant growth characteristics and management methods to select the most suitable plants for the user's needs. For example, it can suggest easy-to-care-for plants for beginners or plants suited to specific environmental conditions. Furthermore, the selection unit also selects plants that match the user's preferences and interior style. This allows for proposals that consider not only plant selection but also aesthetic appeal as part of the interior. The selection unit provides detailed information on the placement and management methods of the selected plants, supporting the user in properly growing them. This enables the selection unit to select the most suitable plants and propose their placement based on the user's indoor environment and behavioral patterns.

[0033] The scheduling unit creates management schedules for plants selected by the selection unit. For example, the scheduling unit can adjust watering and fertilizing timings based on photos of the plants and their environment. Specifically, it analyzes the plant's growth stage and environment to create an optimal management schedule. For instance, it analyzes the color and shape of the plant's leaves to adjust watering timing. It also analyzes the light intensity and temperature of the environment to optimize fertilizing timing. The scheduling unit can also propose management schedules tailored to the user's lifestyle and behavioral patterns. For example, it can set watering and fertilizing times to avoid busy periods, making it easier for users to continue caring for their plants. Furthermore, the scheduling unit also creates long-term management schedules that adapt to seasonal environmental changes and the plant's growth cycle. This helps maintain plant health and supports long-term growth. The scheduling unit notifies users of the created schedules on their smartphones and tablets, providing a reminder function. This ensures users remember to care for their plants. Based on user feedback, the scheduling unit continuously improves the schedules, providing more accurate management schedules.

[0034] The support department provides interactive support to users based on schedules created by the scheduling department. For example, the support department can provide Q&A via an AI chatbot. Specifically, the AI ​​chatbot answers user questions in real time and provides advice on plant care and troubleshooting. For instance, the AI ​​chatbot can quickly answer questions about what to do if a plant's leaves turn yellow or the appropriate watering frequency. The support department can also provide individually customized advice based on the user's behavior patterns and past question history, ensuring users receive support best suited to their situation. Furthermore, the support department provides reminders for regular maintenance and seasonal care, helping users remember to care for their plants. The support department collects user feedback to continuously improve the accuracy of the AI ​​chatbot's responses and the content of its support. This enables the support department to provide users with prompt and accurate support, helping them maintain the health of their plants.

[0035] The Interior Design Department provides interior design proposals based on the support provided by the Support Department. For example, the Interior Design Department can make interior design proposals based on the indoor environment (such as the color scheme of the room and pots) and guide users to the purchase site. Specifically, it proposes plant placement and pot selection that match the color scheme and style of the user's room. For example, it proposes simple pot designs for modern interiors and wooden pots for natural interiors. It also proposes plant placement and combinations, creating an interior design that considers the overall balance of the room. The Interior Design Department can also make customized proposals that match the user's preferences and lifestyle. For example, it proposes relaxing plants for the living room and practical plants such as herbs for the kitchen. Furthermore, the Interior Design Department provides links to online shops so that users can easily purchase the proposed items. This allows users to easily decorate their interiors. The Interior Design Department can continuously improve its proposals based on user feedback, providing more satisfying interior design proposals. In this way, the Interior Design Department can analyze the user's indoor environment and make optimal interior design proposals.

[0036] The measurement unit can automatically measure indoor environmental conditions such as temperature and light intensity. For example, the measurement unit can measure the indoor temperature using a temperature sensor and measure the indoor light intensity using a light sensor. The measurement unit can measure the indoor temperature in real time using a temperature sensor and measure the indoor light intensity in real time using a light sensor. For example, the measurement unit can measure the indoor temperature using a temperature sensor and measure the indoor light intensity using a light sensor. This allows for the acquisition of accurate data through automatic measurement of the indoor environment. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data acquired from the temperature sensor and light sensor into a generating AI, which can analyze the data and output the indoor environment measurement results.

[0037] The analysis unit can analyze user behavior patterns. For example, the analysis unit can use algorithms based on collected data to analyze user behavior patterns. The analysis unit can use machine learning algorithms based on collected data to analyze user behavior patterns. By analyzing user behavior patterns, it becomes possible to select more appropriate plants. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input collected data into a generating AI, which can analyze the data and output user behavior patterns.

[0038] The selection unit can select and propose the optimal plants and their placement. For example, the selection unit can select the optimal plants and propose their placement based on the user's indoor environment and behavioral patterns. This improves the user's gardening experience through the selection and placement of optimal plants. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input data on the user's indoor environment and behavioral patterns into a generating AI, which can analyze the data and output the optimal plant selection and placement.

[0039] The scheduling unit can adjust the timing of watering and fertilizing based on photos of plants and their surroundings. For example, the scheduling unit can analyze photos of plants and their surroundings to adjust the timing of watering and fertilizing. This allows for proper management by adjusting the timing of watering and fertilizing based on photos of plants and their surroundings. Some or all of the above-described processes in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input photos of plants and their surroundings into a generating AI, which can then analyze the data to adjust the timing of watering and fertilizing.

[0040] The scheduling unit can provide pruning advice based on tree shape photographs. For example, the scheduling unit can analyze tree shape photographs and provide pruning advice. The scheduling unit can analyze tree shape photographs and provide pruning advice. For example, the scheduling unit can analyze tree shape photographs and provide pruning advice. This makes it possible to perform appropriate pruning by providing pruning advice based on tree shape photographs. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input tree shape photographs into a generating AI, and the generating AI can analyze the data and provide pruning advice.

[0041] The support department can provide Q&A via an AI chatbot. For example, the support department can use an AI chatbot to answer user questions in real time. This allows for a quick response to user inquiries by providing Q&A via an AI chatbot. Some or all of the above-described processes in the support department may be performed using AI or not. For example, the support department can input a user's question into a generating AI, which can then output an answer to the question.

[0042] The interior design proposal department can provide interior design proposals based on the indoor environment and guide users to the purchase site. For example, the interior design proposal department can analyze the indoor environment and provide the user with the most suitable interior design proposal. The interior design proposal department can analyze the indoor environment and provide the user with the most suitable interior design proposal. For example, the interior design proposal department can analyze the indoor environment and provide the user with the most suitable interior design proposal. This promotes user purchasing behavior by providing interior design proposals based on the indoor environment and guiding users to the purchase site. Some or all of the above processing in the interior design proposal department may be performed using AI or not. For example, the interior design proposal department can input indoor environment data into a generating AI, and the generating AI can analyze the data and output interior design proposals.

[0043] The measurement unit can select the optimal measurement method by referring to the user's past measurement data during measurement. The measurement unit can propose the optimal measurement method based on the measurement method the user has used in the past. The measurement unit can propose the optimal measurement method based on the measurement method the user has used in the past. The measurement unit can select the most accurate measurement method from the user's past measurement data. The measurement unit can select the most accurate measurement method from the user's past measurement data. The measurement unit can analyze the user's past measurement data and customize the measurement method. The measurement unit can analyze the user's past measurement data and customize the measurement method. This allows the optimal measurement method to be selected by referring to the user's past measurement data. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the user's past measurement data into a generating AI, and the generating AI can analyze the data and select the optimal measurement method.

[0044] The measurement unit can adjust the measurement frequency based on the user's daily rhythm during measurement. For example, if the user is a morning person, the measurement unit can concentrate measurements in the morning hours. For example, if the user is a night owl, the measurement unit can concentrate measurements in the evening hours. For example, the measurement unit can dynamically adjust the measurement frequency to match the user's daily rhythm. By adjusting the measurement frequency based on the user's daily rhythm, the efficiency of measurement is improved. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input data on the user's daily rhythm into a generating AI, which can then analyze the data and adjust the measurement frequency.

[0045] The measurement unit can prioritize measuring highly relevant data by considering the user's geographical location information during measurement. For example, if the user is at home, the measurement unit can prioritize measuring indoor environment data. For example, if the user is out, the measurement unit can prioritize measuring external environment data. For example, the measurement unit can dynamically adjust the priority of measurement data based on the user's geographical location information. This allows the measurement unit to prioritize measuring highly relevant data by considering the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the user's geographical location information into a generating AI, which can analyze the data and prioritize measuring highly relevant data.

[0046] The measurement unit can analyze the user's social media activity and measure relevant data during measurement. For example, if the user posts about plants on social media, the measurement unit will prioritize measuring plant-related data. The measurement unit can prioritize measuring plant-related data if the user posts about plants on social media. For example, if the user posts about interior design on social media, the measurement unit will prioritize measuring interior design-related data. The measurement unit can prioritize measuring interior design-related data if the user posts about interior design on social media. For example, the measurement unit can analyze the user's social media activity and dynamically adjust the priority of the measurement data. The measurement unit can analyze the user's social media activity and dynamically adjust the priority of the measurement data. This allows for the priority measurement of relevant data by analyzing the user's social media activity. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input data on the user's social media activity into a generating AI, which can analyze the data and measure relevant data.

[0047] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the analysis unit can analyze past analysis data and customize the analysis algorithm. The analysis unit can analyze past analysis data and customize the analysis algorithm. For example, the analysis unit can improve the accuracy of the analysis algorithm by referring to past analysis data. The analysis unit can improve the accuracy of the analysis algorithm by referring to past analysis data. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis data. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can analyze the data and optimize the analysis algorithm.

[0048] The analysis unit can adjust the level of detail of the analysis based on the user's behavior patterns during the analysis. For example, the analysis unit can perform a detailed analysis based on actions that the user frequently performs. The analysis unit can perform a detailed analysis based on actions that the user frequently performs. For example, the analysis unit can perform a simplified analysis based on actions that the user rarely performs. The analysis unit can perform a simplified analysis based on actions that the user rarely performs. For example, the analysis unit can dynamically adjust the level of detail of the analysis in accordance with the user's behavior patterns. The analysis unit can dynamically adjust the level of detail of the analysis in accordance with the user's behavior patterns. This improves the efficiency of the analysis by adjusting the level of detail of the analysis based on the user's behavior patterns. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input user behavior pattern data into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0049] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is at home, the analysis unit can perform analysis based on indoor environment data. For example, if the user is out, the analysis unit can perform analysis based on external environment data. The analysis unit can dynamically adjust the analysis method based on the user's geographical location information. This makes it possible to perform more appropriate analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data and perform the analysis.

[0050] The analysis unit can analyze a user's social media activity and analyze relevant data during the analysis process. For example, if a user posts about plants on social media, the analysis unit will prioritize analyzing plant-related data. The analysis unit can prioritize analyzing plant-related data if a user posts about plants on social media. For example, if a user posts about interior design on social media, the analysis unit will prioritize analyzing interior design-related data. The analysis unit can, for example, analyze a user's social media activity and dynamically adjust the priority of the analysis data. This allows for the prioritization of relevant data by analyzing a user's social media activity. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs data on the user's social media activity into a generating AI, which analyzes the data and then analyzes relevant data.

[0051] The selection unit can optimize the selection algorithm by referring to past selection data during the selection process. For example, the selection unit can select the optimal selection algorithm based on past selection data. The selection unit can select the optimal selection algorithm based on past selection data. For example, the selection unit can analyze past selection data and customize the selection algorithm. The selection unit can analyze past selection data and customize the selection algorithm. For example, the selection unit can improve the accuracy of the selection algorithm by referring to past selection data. The selection unit can improve the accuracy of the selection algorithm by referring to past selection data. As a result, the accuracy of the selection algorithm is improved by referring to past selection data. Some or all of the above processes in the selection unit are performed using AI. For example, the selection unit can input past selection data into a generating AI, which can analyze the data and optimize the selection algorithm.

[0052] The selection unit can select plants based on the user's lifestyle. For example, if the user is a morning person, the selection unit will select plants that are easy to care for in the morning. If the user is a night owl, the selection unit will select plants that are easy to care for at night. The selection unit can dynamically adjust the plant selection to match the user's lifestyle. This allows for the selection of more appropriate plants based on the user's lifestyle. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit inputs data on the user's lifestyle into a generating AI, which analyzes the data to select plants.

[0053] The selection unit can select the most suitable plant by considering the user's geographical location information during the selection process. For example, if the user is at home, the selection unit can select a plant suitable for the indoor environment. For example, if the user is out, the selection unit can select a plant suitable for the outdoor environment. For example, the selection unit can dynamically select the most suitable plant based on the user's geographical location information. This allows the selection of the most suitable plant to be made by considering the user's geographical location information. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input the user's geographical location information into a generating AI, which can analyze the data and select the most suitable plant.

[0054] The selection unit can analyze the user's social media activity and select relevant plants during the selection process. For example, if the user has posted about plants on social media, the selection unit will prioritize selecting plant-related data. The selection unit can prioritize selecting plant-related data if the user has posted about plants on social media. For example, if the user has posted about interior design on social media, the selection unit will prioritize selecting interior design data. The selection unit can analyze the user's social media activity and dynamically adjust the priority of the selected data. This allows the selection unit to prioritize selecting relevant plants by analyzing the user's social media activity. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input data on the user's social media activity into a generating AI, which can analyze the data and select relevant plants.

[0055] The schedule creation unit can optimize the schedule algorithm by referring to past schedule data when creating a schedule. For example, the schedule creation unit can select the optimal schedule algorithm based on past schedule data. The schedule creation unit can select the optimal schedule algorithm based on past schedule data. For example, the schedule creation unit can analyze past schedule data and customize the schedule algorithm. The schedule creation unit can analyze past schedule data and customize the schedule algorithm. For example, the schedule creation unit can improve the accuracy of the schedule algorithm by referring to past schedule data. The schedule creation unit can improve the accuracy of the schedule algorithm by referring to past schedule data. As a result, the accuracy of the schedule algorithm is improved by referring to past schedule data. Some or all of the above processes in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input past schedule data into a generating AI, and the generating AI can analyze the data and optimize the schedule algorithm.

[0056] The schedule creation unit can adjust the schedule based on the user's lifestyle rhythm when creating it. For example, if the user is a morning person, the schedule creation unit can create a schedule that concentrates tasks in the morning hours. For example, if the user is a night owl, the schedule creation unit can create a schedule that concentrates tasks in the evening hours. The schedule creation unit can dynamically adjust the schedule to match the user's lifestyle rhythm. This allows for the creation of a more appropriate schedule by adjusting it based on the user's lifestyle rhythm. Some or all of the above processing in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input data on the user's lifestyle rhythm into a generating AI, which can then analyze the data and adjust the schedule.

[0057] The schedule creation unit can create a schedule while taking the user's geographical location information into consideration. For example, if the user is at home, the schedule creation unit can prioritize scheduling tasks to be performed at home. For example, if the user is out, the schedule creation unit can prioritize scheduling tasks to be performed at their destination. For example, the schedule creation unit can dynamically adjust the schedule based on the user's geographical location information. This allows for the creation of a more appropriate schedule by taking the user's geographical location information into consideration. Some or all of the above-described processes in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input the user's geographical location information into a generating AI, which can then analyze the data and create a schedule.

[0058] The scheduling unit can analyze the user's social media activity and create relevant schedules when creating a schedule. For example, if the user posts about plants on social media, the scheduling unit will prioritize scheduling plant-related tasks. The scheduling unit can prioritize scheduling plant-related tasks if the user posts about plants on social media. For example, if the user posts about interior design on social media, the scheduling unit will prioritize scheduling interior design tasks. The scheduling unit can prioritize scheduling interior design tasks if the user posts about interior design on social media. The scheduling unit can analyze the user's social media activity and dynamically adjust schedule priorities. This allows the scheduling unit to prioritize the creation of relevant schedules by analyzing the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input data on the user's social media activity into a generating AI, which can analyze the data and create relevant schedules.

[0059] The support unit can optimize the support algorithm by referring to past support data when providing support. For example, the support unit can select the optimal support algorithm based on past support data. The support unit can select the optimal support algorithm based on past support data. For example, the support unit can analyze past support data and customize the support algorithm. The support unit can analyze past support data and customize the support algorithm. For example, the support unit can improve the accuracy of the support algorithm by referring to past support data. The support unit can improve the accuracy of the support algorithm by referring to past support data. As a result, the accuracy of the support algorithm is improved by referring to past support data. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input past support data into a generating AI, and the generating AI can analyze the data and optimize the support algorithm.

[0060] The support unit can adjust support based on the user's lifestyle when providing support. For example, if the user is a morning person, the support unit can provide support during the morning hours. For example, if the user is a night owl, the support unit can provide support during the evening hours. For example, the support unit can dynamically adjust the method of providing support to match the user's lifestyle. By adjusting support based on the user's lifestyle, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input data on the user's lifestyle into a generating AI, and the generating AI can analyze the data to adjust the support.

[0061] The support unit can provide support while taking the user's geographical location into consideration. For example, if the user is at home, the support unit can prioritize providing support that can be performed at home. For example, if the user is out, the support unit can prioritize providing support that can be performed at their location. The support unit can dynamically adjust the method of providing support based on the user's geographical location. This allows for the provision of more appropriate support by taking the user's geographical location into consideration. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the user's geographical location into a generating AI, and the generating AI can analyze the data to provide support.

[0062] The support department can analyze a user's social media activity when providing support and provide relevant support. For example, if a user posts about plants on social media, the support department can prioritize providing plant-related support. The support department can prioritize providing interior-related support if a user posts about interior design on social media. The support department can, for example, analyze a user's social media activity and dynamically adjust the support priority. This allows the support department to prioritize relevant support by analyzing a user's social media activity. Some or all of the above processing in the support department may be performed using AI or not. For example, the support department can input data on a user's social media activity into a generating AI, which can analyze the data and provide relevant support.

[0063] The interior design proposal department can optimize its proposal algorithm by referring to past proposal data when making interior design proposals. For example, the interior design proposal department can select the optimal proposal algorithm based on past proposal data. The interior design proposal department can select the optimal proposal algorithm based on past proposal data. For example, the interior design proposal department can analyze past proposal data and customize the proposal algorithm. The interior design proposal department can analyze past proposal data and customize the proposal algorithm. For example, the interior design proposal department can improve the accuracy of its proposal algorithm by referring to past proposal data. The interior design proposal department can improve the accuracy of its proposal algorithm by referring to past proposal data. As a result, the accuracy of the proposal algorithm is improved by referring to past proposal data. Some or all of the above processes in the interior design proposal department may be performed using AI or not. For example, the interior design proposal department can input past proposal data into a generating AI, and the generating AI can analyze the data and optimize the proposal algorithm.

[0064] The interior design proposal unit can adjust its proposals based on the user's lifestyle. For example, if the user is a morning person, the interior design proposal unit can provide interior design proposals suitable for the morning hours. For example, if the user is a night owl, the interior design proposal unit can provide interior design proposals suitable for the evening hours. For example, the interior design proposal unit can dynamically adjust its interior design proposals to match the user's lifestyle. By adjusting proposals based on the user's lifestyle, more appropriate interior design proposals become possible. Some or all of the above processing in the interior design proposal unit may be performed using AI or not. For example, the interior design proposal unit can input data on the user's lifestyle into a generating AI, which can then analyze the data and adjust the proposals.

[0065] The interior design proposal unit can make proposals that take into account the user's geographical location information. For example, if the user is at home, the interior design proposal unit can make interior design proposals that are suitable for the environment of the user's home. For example, if the user is out, the interior design proposal unit can make interior design proposals that are suitable for the environment of the user's destination. For example, the interior design proposal unit can dynamically adjust interior design proposals based on the user's geographical location information. This makes it possible to make more appropriate interior design proposals by taking the user's geographical location information into consideration. Some or all of the above processing in the interior design proposal unit may be performed using AI or not. For example, the interior design proposal unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data and make proposals.

[0066] The Interior Design Proposal Department can analyze a user's social media activity and make relevant suggestions when providing interior design proposals. For example, if a user posts about interiors on social media, the Interior Design Proposal Department will prioritize interior-related suggestions. The Interior Design Proposal Department can prioritize interior-related suggestions if a user posts about interiors on social media. For example, if a user posts about plants on social media, the Interior Design Proposal Department will prioritize plant-related suggestions. The Interior Design Proposal Department can, for example, analyze a user's social media activity and dynamically adjust the priority of suggestions. The Interior Design Proposal Department can analyze a user's social media activity and dynamically adjust the priority of suggestions. This allows for prioritizing relevant suggestions by analyzing a user's social media activity. Some or all of the above processing in the Interior Design Proposal Department may be performed using AI or not. For example, the Interior Design Proposal Department can input data on a user's social media activity into a generating AI, which can analyze the data and make relevant suggestions.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The gardening support system can also be equipped with a plant growth prediction unit. This unit predicts plant growth based on data obtained from the measurement and analysis units. For example, it can analyze environmental data such as temperature, light intensity, and humidity to predict plant growth rate and health. It can also refer to past growth data to predict future growth. Furthermore, the plant growth prediction unit can provide users with advice based on the growth prediction. This allows users to predict plant growth and manage their plants appropriately.

[0069] The gardening support system can also be equipped with a plant health check unit. This unit diagnoses the health of plants based on data obtained from the measurement and analysis units. For example, it can analyze leaf color and shape, stem condition, etc., to detect signs of disease or pests. Furthermore, the plant health check unit can suggest countermeasures to the user based on the health check results. It can also refer to past health check data to suggest preventative measures. This allows users to understand the health of their plants and take appropriate action.

[0070] The gardening support system can also be equipped with a plant growth record unit. This unit creates plant growth records based on data obtained from the measurement and analysis units. For example, it can record the plant's height, the number of leaves, the number of flowers, etc., visualizing the growth process. Furthermore, the plant growth record unit can provide users with advice based on the growth records. It can also refer to past growth records to analyze growth trends. This allows users to record plant growth and understand the growth process.

[0071] The gardening support system can also be equipped with a plant nutrition management unit. This unit manages the nutritional status of plants based on data obtained from the measurement and analysis units. For example, it can analyze soil nutrients and moisture content and suggest appropriate fertilizer and watering timings. It can also provide users with nutrition-based advice. Furthermore, it can refer to past nutrition management data and analyze trends in nutrition management. This allows users to manage the nutritional status of their plants and provide appropriate nutrition.

[0072] The gardening support system can also be equipped with a plant environmental adaptation unit. This unit assists plants in adapting to their environment based on data obtained from the measurement and analysis units. For example, it can analyze environmental data such as temperature, humidity, and light intensity, and adjust the plant's environment to ensure optimal growth. It can also provide users with environmentally-based advice. Furthermore, it can refer to past environmental adaptation data to analyze trends in adaptation. This allows users to support plant adaptation and ensure optimal growth.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The measurement unit automatically measures the user's room layout and indoor environment. The measurement unit uses temperature sensors and light sensors to measure indoor temperature and light levels in real time. The measurement unit can also collect data to analyze the user's behavior patterns. Step 2: The analysis unit analyzes the data measured by the measurement unit. The analysis unit uses algorithms to analyze the collected data and analyze the user's behavior patterns. Step 3: The selection unit selects the optimal plants based on the data analyzed by the analysis unit. The selection unit selects the optimal plants and proposes their placement based on the user's indoor environment and behavioral patterns. Step 4: The scheduling unit creates a management schedule for the plants selected by the selection unit. The scheduling unit analyzes photos of the plants and their environment to adjust the timing of watering and fertilizing. Step 5: The support department provides interactive support to users based on the schedule created by the scheduling department. The support department uses an AI chatbot to answer user questions in real time. Step 6: The Interior Design Department makes interior design proposals based on the support provided by the Support Department. The Interior Design Department analyzes the indoor environment and makes the most suitable interior design proposals for the user.

[0075] (Example of form 2) The gardening support system according to an embodiment of the present invention is a system that proposes the optimal plants and their management methods based on the region and indoor environment, and provides a total proposal that includes interior design. This gardening support system automatically measures the user's floor plan and indoor environment (temperature, light intensity, etc.) and analyzes the user's behavior patterns. Next, it proposes the selection and placement of the optimal plants based on this data. Furthermore, it creates and proposes a management schedule for each individual plant. For example, it adjusts the timing of watering and fertilizing based on photos of the plants and their placement environment, and provides pruning advice based on photos of the tree's shape. It also provides interactive support through an AI chatbot to answer the user's questions. Furthermore, it accumulates the user's reactions to past advice and adjusts their preferences. Finally, it provides interior design suggestions based on the indoor environment (room and pot color scheme, etc.) and guides the user to a purchase site. Through this mechanism, the user is supported in making gardening decisions, tasks are automated, and a personalized experience is provided. As a result, the gardening support system can propose the optimal plants and management methods based on the user's floor plan and indoor environment, and provide a total proposal that includes interior design.

[0076] The gardening support system according to this embodiment comprises a measurement unit, an analysis unit, a selection unit, a schedule creation unit, a support unit, and an interior design proposal unit. The measurement unit automatically measures the user's floor plan and indoor environment. The measurement unit can automatically measure indoor environmental factors such as temperature and light intensity. The measurement unit measures indoor temperature and light intensity in real time using a temperature sensor and a light sensor. For example, the measurement unit can measure indoor temperature using a temperature sensor and measure indoor light intensity using a light sensor. The measurement unit can also collect data for analyzing the user's behavior patterns. The analysis unit analyzes the data measured by the measurement unit. For example, the analysis unit can analyze the user's behavior patterns using an algorithm based on the collected data. For example, the analysis unit can perform analysis using a machine learning algorithm based on the collected data to analyze the user's behavior patterns. The selection unit selects the optimal plants based on the data analyzed by the analysis unit. For example, the selection unit can propose the optimal plant selection and its placement. The selection unit selects the most suitable plants and proposes their placement based on the user's indoor environment and behavioral patterns. The scheduling unit creates a management schedule for the plants selected by the selection unit. The scheduling unit can, for example, adjust watering and fertilizing timing based on photos of the plants and their surrounding environment. The scheduling unit analyzes photos of the plants and their surrounding environment to adjust watering and fertilizing timing. The scheduling unit analyzes photos of the plants and their surrounding environment to adjust watering and fertilizing timing. The support unit provides interactive support to the user based on the schedule created by the scheduling unit. The support unit can, for example, provide Q&A via an AI chatbot. The support unit uses an AI chatbot to answer user questions in real time.The Interior Design Department makes interior design proposals based on the support provided by the Support Department. The Interior Design Department can make interior design proposals based on the indoor environment (such as the color scheme of the room and pots) and guide users to the purchase site. The Interior Design Department can analyze the indoor environment and make the most suitable interior design proposals for the user. The Interior Design Department can analyze the indoor environment and make the most suitable interior design proposals for the user. As a result, the gardening support system according to the embodiment can propose the most suitable plants and care methods based on the user's floor plan and indoor environment, and can make total proposals that include interior design.

[0077] The measurement unit automatically measures the user's room layout and indoor environment. For example, the measurement unit can automatically measure indoor environmental factors such as temperature and light intensity. The measurement unit uses temperature sensors and light sensors to measure indoor temperature and light intensity in real time. Specifically, temperature sensors are installed in various locations throughout the room, allowing for detailed temperature measurements at different locations. This makes it possible to understand temperature differences between rooms and temperature changes over time. Light sensors are installed near windows or in the center of rooms to measure the amount of natural light during the day and the influence of artificial lighting. This allows for the collection of data to provide a suitable light environment for plants. The measurement unit can also collect data to analyze the user's behavior patterns. For example, sensors can detect the time of day the user is in the room and their movement routes to understand their behavior patterns. This allows for the provision of information to optimize plant placement and management methods. Furthermore, by adding humidity sensors and carbon dioxide sensors, the measurement unit can also measure indoor humidity and air quality. This allows for a comprehensive understanding of environmental factors affecting plant growth, enabling more accurate gardening support. The measurement department sends this data to a cloud server and uses it for analysis and proposals in collaboration with other departments.

[0078] The analysis unit analyzes the data measured by the measurement unit. For example, the analysis unit can analyze user behavior patterns. To analyze user behavior patterns, the analysis unit uses algorithms based on the collected data. Specifically, it uses machine learning algorithms to analyze user behavior patterns and optimize plant placement and management methods. For example, if a user is often in the room at a specific time, it can suggest providing the optimal amount of light and temperature for that time. It can also suggest placing plants along paths that the user frequently travels to make plant care easier. Furthermore, the analysis unit can perform trend analysis based on past data to predict seasonal environmental changes and changes in user behavior patterns. This allows it to provide information for long-term gardening planning. The analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a sudden change in temperature or light intensity is detected, it can suggest measures to minimize the impact on plants. In this way, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0079] The selection unit selects the most suitable plants based on data analyzed by the analysis unit. For example, the selection unit can propose the optimal plant selection and placement. Specifically, it determines the type and location of plants based on the user's indoor environment and behavioral patterns. For instance, it might place sunlight-loving plants near windows where sufficient light is available, and temperature-sensitive plants in areas with stable temperatures. It also suggests placing plants in frequently seen and easily accessible locations based on the user's behavioral patterns, making plant care easier. The selection unit utilizes a database of plant growth characteristics and management methods to select the most suitable plants for the user's needs. For example, it can suggest easy-to-care-for plants for beginners or plants suited to specific environmental conditions. Furthermore, the selection unit also selects plants that match the user's preferences and interior style. This allows for proposals that consider not only plant selection but also aesthetic appeal as part of the interior. The selection unit provides detailed information on the placement and management methods of the selected plants, supporting the user in properly growing them. This enables the selection unit to select the most suitable plants and propose their placement based on the user's indoor environment and behavioral patterns.

[0080] The scheduling unit creates management schedules for plants selected by the selection unit. For example, the scheduling unit can adjust watering and fertilizing timings based on photos of the plants and their environment. Specifically, it analyzes the plant's growth stage and environment to create an optimal management schedule. For instance, it analyzes the color and shape of the plant's leaves to adjust watering timing. It also analyzes the light intensity and temperature of the environment to optimize fertilizing timing. The scheduling unit can also propose management schedules tailored to the user's lifestyle and behavioral patterns. For example, it can set watering and fertilizing times to avoid busy periods, making it easier for users to continue caring for their plants. Furthermore, the scheduling unit also creates long-term management schedules that adapt to seasonal environmental changes and the plant's growth cycle. This helps maintain plant health and supports long-term growth. The scheduling unit notifies users of the created schedules on their smartphones and tablets, providing a reminder function. This ensures users remember to care for their plants. Based on user feedback, the scheduling unit continuously improves the schedules, providing more accurate management schedules.

[0081] The support department provides interactive support to users based on schedules created by the scheduling department. For example, the support department can provide Q&A via an AI chatbot. Specifically, the AI ​​chatbot answers user questions in real time and provides advice on plant care and troubleshooting. For instance, the AI ​​chatbot can quickly answer questions about what to do if a plant's leaves turn yellow or the appropriate watering frequency. The support department can also provide individually customized advice based on the user's behavior patterns and past question history, ensuring users receive support best suited to their situation. Furthermore, the support department provides reminders for regular maintenance and seasonal care, helping users remember to care for their plants. The support department collects user feedback to continuously improve the accuracy of the AI ​​chatbot's responses and the content of its support. This enables the support department to provide users with prompt and accurate support, helping them maintain the health of their plants.

[0082] The Interior Design Department provides interior design proposals based on the support provided by the Support Department. For example, the Interior Design Department can make interior design proposals based on the indoor environment (such as the color scheme of the room and pots) and guide users to the purchase site. Specifically, it proposes plant placement and pot selection that match the color scheme and style of the user's room. For example, it proposes simple pot designs for modern interiors and wooden pots for natural interiors. It also proposes plant placement and combinations, creating an interior design that considers the overall balance of the room. The Interior Design Department can also make customized proposals that match the user's preferences and lifestyle. For example, it proposes relaxing plants for the living room and practical plants such as herbs for the kitchen. Furthermore, the Interior Design Department provides links to online shops so that users can easily purchase the proposed items. This allows users to easily decorate their interiors. The Interior Design Department can continuously improve its proposals based on user feedback, providing more satisfying interior design proposals. In this way, the Interior Design Department can analyze the user's indoor environment and make optimal interior design proposals.

[0083] The measurement unit can automatically measure indoor environmental conditions such as temperature and light intensity. For example, the measurement unit can measure the indoor temperature using a temperature sensor and measure the indoor light intensity using a light sensor. The measurement unit can measure the indoor temperature in real time using a temperature sensor and measure the indoor light intensity in real time using a light sensor. For example, the measurement unit can measure the indoor temperature using a temperature sensor and measure the indoor light intensity using a light sensor. This allows for the acquisition of accurate data through automatic measurement of the indoor environment. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data acquired from the temperature sensor and light sensor into a generating AI, which can analyze the data and output the indoor environment measurement results.

[0084] The analysis unit can analyze user behavior patterns. For example, the analysis unit can use algorithms based on collected data to analyze user behavior patterns. The analysis unit can use machine learning algorithms based on collected data to analyze user behavior patterns. By analyzing user behavior patterns, it becomes possible to select more appropriate plants. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input collected data into a generating AI, which can analyze the data and output user behavior patterns.

[0085] The selection unit can select and propose the optimal plants and their placement. For example, the selection unit can select the optimal plants and propose their placement based on the user's indoor environment and behavioral patterns. This improves the user's gardening experience through the selection and placement of optimal plants. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input data on the user's indoor environment and behavioral patterns into a generating AI, which can analyze the data and output the optimal plant selection and placement.

[0086] The scheduling unit can adjust the timing of watering and fertilizing based on photos of plants and their surroundings. For example, the scheduling unit can analyze photos of plants and their surroundings to adjust the timing of watering and fertilizing. This allows for proper management by adjusting the timing of watering and fertilizing based on photos of plants and their surroundings. Some or all of the above-described processes in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input photos of plants and their surroundings into a generating AI, which can then analyze the data to adjust the timing of watering and fertilizing.

[0087] The scheduling unit can provide pruning advice based on tree shape photographs. For example, the scheduling unit can analyze tree shape photographs and provide pruning advice. The scheduling unit can analyze tree shape photographs and provide pruning advice. For example, the scheduling unit can analyze tree shape photographs and provide pruning advice. This makes it possible to perform appropriate pruning by providing pruning advice based on tree shape photographs. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input tree shape photographs into a generating AI, and the generating AI can analyze the data and provide pruning advice.

[0088] The support department can provide Q&A via an AI chatbot. For example, the support department can use an AI chatbot to answer user questions in real time. This allows for a quick response to user inquiries by providing Q&A via an AI chatbot. Some or all of the above-described processes in the support department may be performed using AI or not. For example, the support department can input a user's question into a generating AI, which can then output an answer to the question.

[0089] The interior design proposal department can provide interior design proposals based on the indoor environment and guide users to the purchase site. For example, the interior design proposal department can analyze the indoor environment and provide the user with the most suitable interior design proposal. The interior design proposal department can analyze the indoor environment and provide the user with the most suitable interior design proposal. For example, the interior design proposal department can analyze the indoor environment and provide the user with the most suitable interior design proposal. This promotes user purchasing behavior by providing interior design proposals based on the indoor environment and guiding users to the purchase site. Some or all of the above processing in the interior design proposal department may be performed using AI or not. For example, the interior design proposal department can input indoor environment data into a generating AI, and the generating AI can analyze the data and output interior design proposals.

[0090] The measurement unit can estimate the user's emotions and adjust the measurement timing based on the estimated emotions. For example, if the user is relaxed, the measurement unit can reduce the measurement frequency to lessen the user's burden. For example, if the user is stressed, the measurement unit can increase the measurement frequency to collect more detailed data. For example, if the user is busy, the measurement unit can adjust the measurement timing to match the user's schedule. This reduces the user's burden by adjusting the measurement timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input user emotion data into a generating AI, which can then analyze the data and adjust the measurement timing.

[0091] The measurement unit can select the optimal measurement method by referring to the user's past measurement data during measurement. The measurement unit can propose the optimal measurement method based on the measurement method the user has used in the past. The measurement unit can propose the optimal measurement method based on the measurement method the user has used in the past. The measurement unit can select the most accurate measurement method from the user's past measurement data. The measurement unit can select the most accurate measurement method from the user's past measurement data. The measurement unit can analyze the user's past measurement data and customize the measurement method. The measurement unit can analyze the user's past measurement data and customize the measurement method. This allows the optimal measurement method to be selected by referring to the user's past measurement data. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the user's past measurement data into a generating AI, and the generating AI can analyze the data and select the optimal measurement method.

[0092] The measurement unit can adjust the measurement frequency based on the user's daily rhythm during measurement. For example, if the user is a morning person, the measurement unit can concentrate measurements in the morning hours. For example, if the user is a night owl, the measurement unit can concentrate measurements in the evening hours. For example, the measurement unit can dynamically adjust the measurement frequency to match the user's daily rhythm. By adjusting the measurement frequency based on the user's daily rhythm, the efficiency of measurement is improved. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input data on the user's daily rhythm into a generating AI, which can then analyze the data and adjust the measurement frequency.

[0093] The measurement unit can estimate the user's emotions and determine the priority of measurement data based on the estimated user emotions. For example, if the user is relaxed, the measurement unit can prioritize measuring data of lower importance. For example, if the user is stressed, the measurement unit can prioritize measuring data of higher importance. For example, if the user is busy, the measurement unit can adjust the priority of measurement data to match the user's schedule. This allows for the priority measurement of important data by determining the priority of measurement data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input user emotion data into a generating AI, which can then analyze the data and determine the priority of the measurement data.

[0094] The measurement unit can prioritize measuring highly relevant data by considering the user's geographical location information during measurement. For example, if the user is at home, the measurement unit can prioritize measuring indoor environment data. For example, if the user is out, the measurement unit can prioritize measuring external environment data. For example, the measurement unit can dynamically adjust the priority of measurement data based on the user's geographical location information. This allows the measurement unit to prioritize measuring highly relevant data by considering the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the user's geographical location information into a generating AI, which can analyze the data and prioritize measuring highly relevant data.

[0095] The measurement unit can analyze the user's social media activity and measure relevant data during measurement. For example, if the user posts about plants on social media, the measurement unit will prioritize measuring plant-related data. The measurement unit can prioritize measuring plant-related data if the user posts about plants on social media. For example, if the user posts about interior design on social media, the measurement unit will prioritize measuring interior design-related data. The measurement unit can prioritize measuring interior design-related data if the user posts about interior design on social media. For example, the measurement unit can analyze the user's social media activity and dynamically adjust the priority of the measurement data. The measurement unit can analyze the user's social media activity and dynamically adjust the priority of the measurement data. This allows for the priority measurement of relevant data by analyzing the user's social media activity. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input data on the user's social media activity into a generating AI, which can analyze the data and measure relevant data.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is stressed, the analysis unit can perform a simplified analysis to estimate emotions and adjust the analysis method based on the estimated emotions. If the user is busy, the analysis unit can adjust the analysis method to match the user's schedule. By adjusting the analysis method based on the user's emotions, a more appropriate analysis becomes possible. 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. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI, which can then analyze the data and adjust the analysis method.

[0097] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the analysis unit can analyze past analysis data and customize the analysis algorithm. The analysis unit can analyze past analysis data and customize the analysis algorithm. For example, the analysis unit can improve the accuracy of the analysis algorithm by referring to past analysis data. The analysis unit can improve the accuracy of the analysis algorithm by referring to past analysis data. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis data. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can analyze the data and optimize the analysis algorithm.

[0098] The analysis unit can adjust the level of detail of the analysis based on the user's behavior patterns during the analysis. For example, the analysis unit can perform a detailed analysis based on actions that the user frequently performs. The analysis unit can perform a detailed analysis based on actions that the user frequently performs. For example, the analysis unit can perform a simplified analysis based on actions that the user rarely performs. The analysis unit can perform a simplified analysis based on actions that the user rarely performs. For example, the analysis unit can dynamically adjust the level of detail of the analysis in accordance with the user's behavior patterns. The analysis unit can dynamically adjust the level of detail of the analysis in accordance with the user's behavior patterns. This improves the efficiency of the analysis by adjusting the level of detail of the analysis based on the user's behavior patterns. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input user behavior pattern data into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is stressed, the analysis unit can display simplified analysis results. For example, if the user is busy, the analysis unit can adjust the display method of the analysis results to match the user's schedule. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generating AI, which can then analyze the data and adjust how the analysis results are displayed.

[0100] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is at home, the analysis unit can perform analysis based on indoor environment data. For example, if the user is out, the analysis unit can perform analysis based on external environment data. The analysis unit can dynamically adjust the analysis method based on the user's geographical location information. This makes it possible to perform more appropriate analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data and perform the analysis.

[0101] The analysis unit can analyze a user's social media activity and analyze relevant data during the analysis process. For example, if a user posts about plants on social media, the analysis unit will prioritize analyzing plant-related data. The analysis unit can prioritize analyzing plant-related data if a user posts about plants on social media. For example, if a user posts about interior design on social media, the analysis unit will prioritize analyzing interior design-related data. The analysis unit can, for example, analyze a user's social media activity and dynamically adjust the priority of the analysis data. This allows for the prioritization of relevant data by analyzing a user's social media activity. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs data on the user's social media activity into a generating AI, which analyzes the data and then analyzes relevant data.

[0102] The selection unit can estimate the user's emotions and adjust the plant selection criteria based on the estimated emotions. For example, if the user is relaxed, the selection unit can select plants with relaxing effects. For example, if the user is stressed, the selection unit can select plants with stress-reducing effects. For example, if the user is busy, the selection unit can select plants that are easy to care for. By adjusting the plant selection criteria based on the user's emotions, a more appropriate plant can be selected. 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. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input user emotion data into a generating AI, which can then analyze the data and adjust the plant selection criteria.

[0103] The selection unit can optimize the selection algorithm by referring to past selection data during the selection process. For example, the selection unit can select the optimal selection algorithm based on past selection data. The selection unit can select the optimal selection algorithm based on past selection data. For example, the selection unit can analyze past selection data and customize the selection algorithm. The selection unit can analyze past selection data and customize the selection algorithm. For example, the selection unit can improve the accuracy of the selection algorithm by referring to past selection data. The selection unit can improve the accuracy of the selection algorithm by referring to past selection data. As a result, the accuracy of the selection algorithm is improved by referring to past selection data. Some or all of the above processes in the selection unit are performed using AI. For example, the selection unit can input past selection data into a generating AI, which can analyze the data and optimize the selection algorithm.

[0104] The selection unit can select plants based on the user's lifestyle. For example, if the user is a morning person, the selection unit will select plants that are easy to care for in the morning. If the user is a night owl, the selection unit will select plants that are easy to care for at night. The selection unit can dynamically adjust the plant selection to match the user's lifestyle. This allows for the selection of more appropriate plants based on the user's lifestyle. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit inputs data on the user's lifestyle into a generating AI, which analyzes the data to select plants.

[0105] The selection unit can estimate the user's emotions and adjust the plant arrangement based on the estimated emotions. For example, if the user is relaxed, the selection unit can suggest an arrangement that promotes relaxation. For example, if the user is stressed, the selection unit can suggest an arrangement that reduces stress. For example, if the user is busy, the selection unit can suggest an arrangement that is easy to maintain. By adjusting the plant arrangement based on the user's emotions, a more appropriate arrangement becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input user emotion data into a generating AI, which can then analyze the data and adjust the plant placement method.

[0106] The selection unit can select the most suitable plant by considering the user's geographical location information during the selection process. For example, if the user is at home, the selection unit can select a plant suitable for the indoor environment. For example, if the user is out, the selection unit can select a plant suitable for the outdoor environment. For example, the selection unit can dynamically select the most suitable plant based on the user's geographical location information. This allows the selection of the most suitable plant to be made by considering the user's geographical location information. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input the user's geographical location information into a generating AI, which can analyze the data and select the most suitable plant.

[0107] The selection unit can analyze the user's social media activity and select relevant plants during the selection process. For example, if the user has posted about plants on social media, the selection unit will prioritize selecting plant-related data. The selection unit can prioritize selecting plant-related data if the user has posted about plants on social media. For example, if the user has posted about interior design on social media, the selection unit will prioritize selecting interior design data. The selection unit can analyze the user's social media activity and dynamically adjust the priority of the selected data. This allows the selection unit to prioritize selecting relevant plants by analyzing the user's social media activity. Some or all of the above processing in the selection unit is performed using AI. For example, the selection unit can input data on the user's social media activity into a generating AI, which can analyze the data and select relevant plants.

[0108] The schedule creation unit can estimate the user's emotions and adjust the schedule creation method based on the estimated emotions. For example, if the user is relaxed, the schedule creation unit can create a detailed schedule. For example, if the user is stressed, the schedule creation unit can create a simplified schedule. For example, if the user is busy, the schedule creation unit can adjust the schedule creation method to match the user's schedule. By adjusting the schedule creation method based on the user's emotions, a more appropriate schedule can be created. 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. Some or all of the above processing in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input user emotion data into a generating AI, which can then analyze the data and adjust the schedule creation method.

[0109] The schedule creation unit can optimize the schedule algorithm by referring to past schedule data when creating a schedule. For example, the schedule creation unit can select the optimal schedule algorithm based on past schedule data. The schedule creation unit can select the optimal schedule algorithm based on past schedule data. For example, the schedule creation unit can analyze past schedule data and customize the schedule algorithm. The schedule creation unit can analyze past schedule data and customize the schedule algorithm. For example, the schedule creation unit can improve the accuracy of the schedule algorithm by referring to past schedule data. The schedule creation unit can improve the accuracy of the schedule algorithm by referring to past schedule data. As a result, the accuracy of the schedule algorithm is improved by referring to past schedule data. Some or all of the above processes in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input past schedule data into a generating AI, and the generating AI can analyze the data and optimize the schedule algorithm.

[0110] The schedule creation unit can adjust the schedule based on the user's lifestyle rhythm when creating it. For example, if the user is a morning person, the schedule creation unit can create a schedule that concentrates tasks in the morning hours. For example, if the user is a night owl, the schedule creation unit can create a schedule that concentrates tasks in the evening hours. The schedule creation unit can dynamically adjust the schedule to match the user's lifestyle rhythm. This allows for the creation of a more appropriate schedule by adjusting it based on the user's lifestyle rhythm. Some or all of the above processing in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input data on the user's lifestyle rhythm into a generating AI, which can then analyze the data and adjust the schedule.

[0111] The scheduling unit can estimate the user's emotions and determine scheduling priorities based on those emotions. For example, if the user is relaxed, the scheduling unit will prioritize scheduling less important tasks. If the user is stressed, the scheduling unit will prioritize scheduling more important tasks. If the user is busy, the scheduling unit will adjust scheduling priorities to match the user's schedule. This allows important tasks to be prioritized by determining scheduling priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input user emotion data into a generating AI, which can then analyze the data to determine schedule priorities.

[0112] The schedule creation unit can create a schedule while taking the user's geographical location information into consideration. For example, if the user is at home, the schedule creation unit can prioritize scheduling tasks to be performed at home. For example, if the user is out, the schedule creation unit can prioritize scheduling tasks to be performed at their destination. For example, the schedule creation unit can dynamically adjust the schedule based on the user's geographical location information. This allows for the creation of a more appropriate schedule by taking the user's geographical location information into consideration. Some or all of the above-described processes in the schedule creation unit may be performed using AI or not. For example, the schedule creation unit can input the user's geographical location information into a generating AI, which can then analyze the data and create a schedule.

[0113] The scheduling unit can analyze the user's social media activity and create relevant schedules when creating a schedule. For example, if the user posts about plants on social media, the scheduling unit will prioritize scheduling plant-related tasks. The scheduling unit can prioritize scheduling plant-related tasks if the user posts about plants on social media. For example, if the user posts about interior design on social media, the scheduling unit will prioritize scheduling interior design tasks. The scheduling unit can prioritize scheduling interior design tasks if the user posts about interior design on social media. The scheduling unit can analyze the user's social media activity and dynamically adjust schedule priorities. This allows the scheduling unit to prioritize the creation of relevant schedules by analyzing the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input data on the user's social media activity into a generating AI, which can analyze the data and create relevant schedules.

[0114] The support unit can estimate the user's emotions and adjust the method of support provided based on the estimated emotions. For example, if the user is relaxed, the support unit can provide detailed support. If the user is relaxed, the support unit can provide detailed support. If the user is stressed, the support unit can provide simple support. If the user is stressed, the support unit can provide simple support. If the user is busy, the support unit can adjust the method of support provided to match the user's schedule. If the user is busy, the support unit can adjust the method of support provided to match the user's schedule. This allows for more appropriate support to be provided by adjusting the method of support provided based on the user's emotions. 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. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the method of support provided.

[0115] The support unit can optimize the support algorithm by referring to past support data when providing support. For example, the support unit can select the optimal support algorithm based on past support data. The support unit can select the optimal support algorithm based on past support data. For example, the support unit can analyze past support data and customize the support algorithm. The support unit can analyze past support data and customize the support algorithm. For example, the support unit can improve the accuracy of the support algorithm by referring to past support data. The support unit can improve the accuracy of the support algorithm by referring to past support data. As a result, the accuracy of the support algorithm is improved by referring to past support data. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input past support data into a generating AI, and the generating AI can analyze the data and optimize the support algorithm.

[0116] The support unit can adjust support based on the user's lifestyle when providing support. For example, if the user is a morning person, the support unit can provide support during the morning hours. For example, if the user is a night owl, the support unit can provide support during the evening hours. For example, the support unit can dynamically adjust the method of providing support to match the user's lifestyle. By adjusting support based on the user's lifestyle, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input data on the user's lifestyle into a generating AI, and the generating AI can analyze the data to adjust the support.

[0117] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is relaxed, the support unit can prioritize providing support of lower importance. If the user is stressed, the support unit can prioritize providing support of higher importance. If the user is busy, the support unit can adjust the priority of support to match the user's schedule. This allows important support to be prioritized by determining the priority of support based on the user's emotions. 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. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support department can input user emotion data into a generating AI, which can then analyze the data to determine support priorities.

[0118] The support unit can provide support while taking the user's geographical location into consideration. For example, if the user is at home, the support unit can prioritize providing support that can be performed at home. For example, if the user is out, the support unit can prioritize providing support that can be performed at their location. The support unit can dynamically adjust the method of providing support based on the user's geographical location. This allows for the provision of more appropriate support by taking the user's geographical location into consideration. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the user's geographical location into a generating AI, and the generating AI can analyze the data to provide support.

[0119] The support department can analyze a user's social media activity when providing support and provide relevant support. For example, if a user posts about plants on social media, the support department can prioritize providing plant-related support. The support department can prioritize providing interior-related support if a user posts about interior design on social media. The support department can, for example, analyze a user's social media activity and dynamically adjust the support priority. This allows the support department to prioritize relevant support by analyzing a user's social media activity. Some or all of the above processing in the support department may be performed using AI or not. For example, the support department can input data on a user's social media activity into a generating AI, which can analyze the data and provide relevant support.

[0120] The interior design proposal unit can estimate the user's emotions and adjust its interior design proposal method based on the estimated emotions. For example, if the user is relaxed, the interior design proposal unit can make interior design proposals that have a relaxing effect. For example, if the user is stressed, the interior design proposal unit can make interior design proposals that have a stress-reducing effect. For example, if the user is busy, the interior design proposal unit can make interior design proposals that are easy to maintain. By adjusting the interior design proposal method based on the user's emotions, more appropriate interior design proposals become possible. 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. Some or all of the above processing in the interior design proposal unit may be performed using AI or not. For example, the interior design proposal department can input user emotional data into a generating AI, which can then analyze the data and adjust the method of interior design proposals.

[0121] The interior design proposal department can optimize its proposal algorithm by referring to past proposal data when making interior design proposals. For example, the interior design proposal department can select the optimal proposal algorithm based on past proposal data. The interior design proposal department can select the optimal proposal algorithm based on past proposal data. For example, the interior design proposal department can analyze past proposal data and customize the proposal algorithm. The interior design proposal department can analyze past proposal data and customize the proposal algorithm. For example, the interior design proposal department can improve the accuracy of its proposal algorithm by referring to past proposal data. The interior design proposal department can improve the accuracy of its proposal algorithm by referring to past proposal data. As a result, the accuracy of the proposal algorithm is improved by referring to past proposal data. Some or all of the above processes in the interior design proposal department may be performed using AI or not. For example, the interior design proposal department can input past proposal data into a generating AI, and the generating AI can analyze the data and optimize the proposal algorithm.

[0122] The interior design proposal unit can adjust its proposals based on the user's lifestyle. For example, if the user is a morning person, the interior design proposal unit can provide interior design proposals suitable for the morning hours. For example, if the user is a night owl, the interior design proposal unit can provide interior design proposals suitable for the evening hours. For example, the interior design proposal unit can dynamically adjust its interior design proposals to match the user's lifestyle. By adjusting proposals based on the user's lifestyle, more appropriate interior design proposals become possible. Some or all of the above processing in the interior design proposal unit may be performed using AI or not. For example, the interior design proposal unit can input data on the user's lifestyle into a generating AI, which can then analyze the data and adjust the proposals.

[0123] The interior design proposal department can estimate the user's emotions and determine the priority of interior design proposals based on those emotions. For example, if the user is relaxed, the interior design proposal department will prioritize less important proposals. For example, if the user is stressed, the interior design proposal department will prioritize more important proposals. For example, if the user is busy, the interior design proposal department will adjust the priority of proposals to match the user's schedule. This allows for prioritizing important proposals by determining the priority of interior design proposals based on 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) or multimodal generation AI. Some or all of the processes described above in the interior design proposal department may be performed using AI or not. For example, the interior design proposal department can input user emotion data into a generating AI, which can then analyze the data to determine the priority of interior design proposals.

[0124] The interior design proposal unit can make proposals that take into account the user's geographical location information. For example, if the user is at home, the interior design proposal unit can make interior design proposals that are suitable for the environment of the user's home. For example, if the user is out, the interior design proposal unit can make interior design proposals that are suitable for the environment of the user's destination. For example, the interior design proposal unit can dynamically adjust interior design proposals based on the user's geographical location information. This makes it possible to make more appropriate interior design proposals by taking the user's geographical location information into consideration. Some or all of the above processing in the interior design proposal unit may be performed using AI or not. For example, the interior design proposal unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data and make proposals.

[0125] The Interior Design Proposal Department can analyze a user's social media activity and make relevant suggestions when providing interior design proposals. For example, if a user posts about interiors on social media, the Interior Design Proposal Department will prioritize interior-related suggestions. The Interior Design Proposal Department can prioritize interior-related suggestions if a user posts about interiors on social media. For example, if a user posts about plants on social media, the Interior Design Proposal Department will prioritize plant-related suggestions. The Interior Design Proposal Department can, for example, analyze a user's social media activity and dynamically adjust the priority of suggestions. The Interior Design Proposal Department can analyze a user's social media activity and dynamically adjust the priority of suggestions. This allows for prioritizing relevant suggestions by analyzing a user's social media activity. Some or all of the above processing in the Interior Design Proposal Department may be performed using AI or not. For example, the Interior Design Proposal Department can input data on a user's social media activity into a generating AI, which can analyze the data and make relevant suggestions.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The gardening support system can also be equipped with a plant growth prediction unit. This unit predicts plant growth based on data obtained from the measurement and analysis units. For example, it can analyze environmental data such as temperature, light intensity, and humidity to predict plant growth rate and health. It can also refer to past growth data to predict future growth. Furthermore, the plant growth prediction unit can provide users with advice based on the growth prediction. This allows users to predict plant growth and manage their plants appropriately.

[0128] The gardening support system can also be equipped with a plant health check unit. This unit diagnoses the health of plants based on data obtained from the measurement and analysis units. For example, it can analyze leaf color and shape, stem condition, etc., to detect signs of disease or pests. Furthermore, the plant health check unit can suggest countermeasures to the user based on the health check results. It can also refer to past health check data to suggest preventative measures. This allows users to understand the health of their plants and take appropriate action.

[0129] The gardening support system can also be equipped with a plant growth record unit. This unit creates plant growth records based on data obtained from the measurement and analysis units. For example, it can record the plant's height, the number of leaves, the number of flowers, etc., visualizing the growth process. Furthermore, the plant growth record unit can provide users with advice based on the growth records. It can also refer to past growth records to analyze growth trends. This allows users to record plant growth and understand the growth process.

[0130] The gardening support system can also be equipped with a plant nutrition management unit. This unit manages the nutritional status of plants based on data obtained from the measurement and analysis units. For example, it can analyze soil nutrients and moisture content and suggest appropriate fertilizer and watering timings. It can also provide users with nutrition-based advice. Furthermore, it can refer to past nutrition management data and analyze trends in nutrition management. This allows users to manage the nutritional status of their plants and provide appropriate nutrition.

[0131] The gardening support system can also be equipped with a plant environmental adaptation unit. This unit assists plants in adapting to their environment based on data obtained from the measurement and analysis units. For example, it can analyze environmental data such as temperature, humidity, and light intensity, and adjust the plant's environment to ensure optimal growth. It can also provide users with environmentally-based advice. Furthermore, it can refer to past environmental adaptation data to analyze trends in adaptation. This allows users to support plant adaptation and ensure optimal growth.

[0132] The gardening support system can further estimate the user's emotions and select plants based on those emotions. For example, if the user is relaxed, it can select plants with relaxing effects. If the user is stressed, it can select plants with stress-reducing effects. Furthermore, if the user is busy, it can select plants that are easy to care for. In this way, by selecting plants based on the user's emotions, it can select more appropriate plants.

[0133] The gardening support system can further estimate the user's emotions and suggest plant placements based on those emotions. For example, if the user is relaxed, it can suggest a relaxing arrangement. If the user is stressed, it can suggest a stress-reducing arrangement. Furthermore, if the user is busy, it can suggest an easy-to-maintain arrangement. By suggesting plant placements based on the user's emotions, it can achieve more appropriate plant placements.

[0134] The gardening support system can further estimate the user's emotions and adjust the plant care schedule based on those emotions. For example, if the user is relaxed, a detailed care schedule can be created. If the user is stressed, a simpler care schedule can be created. Furthermore, if the user is busy, the care schedule can be adjusted to fit the user's schedule. In this way, by adjusting the plant care schedule based on the user's emotions, more appropriate care can be achieved.

[0135] The gardening support system can further estimate the user's emotions and provide interior design suggestions based on those emotions. For example, if the user is relaxed, it can suggest interior designs that promote relaxation. If the user is stressed, it can suggest interior designs that reduce stress. Furthermore, if the user is busy, it can suggest interior designs that are easy to maintain. In this way, by providing interior design suggestions based on the user's emotions, a more appropriate interior can be achieved.

[0136] The gardening support system can further estimate the user's emotions and adjust the support provided based on those emotions. For example, if the user is relaxed, detailed support can be provided. If the user is stressed, simpler support can be provided. Furthermore, if the user is busy, the support can be adjusted to fit the user's schedule. In this way, by adjusting the support provided based on the user's emotions, more appropriate support can be achieved.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The measurement unit automatically measures the user's room layout and indoor environment. The measurement unit uses temperature sensors and light sensors to measure indoor temperature and light levels in real time. The measurement unit can also collect data to analyze the user's behavior patterns. Step 2: The analysis unit analyzes the data measured by the measurement unit. The analysis unit uses algorithms to analyze the collected data and analyze the user's behavior patterns. Step 3: The selection unit selects the optimal plants based on the data analyzed by the analysis unit. The selection unit selects the optimal plants and proposes their placement based on the user's indoor environment and behavioral patterns. Step 4: The scheduling unit creates a management schedule for the plants selected by the selection unit. The scheduling unit analyzes photos of the plants and their environment to adjust the timing of watering and fertilizing. Step 5: The support department provides interactive support to users based on the schedule created by the scheduling department. The support department uses an AI chatbot to answer user questions in real time. Step 6: The Interior Design Department makes interior design proposals based on the support provided by the Support Department. The Interior Design Department analyzes the indoor environment and makes the most suitable interior design proposals for the user.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the measurement unit, analysis unit, selection unit, schedule creation unit, support unit, and interior design proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the measurement unit measures the indoor environment using the temperature sensor and light sensor of the smart device 14, and analyzes the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the measurement data using, for example, the specific processing unit 290 of the data processing unit 12, and the selection unit selects the optimal plant based on the analysis results. The schedule creation unit creates a management schedule using, for example, the specific processing unit 290 of the data processing unit 12, and the support unit provides interactive support using the AI ​​chatbot of the smart device 14. The interior design proposal unit makes interior design proposals using, for example, the control unit 46A of the smart device 14 and guides the user to a purchase site. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the measurement unit, analysis unit, selection unit, schedule creation unit, support unit, and interior design proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the measurement unit measures the indoor environment using the temperature sensor and light sensor of the smart glasses 214, and analyzes the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the measurement data using the specific processing unit 290 of the data processing unit 12, for example, and the selection unit selects the optimal plant based on the analysis results. The schedule creation unit creates a management schedule using the specific processing unit 290 of the data processing unit 12, for example, and the support unit provides interactive support using the AI ​​chatbot of the smart glasses 214. The interior design proposal unit makes interior design proposals using the control unit 46A of the smart glasses 214 and guides the user to a purchase site. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the measurement unit, analysis unit, selection unit, schedule creation unit, support unit, and interior design proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the measurement unit measures the indoor environment using the temperature sensor and light sensor of the headset terminal 314 and analyzes the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the measurement data using, for example, the specific processing unit 290 of the data processing unit 12, and the selection unit selects the optimal plant based on the analysis results. The schedule creation unit creates a management schedule using, for example, the specific processing unit 290 of the data processing unit 12, and the support unit provides interactive support using the AI ​​chatbot of the headset terminal 314. The interior design proposal unit makes interior design proposals using, for example, the control unit 46A of the headset terminal 314 and guides the user to a purchase site. 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.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the measurement unit, analysis unit, selection unit, schedule creation unit, support unit, and interior design proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the measurement unit measures the indoor environment using the temperature sensor and light sensor of the robot 414, and analyzes the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the measurement data using, for example, the specific processing unit 290 of the data processing unit 12, and the selection unit selects the optimal plant based on the analysis results. The schedule creation unit creates a management schedule using, for example, the specific processing unit 290 of the data processing unit 12, and the support unit provides interactive support using the robot 414's AI chatbot. The interior design proposal unit makes interior design proposals using, for example, the control unit 46A of the robot 414 and guides the user to a purchase site. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) A measurement unit that automatically measures the user's floor plan and indoor environment, An analysis unit analyzes the data measured by the measurement unit, A selection unit that selects the optimal plant based on the data analyzed by the aforementioned analysis unit, A schedule creation unit creates a management schedule for the plants selected by the selection unit, A support unit provides interactive support to the user based on the schedule created by the aforementioned schedule creation unit, The system comprises an interior design proposal unit that makes interior design proposals based on the support provided by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned measuring unit is Automatically measures indoor environmental conditions such as temperature and light intensity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze user behavior patterns The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is We propose the optimal selection and placement of plants. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned schedule creation unit, The timing of watering and fertilizing is adjusted based on photos of the plants and their surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned schedule creation unit, We provide pruning advice based on photographs of the tree's shape. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit is We provide Q&A via AI chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned interior design proposal department, We provide interior design suggestions based on the indoor environment and guide users to the purchase site. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the measurement timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned measuring unit is During measurement, the system selects the optimal measurement method by referring to the user's past measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned measuring unit is During measurement, the measurement frequency is adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned measuring unit is It estimates the user's emotions and prioritizes measurement data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned measuring unit is During measurement, the system prioritizes measuring highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned measuring unit is During measurement, the system analyzes users' social media activity and measures relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the user's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed, and related data is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is The system estimates the user's emotions and adjusts the plant selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is During the selection process, the selection algorithm is optimized by referring to past selection data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is During the selection process, plants are chosen based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is The system estimates the user's emotions and adjusts the plant placement based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is During the selection process, the optimal plant is chosen considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is During the selection process, we analyze the user's social media activity and select plants that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned schedule creation unit, It estimates the user's emotions and adjusts how the schedule is created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned schedule creation unit, When creating a schedule, the scheduling algorithm is optimized by referring to past schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned schedule creation unit, When creating a schedule, adjust the schedule based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned schedule creation unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned schedule creation unit, When creating a schedule, the schedule will be created taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned schedule creation unit, When creating a schedule, analyze the user's social media activity and create relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is We estimate the user's emotions and adjust how support is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is When providing support, we optimize the support algorithm by referring to past support data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is When providing support, we adjust the support based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit is When providing support, we take the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit is When providing support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned interior design proposal department, The system estimates the user's emotions and adjusts the interior design suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned interior design proposal department, When making interior design proposals, we optimize the proposal algorithm by referring to past proposal data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned interior design proposal department, When proposing interior designs, we adjust the proposals based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned interior design proposal department, The system estimates the user's emotions and prioritizes interior design suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned interior design proposal department, When making interior design proposals, we take the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned interior design proposal department, When making interior design proposals, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0211] 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 measurement unit that automatically measures the user's floor plan and indoor environment, An analysis unit analyzes the data measured by the measurement unit, A selection unit that selects the optimal plant based on the data analyzed by the aforementioned analysis unit, A schedule creation unit creates a management schedule for the plants selected by the selection unit, A support unit provides interactive support to the user based on the schedule created by the aforementioned schedule creation unit, The system comprises an interior design proposal unit that makes interior design proposals based on the support provided by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned measuring unit is Automatically measures indoor environmental conditions such as temperature and light intensity. The system according to feature 1.

3. The aforementioned analysis unit, Analyze user behavior patterns The system according to feature 1.

4. The aforementioned selection unit is We propose the optimal selection and placement of plants. The system according to feature 1.

5. The aforementioned schedule creation unit, The timing of watering and fertilizing is adjusted based on photos of the plants and their surrounding environment. The system according to feature 1.

6. The aforementioned schedule creation unit, We provide pruning advice based on photographs of the tree's shape. The system according to feature 1.

7. The aforementioned support unit is We provide Q&A via AI chatbot. The system according to feature 1.

8. The aforementioned interior design proposal department, We provide interior design suggestions based on the indoor environment and guide users to the purchase site. The system according to feature 1.

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