system

Abstract

The system according to this embodiment aims to provide users with appropriate care methods and timings for keeping cut flowers and plants fresh longer. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and an instruction unit. The acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The analysis unit analyzes the data acquired by the acquisition unit. The instruction unit issues instructions to the user based on the data analyzed by the analysis unit.

Description

Technical Field

[0004] ,

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[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 conventional technology, there is a problem that an appropriate maintenance method and timing for prolonging the life of cut flowers and plants are unknown, and it is difficult to take time for maintenance in a busy life.

[0005] The system according to the embodiment aims to provide a user with an appropriate maintenance method and timing for prolonging the life of cut flowers and plants.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, and an instruction unit. The acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The analysis unit analyzes the data acquired by the acquisition unit. The instruction unit issues instructions to the user based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with appropriate care methods and timings for keeping cut flowers and plants fresh longer. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 three or more matters are connected and expressed by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The plant management system according to an embodiment of the present invention is a system that provides optimal care methods for extending the life of cut flowers and plants. This plant management system acquires data using a vase equipped with sensors that detect temperature, humidity, moisture content, light intensity, etc., and generating AI, and instructs the user on the optimal timing and method of care based on the user's schedule. For example, the plant management system acquires data such as temperature, humidity, moisture content, and light intensity using sensors mounted on the vase. Next, the plant management system's generating AI analyzes this data and the user's schedule to instruct the user on the appropriate timing and method of care. For example, it instructs the user on the timing of water changes and the optimal amount of light for the plant. In addition, the plant management system can display virtual flowers and plants in the real world using AR functionality, allowing the user to visually confirm how the combination will look. Furthermore, the plant management system proposes flower and plant designs and care methods appropriate for the period, in accordance with seasonal changes and events (Christmas, Valentine's Day, etc.). The plant management system's AI monitors the condition of cut flowers and plants in a timely manner and notifies the user in real time when care is needed. This system allows anyone to easily enjoy a life surrounded by beautiful cut flowers and plants at all times. This allows the plant management system to provide optimal care methods for keeping cut flowers and plants fresh longer.

[0029] The plant management system according to this embodiment comprises an acquisition unit, an analysis unit, and an instruction unit. The acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The acquisition unit acquires data using, for example, a temperature sensor, humidity sensor, soil moisture sensor, and light sensor. The acquisition unit can acquire data in real time or periodically. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using a generation AI. The analysis unit analyzes the data using, for example, methods such as calculating the average value of the data, trend analysis, and anomaly detection. The instruction unit issues instructions to the user based on the data analyzed by the analysis unit. The instruction unit issues instructions to the user using, for example, email or app notifications. The instruction unit provides the user with specific action instructions and warnings. As a result, the plant management system acquires and analyzes data such as temperature, humidity, moisture content, and light intensity, and issues instructions to the user, enabling optimal plant care.

[0030] The data acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The unit uses sensors such as temperature sensors, humidity sensors, soil moisture sensors, and light sensors to acquire data. Specifically, temperature sensors measure ambient temperature, humidity sensors detect humidity in the air, soil moisture sensors measure soil moisture content to determine how much water plant roots can absorb, and light sensors measure the intensity and amount of light received by plants, providing data to ensure sufficient light for photosynthesis. These sensors are installed to monitor the plant's growing environment in detail and can acquire data in real time. Furthermore, the data acquisition unit can adjust the data acquisition frequency; for example, it can acquire data frequently during periods of significant temperature fluctuations during the day and reduce the frequency at night. This allows the acquisition unit to provide data that keeps the plant's growing environment optimal at all times. The acquisition unit also transmits data to a cloud server for access by the analysis and control units. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit uses a generation AI to analyze the data. Specifically, the generation AI receives data such as temperature, humidity, water content, and light intensity as input, and evaluates the plant's growth status based on this data. The generation AI calculates the average value of the data and performs trend analysis to determine whether the plant's growing environment is appropriate. For example, it calculates the average value of temperature data to check if it is within an appropriate temperature range. It also analyzes the trend of humidity data to evaluate whether the humidity is fluctuating within an appropriate range. Furthermore, the generation AI uses an anomaly detection algorithm to detect unusual patterns and abnormal data. For example, if the data from the soil moisture sensor decreases sharply, the generation AI will determine this as an anomaly and warn that the plant may be suffering from a lack of water. This allows the analysis unit to quickly and accurately analyze the acquired data and understand the plant's growing environment in real time. In addition, the analysis unit can also utilize historical data and statistical information to perform long-term growth trend analysis and risk assessment. This allows the analysis unit to not only understand the situation in real time but also to handle long-term plant management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The instruction unit issues instructions to the user based on data analyzed by the analysis unit. The instruction unit issues instructions to the user through methods such as email and app notifications. Specifically, based on the data provided by the analysis unit, the instruction unit provides the user with specific action instructions to optimize the plant's growing environment. For example, if the temperature exceeds the appropriate range, the instruction unit instructs the user to adjust the temperature. Also, if the soil moisture is insufficient, the instruction unit notifies the user to water the plants. Furthermore, if there is insufficient light, the instruction unit instructs the user to move the plants to a brighter location. In this way, the instruction unit can quickly provide the user with appropriate action instructions and optimize the plant's growing environment. In addition, the instruction unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, it receives feedback on the results of the user's actions following the instructions and revises the instructions in cooperation with the analysis unit. The instruction unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only email notifications but also in-app notifications, SMS, and voice calls. This allows the control unit to provide users with quick and reliable instructions, optimizing the plant's growing environment.

[0033] The control unit can provide instructions on when and how to perform maintenance based on the user's schedule. The control unit obtains the user's schedule information through methods such as integration with a calendar app or manual input. Based on the user's schedule, the control unit provides specific instructions on when and how to perform maintenance, such as watering once a week or how much sunlight to provide. This allows for optimal maintenance tailored to the user's lifestyle by providing instructions on when and how to perform maintenance based on the user's schedule.

[0034] The plant management system according to this embodiment includes a display unit that provides AR functionality. The display unit can provide AR functionality. The display unit needs to clarify, for example, the type of AR software and device to be used. The display unit can display virtual flowers and plants in the real world and visually confirm their combinations. Thus, by providing AR functionality, virtual flowers and plants can be displayed in the real world and visually confirmed.

[0035] The display unit shows virtual flowers and plants in the real world, allowing users to visually confirm their combinations. The display unit needs to clearly define, for example, the type of 3D model and the display resolution. The display unit allows users to pre-confirm the placement of flowers and plants. This enables users to pre-confirm the placement of flowers and plants by displaying virtual flowers and plants in the real world and visually confirming their combinations.

[0036] The plant management system according to this embodiment includes a suggestion unit that makes suggestions in response to seasonal changes and events. The suggestion unit can make suggestions in response to seasonal changes and events. For example, the suggestion unit makes suggestions based on definitions of spring, summer, autumn, and winter, or specific events (Christmas, Halloween, etc.). The suggestion unit enables users to learn about flower and plant designs and care methods appropriate for that time of year. In this way, by making suggestions in response to seasonal changes and events, users can learn about flower and plant designs and care methods appropriate for that time of year.

[0037] The proposal department can suggest flower and plant designs and care methods suitable for seasonal changes and events. For example, the proposal department needs to clearly define design examples and specific care procedures. The proposal department ensures that users can enjoy the most suitable flowers and plants for each season. By suggesting flower and plant designs and care methods suitable for seasonal changes and events, users can enjoy the most suitable flowers and plants for each season.

[0038] The plant management system according to this embodiment includes a monitoring unit that monitors the condition of cut flowers and plants. The monitoring unit can monitor the condition of cut flowers and plants. The monitoring unit needs to clarify, for example, the type of sensor to be used and the frequency of monitoring. The monitoring unit enables the user to perform maintenance in a timely manner. By monitoring the condition of cut flowers and plants, the user can perform maintenance in a timely manner.

[0039] The monitoring unit can monitor the condition of cut flowers and plants in a timely manner and notify the user in real time when care is needed. The monitoring unit notifies the user, for example, when a certain threshold is exceeded or through real-time notifications. The monitoring unit ensures that users can always enjoy beautiful flowers and plants. In this way, by monitoring the condition of cut flowers and plants in a timely manner and notifying the user in real time when care is needed, users can always enjoy beautiful flowers and plants.

[0040] The data acquisition unit can automatically select different sensors according to the type of plant to acquire optimal data. For example, the acquisition unit prioritizes the use of the soil moisture sensor for drought-tolerant plants such as cacti. It can also prioritize the use of the humidity sensor for plants that prefer humidity, such as ferns. Furthermore, the acquisition unit can adjust the light intensity sensor according to the type of flower to measure the optimal light intensity. This allows for the acquisition of more accurate data by selecting the optimal sensor according to the type of plant. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI.

[0041] The data acquisition unit can automatically adjust the sensitivity of the sensors in response to environmental changes. For example, if the room temperature changes rapidly, the data acquisition unit can increase the sensitivity of the temperature sensor to acquire data. Similarly, if the humidity changes rapidly, the data acquisition unit can increase the sensitivity of the humidity sensor to acquire data. Furthermore, if the light intensity changes rapidly, the data acquisition unit can increase the sensitivity of the light intensity sensor to acquire data. By adjusting the sensor sensitivity in response to environmental changes, more accurate data can be acquired. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI.

[0042] The data acquisition unit can acquire region-specific environmental data while considering the user's geographical location. For example, if the user lives in an arid region, the data acquisition unit will focus on acquiring humidity data. If the user lives in a high-temperature region, the data acquisition unit will focus on acquiring temperature data. Furthermore, if the user lives in a rainy region, the data acquisition unit will focus on acquiring moisture content data. In this way, region-specific environmental data can be acquired by considering the user's geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI.

[0043] The data acquisition unit can analyze a user's social media activity and acquire relevant environmental data. For example, if a user frequently posts about plants on social media, the data acquisition unit can focus on acquiring data related to the health of those plants. Similarly, if a user posts about a specific flower on social media, the data acquisition unit can focus on acquiring data related to that flower. Furthermore, if a user posts about seasonal events on social media, the data acquisition unit can focus on acquiring data related to those events. This allows for the acquisition of environmental data based on the user's interests by analyzing their social media activity. Some or all of the processing described above in the data acquisition unit may be performed using AI, for example, or without AI.

[0044] The analysis unit can optimize its analysis algorithm by referring to the plant's growth history. For example, the analysis unit can analyze the optimal water content based on the plant's past growth data. It can also analyze the optimal light content based on the plant's past growth data. Furthermore, it can analyze the optimal temperature and humidity based on the plant's past growth data. By referring to the plant's growth history, the analysis algorithm can be optimized, providing more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0045] The analysis unit can compare data from different plants and select the optimal analysis method. For example, the analysis unit can compare data from different types of flowers and analyze the optimal water content. It can also compare data from different types of plants and analyze the optimal light intensity. Furthermore, it can compare data from different types of plants and analyze the optimal temperature and humidity. This allows for the selection of the optimal analysis method by comparing data from different plants, thereby providing more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0046] The analysis unit can determine the priority of analysis based on the data acquisition timing. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. Furthermore, the analysis unit can analyze long-term trends based on past data. In addition, the analysis unit can prioritize the analysis of data from specific events (e.g., after a water change). This allows for the provision of real-time information by prioritizing analysis based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature data. For example, the analysis unit can improve its analysis algorithm by referring to the latest research papers. Furthermore, the analysis unit can improve the reliability of its analysis results by referring to data in specialized books. In addition, the analysis unit can supplement its analysis results by referring to online databases. This allows for improved accuracy of the analysis and the provision of more reliable information by referring to relevant literature data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0048] The instruction unit can provide different instructions depending on the type of plant. For example, it can provide instructions to reduce the frequency of watering for cacti. It can also provide instructions to maintain high humidity for ferns. Furthermore, it can provide instructions to provide the optimal amount of light depending on the type of flower. By providing instructions tailored to the type of plant, it becomes possible to provide optimal care for each plant. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without using AI.

[0049] The instruction unit can select the optimal instruction by referring to the user's past behavior history. For example, the instruction unit can provide the optimal instruction based on the user's past maintenance methods. The instruction unit can also provide instructions on the optimal watering timing based on the user's past behavior history. Furthermore, the instruction unit can analyze the user's past behavior history and provide instructions to provide the optimal amount of light. In this way, the instruction unit can provide the user with the most suitable instructions by referring to the user's past behavior history. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without using AI.

[0050] The instruction unit can provide region-specific instructions by taking into account the user's geographical location. For example, if the user lives in an arid region, the instruction unit can provide instructions to maintain high humidity. If the user lives in a hot region, the instruction unit can provide instructions to lower the temperature. Furthermore, if the user lives in a rainy region, the instruction unit can provide instructions to reduce the frequency of watering. In this way, region-specific instructions can be provided by taking into account the user's geographical location. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without using AI.

[0051] The instruction unit can analyze a user's social media activity and provide relevant instructions. For example, if a user frequently posts about plants on social media, the instruction unit can provide instructions regarding the health of plants. It can also provide instructions about specific flowers if a user posts about a particular flower on social media. Furthermore, if a user posts about seasonal events on social media, the instruction unit can provide instructions related to those events. This allows for the provision of user-interest-based instructions by analyzing the user's social media activity. Some or all of the processing described above in the instruction unit may be performed using AI, for example, or without AI.

[0052] The display unit can provide different display methods depending on the plant's growth stage. For example, when the plant is in its early growth stage, the display unit can display basic information. When the plant is in its mid-growth stage, the display unit can display detailed growth data. Furthermore, when the plant is in its late growth stage, the display unit can display the optimal harvest time and care methods. This allows users to properly manage the plant's growth by providing display methods tailored to the plant's growth stage. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0053] The display unit can select the optimal display based on the user's past visual preferences. For example, the display unit can adjust the colors of the displayed content based on the user's past preferred color schemes. Furthermore, the display unit can adjust the layout of the displayed content based on the user's past preferred layouts. In addition, the display unit can adjust the font of the displayed content based on the user's past preferred fonts. This allows the display unit to provide information that is easy for the user to read by selecting the optimal display based on the user's past visual preferences. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0054] The display unit can provide an optimal display method considering the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows for the provision of an optimal display method for each device by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0055] The display unit can analyze the user's social media activity and provide relevant display content. For example, if the user frequently posts about plants on social media, the display unit can display information about the health of the plants. It can also display information about specific flowers if the user posts about them on social media. Furthermore, if the user posts about seasonal events on social media, the display unit can display information related to those events. This allows the display unit to provide content based on the user's interests by analyzing their social media activity. Some or all of the processing described above in the display unit may be performed using AI, for example, or without AI.

[0056] The suggestion section can provide different suggestions depending on the season. For example, in spring, it can provide suggestions regarding flower types and colors. In summer, it can provide suggestions regarding watering plants and creating shade. Furthermore, in autumn, it can provide suggestions regarding pruning and harvesting plants. By providing suggestions that are appropriate for the changing seasons, users can enjoy the flowers and plants that are best suited to that time of year. Some or all of the processing described above in the suggestion section may be performed using AI, for example, or without AI.

[0057] The suggestion unit can select the most suitable suggestion by referring to the user's past preference history. For example, the suggestion unit can provide the most suitable suggestion based on the types of flowers the user has liked in the past. The suggestion unit can also suggest the most suitable plant care method based on the user's past preference history. Furthermore, the suggestion unit can analyze the user's past preference history and suggest the most suitable plant arrangement method. In this way, by referring to the user's past preference history, the suggestion unit can provide the user with the most suitable suggestion. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0058] The suggestion unit can provide region-specific suggestions by taking into account the user's geographical location. For example, if the user lives in an arid region, the suggestion unit can suggest maintaining high humidity. If the user lives in a hot region, the suggestion unit can suggest lowering the temperature. Furthermore, if the user lives in a rainy region, the suggestion unit can suggest reducing the frequency of watering. In this way, region-specific suggestions can be provided by taking into account the user's geographical location. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or without using AI.

[0059] The suggestion unit can analyze a user's social media activity and provide relevant suggestions. For example, if a user frequently posts about plants on social media, the suggestion unit can provide suggestions regarding the health of plants. It can also provide suggestions about specific flowers if a user posts about them. Furthermore, if a user posts about seasonal events on social media, the suggestion unit can provide suggestions related to those events. This allows for the provision of suggestions based on user interests by analyzing the user's social media activity. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or without AI.

[0060] The monitoring unit can provide different monitoring methods depending on the plant's growth stage. For example, when the plant is in its early growth stage, the monitoring unit performs basic monitoring. When the plant is in its mid-growth stage, the monitoring unit can monitor detailed growth data. Furthermore, when the plant is in its late growth stage, the monitoring unit can monitor the optimal harvest time and care methods. This allows users to properly manage plant growth by providing monitoring methods tailored to the plant's growth stage. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI.

[0061] The monitoring unit can select the optimal monitoring by referring to the user's past behavior history. For example, the monitoring unit can provide optimal monitoring based on the user's past maintenance methods. The monitoring unit can also monitor the optimal watering timing from the user's past behavior history. Furthermore, the monitoring unit can analyze the user's past behavior history and provide monitoring that provides the optimal amount of light. In this way, the monitoring unit can provide the user with optimal monitoring by referring to the user's past behavior history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0062] The monitoring unit can provide region-specific monitoring by taking into account the user's geographical location information. For example, if the user lives in an arid region, the monitoring unit can provide monitoring to maintain high humidity. If the user lives in a high-temperature region, the monitoring unit can provide monitoring to lower the temperature. Furthermore, if the user lives in a rainy region, the monitoring unit can provide monitoring to reduce the frequency of watering. In this way, region-specific monitoring can be provided by taking into account the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0063] The monitoring unit can analyze a user's social media activity and provide relevant monitoring content. For example, if a user frequently posts about plants on social media, the monitoring unit can provide monitoring on the health of those plants. Furthermore, if a user posts about a specific flower on social media, the monitoring unit can provide monitoring related to that flower. Additionally, if a user posts about a seasonal event on social media, the monitoring unit can provide monitoring related to that event. This allows for the provision of monitoring content based on the user's interests by analyzing their social media activity. Some or all of the processing described above in the monitoring unit may be performed using AI, for example, or without AI.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The plant management system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and answer questions about the plant's condition and care methods in real time. For example, if the user asks, "When is the best time to water this flower?", the voice recognition unit will answer with the optimal watering time based on data from the acquisition unit. Also, if the user asks, "What is the appropriate amount of light for this plant?", the voice recognition unit can answer with the appropriate amount of light based on data from the analysis unit. Furthermore, if the user asks, "When is the next time to care for the plant?", the voice recognition unit can answer with the next care timing based on data from the instruction unit. In this way, by using the voice recognition unit, users can easily obtain information about the plant's condition and care methods through voice commands.

[0066] The plant management system can also suggest different care methods depending on the plant's growth stage. For example, in the early stages of growth, it can suggest basic watering and light management. In the middle stages of growth, it can suggest additional fertilizer and pruning methods. Furthermore, in the later stages of growth, it can suggest harvesting time and optimal storage methods. This allows users to properly manage plant growth by suggesting care methods tailored to each stage of development.

[0067] The plant management system can also suggest region-specific plants by considering the user's geographical location. For example, if the user lives in an arid region, it can suggest drought-tolerant plants (e.g., cacti). If the user lives in a hot region, it can suggest plants that can withstand high temperatures (e.g., olive trees). Furthermore, if the user lives in a rainy region, it can suggest plants suited to high humidity (e.g., ferns). In this way, by considering the user's geographical location, it can suggest region-specific plants.

[0068] The plant management system can further analyze users' social media activity and provide relevant plant information. For example, if a user posts about a specific flower on social media, it can provide care instructions and growth information for that flower. Similarly, if a user posts about seasonal events on social media, it can provide plant designs and care instructions related to those events. Furthermore, if a user frequently posts about plants on social media, it can provide information about the plant's health. In this way, by analyzing users' social media activity, the system can provide plant information tailored to their interests.

[0069] The plant management system can automatically select different sensors depending on the plant species to obtain optimal data. For example, it prioritizes the use of soil moisture sensors for drought-tolerant plants like cacti, and prioritizes the use of humidity sensors for plants that prefer humidity, such as ferns. Furthermore, it can adjust light intensity sensors according to the type of flower to measure the optimal light level. This allows for more accurate data acquisition by selecting the most suitable sensors for each plant species.

[0070] The plant management system can further select optimal instructions by referring to the user's past behavior history. For example, it can provide optimal instructions based on the user's past care methods. It can also provide instructions on the optimal watering timing based on the user's past behavior history. Furthermore, it can analyze the user's past behavior history and provide instructions on the optimal amount of light. In this way, by referring to the user's past behavior history, it can provide the most suitable instructions to the user.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The acquisition unit acquires data using, for example, a temperature sensor, humidity sensor, soil moisture sensor, and light sensor. The acquisition unit can acquire data in real time or periodically. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using a generation AI. The analysis unit analyzes the data using methods such as calculating the average value of the data, trend analysis, and anomaly detection. Step 3: The instruction unit issues instructions to the user based on the data analyzed by the analysis unit. The instruction unit issues instructions to the user, for example, via email or app notification. The instruction unit provides the user with specific action instructions, warnings, and other relevant information.

[0073] (Example of form 2) The plant management system according to an embodiment of the present invention is a system that provides optimal care methods for extending the life of cut flowers and plants. This plant management system acquires data using a vase equipped with sensors that detect temperature, humidity, moisture content, light intensity, etc., and generating AI, and instructs the user on the optimal timing and method of care based on the user's schedule. For example, the plant management system acquires data such as temperature, humidity, moisture content, and light intensity using sensors mounted on the vase. Next, the plant management system's generating AI analyzes this data and the user's schedule to instruct the user on the appropriate timing and method of care. For example, it instructs the user on the timing of water changes and the optimal amount of light for the plant. In addition, the plant management system can display virtual flowers and plants in the real world using AR functionality, allowing the user to visually confirm how the combination will look. Furthermore, the plant management system proposes flower and plant designs and care methods appropriate for the period, in accordance with seasonal changes and events (Christmas, Valentine's Day, etc.). The plant management system's AI monitors the condition of cut flowers and plants in a timely manner and notifies the user in real time when care is needed. This system allows anyone to easily enjoy a life surrounded by beautiful cut flowers and plants at all times. This allows the plant management system to provide optimal care methods for keeping cut flowers and plants fresh longer.

[0074] The plant management system according to this embodiment comprises an acquisition unit, an analysis unit, and an instruction unit. The acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The acquisition unit acquires data using, for example, a temperature sensor, humidity sensor, soil moisture sensor, and light sensor. The acquisition unit can acquire data in real time or periodically. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using a generation AI. The analysis unit analyzes the data using, for example, methods such as calculating the average value of the data, trend analysis, and anomaly detection. The instruction unit issues instructions to the user based on the data analyzed by the analysis unit. The instruction unit issues instructions to the user using, for example, email or app notifications. The instruction unit provides the user with specific action instructions and warnings. As a result, the plant management system acquires and analyzes data such as temperature, humidity, moisture content, and light intensity, and issues instructions to the user, enabling optimal plant care.

[0075] The data acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The unit uses sensors such as temperature sensors, humidity sensors, soil moisture sensors, and light sensors to acquire data. Specifically, temperature sensors measure ambient temperature, humidity sensors detect humidity in the air, soil moisture sensors measure soil moisture content to determine how much water plant roots can absorb, and light sensors measure the intensity and amount of light received by plants, providing data to ensure sufficient light for photosynthesis. These sensors are installed to monitor the plant's growing environment in detail and can acquire data in real time. Furthermore, the data acquisition unit can adjust the data acquisition frequency; for example, it can acquire data frequently during periods of significant temperature fluctuations during the day and reduce the frequency at night. This allows the acquisition unit to provide data that keeps the plant's growing environment optimal at all times. The acquisition unit also transmits data to a cloud server for access by the analysis and control units. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0076] The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit uses a generation AI to analyze the data. Specifically, the generation AI receives data such as temperature, humidity, water content, and light intensity as input, and evaluates the plant's growth status based on this data. The generation AI calculates the average value of the data and performs trend analysis to determine whether the plant's growing environment is appropriate. For example, it calculates the average value of temperature data to check if it is within an appropriate temperature range. It also analyzes the trend of humidity data to evaluate whether the humidity is fluctuating within an appropriate range. Furthermore, the generation AI uses an anomaly detection algorithm to detect unusual patterns and abnormal data. For example, if the data from the soil moisture sensor decreases sharply, the generation AI will determine this as an anomaly and warn that the plant may be suffering from a lack of water. This allows the analysis unit to quickly and accurately analyze the acquired data and understand the plant's growing environment in real time. In addition, the analysis unit can also utilize historical data and statistical information to perform long-term growth trend analysis and risk assessment. This allows the analysis unit to not only understand the situation in real time but also to handle long-term plant management and anomaly detection, improving the reliability and safety of the entire system.

[0077] The instruction unit issues instructions to the user based on data analyzed by the analysis unit. The instruction unit issues instructions to the user through methods such as email and app notifications. Specifically, based on the data provided by the analysis unit, the instruction unit provides the user with specific action instructions to optimize the plant's growing environment. For example, if the temperature exceeds the appropriate range, the instruction unit instructs the user to adjust the temperature. Also, if the soil moisture is insufficient, the instruction unit notifies the user to water the plants. Furthermore, if there is insufficient light, the instruction unit instructs the user to move the plants to a brighter location. In this way, the instruction unit can quickly provide the user with appropriate action instructions and optimize the plant's growing environment. In addition, the instruction unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, it receives feedback on the results of the user's actions following the instructions and revises the instructions in cooperation with the analysis unit. The instruction unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only email notifications but also in-app notifications, SMS, and voice calls. This allows the control unit to provide users with quick and reliable instructions, optimizing the plant's growing environment.

[0078] The control unit can provide instructions on when and how to perform maintenance based on the user's schedule. The control unit obtains the user's schedule information through methods such as integration with a calendar app or manual input. Based on the user's schedule, the control unit provides specific instructions on when and how to perform maintenance, such as watering once a week or how much sunlight to provide. This allows for optimal maintenance tailored to the user's lifestyle by providing instructions on when and how to perform maintenance based on the user's schedule.

[0079] The plant management system according to this embodiment includes a display unit that provides AR functionality. The display unit can provide AR functionality. The display unit needs to clarify, for example, the type of AR software and device to be used. The display unit can display virtual flowers and plants in the real world and visually confirm their combinations. Thus, by providing AR functionality, virtual flowers and plants can be displayed in the real world and visually confirmed.

[0080] The display unit shows virtual flowers and plants in the real world, allowing users to visually confirm their combinations. The display unit needs to clearly define, for example, the type of 3D model and the display resolution. The display unit allows users to pre-confirm the placement of flowers and plants. This enables users to pre-confirm the placement of flowers and plants by displaying virtual flowers and plants in the real world and visually confirming their combinations.

[0081] The plant management system according to this embodiment includes a suggestion unit that makes suggestions in response to seasonal changes and events. The suggestion unit can make suggestions in response to seasonal changes and events. For example, the suggestion unit makes suggestions based on definitions of spring, summer, autumn, and winter, or specific events (Christmas, Halloween, etc.). The suggestion unit enables users to learn about flower and plant designs and care methods appropriate for that time of year. In this way, by making suggestions in response to seasonal changes and events, users can learn about flower and plant designs and care methods appropriate for that time of year.

[0082] The proposal department can suggest flower and plant designs and care methods suitable for seasonal changes and events. For example, the proposal department needs to clearly define design examples and specific care procedures. The proposal department ensures that users can enjoy the most suitable flowers and plants for each season. By suggesting flower and plant designs and care methods suitable for seasonal changes and events, users can enjoy the most suitable flowers and plants for each season.

[0083] The plant management system according to this embodiment includes a monitoring unit that monitors the condition of cut flowers and plants. The monitoring unit can monitor the condition of cut flowers and plants. The monitoring unit needs to clarify, for example, the type of sensor to be used and the frequency of monitoring. The monitoring unit enables the user to perform maintenance in a timely manner. By monitoring the condition of cut flowers and plants, the user can perform maintenance in a timely manner.

[0084] The monitoring unit can monitor the condition of cut flowers and plants in a timely manner and notify the user in real time when care is needed. The monitoring unit notifies the user, for example, when a certain threshold is exceeded or through real-time notifications. The monitoring unit ensures that users can always enjoy beautiful flowers and plants. In this way, by monitoring the condition of cut flowers and plants in a timely manner and notifying the user in real time when care is needed, users can always enjoy beautiful flowers and plants.

[0085] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the user is relaxed, the data acquisition unit can set a lower data acquisition frequency to reduce the user's burden. Conversely, if the user is busy, the data acquisition unit can set a higher data acquisition frequency to quickly issue care instructions. Furthermore, if the user is stressed, the data acquisition unit can adjust the timing of data acquisition to match the user's schedule. In this way, by adjusting the timing of data acquisition based on the user's emotions, the user's burden is reduced and data can be acquired at the optimal time. 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.

[0086] The data acquisition unit can automatically select different sensors according to the type of plant to acquire optimal data. For example, the acquisition unit prioritizes the use of the soil moisture sensor for drought-tolerant plants such as cacti. It can also prioritize the use of the humidity sensor for plants that prefer humidity, such as ferns. Furthermore, the acquisition unit can adjust the light intensity sensor according to the type of flower to measure the optimal light intensity. This allows for the acquisition of more accurate data by selecting the optimal sensor according to the type of plant. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI.

[0087] The data acquisition unit can automatically adjust the sensitivity of the sensors in response to environmental changes. For example, if the room temperature changes rapidly, the data acquisition unit can increase the sensitivity of the temperature sensor to acquire data. Similarly, if the humidity changes rapidly, the data acquisition unit can increase the sensitivity of the humidity sensor to acquire data. Furthermore, if the light intensity changes rapidly, the data acquisition unit can increase the sensitivity of the light intensity sensor to acquire data. By adjusting the sensor sensitivity in response to environmental changes, more accurate data can be acquired. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI.

[0088] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the user is relaxed, the unit will prioritize acquiring data related to the plant's health. If the user is busy, the unit can prioritize acquiring the most important data (e.g., water content). Furthermore, if the user is stressed, the unit can prioritize acquiring data related to the plant's aesthetics. By prioritizing data based on the user's emotions, the system can prioritize acquiring data that meets the user's needs. 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.

[0089] The data acquisition unit can acquire region-specific environmental data while considering the user's geographical location. For example, if the user lives in an arid region, the data acquisition unit will focus on acquiring humidity data. If the user lives in a high-temperature region, the data acquisition unit will focus on acquiring temperature data. Furthermore, if the user lives in a rainy region, the data acquisition unit will focus on acquiring moisture content data. In this way, region-specific environmental data can be acquired by considering the user's geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI.

[0090] The data acquisition unit can analyze a user's social media activity and acquire relevant environmental data. For example, if a user frequently posts about plants on social media, the data acquisition unit can focus on acquiring data related to the health of those plants. Similarly, if a user posts about a specific flower on social media, the data acquisition unit can focus on acquiring data related to that flower. Furthermore, if a user posts about seasonal events on social media, the data acquisition unit can focus on acquiring data related to those events. This allows for the acquisition of environmental data based on the user's interests by analyzing their social media activity. Some or all of the processing described above in the data acquisition unit may be performed using AI, for example, or without AI.

[0091] 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 and provide more information to the user. If the user is busy, the analysis unit can perform a concise analysis and provide only the necessary information. Furthermore, if the user is stressed, the analysis unit can display the analysis results in an easy-to-understand visual format. In this way, by adjusting the analysis method based on the user's emotions, the system can provide the user with the most appropriate information. 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.

[0092] The analysis unit can optimize its analysis algorithm by referring to the plant's growth history. For example, the analysis unit can analyze the optimal water content based on the plant's past growth data. It can also analyze the optimal light content based on the plant's past growth data. Furthermore, it can analyze the optimal temperature and humidity based on the plant's past growth data. By referring to the plant's growth history, the analysis algorithm can be optimized, providing more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0093] The analysis unit can compare data from different plants and select the optimal analysis method. For example, the analysis unit can compare data from different types of flowers and analyze the optimal water content. It can also compare data from different types of plants and analyze the optimal light intensity. Furthermore, it can compare data from different types of plants and analyze the optimal temperature and humidity. This allows for the selection of the optimal analysis method by comparing data from different plants, thereby providing more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is busy, the analysis unit can display concise analysis results. Furthermore, if the user is stressed, the analysis unit can display visually easy-to-understand analysis results. In this way, by adjusting the display method of the analysis results based on the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The analysis unit can determine the priority of analysis based on the data acquisition timing. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. Furthermore, the analysis unit can analyze long-term trends based on past data. In addition, the analysis unit can prioritize the analysis of data from specific events (e.g., after a water change). This allows for the provision of real-time information by prioritizing analysis based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0096] The analysis unit can improve the accuracy of its analysis by referring to relevant literature data. For example, the analysis unit can improve its analysis algorithm by referring to the latest research papers. Furthermore, the analysis unit can improve the reliability of its analysis results by referring to data in specialized books. In addition, the analysis unit can supplement its analysis results by referring to online databases. This allows for improved accuracy of the analysis and the provision of more reliable information by referring to relevant literature data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0097] The instruction unit can estimate the user's emotions and adjust the way instructions are presented based on those emotions. For example, if the user is relaxed, the instruction unit can provide detailed instructions. If the user is busy, it can provide concise instructions. Furthermore, if the user is stressed, it can provide visually clear instructions. In this way, by adjusting the way instructions are presented based on the user's emotions, instructions can be made easy for the user to understand. 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.

[0098] The instruction unit can provide different instructions depending on the type of plant. For example, it can provide instructions to reduce the frequency of watering for cacti. It can also provide instructions to maintain high humidity for ferns. Furthermore, it can provide instructions to provide the optimal amount of light depending on the type of flower. By providing instructions tailored to the type of plant, it becomes possible to provide optimal care for each plant. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without using AI.

[0099] The instruction unit can select the optimal instruction by referring to the user's past behavior history. For example, the instruction unit can provide the optimal instruction based on the user's past maintenance methods. The instruction unit can also provide instructions on the optimal watering timing based on the user's past behavior history. Furthermore, the instruction unit can analyze the user's past behavior history and provide instructions to provide the optimal amount of light. In this way, the instruction unit can provide the user with the most suitable instructions by referring to the user's past behavior history. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without using AI.

[0100] The instruction unit can estimate the user's emotions and prioritize instructions based on those emotions. For example, if the user is relaxed, the instruction unit can prioritize detailed instructions. If the user is busy, the instruction unit can prioritize the most important instructions. Furthermore, if the user is stressed, the instruction unit can prioritize visually easy-to-understand instructions. In this way, by prioritizing instructions based on the user's emotions, instructions that are important to the user can be provided preferentially. 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.

[0101] The instruction unit can provide region-specific instructions by taking into account the user's geographical location. For example, if the user lives in an arid region, the instruction unit can provide instructions to maintain high humidity. If the user lives in a hot region, the instruction unit can provide instructions to lower the temperature. Furthermore, if the user lives in a rainy region, the instruction unit can provide instructions to reduce the frequency of watering. In this way, region-specific instructions can be provided by taking into account the user's geographical location. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without using AI.

[0102] The instruction unit can analyze a user's social media activity and provide relevant instructions. For example, if a user frequently posts about plants on social media, the instruction unit can provide instructions regarding the health of plants. It can also provide instructions about specific flowers if a user posts about a particular flower on social media. Furthermore, if a user posts about seasonal events on social media, the instruction unit can provide instructions related to those events. This allows for the provision of user-interest-based instructions by analyzing the user's social media activity. Some or all of the processing described above in the instruction unit may be performed using AI, for example, or without AI.

[0103] The display unit can estimate the user's emotions and adjust the displayed content based on the estimated emotions. For example, if the user is relaxed, the display unit can display detailed information. If the user is busy, the display unit can display concise information. Furthermore, if the user is stressed, the display unit can display visually easy-to-understand information. In this way, by adjusting the displayed content based on the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The display unit can provide different display methods depending on the plant's growth stage. For example, when the plant is in its early growth stage, the display unit can display basic information. When the plant is in its mid-growth stage, the display unit can display detailed growth data. Furthermore, when the plant is in its late growth stage, the display unit can display the optimal harvest time and care methods. This allows users to properly manage the plant's growth by providing display methods tailored to the plant's growth stage. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0105] The display unit can select the optimal display based on the user's past visual preferences. For example, the display unit can adjust the colors of the displayed content based on the user's past preferred color schemes. Furthermore, the display unit can adjust the layout of the displayed content based on the user's past preferred layouts. In addition, the display unit can adjust the font of the displayed content based on the user's past preferred fonts. This allows the display unit to provide information that is easy for the user to read by selecting the optimal display based on the user's past visual preferences. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0106] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, the display unit can prioritize displaying detailed information. If the user is busy, the display unit can prioritize displaying the most important information. Furthermore, if the user is stressed, the display unit can prioritize displaying visually easy-to-understand information. In this way, by determining the display priority based on the user's emotions, information that is important to the user can be displayed preferentially. 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.

[0107] The display unit can provide an optimal display method considering the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows for the provision of an optimal display method for each device by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0108] The display unit can analyze the user's social media activity and provide relevant display content. For example, if the user frequently posts about plants on social media, the display unit can display information about the health of the plants. It can also display information about specific flowers if the user posts about them on social media. Furthermore, if the user posts about seasonal events on social media, the display unit can display information related to those events. This allows the display unit to provide content based on the user's interests by analyzing their social media activity. Some or all of the processing described above in the display unit may be performed using AI, for example, or without AI.

[0109] The suggestion function can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is busy, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide visually easy-to-understand suggestions. In this way, by adjusting the suggestions based on the user's emotions, it can provide suggestions that are easy for the user to understand. 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.

[0110] The suggestion section can provide different suggestions depending on the season. For example, in spring, it can provide suggestions regarding flower types and colors. In summer, it can provide suggestions regarding watering plants and creating shade. Furthermore, in autumn, it can provide suggestions regarding pruning and harvesting plants. By providing suggestions that are appropriate for the changing seasons, users can enjoy the flowers and plants that are best suited to that time of year. Some or all of the processing described above in the suggestion section may be performed using AI, for example, or without AI.

[0111] The suggestion unit can select the most suitable suggestion by referring to the user's past preference history. For example, the suggestion unit can provide the most suitable suggestion based on the types of flowers the user has liked in the past. The suggestion unit can also suggest the most suitable plant care method based on the user's past preference history. Furthermore, the suggestion unit can analyze the user's past preference history and suggest the most suitable plant arrangement method. In this way, by referring to the user's past preference history, the suggestion unit can provide the user with the most suitable suggestion. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0112] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will prioritize detailed suggestions. If the user is busy, the suggestion function can prioritize the most important suggestions. Furthermore, if the user is stressed, the suggestion function can prioritize visually easy-to-understand suggestions. In this way, by prioritizing suggestions based on the user's emotions, the system can prioritize suggestions that are important to the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The suggestion unit can provide region-specific suggestions by taking into account the user's geographical location. For example, if the user lives in an arid region, the suggestion unit can suggest maintaining high humidity. If the user lives in a hot region, the suggestion unit can suggest lowering the temperature. Furthermore, if the user lives in a rainy region, the suggestion unit can suggest reducing the frequency of watering. In this way, region-specific suggestions can be provided by taking into account the user's geographical location. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or without using AI.

[0114] The suggestion unit can analyze a user's social media activity and provide relevant suggestions. For example, if a user frequently posts about plants on social media, the suggestion unit can provide suggestions regarding the health of plants. It can also provide suggestions about specific flowers if a user posts about them. Furthermore, if a user posts about seasonal events on social media, the suggestion unit can provide suggestions related to those events. This allows for the provision of suggestions based on user interests by analyzing the user's social media activity. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or without AI.

[0115] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is relaxed, the monitoring unit can set a lower monitoring frequency to reduce the user's burden. Conversely, if the user is busy, the monitoring unit can set a higher monitoring frequency to quickly provide instructions for care. Furthermore, if the user is stressed, the monitoring unit can adjust the monitoring frequency to match the user's schedule. In this way, by adjusting the monitoring frequency based on the user's emotions, the user's burden is reduced and monitoring can be performed at the optimal time. 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.

[0116] The monitoring unit can provide different monitoring methods depending on the plant's growth stage. For example, when the plant is in its early growth stage, the monitoring unit performs basic monitoring. When the plant is in its mid-growth stage, the monitoring unit can monitor detailed growth data. Furthermore, when the plant is in its late growth stage, the monitoring unit can monitor the optimal harvest time and care methods. This allows users to properly manage plant growth by providing monitoring methods tailored to the plant's growth stage. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI.

[0117] The monitoring unit can select the optimal monitoring by referring to the user's past behavior history. For example, the monitoring unit can provide optimal monitoring based on the user's past maintenance methods. The monitoring unit can also monitor the optimal watering timing from the user's past behavior history. Furthermore, the monitoring unit can analyze the user's past behavior history and provide monitoring that provides the optimal amount of light. In this way, the monitoring unit can provide the user with optimal monitoring by referring to the user's past behavior history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0118] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is relaxed, the monitoring unit can prioritize providing detailed monitoring. If the user is busy, the monitoring unit can prioritize providing the most important monitoring. Furthermore, if the user is stressed, the monitoring unit can prioritize providing visually easy-to-understand monitoring. In this way, by determining monitoring priorities based on the user's emotions, monitoring that is important to the user can be provided preferentially. 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.

[0119] The monitoring unit can provide region-specific monitoring by taking into account the user's geographical location information. For example, if the user lives in an arid region, the monitoring unit can provide monitoring to maintain high humidity. If the user lives in a high-temperature region, the monitoring unit can provide monitoring to lower the temperature. Furthermore, if the user lives in a rainy region, the monitoring unit can provide monitoring to reduce the frequency of watering. In this way, region-specific monitoring can be provided by taking into account the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0120] The monitoring unit can analyze a user's social media activity and provide relevant monitoring content. For example, if a user frequently posts about plants on social media, the monitoring unit can provide monitoring on the health of those plants. Furthermore, if a user posts about a specific flower on social media, the monitoring unit can provide monitoring related to that flower. Additionally, if a user posts about a seasonal event on social media, the monitoring unit can provide monitoring related to that event. This allows for the provision of monitoring content based on the user's interests by analyzing their social media activity. Some or all of the processing described above in the monitoring unit may be performed using AI, for example, or without AI.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The plant management system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and answer questions about the plant's condition and care methods in real time. For example, if the user asks, "When is the best time to water this flower?", the voice recognition unit will answer with the optimal watering time based on data from the acquisition unit. Also, if the user asks, "What is the appropriate amount of light for this plant?", the voice recognition unit can answer with the appropriate amount of light based on data from the analysis unit. Furthermore, if the user asks, "When is the next time to care for the plant?", the voice recognition unit can answer with the next care timing based on data from the instruction unit. In this way, by using the voice recognition unit, users can easily obtain information about the plant's condition and care methods through voice commands.

[0123] The plant management system can further suggest plant types based on the user's emotions using emotion estimation functionality. For example, if the user is stressed, the emotion estimation function can suggest relaxing plants (e.g., lavender). If the user is relaxed, the emotion estimation function can suggest beautiful ornamental flowers (e.g., roses). Furthermore, if the user is busy, the emotion estimation function can suggest easy-to-care-for plants (e.g., cacti). In this way, by using emotion estimation functionality, the system can suggest the most suitable plant type according to the user's emotions.

[0124] The plant management system can also suggest different care methods depending on the plant's growth stage. For example, in the early stages of growth, it can suggest basic watering and light management. In the middle stages of growth, it can suggest additional fertilizer and pruning methods. Furthermore, in the later stages of growth, it can suggest harvesting time and optimal storage methods. This allows users to properly manage plant growth by suggesting care methods tailored to each stage of development.

[0125] The plant management system can also suggest region-specific plants by considering the user's geographical location. For example, if the user lives in an arid region, it can suggest drought-tolerant plants (e.g., cacti). If the user lives in a hot region, it can suggest plants that can withstand high temperatures (e.g., olive trees). Furthermore, if the user lives in a rainy region, it can suggest plants suited to high humidity (e.g., ferns). In this way, by considering the user's geographical location, it can suggest region-specific plants.

[0126] The plant management system can further analyze users' social media activity and provide relevant plant information. For example, if a user posts about a specific flower on social media, it can provide care instructions and growth information for that flower. Similarly, if a user posts about seasonal events on social media, it can provide plant designs and care instructions related to those events. Furthermore, if a user frequently posts about plants on social media, it can provide information about the plant's health. In this way, by analyzing users' social media activity, the system can provide plant information tailored to their interests.

[0127] The plant management system can further suggest plant placement methods based on the user's emotions using emotion estimation functionality. For example, if the user is relaxed, the emotion estimation function can suggest a relaxing arrangement (e.g., lavender in the living room). If the user is busy, the emotion estimation function can suggest an easy-to-care-for arrangement (e.g., a cactus on the desk). Furthermore, if the user is stressed, the emotion estimation function can suggest an arrangement that has a stress-reducing effect (e.g., a houseplant in the bedroom). In this way, by using emotion estimation functionality, the system can suggest the optimal plant placement method according to the user's emotions.

[0128] The plant management system can automatically select different sensors depending on the plant species to obtain optimal data. For example, it prioritizes the use of soil moisture sensors for drought-tolerant plants like cacti, and prioritizes the use of humidity sensors for plants that prefer humidity, such as ferns. Furthermore, it can adjust light intensity sensors according to the type of flower to measure the optimal light level. This allows for more accurate data acquisition by selecting the most suitable sensors for each plant species.

[0129] The plant management system can further adjust the frequency of care based on the user's emotions using emotion estimation functionality. For example, if the user is relaxed, the care frequency can be set lower to reduce the user's burden. Conversely, if the user is busy, the care frequency can be set higher to provide quick care instructions. Furthermore, if the user is stressed, the care frequency can be adjusted to match the user's schedule. In this way, the emotion estimation function allows the care frequency to be adjusted based on the user's emotions.

[0130] The plant management system can further select optimal instructions by referring to the user's past behavior history. For example, it can provide optimal instructions based on the user's past care methods. It can also provide instructions on the optimal watering timing based on the user's past behavior history. Furthermore, it can analyze the user's past behavior history and provide instructions on the optimal amount of light. In this way, by referring to the user's past behavior history, it can provide the most suitable instructions to the user.

[0131] The plant management system can further adjust the way instructions are presented based on the user's emotions using emotion estimation functionality. For example, if the user is relaxed, it can provide detailed instructions. If the user is busy, it can provide concise instructions. Furthermore, if the user is stressed, it can provide visually easy-to-understand instructions. In this way, by using emotion estimation functionality, the way instructions are presented can be adjusted based on the user's emotions.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity. The acquisition unit acquires data using, for example, a temperature sensor, humidity sensor, soil moisture sensor, and light sensor. The acquisition unit can acquire data in real time or periodically. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using a generation AI. The analysis unit analyzes the data using methods such as calculating the average value of the data, trend analysis, and anomaly detection. Step 3: The instruction unit issues instructions to the user based on the data analyzed by the analysis unit. The instruction unit issues instructions to the user, for example, via email or app notification. The instruction unit provides the user with specific action instructions, warnings, and other relevant information.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the acquisition unit, analysis unit, instruction unit, display unit, suggestion unit, and monitor unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity using the sensors of the smart device 14. The analysis unit analyzes the data using AI generated by the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the user using the control unit 46A of the smart device 14. The display unit provides AR functionality using the display 40A of the smart device 14. The suggestion unit makes suggestions according to seasonal changes and events using the specific processing unit 290 of the data processing unit 12. The monitor unit monitors the condition of cut flowers and plants using the sensors of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the acquisition unit, analysis unit, instruction unit, display unit, suggestion unit, and monitor unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity using the sensors of the smart glasses 214. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the user via the control unit 46A of the smart glasses 214. The display unit provides AR functionality using the display of the smart glasses 214. The suggestion unit makes suggestions according to seasonal changes and events via the specific processing unit 290 of the data processing unit 12. The monitor unit monitors the condition of cut flowers and plants using the sensors of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the acquisition unit, analysis unit, instruction unit, display unit, suggestion unit, and monitor unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity using the sensors of the headset terminal 314. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the user using the control unit 46A of the headset terminal 314. The display unit provides AR functionality using the display 343 of the headset terminal 314. The suggestion unit makes suggestions according to seasonal changes and events using the specific processing unit 290 of the data processing unit 12. The monitor unit monitors the condition of cut flowers and plants using the sensors of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements described above, including the acquisition unit, analysis unit, instruction unit, display unit, suggestion unit, and monitor unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires data such as temperature, humidity, moisture content, and light intensity using the sensors of the robot 414. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The instruction unit issues instructions to the user via the control unit 46A of the robot 414. The display unit provides AR functionality using the display of the robot 414. The suggestion unit makes suggestions according to seasonal changes and events via the specific processing unit 290 of the data processing unit 12. The monitor unit monitors the condition of cut flowers and plants using the sensors of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Note 1) An acquisition unit that acquires data such as temperature, humidity, moisture content, and light intensity, An analysis unit analyzes the data acquired by the acquisition unit, The system includes an instruction unit that issues instructions to the user based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The indicator unit is, The system provides instructions on when and how to perform maintenance based on the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a display unit that provides AR functionality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is It allows you to display virtual flowers and plants in the real world and visually check their combinations. The system described in Appendix 3, characterized by the features described herein. (Note 5) We have a proposal department that makes suggestions tailored to seasonal changes and events. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose flower and plant designs and care methods suitable for seasonal changes and events. The system described in Appendix 5, characterized by the features described herein. (Note 7) It is equipped with a monitoring unit to monitor the condition of cut flowers and plants. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitor unit is The system monitors the condition of cut flowers and plants in a timely manner and notifies users in real time when care is needed. The system described in Appendix 7, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system automatically selects different sensors depending on the type of plant to obtain optimal data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The sensor sensitivity is automatically adjusted in response to changes in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, Obtain region-specific environmental data while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, Analyze users' social media activity and obtain relevant environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Optimize the analysis algorithm by referring to the plant's growth history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We compare data from different plants and select the optimal analysis method. 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, Prioritize analysis based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to relevant literature data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, It estimates the user's emotions and adjusts the way instructions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, Provides different instructions depending on the type of plant. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, The system selects the most appropriate instructions by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The indicator unit is, It estimates the user's emotions and determines the priority of instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The indicator unit is, Provides region-specific instructions that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The indicator unit is, Analyze users' social media activity and provide relevant guidance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned display unit is Provides different display methods depending on the plant's growth stage. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned display unit is Select the optimal display based on the user's past visual preferences. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned display unit is Provides the optimal display method considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned display unit is Analyze users' social media activity and provide relevant content. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned proposal section is, We offer different suggestions depending on the season. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned proposal section is, The system selects the most suitable suggestions by referring to the user's past preference history. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned proposal section is, Providing region-specific suggestions that take into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned proposal section is, Analyze users' social media activity and provide relevant suggestions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned monitor unit is It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned monitor unit is Provides different monitoring methods depending on the plant's growth stage. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned monitor unit is Select the optimal monitoring method by referring to the user's past behavior history. The system described in Appendix 5, characterized by the features described herein. (Note 42) The aforementioned monitor unit is It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned monitor unit is Provides region-specific monitoring that takes into account the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned monitor unit is Analyze users' social media activity and provide relevant monitoring information. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0206] 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. An acquisition unit that acquires data such as temperature, humidity, moisture content, and light intensity, An analysis unit analyzes the data acquired by the acquisition unit, The system includes an instruction unit that issues instructions to the user based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The indicator unit is, The system provides instructions on when and how to perform maintenance based on the user's schedule. The system according to feature 1.

3. Equipped with a display unit that provides AR functionality. The system according to feature 1.

4. The aforementioned display unit is It allows you to display virtual flowers and plants in the real world and visually check their combinations. The system according to claim 3.

5. We have a proposal department that makes suggestions tailored to seasonal changes and events. The system according to feature 1.

6. The aforementioned proposal section is, We propose flower and plant designs and care methods suitable for seasonal changes and events. The system according to claim 5, characterized in that it is the same as described in claim 5.

7. It is equipped with a monitoring unit to monitor the condition of cut flowers and plants. The system according to feature 1.

8. The aforementioned monitor unit is The system monitors the condition of cut flowers and plants in a timely manner and notifies users in real time when care is needed. The system according to feature 7.

9. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system according to feature 1.

10. The acquisition unit is, The system automatically selects different sensors depending on the type of plant to obtain optimal data. The system according to feature 1.

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