system

Abstract

The system according to this embodiment aims to monitor the growth and dryness of houseplants in real time and to provide instructions for appropriate care. [Solution] The system according to the embodiment comprises a sensor unit, a camera unit, an analysis unit, an instruction unit, and a reminder unit. The sensor unit detects the growth and dryness of the houseplant. The camera unit takes pictures of the houseplant. The analysis unit analyzes the data collected by the sensor unit and the camera unit. The instruction unit instructs appropriate care based on the analysis results obtained by the analysis unit. The reminder unit reminds the user of the care instructed by the instruction unit.

Description

Technical Field

[0006] , , , ,

[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 the chatbot's 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, the growth condition and dryness degree of foliage plants have not been sufficiently monitored in real time to give appropriate care instructions, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the growth condition and dryness degree of foliage plants in real time and give appropriate care instructions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sensor unit, a camera unit, an analysis unit, an instruction unit, and a reminder unit. The sensor unit detects the growth and dryness of the houseplant. The camera unit takes images of the houseplant. The analysis unit analyzes the data collected by the sensor unit and the camera unit. The instruction unit instructs appropriate care based on the analysis results obtained by the analysis unit. The reminder unit reminds the user of the care instructed by the instruction unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the growth and dryness of houseplants in real time and provide instructions for appropriate care. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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) An embodiment of the present invention provides a houseplant monitoring system that incorporates a sensor and a camera into a potted plant. This houseplant monitoring system monitors the growth and dryness of houseplants in real time. The houseplant monitoring system uses the sensor and camera to monitor the growth and dryness of the plant. Next, a generating AI analyzes this data and provides instructions for appropriate care. This includes, for example, the timing of watering, adjusting sunlight, and adding fertilizer. It also provides care advice tailored to the current season, weather, and even the upcoming weather forecast. Furthermore, it automatically sets reminders according to the user's schedule, minimizing maintenance time while maintaining the plant's health. As an additional function, the AI ​​provides knowledge about the plant and supports troubleshooting during the growing process. For example, the sensor measures the dryness of the plant, and the camera photographs the plant's growth. This allows for real-time monitoring of the plant's condition. Next, the generating AI analyzes the collected data. The generating AI analyzes data such as temperature, humidity, and mass to understand the plant's growth state and dryness. For example, the generating AI analyzes the dryness of plants and instructs when to water them. It also analyzes the plant's growth and instructs on adjusting sunlight and adding fertilizer. This provides appropriate care to maintain plant health. Furthermore, the generating AI provides care advice based on the current season, weather, and even the upcoming weather forecast. For instance, it analyzes the weather forecast and instructs against watering on rainy days. It also analyzes seasonal changes and suggests appropriate care methods for each season. This provides optimal care to maintain plant health. The generating AI also automatically sets reminders based on the user's schedule. For example, it analyzes the user's schedule and reminds them of care times. This allows users to maintain plant health while minimizing care time. Finally, the generating AI provides knowledge about plants and supports troubleshooting during the growing process. For example, it analyzes signs of plant diseases and pests and instructs on how to deal with them.This makes it easier for users to maintain the health of their plants. The plant monitoring system can then monitor the growth and dryness of the plants in real time and provide appropriate care instructions.

[0029] The ornamental plant monitoring system according to this embodiment comprises a sensor unit, a camera unit, an analysis unit, an instruction unit, and a reminder unit. The sensor unit monitors the growth and dryness of the plant. The sensor unit measures the state of the plant using, for example, a temperature sensor, a humidity sensor, and a mass sensor. For example, the temperature sensor can measure the temperature around the plant. The humidity sensor can measure the humidity around the plant. The mass sensor can measure the mass of the plant. The camera unit photographs the growth of the plant. For example, the camera unit can photograph the growth of the plant using a high-resolution camera. The camera unit can periodically photograph the growth of the plant and save it as image data. For example, the camera unit can photograph changes in the color and shape of the plant's leaves. The analysis unit analyzes the data collected by the sensor unit and the camera unit. For example, the analysis unit can analyze the data using generative AI. The analysis unit can analyze data such as temperature, humidity, and mass to understand the growth state and dryness of the plant. For example, the analysis unit can analyze temperature data to identify a temperature range suitable for plant growth. The system can analyze humidity data to identify the optimal humidity range for plant growth. It can also analyze mass data to understand changes in mass associated with plant growth. The instruction unit provides appropriate care instructions based on the analysis results obtained by the analysis unit. The instruction unit can, for example, use generating AI to provide care instructions. The instruction unit can provide instructions for watering timing, adjusting sunlight, and adding fertilizer. For example, the instruction unit can instruct watering timing, ensuring appropriate watering according to the plant's dryness. It can also instruct sunlight adjustment to ensure optimal sunlight for plant growth. Furthermore, it can instruct fertilizer addition to supply the nutrients necessary for plant growth. The reminder unit reminds the user of the care instructed by the instruction unit. The reminder unit can, for example, set reminders using AI. The reminder unit can set reminders according to the user's schedule. For example, the reminder unit can refer to the user's calendar information and set reminders at appropriate times.By referring to the alarm settings, the system can remind the user to take care of the plants. This allows the houseplant monitoring system according to the embodiment to monitor the growth and dryness of the plants in real time and provide instructions for appropriate care.

[0030] The sensor unit monitors the growth and dryness of plants. It measures the plant's condition using sensors such as temperature sensors, humidity sensors, and mass sensors. Specifically, the temperature sensor accurately measures the temperature around the plant to confirm that it is growing in an appropriate temperature environment. The humidity sensor measures the humidity around the plant to determine if it is too dry or too wet. The mass sensor measures the plant's mass to understand changes in mass as it grows. This allows for monitoring whether the plant is growing healthily. The sensor unit collects this data in real time and transmits it to a central database. Furthermore, the sensor unit has a function to issue alerts when abnormal data is detected, allowing for a quick response when the plant's condition changes rapidly. For example, if the temperature rises sharply or the humidity drops sharply, the sensor unit immediately issues an alert and notifies the user. This ensures that the plant's health is always kept optimal.

[0031] The camera unit photographs the growth of plants. For example, it can use a high-resolution camera to photograph the plant's growth. Specifically, the camera unit periodically photographs changes in the color and shape of the plant's leaves and saves the data as image data. This allows for a detailed record of the plant's growth process, which can then be reviewed. The camera unit periodically photographs the plant's growth and transmits the image data to the analysis unit. Furthermore, the camera unit also has a function to detect abnormal changes; for example, it can issue an alert if the leaves suddenly change color or wilt. This allows for constant monitoring of the plant's health and a quick response when an abnormality occurs. The camera unit can also capture images from different angles to understand the plant's overall appearance. This allows for more detailed monitoring of the plant's growth.

[0032] The analysis unit analyzes data collected by the sensor unit and camera unit. The analysis unit can analyze data using, for example, generative AI. Specifically, it analyzes data such as temperature, humidity, and mass to understand the growth state and dryness of plants. Based on this data, the generative AI identifies environmental conditions suitable for plant growth. For example, it can analyze temperature data to identify a temperature range suitable for plant growth. It can analyze humidity data to identify a humidity range suitable for plant growth. It can analyze mass data to understand changes in mass associated with plant growth. Furthermore, the generative AI can analyze image data from the camera unit to detect changes in the color and shape of plant leaves. This allows for a detailed understanding of the plant's health and provides information for appropriate care. The analysis unit can also perform trend analysis based on past data and predict future growth. This allows for long-term monitoring of plant growth and optimal care.

[0033] The instruction unit provides appropriate care instructions based on the analysis results obtained by the analysis unit. The instruction unit can, for example, use a generating AI to provide care instructions. Specifically, it can instruct on care such as the timing of watering, adjusting sunlight, and adding fertilizer. Based on the analysis results, the generating AI proposes optimal care according to the degree of dryness and growth stage of the plant. For example, it can instruct on the timing of watering, allowing for appropriate watering according to the degree of dryness of the plant. It can instruct on adjusting sunlight, ensuring that the plant receives sunlight suitable for growth. It can instruct on adding fertilizer, supplying the nutrients necessary for plant growth. The instruction unit can also provide specific care methods to the user. For example, it can provide detailed instructions on the amount and frequency of watering, how to adjust sunlight, and the type and amount of fertilizer. This allows the user to perform specific care to maintain the optimal health of the plant.

[0034] The reminder unit reminds the user of the care instructed by the instruction unit. The reminder unit can set reminders using, for example, AI. Specifically, it sets reminders according to the user's schedule and notifies them to perform care at the appropriate time. The reminder unit can refer to the user's calendar information and set reminders at the appropriate time. For example, it can remind the user when it is time to water, notifying them so that they do not forget. It can also remind users to adjust sunlight and add fertilizer in a similar way. The reminder unit can also refer to alarm settings and remind the user so that they do not forget to take care of the plants. This allows the user to properly care for the plants and maintain their health at an optimal level. Furthermore, the reminder unit can adjust the accuracy and timing of reminders based on user feedback. This allows for flexible reminder settings tailored to the user's lifestyle, making plant care more effective.

[0035] The analysis unit can analyze data such as temperature, humidity, and mass to understand the growth state and dryness of plants. For example, the analysis unit can analyze temperature data to identify a temperature range suitable for plant growth. The analysis unit can analyze humidity data to identify a humidity range suitable for plant growth. The analysis unit can analyze mass data to understand the changes in mass associated with plant growth. This allows for an accurate understanding of the plant's growth state and dryness. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input data such as temperature, humidity, and mass into a generation AI, which can then analyze the data to understand the plant's growth state and dryness.

[0036] The instruction unit can provide instructions for care such as watering timing, adjusting sunlight, and adding fertilizer. For example, the instruction unit can instruct the timing of watering, allowing for appropriate watering according to the plant's dryness. The instruction unit can instruct the adjustment of sunlight, ensuring sufficient sunlight for plant growth. The instruction unit can instruct the addition of fertilizer, supplying nutrients necessary for plant growth. This provides appropriate care to maintain the plant's health. Some or all of the above processes in the instruction unit may be performed using or without a generating AI. For example, the instruction unit can input data on the plant's dryness into the generating AI, which can then instruct the timing of watering.

[0037] The reminder unit can set reminders according to the user's schedule. For example, the reminder unit can refer to the user's calendar information and set reminders at the appropriate time. The reminder unit can refer to alarm settings and remind the user to avoid forgetting maintenance. This allows the system to provide reminders at the appropriate time according to the user's schedule. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's calendar information into a generation AI, which can then set reminders at the appropriate time.

[0038] The analysis unit can provide care advice based on weather forecasts and seasonal changes. For example, the analysis unit can analyze the weather forecast and instruct the user to refrain from watering on rainy days. The analysis unit can also analyze seasonal changes and suggest care methods appropriate for each season. This allows the system to provide optimal care methods based on weather forecasts and seasonal changes. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may not be performed using a generating AI. For example, the analysis unit can input weather forecast data into a generating AI, which can then analyze the weather forecast and provide care advice.

[0039] The instruction unit can analyze signs of plant diseases and pests and provide instructions on how to deal with them. For example, the instruction unit can analyze discoloration of plant leaves or insect damage and provide instructions on appropriate countermeasures. The instruction unit can detect signs of plant diseases and pests early and provide appropriate countermeasures. Some or all of the above processing in the instruction unit may be performed using or without a generating AI. For example, the instruction unit can input plant leaf discoloration data into a generating AI, which can then analyze the signs of disease and provide instructions on how to deal with them.

[0040] The sensor unit can automatically adjust its sensitivity according to the plant's growth stage. For example, when the plant is young, the sensor unit can set its sensitivity high to detect subtle changes. When the plant is mature, the sensor unit can set its sensitivity to medium to detect major changes. When the plant is mature, the sensor unit can set its sensitivity low to detect only large changes. By adjusting the sensor sensitivity according to the plant's growth stage, appropriate data can be obtained. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input plant growth stage data into a generating AI, which can then automatically adjust the sensor sensitivity.

[0041] The sensor unit may have a function to immediately notify the user if it detects an abnormality. For example, the sensor unit can immediately notify the user if it detects an abnormal degree of dryness. The sensor unit can immediately notify the user if it detects an abnormal temperature change. The sensor unit can immediately notify the user if it detects an abnormal humidity change. This enables a quick response by immediately notifying the user when an abnormality is detected. Some or all of the above processing in the sensor unit may be performed using AI or not using AI. For example, the sensor unit can input abnormality data into a generating AI, and the generating AI can detect the abnormality and notify the user.

[0042] The sensor unit can add soil nutrient levels to the acquired data. For example, the sensor unit can measure soil nitrogen levels and acquire data. The sensor unit can measure soil phosphorus levels and acquire data. The sensor unit can measure soil potassium levels and acquire data. By adding soil nutrient levels, the health of plants can be understood more accurately. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input soil nutrient data into a generating AI, which can analyze the data to understand the health of plants.

[0043] The sensor unit can add ambient sound environment data to the acquired data and analyze factors that affect plant growth. For example, the sensor unit can measure ambient noise levels and acquire data. The sensor unit can measure the type of music in the surroundings and acquire data. The sensor unit can measure the frequency of human voices in the surroundings and acquire data. By adding ambient sound environment data, factors that affect plant growth can be analyzed more accurately. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input ambient sound environment data into a generating AI, which can then analyze the data to analyze factors that affect plant growth.

[0044] The camera unit can automatically adjust its resolution according to the plant's growth stage. For example, when the plant is young, the camera unit can set the resolution high to capture subtle changes. When the plant is mature, the camera unit can set the resolution medium to capture major changes. When the plant is mature, the camera unit can set the resolution low to capture only major changes. By adjusting the camera resolution according to the plant's growth stage, appropriate data can be obtained. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input plant growth stage data into a generating AI, which can then automatically adjust the camera resolution.

[0045] The camera unit can be equipped with a function to immediately notify the user if it detects an abnormality. For example, if the camera unit detects abnormal leaf discoloration, it can immediately notify the user. If the camera unit detects abnormal leaf shape changes, it can immediately notify the user. If the camera unit detects abnormal growth rate, it can immediately notify the user. This enables a quick response by immediately notifying the user when an abnormality is detected. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input abnormality data into a generating AI, and the generating AI can detect the abnormality and notify the user.

[0046] The camera unit can be equipped with a function to analyze changes in plant color in the data it acquires. For example, the camera unit can analyze changes in the color of plant leaves and acquire data. The camera unit can analyze changes in the color of plant flowers and acquire data. The camera unit can analyze changes in the color of plant stems and acquire data. This allows for a more accurate understanding of the plant's health by analyzing its color changes. Some or all of the above-described processes in the camera unit may be performed using AI or not. For example, the camera unit can input plant color change data into a generating AI, which can then analyze the data to understand the plant's health.

[0047] The camera unit can be equipped with a function to analyze changes in the shape of plant leaves in the acquired data. For example, the camera unit can analyze changes in the shape of plant leaves and acquire data. The camera unit can analyze changes in the shape of plant flowers and acquire data. The camera unit can analyze changes in the shape of plant stems and acquire data. By analyzing changes in the shape of plant leaves, the health status of the plant can be understood more accurately. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input plant leaf shape change data into a generating AI, and the generating AI can analyze the data to understand the health status of the plant.

[0048] The analysis unit can improve the accuracy of the analysis by referring to the plant's growth history data during the analysis. For example, the analysis unit can refer to the plant's past growth data to analyze its current growth state. The analysis unit can refer to the plant's past dryness data to analyze its current dryness state. The analysis unit can refer to the plant's past temperature data to analyze its current temperature state. In this way, the accuracy of the analysis can be improved by referring to the plant's growth history data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the plant's growth history data into a generation AI, and the generation AI can analyze the data to improve the accuracy of the analysis.

[0049] The analysis unit can apply different analysis algorithms to each type of plant during analysis. For example, the analysis unit can apply an analysis algorithm appropriate to the type of ornamental plant to analyze its growth state. The analysis unit can apply an analysis algorithm appropriate to the type of succulent plant to analyze its dryness state. The analysis unit can apply an analysis algorithm appropriate to the type of flowering plant to analyze its temperature state. This allows for improved analysis accuracy by applying the optimal analysis algorithm for each type of plant. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input plant type data into a generation AI, which can then analyze the data and apply the optimal analysis algorithm.

[0050] The analysis unit can improve the accuracy of the analysis by referring to the plant's surrounding environment data during the analysis. For example, the analysis unit can refer to the temperature data surrounding the plant to analyze its growth state. The analysis unit can refer to the humidity data surrounding the plant to analyze its dry state. The analysis unit can refer to the light intensity data surrounding the plant to analyze its growth state. In this way, the accuracy of the analysis can be improved by referring to the plant's surrounding environment data. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the plant's surrounding environment data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0051] The analysis unit can improve the accuracy of the analysis by referring to plant growth prediction data during the analysis. For example, the analysis unit can refer to plant growth prediction data to analyze the current growth state. The analysis unit can refer to plant drought prediction data to analyze the current drought state. The analysis unit can refer to plant temperature prediction data to analyze the current temperature state. In this way, the accuracy of the analysis can be improved by referring to plant growth prediction data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input plant growth prediction data into a generation AI, and the generation AI can analyze the data to improve the accuracy of the analysis.

[0052] The instruction unit can adjust the level of detail in care instructions according to the plant's growth stage at the time of instruction. For example, the instruction unit can provide detailed care instructions when the plant is young. The instruction unit can provide basic care instructions when the plant is mature. The instruction unit can provide concise care instructions when the plant is mature. In this way, appropriate care can be provided by adjusting the level of detail in care instructions according to the plant's growth stage. Some or all of the above processing in the instruction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the instruction unit can input plant growth stage data into a generative AI, and the generative AI can analyze the data and adjust the level of detail in care instructions.

[0053] The instruction unit can apply different care instruction algorithms to each type of plant when instructions are given. For example, the instruction unit can apply a care instruction algorithm according to the type of houseplant to instruct appropriate care. The instruction unit can apply a care instruction algorithm according to the type of succulent plant to instruct appropriate care. The instruction unit can apply a care instruction algorithm according to the type of flowering plant to instruct appropriate care. In this way, appropriate care can be provided by applying the optimal care instruction algorithm according to the type of plant. Some or all of the above processing in the instruction unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the instruction unit can input plant type data into a generative AI, and the generative AI can analyze the data and apply the optimal care instruction algorithm.

[0054] The instruction unit can improve the accuracy of care instructions by referring to the plant's surrounding environment data at the time of instruction. For example, the instruction unit can refer to the plant's surrounding temperature data to provide appropriate care instructions. The instruction unit can refer to the plant's surrounding humidity data to provide appropriate care instructions. The instruction unit can refer to the plant's surrounding light intensity data to provide appropriate care instructions. In this way, the accuracy of care instructions can be improved by referring to the plant's surrounding environment data. Some or all of the above processing in the instruction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the instruction unit can input the plant's surrounding environment data into a generation AI, and the generation AI can analyze the data to improve the accuracy of care instructions.

[0055] The instruction unit can improve the accuracy of care instructions by referring to plant growth prediction data at the time of instruction. For example, the instruction unit can refer to plant growth prediction data and provide appropriate care instructions. The instruction unit can refer to plant drying prediction data and provide appropriate care instructions. The instruction unit can refer to plant temperature prediction data and provide appropriate care instructions. In this way, the accuracy of care instructions can be improved by referring to plant growth prediction data. Some or all of the above processing in the instruction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the instruction unit can input plant growth prediction data into a generation AI, and the generation AI can analyze the data to improve the accuracy of care instructions.

[0056] The reminder unit can provide the optimal reminder by referring to the user's past maintenance history when setting a reminder. For example, the reminder unit can refer to the user's past maintenance history and set the optimal reminder. The reminder unit can refer to the user's past maintenance frequency and set the optimal reminder. The reminder unit can refer to the user's past maintenance content and set the optimal reminder. In this way, the system can provide the optimal reminder by referring to the user's past maintenance history. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's past maintenance history data into a generation AI, and the generation AI can analyze the data and set the optimal reminder.

[0057] The reminder unit can adjust the frequency of reminders according to the user's schedule when setting reminders. For example, the reminder unit can refer to the user's schedule and set the optimal reminder frequency. The reminder unit can set reminders to avoid the user's busy times. The reminder unit can set reminders to coincide with the user's free time. By adjusting the frequency of reminders according to the user's schedule, the system can provide the user with appropriate information. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's schedule data into a generation AI, which can then analyze the data and adjust the reminder frequency.

[0058] The reminder unit can provide the optimal reminder based on the user's device information when a reminder is set. For example, the reminder unit can provide the optimal reminder if the user is using a smartphone. The reminder unit can provide the optimal reminder if the user is using a tablet. The reminder unit can provide the optimal reminder if the user is using a smartwatch. In this way, by providing the optimal reminder based on the user's device information, the system can provide the user with appropriate information. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's device information into a generation AI, and the generation AI can analyze the data to provide the optimal reminder.

[0059] The reminder unit can analyze the user's lifestyle patterns when setting reminders and provide optimal reminders. For example, the reminder unit can analyze the user's lifestyle patterns and set optimal reminders. The reminder unit can set reminders considering the user's wake-up time and bedtime. The reminder unit can set reminders considering the user's meal times and exercise times. By analyzing the user's lifestyle patterns and providing optimal reminders, the system can provide the user with appropriate information. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's lifestyle pattern data into a generation AI, which can then analyze the data and set optimal reminders.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The houseplant monitoring system can also be equipped with a plant growth prediction function. This function can predict the future growth of plants based on past growth data. For example, it can predict the growth rate of plants and allow for the planning of appropriate care in advance. Furthermore, the growth prediction function can identify factors affecting plant growth and provide advice for creating the optimal growing environment. Additionally, it can predict risks associated with plant growth and allow for early intervention. This helps maintain plant health and promotes optimal growth.

[0062] The houseplant monitoring system can also be equipped with a plant health check function. This function comprehensively evaluates the plant's condition and diagnoses its health. For example, it can comprehensively evaluate changes in leaf color and shape, growth rate, and degree of dryness to diagnose the plant's health. Furthermore, the health check function can provide care advice tailored to the plant's health condition. Additionally, the health check function can regularly monitor the plant's health and detect abnormalities early. This helps maintain plant health and provide optimal care.

[0063] The houseplant monitoring system can also be equipped with a function to measure the stress level of the plants. This stress level measurement function allows for the measurement of the stress the plants experience and the implementation of appropriate countermeasures. For example, it can measure the stress caused by sudden changes in temperature and humidity, or insufficient light, and implement appropriate measures. Furthermore, the stress level measurement function can provide care advice tailored to the plant's stress level. Additionally, the stress level measurement function allows for regular monitoring of the plant's stress level, enabling early detection of abnormalities. This helps maintain the plant's health and provides optimal care.

[0064] The houseplant monitoring system can also be equipped with functions to optimize the plant's growth environment. This growth environment optimization function can provide advice to ensure the optimal environment for plant growth. For example, it can optimize environmental factors such as temperature, humidity, and light intensity to promote plant growth. Furthermore, it can identify factors affecting plant growth and provide advice to ensure the optimal environment. Additionally, the growth environment optimization function can regularly monitor the plant's growth environment and detect abnormalities early. This helps maintain plant health and promotes optimal growth.

[0065] The houseplant monitoring system can also be equipped with a function to record the plant's growth history. This growth history recording function allows for detailed recording of the plant's growth process, which can be used for future care. For example, it can record details such as the plant's growth rate, dryness, and changes in temperature and humidity, which can be used for future care. Furthermore, the growth history recording function can provide appropriate care advice based on the plant's growth history. Additionally, the growth history recording function allows for regular monitoring of the plant's growth history, enabling early detection of abnormalities. This helps maintain the plant's health and provides optimal care.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The sensor unit monitors the growth and dryness of the plant. The sensor unit measures the plant's condition using temperature sensors, humidity sensors, mass sensors, etc. For example, the temperature sensor measures the temperature around the plant, the humidity sensor measures the humidity around the plant, and the mass sensor measures the plant's mass. Step 2: The camera unit photographs the plant's growth. The camera unit uses a high-resolution camera to periodically photograph the plant's growth and save the images as data. For example, it photographs changes in the color and shape of the plant's leaves. Step 3: The analysis unit analyzes the data collected by the sensor unit and camera unit. The analysis unit uses a generation AI to analyze the data, and analyzes data such as temperature, humidity, and mass to understand the growth state and dryness of the plants. For example, it analyzes temperature data to identify a temperature range suitable for plant growth, analyzes humidity data to identify a humidity range suitable for plant growth, and analyzes mass data to understand the changes in mass associated with plant growth. Step 4: The instruction unit provides instructions for appropriate care based on the analysis results obtained by the analysis unit. The instruction unit uses generated AI to provide instructions for care such as watering timing, adjusting sunlight, and adding fertilizer. For example, it may instruct on watering timing to water appropriately according to the degree of dryness of the plants, instruct on adjusting sunlight to ensure sufficient sunlight for plant growth, and instruct on adding fertilizer to supply the nutrients necessary for plant growth. Step 5: The reminder unit reminds the user of the care instructed by the instruction unit. The reminder unit uses AI to set reminders and sets reminders according to the user's schedule. For example, it refers to the user's calendar information, sets reminders at the appropriate time, and refers to alarm settings to remind the user not to forget the care.

[0068] (Example of form 2) An embodiment of the present invention provides a houseplant monitoring system that incorporates a sensor and a camera into a potted plant. This houseplant monitoring system monitors the growth and dryness of houseplants in real time. The houseplant monitoring system uses the sensor and camera to monitor the growth and dryness of the plant. Next, a generating AI analyzes this data and provides instructions for appropriate care. This includes, for example, the timing of watering, adjusting sunlight, and adding fertilizer. It also provides care advice tailored to the current season, weather, and even the upcoming weather forecast. Furthermore, it automatically sets reminders according to the user's schedule, minimizing maintenance time while maintaining the plant's health. As an additional function, the AI ​​provides knowledge about the plant and supports troubleshooting during the growing process. For example, the sensor measures the dryness of the plant, and the camera photographs the plant's growth. This allows for real-time monitoring of the plant's condition. Next, the generating AI analyzes the collected data. The generating AI analyzes data such as temperature, humidity, and mass to understand the plant's growth state and dryness. For example, the generating AI analyzes the dryness of plants and instructs when to water them. It also analyzes the plant's growth and instructs on adjusting sunlight and adding fertilizer. This provides appropriate care to maintain plant health. Furthermore, the generating AI provides care advice based on the current season, weather, and even the upcoming weather forecast. For instance, it analyzes the weather forecast and instructs against watering on rainy days. It also analyzes seasonal changes and suggests appropriate care methods for each season. This provides optimal care to maintain plant health. The generating AI also automatically sets reminders based on the user's schedule. For example, it analyzes the user's schedule and reminds them of care times. This allows users to maintain plant health while minimizing care time. Finally, the generating AI provides knowledge about plants and supports troubleshooting during the growing process. For example, it analyzes signs of plant diseases and pests and instructs on how to deal with them.This makes it easier for users to maintain the health of their plants. The plant monitoring system can then monitor the growth and dryness of the plants in real time and provide appropriate care instructions.

[0069] The ornamental plant monitoring system according to this embodiment comprises a sensor unit, a camera unit, an analysis unit, an instruction unit, and a reminder unit. The sensor unit monitors the growth and dryness of the plant. The sensor unit measures the state of the plant using, for example, a temperature sensor, a humidity sensor, and a mass sensor. For example, the temperature sensor can measure the temperature around the plant. The humidity sensor can measure the humidity around the plant. The mass sensor can measure the mass of the plant. The camera unit photographs the growth of the plant. For example, the camera unit can photograph the growth of the plant using a high-resolution camera. The camera unit can periodically photograph the growth of the plant and save it as image data. For example, the camera unit can photograph changes in the color and shape of the plant's leaves. The analysis unit analyzes the data collected by the sensor unit and the camera unit. For example, the analysis unit can analyze the data using generative AI. The analysis unit can analyze data such as temperature, humidity, and mass to understand the growth state and dryness of the plant. For example, the analysis unit can analyze temperature data to identify a temperature range suitable for plant growth. The system can analyze humidity data to identify the optimal humidity range for plant growth. It can also analyze mass data to understand changes in mass associated with plant growth. The instruction unit provides appropriate care instructions based on the analysis results obtained by the analysis unit. The instruction unit can, for example, use generating AI to provide care instructions. The instruction unit can provide instructions for watering timing, adjusting sunlight, and adding fertilizer. For example, the instruction unit can instruct watering timing, ensuring appropriate watering according to the plant's dryness. It can also instruct sunlight adjustment to ensure optimal sunlight for plant growth. Furthermore, it can instruct fertilizer addition to supply the nutrients necessary for plant growth. The reminder unit reminds the user of the care instructed by the instruction unit. The reminder unit can, for example, set reminders using AI. The reminder unit can set reminders according to the user's schedule. For example, the reminder unit can refer to the user's calendar information and set reminders at appropriate times.By referring to the alarm settings, the system can remind the user to take care of the plants. This allows the houseplant monitoring system according to the embodiment to monitor the growth and dryness of the plants in real time and provide instructions for appropriate care.

[0070] The sensor unit monitors the growth and dryness of plants. It measures the plant's condition using sensors such as temperature sensors, humidity sensors, and mass sensors. Specifically, the temperature sensor accurately measures the temperature around the plant to confirm that it is growing in an appropriate temperature environment. The humidity sensor measures the humidity around the plant to determine if it is too dry or too wet. The mass sensor measures the plant's mass to understand changes in mass as it grows. This allows for monitoring whether the plant is growing healthily. The sensor unit collects this data in real time and transmits it to a central database. Furthermore, the sensor unit has a function to issue alerts when abnormal data is detected, allowing for a quick response when the plant's condition changes rapidly. For example, if the temperature rises sharply or the humidity drops sharply, the sensor unit immediately issues an alert and notifies the user. This ensures that the plant's health is always kept optimal.

[0071] The camera unit photographs the growth of plants. For example, it can use a high-resolution camera to photograph the plant's growth. Specifically, the camera unit periodically photographs changes in the color and shape of the plant's leaves and saves the data as image data. This allows for a detailed record of the plant's growth process, which can then be reviewed. The camera unit periodically photographs the plant's growth and transmits the image data to the analysis unit. Furthermore, the camera unit also has a function to detect abnormal changes; for example, it can issue an alert if the leaves suddenly change color or wilt. This allows for constant monitoring of the plant's health and a quick response when an abnormality occurs. The camera unit can also capture images from different angles to understand the plant's overall appearance. This allows for more detailed monitoring of the plant's growth.

[0072] The analysis unit analyzes data collected by the sensor unit and camera unit. The analysis unit can analyze data using, for example, generative AI. Specifically, it analyzes data such as temperature, humidity, and mass to understand the growth state and dryness of plants. Based on this data, the generative AI identifies environmental conditions suitable for plant growth. For example, it can analyze temperature data to identify a temperature range suitable for plant growth. It can analyze humidity data to identify a humidity range suitable for plant growth. It can analyze mass data to understand changes in mass associated with plant growth. Furthermore, the generative AI can analyze image data from the camera unit to detect changes in the color and shape of plant leaves. This allows for a detailed understanding of the plant's health and provides information for appropriate care. The analysis unit can also perform trend analysis based on past data and predict future growth. This allows for long-term monitoring of plant growth and optimal care.

[0073] The instruction unit provides appropriate care instructions based on the analysis results obtained by the analysis unit. The instruction unit can, for example, use a generating AI to provide care instructions. Specifically, it can instruct on care such as the timing of watering, adjusting sunlight, and adding fertilizer. Based on the analysis results, the generating AI proposes optimal care according to the degree of dryness and growth stage of the plant. For example, it can instruct on the timing of watering, allowing for appropriate watering according to the degree of dryness of the plant. It can instruct on adjusting sunlight, ensuring that the plant receives sunlight suitable for growth. It can instruct on adding fertilizer, supplying the nutrients necessary for plant growth. The instruction unit can also provide specific care methods to the user. For example, it can provide detailed instructions on the amount and frequency of watering, how to adjust sunlight, and the type and amount of fertilizer. This allows the user to perform specific care to maintain the optimal health of the plant.

[0074] The reminder unit reminds the user of the care instructed by the instruction unit. The reminder unit can set reminders using, for example, AI. Specifically, it sets reminders according to the user's schedule and notifies them to perform care at the appropriate time. The reminder unit can refer to the user's calendar information and set reminders at the appropriate time. For example, it can remind the user when it is time to water, notifying them so that they do not forget. It can also remind users to adjust sunlight and add fertilizer in a similar way. The reminder unit can also refer to alarm settings and remind the user so that they do not forget to take care of the plants. This allows the user to properly care for the plants and maintain their health at an optimal level. Furthermore, the reminder unit can adjust the accuracy and timing of reminders based on user feedback. This allows for flexible reminder settings tailored to the user's lifestyle, making plant care more effective.

[0075] The analysis unit can analyze data such as temperature, humidity, and mass to understand the growth state and dryness of plants. For example, the analysis unit can analyze temperature data to identify a temperature range suitable for plant growth. The analysis unit can analyze humidity data to identify a humidity range suitable for plant growth. The analysis unit can analyze mass data to understand the changes in mass associated with plant growth. This allows for an accurate understanding of the plant's growth state and dryness. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input data such as temperature, humidity, and mass into a generation AI, which can then analyze the data to understand the plant's growth state and dryness.

[0076] The instruction unit can provide instructions for care such as watering timing, adjusting sunlight, and adding fertilizer. For example, the instruction unit can instruct the timing of watering, allowing for appropriate watering according to the plant's dryness. The instruction unit can instruct the adjustment of sunlight, ensuring sufficient sunlight for plant growth. The instruction unit can instruct the addition of fertilizer, supplying nutrients necessary for plant growth. This provides appropriate care to maintain the plant's health. Some or all of the above processes in the instruction unit may be performed using or without a generating AI. For example, the instruction unit can input data on the plant's dryness into the generating AI, which can then instruct the timing of watering.

[0077] The reminder unit can set reminders according to the user's schedule. For example, the reminder unit can refer to the user's calendar information and set reminders at the appropriate time. The reminder unit can refer to alarm settings and remind the user to avoid forgetting maintenance. This allows the system to provide reminders at the appropriate time according to the user's schedule. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's calendar information into a generation AI, which can then set reminders at the appropriate time.

[0078] The analysis unit can provide care advice based on weather forecasts and seasonal changes. For example, the analysis unit can analyze the weather forecast and instruct the user to refrain from watering on rainy days. The analysis unit can also analyze seasonal changes and suggest care methods appropriate for each season. This allows the system to provide optimal care methods based on weather forecasts and seasonal changes. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may not be performed using a generating AI. For example, the analysis unit can input weather forecast data into a generating AI, which can then analyze the weather forecast and provide care advice.

[0079] The instruction unit can analyze signs of plant diseases and pests and provide instructions on how to deal with them. For example, the instruction unit can analyze discoloration of plant leaves or insect damage and provide instructions on appropriate countermeasures. The instruction unit can detect signs of plant diseases and pests early and provide appropriate countermeasures. Some or all of the above processing in the instruction unit may be performed using or without a generating AI. For example, the instruction unit can input plant leaf discoloration data into a generating AI, which can then analyze the signs of disease and provide instructions on how to deal with them.

[0080] The sensor unit can estimate the user's emotions and adjust the frequency of sensor data acquisition based on the estimated user emotions. For example, if the user is stressed, the sensor unit can set the sensor data acquisition frequency low and reduce notifications. If the user is relaxed, the sensor unit can set the sensor data acquisition frequency high and provide detailed information. If the user is busy, the sensor unit can set the sensor data acquisition frequency to a moderate level and provide only the necessary information. In this way, by adjusting the sensor data acquisition frequency according to the user's emotions, appropriate information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not using AI. For example, the sensor unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the data acquisition frequency.

[0081] The sensor unit can automatically adjust its sensitivity according to the plant's growth stage. For example, when the plant is young, the sensor unit can set its sensitivity high to detect subtle changes. When the plant is mature, the sensor unit can set its sensitivity to medium to detect major changes. When the plant is mature, the sensor unit can set its sensitivity low to detect only large changes. By adjusting the sensor sensitivity according to the plant's growth stage, appropriate data can be obtained. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input plant growth stage data into a generating AI, which can then automatically adjust the sensor sensitivity.

[0082] The sensor unit may have a function to immediately notify the user if it detects an abnormality. For example, the sensor unit can immediately notify the user if it detects an abnormal degree of dryness. The sensor unit can immediately notify the user if it detects an abnormal temperature change. The sensor unit can immediately notify the user if it detects an abnormal humidity change. This enables a quick response by immediately notifying the user when an abnormality is detected. Some or all of the above processing in the sensor unit may be performed using AI or not using AI. For example, the sensor unit can input abnormality data into a generating AI, and the generating AI can detect the abnormality and notify the user.

[0083] The sensor unit can estimate the user's emotions and adjust the timing of sensor data acquisition based on the estimated user emotions. For example, if the user is stressed, the sensor unit can reduce the data acquisition timing and provide fewer notifications. If the user is relaxed, the sensor unit can increase the data acquisition timing and provide more detailed information. If the user is busy, the sensor unit can set the data acquisition timing to a moderate level and provide only the necessary information. In this way, by adjusting the sensor data acquisition timing according to the user's emotions, appropriate information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not using AI. For example, the sensor unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the data acquisition timing.

[0084] The sensor unit can add soil nutrient levels to the acquired data. For example, the sensor unit can measure soil nitrogen levels and acquire data. The sensor unit can measure soil phosphorus levels and acquire data. The sensor unit can measure soil potassium levels and acquire data. By adding soil nutrient levels, the health of plants can be understood more accurately. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input soil nutrient data into a generating AI, which can analyze the data to understand the health of plants.

[0085] The sensor unit can add ambient sound environment data to the acquired data and analyze factors that affect plant growth. For example, the sensor unit can measure ambient noise levels and acquire data. The sensor unit can measure the type of music in the surroundings and acquire data. The sensor unit can measure the frequency of human voices in the surroundings and acquire data. By adding ambient sound environment data, factors that affect plant growth can be analyzed more accurately. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input ambient sound environment data into a generating AI, which can then analyze the data to analyze factors that affect plant growth.

[0086] The camera unit can estimate the user's emotions and adjust the camera's shooting frequency based on the estimated emotions. For example, if the user is stressed, the camera unit can set the camera's shooting frequency low and reduce notifications. If the user is relaxed, the camera unit can set the camera's shooting frequency high and provide detailed information. If the user is busy, the camera unit can set the camera's shooting frequency to a moderate level and provide only the necessary information. In this way, by adjusting the camera's shooting frequency according to the user's emotions, the camera unit can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the shooting frequency.

[0087] The camera unit can automatically adjust its resolution according to the plant's growth stage. For example, when the plant is young, the camera unit can set the resolution high to capture subtle changes. When the plant is mature, the camera unit can set the resolution medium to capture major changes. When the plant is mature, the camera unit can set the resolution low to capture only major changes. By adjusting the camera resolution according to the plant's growth stage, appropriate data can be obtained. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input plant growth stage data into a generating AI, which can then automatically adjust the camera resolution.

[0088] The camera unit can be equipped with a function to immediately notify the user if it detects an abnormality. For example, if the camera unit detects abnormal leaf discoloration, it can immediately notify the user. If the camera unit detects abnormal leaf shape changes, it can immediately notify the user. If the camera unit detects abnormal growth rate, it can immediately notify the user. This enables a quick response by immediately notifying the user when an abnormality is detected. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input abnormality data into a generating AI, and the generating AI can detect the abnormality and notify the user.

[0089] The camera unit can estimate the user's emotions and adjust the camera's shooting timing based on the estimated emotions. For example, if the user is stressed, the camera unit can reduce the shooting timing and provide fewer notifications. If the user is relaxed, the camera unit can increase the shooting timing and provide more detailed information. If the user is busy, the camera unit can set the shooting timing to a moderate level and provide only the necessary information. In this way, by adjusting the camera's shooting timing according to the user's emotions, the camera unit can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the shooting timing.

[0090] The camera unit can be equipped with a function to analyze changes in plant color in the data it acquires. For example, the camera unit can analyze changes in the color of plant leaves and acquire data. The camera unit can analyze changes in the color of plant flowers and acquire data. The camera unit can analyze changes in the color of plant stems and acquire data. This allows for a more accurate understanding of the plant's health by analyzing its color changes. Some or all of the above-described processes in the camera unit may be performed using AI or not. For example, the camera unit can input plant color change data into a generating AI, which can then analyze the data to understand the plant's health.

[0091] The camera unit can be equipped with a function to analyze changes in the shape of plant leaves in the acquired data. For example, the camera unit can analyze changes in the shape of plant leaves and acquire data. The camera unit can analyze changes in the shape of plant flowers and acquire data. The camera unit can analyze changes in the shape of plant stems and acquire data. By analyzing changes in the shape of plant leaves, the health status of the plant can be understood more accurately. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input plant leaf shape change data into a generating AI, and the generating AI can analyze the data to understand the health status of the plant.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can estimate the emotions and adjust the display method of the analysis results.

[0093] The analysis unit can improve the accuracy of the analysis by referring to the plant's growth history data during the analysis. For example, the analysis unit can refer to the plant's past growth data to analyze its current growth state. The analysis unit can refer to the plant's past dryness data to analyze its current dryness state. The analysis unit can refer to the plant's past temperature data to analyze its current temperature state. In this way, the accuracy of the analysis can be improved by referring to the plant's growth history data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the plant's growth history data into a generation AI, and the generation AI can analyze the data to improve the accuracy of the analysis.

[0094] The analysis unit can apply different analysis algorithms to each type of plant during analysis. For example, the analysis unit can apply an analysis algorithm appropriate to the type of ornamental plant to analyze its growth state. The analysis unit can apply an analysis algorithm appropriate to the type of succulent plant to analyze its dryness state. The analysis unit can apply an analysis algorithm appropriate to the type of flowering plant to analyze its temperature state. This allows for improved analysis accuracy by applying the optimal analysis algorithm for each type of plant. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input plant type data into a generation AI, which can then analyze the data and apply the optimal analysis algorithm.

[0095] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important information. If the user is relaxed, the analysis unit can prioritize displaying detailed information. If the user is in a hurry, the analysis unit can prioritize displaying concise information. In this way, by determining the priority of the analysis results according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and determine the priority of the analysis results.

[0096] The analysis unit can improve the accuracy of the analysis by referring to the plant's surrounding environment data during the analysis. For example, the analysis unit can refer to the temperature data surrounding the plant to analyze its growth state. The analysis unit can refer to the humidity data surrounding the plant to analyze its dry state. The analysis unit can refer to the light intensity data surrounding the plant to analyze its growth state. In this way, the accuracy of the analysis can be improved by referring to the plant's surrounding environment data. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the plant's surrounding environment data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0097] The analysis unit can improve the accuracy of the analysis by referring to plant growth prediction data during the analysis. For example, the analysis unit can refer to plant growth prediction data to analyze the current growth state. The analysis unit can refer to plant drought prediction data to analyze the current drought state. The analysis unit can refer to plant temperature prediction data to analyze the current temperature state. In this way, the accuracy of the analysis can be improved by referring to plant growth prediction data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input plant growth prediction data into a generation AI, and the generation AI can analyze the data to improve the accuracy of the analysis.

[0098] The instruction unit can estimate the user's emotions and adjust the way care instructions are expressed based on the estimated emotions. For example, if the user is tense, the instruction unit can provide a simple and easily visible expression. If the user is relaxed, the instruction unit can provide an expression that includes detailed information. If the user is in a hurry, the instruction unit can provide an expression that gets straight to the point. In this way, by adjusting the way care instructions are expressed according to the user's emotions, appropriate information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using or without a generative AI. For example, the instruction unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the way care instructions are expressed.

[0099] The instruction unit can adjust the level of detail in care instructions according to the plant's growth stage at the time of instruction. For example, the instruction unit can provide detailed care instructions when the plant is young. The instruction unit can provide basic care instructions when the plant is mature. The instruction unit can provide concise care instructions when the plant is mature. In this way, appropriate care can be provided by adjusting the level of detail in care instructions according to the plant's growth stage. Some or all of the above processing in the instruction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the instruction unit can input plant growth stage data into a generative AI, and the generative AI can analyze the data and adjust the level of detail in care instructions.

[0100] The instruction unit can apply different care instruction algorithms to each type of plant when instructions are given. For example, the instruction unit can apply a care instruction algorithm according to the type of houseplant to instruct appropriate care. The instruction unit can apply a care instruction algorithm according to the type of succulent plant to instruct appropriate care. The instruction unit can apply a care instruction algorithm according to the type of flowering plant to instruct appropriate care. In this way, appropriate care can be provided by applying the optimal care instruction algorithm according to the type of plant. Some or all of the above processing in the instruction unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the instruction unit can input plant type data into a generative AI, and the generative AI can analyze the data and apply the optimal care instruction algorithm.

[0101] The instruction unit can estimate the user's emotions and determine the priority of care instructions based on the estimated emotions. For example, if the user is stressed, the instruction unit can prioritize important care instructions. If the user is relaxed, the instruction unit can prioritize detailed care instructions. If the user is in a hurry, the instruction unit can prioritize concise care instructions. In this way, by determining the priority of care instructions according to the user's emotions, the user can be provided with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using or without a generative AI. For example, the instruction unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of care instructions.

[0102] The instruction unit can improve the accuracy of care instructions by referring to the plant's surrounding environment data at the time of instruction. For example, the instruction unit can refer to the plant's surrounding temperature data to provide appropriate care instructions. The instruction unit can refer to the plant's surrounding humidity data to provide appropriate care instructions. The instruction unit can refer to the plant's surrounding light intensity data to provide appropriate care instructions. In this way, the accuracy of care instructions can be improved by referring to the plant's surrounding environment data. Some or all of the above processing in the instruction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the instruction unit can input the plant's surrounding environment data into a generation AI, and the generation AI can analyze the data to improve the accuracy of care instructions.

[0103] The instruction unit can improve the accuracy of care instructions by referring to plant growth prediction data at the time of instruction. For example, the instruction unit can refer to plant growth prediction data and provide appropriate care instructions. The instruction unit can refer to plant drying prediction data and provide appropriate care instructions. The instruction unit can refer to plant temperature prediction data and provide appropriate care instructions. In this way, the accuracy of care instructions can be improved by referring to plant growth prediction data. Some or all of the above processing in the instruction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the instruction unit can input plant growth prediction data into a generation AI, and the generation AI can analyze the data to improve the accuracy of care instructions.

[0104] The reminder unit can estimate the user's emotions and adjust the reminder notification method based on the estimated emotions. For example, if the user is stressed, the reminder unit can provide a simple and highly visible notification method. If the user is relaxed, the reminder unit can provide a notification method that includes detailed information. If the user is in a hurry, the reminder unit can provide a notification method that gets straight to the point. In this way, by adjusting the reminder notification method according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using or without a generative AI. For example, the reminder unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the reminder notification method.

[0105] The reminder unit can provide the optimal reminder by referring to the user's past maintenance history when setting a reminder. For example, the reminder unit can refer to the user's past maintenance history and set the optimal reminder. The reminder unit can refer to the user's past maintenance frequency and set the optimal reminder. The reminder unit can refer to the user's past maintenance content and set the optimal reminder. In this way, the system can provide the optimal reminder by referring to the user's past maintenance history. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's past maintenance history data into a generation AI, and the generation AI can analyze the data and set the optimal reminder.

[0106] The reminder unit can adjust the frequency of reminders according to the user's schedule when setting reminders. For example, the reminder unit can refer to the user's schedule and set the optimal reminder frequency. The reminder unit can set reminders to avoid the user's busy times. The reminder unit can set reminders to coincide with the user's free time. By adjusting the frequency of reminders according to the user's schedule, the system can provide the user with appropriate information. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's schedule data into a generation AI, which can then analyze the data and adjust the reminder frequency.

[0107] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit can prioritize important reminders. If the user is relaxed, the reminder unit can prioritize detailed reminders. If the user is in a hurry, the reminder unit can prioritize concise reminders. By prioritizing reminders according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using or without a generative AI. For example, the reminder unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of reminders.

[0108] The reminder unit can provide the optimal reminder based on the user's device information when a reminder is set. For example, the reminder unit can provide the optimal reminder if the user is using a smartphone. The reminder unit can provide the optimal reminder if the user is using a tablet. The reminder unit can provide the optimal reminder if the user is using a smartwatch. In this way, by providing the optimal reminder based on the user's device information, the system can provide the user with appropriate information. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's device information into a generation AI, and the generation AI can analyze the data to provide the optimal reminder.

[0109] The reminder unit can analyze the user's lifestyle patterns when setting reminders and provide optimal reminders. For example, the reminder unit can analyze the user's lifestyle patterns and set optimal reminders. The reminder unit can set reminders considering the user's wake-up time and bedtime. The reminder unit can set reminders considering the user's meal times and exercise times. By analyzing the user's lifestyle patterns and providing optimal reminders, the system can provide the user with appropriate information. Some or all of the above processing in the reminder unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reminder unit can input the user's lifestyle pattern data into a generation AI, which can then analyze the data and set optimal reminders.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The houseplant monitoring system can also be equipped with a plant growth prediction function. This function can predict the future growth of plants based on past growth data. For example, it can predict the growth rate of plants and allow for the planning of appropriate care in advance. Furthermore, the growth prediction function can identify factors affecting plant growth and provide advice for creating the optimal growing environment. Additionally, it can predict risks associated with plant growth and allow for early intervention. This helps maintain plant health and promotes optimal growth.

[0112] The houseplant monitoring system can also be equipped with a plant health check function. This function comprehensively evaluates the plant's condition and diagnoses its health. For example, it can comprehensively evaluate changes in leaf color and shape, growth rate, and degree of dryness to diagnose the plant's health. Furthermore, the health check function can provide care advice tailored to the plant's health condition. Additionally, the health check function can regularly monitor the plant's health and detect abnormalities early. This helps maintain plant health and provide optimal care.

[0113] The houseplant monitoring system can also be equipped with a function to measure the stress level of the plants. This stress level measurement function allows for the measurement of the stress the plants experience and the implementation of appropriate countermeasures. For example, it can measure the stress caused by sudden changes in temperature and humidity, or insufficient light, and implement appropriate measures. Furthermore, the stress level measurement function can provide care advice tailored to the plant's stress level. Additionally, the stress level measurement function allows for regular monitoring of the plant's stress level, enabling early detection of abnormalities. This helps maintain the plant's health and provides optimal care.

[0114] The houseplant monitoring system can also be equipped with functions to optimize the plant's growth environment. This growth environment optimization function can provide advice to ensure the optimal environment for plant growth. For example, it can optimize environmental factors such as temperature, humidity, and light intensity to promote plant growth. Furthermore, it can identify factors affecting plant growth and provide advice to ensure the optimal environment. Additionally, the growth environment optimization function can regularly monitor the plant's growth environment and detect abnormalities early. This helps maintain plant health and promotes optimal growth.

[0115] The houseplant monitoring system can also be equipped with a function to record the plant's growth history. This growth history recording function allows for detailed recording of the plant's growth process, which can be used for future care. For example, it can record details such as the plant's growth rate, dryness, and changes in temperature and humidity, which can be used for future care. Furthermore, the growth history recording function can provide appropriate care advice based on the plant's growth history. Additionally, the growth history recording function allows for regular monitoring of the plant's growth history, enabling early detection of abnormalities. This helps maintain the plant's health and provides optimal care.

[0116] The houseplant monitoring system can estimate the user's emotions and adjust plant care methods based on those emotions. For example, if the user is stressed, it can suggest simple, low-maintenance care methods. If the user is relaxed, it can suggest detailed and thorough care methods. If the user is busy, it can suggest care methods that require minimal effort. In this way, by adjusting plant care methods according to the user's emotions, the system can provide the optimal care method for the user. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The houseplant monitoring system can estimate the user's emotions and adjust the notification method for plant growth status based on the estimated emotions. For example, if the user is stressed, a simple and highly visible notification method can be provided. If the user is relaxed, a notification method containing detailed information can be provided. If the user is in a hurry, a notification method that gets straight to the point can be provided. In this way, by adjusting the notification method for plant growth status according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The houseplant monitoring system can estimate the user's emotions and adjust the display method for the plant's growth status based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method for the plant's growth status according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The houseplant monitoring system can estimate the user's emotions and prioritize the plant's growth status based on those emotions. For example, if the user is stressed, important information can be displayed preferentially. If the user is relaxed, detailed information can be displayed preferentially. If the user is in a hurry, concise information can be displayed preferentially. This allows the system to provide the user with appropriate information by prioritizing the plant's growth status according to their emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The houseplant monitoring system can estimate the user's emotions and adjust the reminder method for plant growth status based on the estimated emotions. For example, if the user is stressed, a simple and highly visible reminder method can be provided. If the user is relaxed, a reminder method with detailed information can be provided. If the user is in a hurry, a concise reminder method can be provided. In this way, by adjusting the reminder method for plant growth status according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The sensor unit monitors the growth and dryness of the plant. The sensor unit measures the plant's condition using temperature sensors, humidity sensors, mass sensors, etc. For example, the temperature sensor measures the temperature around the plant, the humidity sensor measures the humidity around the plant, and the mass sensor measures the plant's mass. Step 2: The camera unit photographs the plant's growth. The camera unit uses a high-resolution camera to periodically photograph the plant's growth and save the images as data. For example, it photographs changes in the color and shape of the plant's leaves. Step 3: The analysis unit analyzes the data collected by the sensor unit and camera unit. The analysis unit uses a generation AI to analyze the data, and analyzes data such as temperature, humidity, and mass to understand the growth state and dryness of the plants. For example, it analyzes temperature data to identify a temperature range suitable for plant growth, analyzes humidity data to identify a humidity range suitable for plant growth, and analyzes mass data to understand the changes in mass associated with plant growth. Step 4: The instruction unit provides instructions for appropriate care based on the analysis results obtained by the analysis unit. The instruction unit uses generated AI to provide instructions for care such as watering timing, adjusting sunlight, and adding fertilizer. For example, it may instruct on watering timing to water appropriately according to the degree of dryness of the plants, instruct on adjusting sunlight to ensure sufficient sunlight for plant growth, and instruct on adding fertilizer to supply the nutrients necessary for plant growth. Step 5: The reminder unit reminds the user of the care instructed by the instruction unit. The reminder unit uses AI to set reminders and sets reminders according to the user's schedule. For example, it refers to the user's calendar information, sets reminders at the appropriate time, and refers to alarm settings to remind the user not to forget the care.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the sensor unit, camera unit, analysis unit, instruction unit, and reminder unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sensor unit monitors the growth and dryness of plants using the sensors of the smart device 14. The camera unit takes pictures of the plant's growth using the camera of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor unit and the camera unit. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and instructs appropriate care based on the analysis results. The reminder unit is implemented by the control unit 46A of the smart device 14 and reminds the user of care. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the sensor unit, camera unit, analysis unit, instruction unit, and reminder unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor unit monitors the growth and dryness of plants using the sensors of the smart glasses 214. The camera unit takes pictures of the plant's growth using the camera of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor unit and the camera unit. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and instructs appropriate care based on the analysis results. The reminder unit is implemented by the control unit 46A of the smart glasses 214 and reminds the user of care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the 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.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 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.

[0158] Each of the multiple elements described above, including the sensor unit, camera unit, analysis unit, instruction unit, and reminder unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor unit monitors the growth and dryness of plants using the sensor of the headset terminal 314. The camera unit takes pictures of the plant's growth using the camera of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor unit and the camera unit. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and instructs appropriate care based on the analysis results. The reminder unit is implemented by the control unit 46A of the headset terminal 314 and reminds the user of care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the sensor unit, camera unit, analysis unit, instruction unit, and reminder unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the sensor unit monitors the growth and dryness of plants using the sensors of the robot 414. The camera unit takes pictures of the plant's growth using the camera of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data collected from the sensor unit and the camera unit. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and instructs appropriate care based on the analysis results. The reminder unit is implemented by the control unit 46A of the robot 414 and reminds the user of care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) Sensor unit, Camera section, An analysis unit that analyzes the data collected by the sensor unit and the camera unit, An instruction unit that instructs appropriate care based on the analysis results obtained by the aforementioned analysis unit, The system includes a reminder unit that reminds the user of the care instructed by the instruction unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, By analyzing data such as temperature, humidity, and mass, we can understand the growth status and dryness of plants. The system described in Appendix 1, characterized by the features described herein. (Note 3) The indicator unit is, The instructions include guidance on watering timing, adjusting sunlight exposure, and adding fertilizer. The system described in Appendix 1, characterized by the features described herein. (Note 4) The reminder unit is, Set reminders according to the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We provide care advice based on weather forecasts and seasonal changes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The indicator unit is, It analyzes signs of plant diseases and pests and provides instructions on how to deal with them. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the frequency of sensor data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sensor unit is The sensor sensitivity is automatically adjusted according to the plant's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sensor unit is It has a function to immediately notify the user if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the timing of sensor data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sensor unit is Add soil nutrient levels to the data to be acquired. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned sensor unit is We will add ambient sound environment data to the acquired data and analyze the factors that influence plant growth. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned camera unit is The camera resolution automatically adjusts according to the plant's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned camera unit is It has a function to immediately notify the user if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting timing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned camera unit is Add a function to analyze plant color changes in the acquired data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned camera unit is Add a function to analyze changes in the shape of plant leaves in the acquired data. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned analysis unit, During analysis, plant growth history data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, different analysis algorithms are applied for each plant species. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, By referencing the surrounding environment data of plants during analysis, the accuracy of the analysis can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, plant growth prediction data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The indicator unit is, The system estimates the user's emotions and adjusts the way care instructions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The indicator unit is, When giving instructions, adjust the level of detail in the care instructions according to the plant's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 27) The indicator unit is, When issuing instructions, different care instruction algorithms are applied for each type of plant. The system described in Appendix 1, characterized by the features described herein. (Note 28) The indicator unit is, The system estimates the user's emotions and prioritizes care instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The indicator unit is, When giving instructions, refer to the surrounding environment data of the plants to improve the accuracy of care instructions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The indicator unit is, When giving instructions, refer to plant growth prediction data to improve the accuracy of care instructions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reminder unit is, It estimates the user's emotions and adjusts how reminders are notified based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reminder unit is, When setting reminders, the system refers to the user's past maintenance history to provide the most suitable reminders. The system described in Appendix 1, characterized by the features described herein. (Note 33) The reminder unit is, When setting a reminder, adjust the frequency of reminders according to the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 34) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The reminder unit is, When setting reminders, the system provides optimal reminders based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The reminder unit is, When setting reminders, the system analyzes the user's lifestyle patterns to provide the most suitable reminders. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. Sensor unit, Camera section, An analysis unit that analyzes the data collected by the sensor unit and the camera unit, An instruction unit that instructs appropriate care based on the analysis results obtained by the aforementioned analysis unit, The system includes a reminder unit that reminds the user of the care instructed by the instruction unit. A system characterized by the following features.

2. The aforementioned analysis unit, By analyzing data such as temperature, humidity, and mass, we can understand the growth status and dryness of plants. The system according to feature 1.

3. The indicator unit is, The instructions include guidance on watering timing, adjusting sunlight exposure, and adding fertilizer. The system according to feature 1.

4. The reminder unit is, Set reminders according to the user's schedule. The system according to feature 1.

5. The aforementioned analysis unit, We provide care advice based on weather forecasts and seasonal changes. The system according to feature 1.

6. The indicator unit is, It analyzes signs of plant diseases and pests and provides instructions on how to deal with them. The system according to feature 1.

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

8. The aforementioned sensor unit is The sensor sensitivity is automatically adjusted according to the plant's growth stage. The system according to feature 1.

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