System

Abstract

To provide a system that discriminates the variety of an indoor foliage plant and provides an appropriate growing method.SOLUTION: A system according to an embodiment includes a discrimination unit, an analysis unit, a provision unit, a prediction unit, and a monitoring unit. The discrimination unit analyzes a photo of an indoor foliage plant taken by a user and discriminates a variety. The analysis unit analyzes a growing situation based on the variety discriminated by the discrimination unit. The provision unit provides an appropriate growing method based on an analysis result obtained by the analysis unit. The prediction unit predicts sunlight and temperature utilizing a weather forecast or location information. The monitoring unit monitors an environment in cooperation with a smart home appliance remote control.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately identified varieties or provided appropriate cultivation methods for indoor ornamental plants, and there is room for improvement.

[0005] The system according to the embodiment aims to identify the variety of an indoor ornamental plant and provide an appropriate method for growing the plant. [Means for solving the problem]

[0006] The system according to the embodiment includes a discrimination unit, an analysis unit, a provision unit, a prediction unit, and a monitoring unit. The discrimination unit analyzes a photo of an indoor houseplant taken by a user and discriminates the variety. The analysis unit analyzes the growing condition based on the variety discriminated by the discrimination unit. The provision unit provides an appropriate growing method based on the analysis result obtained by the analysis unit. The prediction unit predicts sunlight and temperature using a weather forecast or location information. The monitoring unit monitors the environment in cooperation with a smart home appliance remote control. Effect of the Invention

[0007] The system according to the embodiment can identify the variety of an indoor ornamental plant and provide an appropriate cultivation method. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The indoor houseplant cultivation support system according to the embodiment of the present invention is a system that analyzes photos of indoor houseplants taken by a user, identifies the variety, analyzes the growth status, and provides an appropriate cultivation method. In this system, a user takes a photo of an indoor houseplant with a smartphone, and AI analyzes the photo to identify the variety of the houseplant. Based on the identified variety, AI analyzes the daily growth status and provides an appropriate cultivation method. In addition, it uses weather forecasts and location information to predict sunlight and temperature, and provides advice on providing an optimal environment for houseplants. It also monitors the environment in cooperation with a smart home appliance remote control, monitors the indoor temperature and humidity, and adjusts the air conditioner and humidifier as necessary. In addition, it analyzes the optimal timing for tasks essential for cultivation, such as watering, fertilizing, and repotting, and notifies the user with a reminder. This allows the user to perform tasks necessary for cultivating houseplants without forgetting them. In this application, AI supports the cultivation of indoor houseplants by providing reliable information and accurate advice. For example, it suggests the optimal frequency of watering and type of fertilizer according to the variety of the houseplant. It is also possible to advise the user to move the plant to a window on a sunny day based on the weather forecast. This allows the indoor plant growing support system to analyze photos of indoor plants taken by the user, identify the species, analyze the growing conditions, and provide appropriate growing methods.

[0029] The indoor foliage plant growth support system according to the embodiment includes a discrimination unit, an analysis unit, a provision unit, a prediction unit, and a monitoring unit. The discrimination unit analyzes a photo of an indoor foliage plant taken by a user and discriminates the variety. The discrimination unit extracts features of the foliage plant using, for example, an image analysis algorithm and discriminates the variety. For example, the discrimination unit can identify the variety based on features such as the shape and color of the leaves and the thickness of the stem. The discrimination unit can also discriminate the variety of the foliage plant with high accuracy using AI. For example, the discrimination unit can analyze an image of the foliage plant and discriminate the variety using a deep learning model. The analysis unit analyzes the daily growth status based on the variety discriminated by the discrimination unit. The analysis unit analyzes the growth status based on, for example, the growth speed of the foliage plant, the color and shape of the leaves, and the like. For example, the analysis unit can measure the growth speed of the foliage plant and propose appropriate measures if the growth is delayed. The analysis unit can also analyze the growth status with high accuracy using AI. For example, the analysis unit can use a machine learning model to analyze the growth pattern of a foliage plant and detect an abnormality. The provision unit provides an appropriate cultivation method based on the analysis result obtained by the analysis unit. For example, the provision unit suggests the frequency of watering and the type of fertilizer according to the variety of the foliage plant. For example, the provision unit can notify the optimal watering timing by a reminder according to the variety of the foliage plant. In addition, the provision unit can use AI to provide a cultivation method with high accuracy. For example, the provision unit can use a machine learning model to suggest an optimal method for cultivating a foliage plant. The prediction unit predicts sunlight and temperature using a weather forecast and location information. The prediction unit can predict the optimal sunlight and temperature for a foliage plant based on weather data, for example. For example, the prediction unit can advise to move the foliage plant to a window on a sunny day based on a weather forecast. In addition, the prediction unit can predict sunlight and temperature with high accuracy using AI. For example, the prediction unit can predict the optimal environment for a foliage plant using a machine learning model. The monitoring unit monitors the environment in cooperation with a smart home appliance remote control. The monitoring unit can, for example, monitor the temperature and humidity in a room and adjust the air conditioner or humidifier as necessary.For example, the monitoring unit can monitor the indoor environment in real time using a temperature sensor and a humidity sensor. In addition, the monitoring unit can monitor the environment with high accuracy using AI. For example, the monitoring unit can analyze the indoor environment using a machine learning model and propose an optimal adjustment method. As a result, the indoor houseplant cultivation support system according to the embodiment can analyze a photo of an indoor houseplant taken by a user, determine the variety, analyze the growth situation, and provide an appropriate cultivation method. For example, the output unit displays the cultivation method to the user through a web application or a mobile application. If feedback on paper media is desired, the results are printed using a printer. Transmission by email provides quick feedback by directly transmitting the results to the user. A part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI.

[0030] The discrimination unit analyzes a photo of an indoor houseplant taken by a user and discriminates the variety. For example, the discrimination unit uses an image analysis algorithm to extract features of the houseplant and discriminate the variety. Specifically, the image analysis algorithm extracts features such as the leaf shape, color, stem thickness, and vein pattern, and identifies the variety by comparing them with an existing database. For example, if the leaf shape is elongated and the color is dark green, it is highly likely to be a specific houseplant variety. In addition, the discrimination unit can use AI to discriminate the variety of the houseplant with high accuracy. Specifically, the discrimination unit uses a deep learning model to analyze the image of the houseplant and discriminate the variety. The deep learning model can learn a large amount of image data of houseplants and recognize the characteristics of each variety with high accuracy. For example, by using a convolutional neural network (CNN), it is possible to analyze the details of the image and detect subtle differences. This allows the discrimination unit to quickly and accurately identify the variety of the houseplant from the image taken by the user. Furthermore, the discrimination unit can improve discrimination accuracy by integrating and analyzing multiple images taken by the user at different angles and lighting conditions. For example, by integrating images taken from different angles, characteristics such as leaf shape and stem thickness can be grasped more accurately. This allows the discrimination unit to achieve highly accurate variety discrimination without relying on the quality and conditions of image data provided by the user.

[0031] The analysis unit analyzes the daily growth status based on the variety identified by the discrimination unit. The analysis unit analyzes the growth status based on, for example, the growth rate of the foliage plant, the color of the leaves, the shape, etc. Specifically, the growth rate of the foliage plant is measured, and appropriate measures can be proposed if the growth is delayed. For example, the growth rate is evaluated by comparing images taken periodically and measuring changes in the size of the leaves and the length of the stem. The health status can also be evaluated by analyzing changes in the color and shape of the leaves. For example, if the color of the leaves has turned yellow, this may be due to nutrient deficiency or excessive sunlight. The analysis unit can use AI to analyze the growth status with high accuracy. Specifically, the machine learning model can be used to analyze the growth pattern of the foliage plant and detect abnormalities. For example, by learning normal growth patterns based on past data and comparing them with the current growth status, abnormalities can be detected early. This allows the analysis unit to monitor the health status of the foliage plant in real time, and take prompt measures if an abnormality occurs. Furthermore, the analysis unit can integrate environmental data and growth history to perform more accurate analysis. For example, by considering environmental data such as indoor temperature, humidity, and light intensity, it is possible to identify factors that affect the growth of houseplants and propose optimal growing conditions. This allows the analysis unit to comprehensively evaluate the health of houseplants and provide appropriate growing advice to the user.

[0032] The providing unit provides an appropriate cultivation method based on the analysis results obtained by the analyzing unit. For example, the providing unit suggests the frequency of watering and the type of fertilizer according to the variety of the houseplant. Specifically, the providing unit can calculate the optimal watering timing and amount for each variety of the houseplant and notify the user by a reminder. For example, for a variety that prefers dryness, watering once a week is suggested, and for a variety that prefers humidity, watering is suggested daily. In addition, the providing unit can use AI to provide a cultivation method with high accuracy. Specifically, the machine learning model is used to suggest the optimal method for cultivating a houseplant. For example, based on past cultivation data, the type and amount of fertilizer optimal for a specific variety can be learned and suggested to the user. This allows the providing unit to provide the optimal cultivation method for maintaining the health of the houseplant and promoting its growth. Furthermore, the providing unit can provide cultivation advice that is individually customized taking into account the user's cultivation history and environmental data. For example, advice according to the current cultivation situation is provided based on the user's past watering and fertilizer use history. In addition, the providing unit can provide information on cultivation methods to the user through a web application or a mobile application. This allows the user to check information on training methods anytime and anywhere and to practice appropriate training methods.

[0033] The prediction unit predicts sunlight and temperature using weather forecasts and location information. The prediction unit can predict the optimal sunlight and temperature for houseplants, for example, based on weather data. Specifically, based on the weather forecast, advice can be given to move the houseplant to a window on a sunny day. For example, on a sunny day, placing the houseplant near a window allows the houseplant to receive sufficient sunlight. The prediction unit can also use AI to predict sunlight and temperature with high accuracy. Specifically, using a machine learning model, predict the optimal environment for the houseplant. For example, based on past weather data, it is possible to learn the patterns of sunlight and temperature in a specific region or season and predict future weather conditions. This allows the prediction unit to provide advice for providing the optimal environment for growing houseplants. Furthermore, the prediction unit can provide advice according to the weather conditions of each region by utilizing the user's location information. For example, the prediction unit proposes the optimal growing method taking into account the weather conditions of the region where the user lives. The prediction unit can also continuously revise the prediction results based on weather data updated in real time to respond to the latest situation. This enables the prediction unit to always provide highly accurate predictions based on the latest information and support the cultivation of ornamental plants.

[0034] The monitoring unit monitors the environment in cooperation with the smart home appliance remote control. The monitoring unit can, for example, monitor the temperature and humidity in the room and adjust the air conditioner and humidifier as necessary. Specifically, the indoor environment can be monitored in real time using a temperature sensor and a humidity sensor. For example, if the temperature in the room is too high, the air conditioner can be automatically adjusted to maintain an appropriate temperature. Also, if the humidity is too low, the humidifier can be operated to maintain an appropriate humidity. The monitoring unit can monitor the environment with high accuracy using AI. Specifically, the monitoring unit can analyze the indoor environment and propose the optimal adjustment method using a machine learning model. For example, the monitoring unit can learn the optimal temperature and humidity under specific conditions based on past environmental data and propose an adjustment method according to the current environment. This allows the monitoring unit to support maintaining an optimal environment for growing houseplants. Furthermore, the monitoring unit can make individually customized environmental adjustments taking into account the user's lifestyle and indoor layout. For example, when the user is absent during the day, the settings of the air conditioner and humidifier are automatically adjusted to optimize energy efficiency. In addition, the monitoring unit provides a function that allows users to remotely monitor and adjust the indoor environment through the smart home appliance remote control, allowing users to manage the indoor environment anytime and anywhere and support the growth of houseplants.

[0035] The discrimination unit can evaluate the quality of the photograph taken and provide feedback to obtain a discrimination result. For example, if the photograph is dark, the discrimination unit suggests retaking the photograph in a bright place. In addition, if the photograph is blurred, the discrimination unit can also provide advice to prevent camera shake. Furthermore, if the composition of the photograph is inappropriate, the discrimination unit can instruct the photograph to be retaken with an appropriate composition. This makes it possible to evaluate the quality of the photograph and provide feedback to obtain an optimal discrimination result. Some or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI or may be performed without using AI. For example, the discrimination unit can input quality data of the photograph taken to the generation AI and cause the generation AI to perform quality evaluation.

[0036] The discrimination unit can integrate multiple photos to perform more accurate variety discrimination. The discrimination unit, for example, integrates photos taken from multiple angles to grasp the overall picture. The discrimination unit can also integrate photos taken at different times of the day to eliminate the influence of light. Furthermore, the discrimination unit can also integrate photos taken with different devices to improve analysis accuracy. As a result, more accurate variety discrimination can be performed by integrating multiple photos. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input multiple photo data to the generation AI and cause the generation AI to execute photo integration.

[0037] The discrimination unit can analyze background information of the photographed photo and improve the accuracy of variety discrimination. The discrimination unit, for example, identifies other plants in the background and uses them as a reference for variety discrimination. The discrimination unit can also analyze the light conditions of the background and correct the brightness of the photo. Furthermore, the discrimination unit can analyze the color tone of the background and accurately discriminate the color of the plant. In this way, the accuracy of variety discrimination can be improved by analyzing the background information of the photo. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI, or may be performed without using AI. For example, the discrimination unit can input background information data to the generation AI and cause the generation AI to execute the analysis of the background information.

[0038] The discrimination unit can improve discrimination accuracy by comparing with a database of plants of the same species photographed by other users. The discrimination unit, for example, compares the plant of the same species in the database to identify the variety. The discrimination unit can also improve discrimination accuracy by comparing with similar varieties in the database. Furthermore, the discrimination unit can compare with photos of different growth stages in the database to perform discrimination taking into account the growth stage. This allows for improved discrimination accuracy by comparing with a database of plants of the same species photographed by other users. Part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI, or may be performed without using AI. For example, the discrimination unit can input plant data from the database to the generation AI and cause the generation AI to perform the comparison.

[0039] The analysis unit can predict the current growth status based on the past growth data. The analysis unit can predict the current moisture status based on, for example, the frequency of watering in the past. The analysis unit can also predict the current nutritional status based on the history of past fertilizer use. Furthermore, the analysis unit can predict the current growth stage based on the past growth rate. This makes it possible to predict the current growth status by referring to the past growth data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the past growth data to the generation AI and cause the generation AI to execute a prediction of the current growth status.

[0040] The analysis unit can detect abnormalities in the growth condition and quickly propose countermeasures. The analysis unit can, for example, detect changes in the color of leaves and warn of nutrient deficiency. The analysis unit can also detect a decrease in growth rate and propose appropriate countermeasures. Furthermore, the analysis unit can detect abnormalities in moisture status and propose the timing of watering. This makes it possible to detect abnormalities in the growth condition and propose countermeasures early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input growth condition data to the generation AI and cause the generation AI to detect abnormalities and propose countermeasures.

[0041] The analysis unit can detect anomalies by comparing with the growth data of other plants. For example, the analysis unit can detect anomalies in the growth rate by comparing with other plants of the same variety. The analysis unit can also adjust the frequency of watering by comparing with the moisture status of other plants. Furthermore, the analysis unit can adjust the amount of fertilizer used by comparing with the nutritional status of other plants. In this way, anomalies can be detected by comparing with the growth data of other plants. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the growth data of other plants to the generation AI and cause the generation AI to detect anomalies.

[0042] The analysis unit can share the analysis result of the growth status with other users and obtain feedback in the community. For example, the analysis unit can post the analysis result of the growth status to the community and receive advice from other users. The analysis unit can also check the condition of one's own plant by comparing it with the growth status of other users. Furthermore, the analysis unit can improve the cultivation method based on the feedback in the community. In this way, by sharing the analysis result of the growth status with other users, feedback in the community can be obtained. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input feedback data of the community to the generation AI and cause the generation AI to execute analysis of the feedback.

[0043] The providing unit can provide optimal advice based on the user's past cultivation history when providing the cultivation method. The providing unit can, for example, suggest optimal fertilizer based on the type of fertilizer used by the user in the past. The providing unit can also suggest optimal watering timing based on the frequency of watering performed by the user in the past. Furthermore, the providing unit can also suggest optimal repotting timing based on the timing of repotting performed by the user in the past. In this way, optimal advice can be provided by referring to the user's past cultivation history. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past cultivation history data to the generating AI and cause the generating AI to provide optimal advice.

[0044] The providing unit can provide individual advice according to the growth stage of the plant when providing the cultivation method. For example, the providing unit can suggest appropriate watering and fertilizer use methods in the early growth stage. The providing unit can also suggest appropriate pruning methods in the middle growth stage. Furthermore, the providing unit can also suggest appropriate repotting times and methods in the later growth stage. This makes it possible to provide different advice according to the growth stage of the plant. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input plant growth stage data to the generating AI and cause the generating AI to provide individual advice.

[0045] The providing unit can provide optimal advice by comparing with the cultivation method of other users. For example, the providing unit can suggest optimal fertilizer by referring to the type of fertilizer used by other users. The providing unit can also suggest optimal watering timing by referring to the frequency of watering performed by other users. Furthermore, the providing unit can also suggest optimal repotting timing by referring to the timing of repotting performed by other users. This makes it possible to provide optimal advice by comparing with the cultivation method of other users. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input cultivation data of other users to the generating AI and cause the generating AI to provide optimal advice.

[0046] The providing unit can customize the cultivation method to suit the user's lifestyle when providing the cultivation method. For example, when the user is busy, the providing unit proposes an easy cultivation method. In addition, when the user is traveling, the providing unit can also propose a cultivation method that can be implemented even during the trip. Furthermore, when the user has a home garden, the providing unit can also propose a cultivation method suitable for the home garden. In this way, by customizing to suit the user's lifestyle, it is possible to provide an optimal cultivation method. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle data into the generating AI and cause the generating AI to provide the customized cultivation method.

[0047] The prediction unit can perform more accurate prediction of sunlight and temperature based on past weather data. The prediction unit, for example, predicts a sunny time period based on past weather data. The prediction unit can also predict the optimal temperature based on past temperature data. Furthermore, the prediction unit can predict future weather based on past weather patterns. In this way, by referring to past weather data, more accurate prediction of sunlight and temperature can be performed. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past weather data to the generation AI and cause the generation AI to execute prediction of sunlight and temperature.

[0048] The prediction unit can suggest an appropriate placement location for the plant based on the prediction result. The prediction unit can suggest, for example, a sunny location and instruct the plant to be moved. The prediction unit can also suggest a location with stable temperature and instruct the plant to be moved. Furthermore, the prediction unit can also suggest a location with appropriate humidity and instruct the plant to be moved. In this way, it is possible to suggest an optimal placement location for the plant based on the prediction result. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the prediction result data to the generation AI and cause the generation AI to execute a proposal for an optimal placement location.

[0049] The prediction unit can improve the prediction accuracy based on the weather data of other regions. The prediction unit, for example, refers to the weather data of other regions and improves the prediction accuracy. The prediction unit can also refer to the temperature data of other regions and improve the prediction accuracy. Furthermore, the prediction unit can also refer to the humidity data of other regions and improve the prediction accuracy. This makes it possible to improve the prediction accuracy by comparing with the weather data of other regions. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the weather data of other regions to the generation AI and cause the generation AI to improve the prediction accuracy.

[0050] The prediction unit can share the prediction result with other users and obtain feedback in the community. For example, the prediction unit posts the prediction result to the community and receives feedback from other users. The prediction unit can also compare its own prediction result with the prediction results of other users and confirm it. Furthermore, the prediction unit can improve the prediction method based on the feedback in the community. As a result, the prediction result can be shared with other users to obtain feedback in the community. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input feedback data of the community to the generation AI and cause the generation AI to execute an analysis of the feedback.

[0051] The monitoring unit can detect an abnormality in the environment based on the monitoring results and quickly propose countermeasures. For example, if the temperature in the room is too high, the monitoring unit can suggest adjusting the air conditioner. Also, if the humidity in the room is too low, the monitoring unit can suggest using a humidifier. Furthermore, if the light conditions in the room are inappropriate, the monitoring unit can suggest adjusting the lighting. This makes it possible to detect an abnormality in the environment based on the monitoring results and propose countermeasures early. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input environmental data to the generation AI and cause the generation AI to detect an abnormality and propose countermeasures.

[0052] The monitoring unit can compare the monitoring results with past data to analyze changes in the environment. The monitoring unit can compare the monitoring results with past temperature data to analyze changes in the current temperature. The monitoring unit can also compare the monitoring results with past humidity data to analyze changes in the current humidity. The monitoring unit can also compare the monitoring results with past light conditions to analyze changes in the current light. This makes it possible to analyze changes in the environment by comparing the monitoring results with past data. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or can be performed without using AI. For example, the monitoring unit can input past environmental data to the generation AI and cause the generation AI to analyze changes in the environment.

[0053] The monitoring unit can adjust the environment in cooperation with other smart home appliances. The monitoring unit can, for example, work with an air conditioner to optimize the temperature in the room. The monitoring unit can also work with a humidifier to optimize the humidity in the room. Furthermore, the monitoring unit can also optimize the light conditions in the room in cooperation with lighting. This allows the environment to be optimized by working with other smart home appliances. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data of the smart home appliances to the generation AI and cause the generation AI to adjust the environment.

[0054] The monitoring unit can share the monitoring results with other users and obtain feedback in the community. For example, the monitoring unit posts the monitoring results to the community and receives feedback from other users. The monitoring unit can also check its own monitoring results by comparing them with the monitoring results of other users. Furthermore, the monitoring unit can improve the monitoring method based on the feedback in the community. This makes it possible to obtain feedback in the community by sharing the monitoring results with other users. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input feedback data of the community to the generation AI and cause the generation AI to analyze the feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The discrimination unit can evaluate the quality of the photograph taken and provide feedback to obtain a discrimination result. For example, if the photograph is dark, it suggests retaking the photograph in a bright place. Also, if the photograph is blurred, it can provide advice to prevent camera shake. Furthermore, if the composition of the photograph is inappropriate, it can instruct to retake the photograph with an appropriate composition. This makes it possible to evaluate the quality of the photograph and provide feedback to obtain an optimal discrimination result. Some or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI or may be performed without using AI. For example, the discrimination unit can input quality data of the photograph taken to the generation AI and cause the generation AI to perform quality evaluation.

[0057] The discrimination unit can integrate multiple photos to perform more accurate variety discrimination. For example, photos taken from multiple angles can be integrated to grasp the overall picture. Photos taken at different times can also be integrated to eliminate the influence of light. Furthermore, photos taken with different devices can also be integrated to improve the analysis accuracy. As a result, more accurate variety discrimination can be performed by integrating multiple photos. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input multiple photo data to the generation AI and cause the generation AI to execute the integration of the photos.

[0058] The discrimination unit can analyze background information of the photographed photo to improve the accuracy of variety discrimination. For example, other plants in the background can be identified and used as a reference for variety discrimination. In addition, the light conditions of the background can be analyzed to correct the brightness of the photo. Furthermore, the color tone of the background can be analyzed to accurately discriminate the color of the plant. In this way, the accuracy of variety discrimination can be improved by analyzing the background information of the photo. Some or all of the above-mentioned processing in the discrimination unit can be performed, for example, using AI, or can be performed without using AI. For example, the discrimination unit can input background information data to the generation AI and cause the generation AI to execute the analysis of the background information.

[0059] The analysis unit can predict the current growth status based on past growth data. For example, the current moisture status can be predicted based on the frequency of watering in the past. The current nutritional status can also be predicted based on the history of fertilizer use in the past. Furthermore, the current growth stage can also be predicted based on the growth rate in the past. In this way, the current growth status can be predicted by referring to the past growth data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past growth data to the generation AI and cause the generation AI to execute a prediction of the current growth status.

[0060] The analysis unit can detect abnormalities in the growth conditions and quickly propose countermeasures. For example, it can detect changes in leaf color and warn of nutrient deficiency. It can also detect a decrease in growth rate and propose appropriate countermeasures. It can also detect abnormalities in moisture status and propose the timing of watering. This makes it possible to detect abnormalities in the growth conditions and propose countermeasures early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input growth condition data to the generation AI and cause the generation AI to detect abnormalities and propose countermeasures.

[0061] The analysis unit can detect abnormalities by comparing with the growth data of other plants. For example, it can detect abnormalities in the growth rate by comparing with other plants of the same variety. It can also adjust the frequency of watering by comparing with the moisture status of other plants. It can also adjust the amount of fertilizer used by comparing with the nutritional status of other plants. In this way, it is possible to detect abnormalities by comparing with the growth data of other plants. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the growth data of other plants to the generation AI and cause the generation AI to detect abnormalities.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The discrimination unit analyzes photos of indoor plants taken by the user and discriminates the variety. The discrimination unit uses an image analysis algorithm to extract the features of the plant and identifies the variety based on the features such as the shape and color of the leaves and the thickness of the stem. In addition, the unit can discriminate the variety with high accuracy using a deep learning model. Step 2: The analysis unit analyzes the daily growth status based on the variety identified by the discrimination unit. The analysis unit analyzes the growth status based on the growth rate, leaf color, shape, etc. of the houseplant, and suggests appropriate measures if growth is slow. It can also use machine learning models to analyze growth patterns and detect abnormalities. Step 3: The provision unit provides an appropriate cultivation method based on the analysis results obtained by the analysis unit. The provision unit suggests watering frequency and type of fertilizer according to the variety of houseplant, and notifies the user of the optimal watering timing by reminder. In addition, the provision unit provides cultivation methods with high accuracy using a machine learning model. Step 4: The prediction unit uses weather forecasts and location information to predict sunlight and temperature. The prediction unit predicts the optimal sunlight and temperature for houseplants based on meteorological data, and advises users to move the houseplant to a window on sunny days based on the weather forecast. It also uses machine learning models to predict sunlight and temperature with high accuracy. Step 5: The monitoring unit monitors the environment in cooperation with the smart home appliance remote control. The monitoring unit monitors the temperature and humidity in the room and adjusts the air conditioner and humidifier as necessary. It uses temperature and humidity sensors to monitor the indoor environment in real time and uses machine learning models to suggest the optimal adjustment method.

[0064] (Example 2) The indoor houseplant cultivation support system according to the embodiment of the present invention is a system that analyzes photos of indoor houseplants taken by a user, identifies the variety, analyzes the growth status, and provides an appropriate cultivation method. In this system, a user takes a photo of an indoor houseplant with a smartphone, and AI analyzes the photo to identify the variety of the houseplant. Based on the identified variety, AI analyzes the daily growth status and provides an appropriate cultivation method. In addition, it uses weather forecasts and location information to predict sunlight and temperature, and provides advice on providing the best environment for houseplants. It also monitors the environment in cooperation with a smart home appliance remote control, monitors the indoor temperature and humidity, and adjusts the air conditioner and humidifier as necessary. It also analyzes the optimal timing for tasks essential to cultivation, such as watering, fertilizing, and repotting, and notifies the user with a reminder. This allows the user to perform the tasks necessary for cultivating houseplants without forgetting them. In this application, AI supports the cultivation of indoor houseplants by providing reliable information and accurate advice. For example, it suggests the optimal frequency of watering and type of fertilizer according to the variety of the houseplant. It is also possible to advise the user to move the plant to a window on a sunny day based on the weather forecast. This allows the indoor plant growing support system to analyze photos of indoor plants taken by the user, identify the species, analyze the growing conditions, and provide appropriate growing methods.

[0065] The indoor foliage plant growth support system according to the embodiment includes a discrimination unit, an analysis unit, a provision unit, a prediction unit, and a monitoring unit. The discrimination unit analyzes a photo of an indoor foliage plant taken by a user and discriminates the variety. The discrimination unit extracts features of the foliage plant using, for example, an image analysis algorithm and discriminates the variety. For example, the discrimination unit can identify the variety based on features such as the shape and color of the leaves and the thickness of the stem. The discrimination unit can also discriminate the variety of the foliage plant with high accuracy using AI. For example, the discrimination unit can analyze an image of the foliage plant and discriminate the variety using a deep learning model. The analysis unit analyzes the daily growth status based on the variety discriminated by the discrimination unit. The analysis unit analyzes the growth status based on, for example, the growth speed of the foliage plant, the color and shape of the leaves, and the like. For example, the analysis unit can measure the growth speed of the foliage plant and propose appropriate measures if the growth is delayed. The analysis unit can also analyze the growth status with high accuracy using AI. For example, the analysis unit can use a machine learning model to analyze the growth pattern of a foliage plant and detect an abnormality. The provision unit provides an appropriate cultivation method based on the analysis result obtained by the analysis unit. For example, the provision unit suggests the frequency of watering and the type of fertilizer according to the variety of the foliage plant. For example, the provision unit can notify the optimal watering timing by a reminder according to the variety of the foliage plant. In addition, the provision unit can use AI to provide a cultivation method with high accuracy. For example, the provision unit can use a machine learning model to suggest an optimal method for cultivating a foliage plant. The prediction unit predicts sunlight and temperature using a weather forecast and location information. The prediction unit can predict the optimal sunlight and temperature for a foliage plant based on weather data, for example. For example, the prediction unit can advise to move the foliage plant to a window on a sunny day based on a weather forecast. In addition, the prediction unit can predict sunlight and temperature with high accuracy using AI. For example, the prediction unit can predict the optimal environment for a foliage plant using a machine learning model. The monitoring unit monitors the environment in cooperation with a smart home appliance remote control. The monitoring unit can, for example, monitor the temperature and humidity in a room and adjust the air conditioner or humidifier as necessary.For example, the monitoring unit can monitor the indoor environment in real time using a temperature sensor and a humidity sensor. In addition, the monitoring unit can monitor the environment with high accuracy using AI. For example, the monitoring unit can analyze the indoor environment using a machine learning model and propose an optimal adjustment method. As a result, the indoor houseplant cultivation support system according to the embodiment can analyze a photo of an indoor houseplant taken by a user, determine the variety, analyze the growth situation, and provide an appropriate cultivation method. For example, the output unit displays the cultivation method to the user through a web application or a mobile application. If feedback on paper media is desired, the results are printed using a printer. Transmission by email provides quick feedback by directly transmitting the results to the user. A part or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The discrimination unit analyzes a photo of an indoor houseplant taken by a user and discriminates the variety. For example, the discrimination unit uses an image analysis algorithm to extract features of the houseplant and discriminate the variety. Specifically, the image analysis algorithm extracts features such as the leaf shape, color, stem thickness, and vein pattern, and identifies the variety by comparing them with an existing database. For example, if the leaf shape is elongated and the color is dark green, it is highly likely to be a specific houseplant variety. In addition, the discrimination unit can use AI to discriminate the variety of the houseplant with high accuracy. Specifically, the discrimination unit uses a deep learning model to analyze the image of the houseplant and discriminate the variety. The deep learning model can learn a large amount of image data of houseplants and recognize the characteristics of each variety with high accuracy. For example, by using a convolutional neural network (CNN), it is possible to analyze the details of the image and detect subtle differences. This allows the discrimination unit to quickly and accurately identify the variety of the houseplant from the image taken by the user. Furthermore, the discrimination unit can improve discrimination accuracy by integrating and analyzing multiple images taken by the user at different angles and lighting conditions. For example, by integrating images taken from different angles, characteristics such as leaf shape and stem thickness can be grasped more accurately. This allows the discrimination unit to achieve highly accurate variety discrimination without relying on the quality and conditions of image data provided by the user.

[0067] The analysis unit analyzes the daily growth status based on the variety identified by the discrimination unit. The analysis unit analyzes the growth status based on, for example, the growth rate of the foliage plant, the color of the leaves, the shape, etc. Specifically, the growth rate of the foliage plant is measured, and appropriate measures can be proposed if the growth is delayed. For example, the growth rate is evaluated by comparing images taken periodically and measuring changes in the size of the leaves and the length of the stem. The health status can also be evaluated by analyzing changes in the color and shape of the leaves. For example, if the color of the leaves has turned yellow, this may be due to nutrient deficiency or excessive sunlight. The analysis unit can use AI to analyze the growth status with high accuracy. Specifically, the machine learning model can be used to analyze the growth pattern of the foliage plant and detect abnormalities. For example, by learning normal growth patterns based on past data and comparing them with the current growth status, abnormalities can be detected early. This allows the analysis unit to monitor the health status of the foliage plant in real time, and take prompt measures if an abnormality occurs. Furthermore, the analysis unit can integrate environmental data and growth history to perform more accurate analysis. For example, by considering environmental data such as indoor temperature, humidity, and light intensity, it is possible to identify factors that affect the growth of houseplants and propose optimal growing conditions. This allows the analysis unit to comprehensively evaluate the health of houseplants and provide appropriate growing advice to the user.

[0068] The providing unit provides an appropriate cultivation method based on the analysis results obtained by the analyzing unit. For example, the providing unit suggests the frequency of watering and the type of fertilizer according to the variety of the houseplant. Specifically, the providing unit can calculate the optimal watering timing and amount for each variety of the houseplant and notify the user by a reminder. For example, for a variety that prefers dryness, watering once a week is suggested, and for a variety that prefers humidity, watering is suggested daily. In addition, the providing unit can use AI to provide a cultivation method with high accuracy. Specifically, the machine learning model is used to suggest the optimal method for cultivating a houseplant. For example, based on past cultivation data, the type and amount of fertilizer optimal for a specific variety can be learned and suggested to the user. This allows the providing unit to provide the optimal cultivation method for maintaining the health of the houseplant and promoting its growth. Furthermore, the providing unit can provide cultivation advice that is individually customized taking into account the user's cultivation history and environmental data. For example, advice according to the current cultivation situation is provided based on the user's past watering and fertilizer use history. In addition, the providing unit can provide information on cultivation methods to the user through a web application or a mobile application. This allows the user to check information on training methods anytime and anywhere and to practice appropriate training methods.

[0069] The prediction unit predicts sunlight and temperature using weather forecasts and location information. The prediction unit can predict the optimal sunlight and temperature for houseplants, for example, based on weather data. Specifically, based on the weather forecast, advice can be given to move the houseplant to a window on a sunny day. For example, on a sunny day, placing the houseplant near a window allows the houseplant to receive sufficient sunlight. The prediction unit can also use AI to predict sunlight and temperature with high accuracy. Specifically, using a machine learning model, predict the optimal environment for the houseplant. For example, based on past weather data, it is possible to learn the patterns of sunlight and temperature in a specific region or season and predict future weather conditions. This allows the prediction unit to provide advice for providing the optimal environment for growing houseplants. Furthermore, the prediction unit can provide advice according to the weather conditions of each region by utilizing the user's location information. For example, the prediction unit proposes the optimal growing method taking into account the weather conditions of the region where the user lives. The prediction unit can also continuously revise the prediction results based on weather data updated in real time to respond to the latest situation. This enables the prediction unit to always provide highly accurate predictions based on the latest information and support the cultivation of ornamental plants.

[0070] The monitoring unit monitors the environment in cooperation with the smart home appliance remote control. The monitoring unit can, for example, monitor the temperature and humidity in the room and adjust the air conditioner and humidifier as necessary. Specifically, the indoor environment can be monitored in real time using a temperature sensor and a humidity sensor. For example, if the temperature in the room is too high, the air conditioner can be automatically adjusted to maintain an appropriate temperature. Also, if the humidity is too low, the humidifier can be operated to maintain an appropriate humidity. The monitoring unit can monitor the environment with high accuracy using AI. Specifically, the monitoring unit can analyze the indoor environment and propose the optimal adjustment method using a machine learning model. For example, the monitoring unit can learn the optimal temperature and humidity under specific conditions based on past environmental data and propose an adjustment method according to the current environment. This allows the monitoring unit to support maintaining an optimal environment for growing houseplants. Furthermore, the monitoring unit can make individually customized environmental adjustments taking into account the user's lifestyle and indoor layout. For example, when the user is absent during the day, the settings of the air conditioner and humidifier are automatically adjusted to optimize energy efficiency. In addition, the monitoring unit provides a function that allows users to remotely monitor and adjust the indoor environment through the smart home appliance remote control, allowing users to manage the indoor environment anytime and anywhere and support the growth of houseplants.

[0071] The discrimination unit can estimate the user's emotion and adjust the timing of taking a photo according to the estimated emotion of the user. For example, if the user is relaxed, the discrimination unit suggests waiting for optimal light conditions before taking a photo. In addition, if the user is in a hurry, the discrimination unit can also prompt the user to take a photo immediately. Furthermore, if the user is excited, the discrimination unit can also suggest taking photos from multiple angles. This makes it possible to provide the optimal timing of taking a photo according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The discrimination unit can evaluate the quality of the photograph taken and provide feedback to obtain a discrimination result. For example, if the photograph is dark, the discrimination unit suggests retaking the photograph in a bright place. In addition, if the photograph is blurred, the discrimination unit can also provide advice to prevent camera shake. Furthermore, if the composition of the photograph is inappropriate, the discrimination unit can instruct the photograph to be retaken with an appropriate composition. This makes it possible to evaluate the quality of the photograph and provide feedback to obtain an optimal discrimination result. Some or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI or may be performed without using AI. For example, the discrimination unit can input quality data of the photograph taken to the generation AI and cause the generation AI to perform quality evaluation.

[0073] The discrimination unit can integrate multiple photos to perform more accurate variety discrimination. The discrimination unit, for example, integrates photos taken from multiple angles to grasp the overall picture. The discrimination unit can also integrate photos taken at different times of the day to eliminate the influence of light. Furthermore, the discrimination unit can also integrate photos taken with different devices to improve analysis accuracy. As a result, more accurate variety discrimination can be performed by integrating multiple photos. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input multiple photo data to the generation AI and cause the generation AI to execute photo integration.

[0074] The discrimination unit can estimate the user's emotion and adjust the display method of the discrimination result according to the estimated user's emotion. For example, when the user is nervous, the discrimination unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the discrimination unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the discrimination unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the discrimination result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The discrimination unit can analyze background information of the photographed photo and improve the accuracy of variety discrimination. The discrimination unit, for example, identifies other plants in the background and uses them as a reference for variety discrimination. The discrimination unit can also analyze the light conditions of the background and correct the brightness of the photo. Furthermore, the discrimination unit can analyze the color tone of the background and accurately discriminate the color of the plant. In this way, the accuracy of variety discrimination can be improved by analyzing the background information of the photo. A part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI, or may be performed without using AI. For example, the discrimination unit can input background information data to the generation AI and cause the generation AI to execute the analysis of the background information.

[0076] The discrimination unit can improve discrimination accuracy by comparing with a database of plants of the same species photographed by other users. The discrimination unit, for example, compares the plant of the same species in the database to identify the variety. The discrimination unit can also improve discrimination accuracy by comparing with similar varieties in the database. Furthermore, the discrimination unit can compare with photos of different growth stages in the database to perform discrimination taking into account the growth stage. This allows for improved discrimination accuracy by comparing with a database of plants of the same species photographed by other users. Part or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI, or may be performed without using AI. For example, the discrimination unit can input plant data from the database to the generation AI and cause the generation AI to perform the comparison.

[0077] The analysis unit can estimate the user's emotion and adjust the analysis frequency of the growth status according to the estimated emotion of the user. For example, when the user is relaxed, the analysis unit performs regular analysis. Also, when the user is in a hurry, the analysis unit can perform the minimum necessary analysis. Furthermore, when the user is excited, the analysis unit can perform detailed analysis frequently. This makes it possible to adjust the analysis frequency of the growth status according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The analysis unit can predict the current growth status based on the past growth data. The analysis unit can predict the current moisture status based on, for example, the frequency of watering in the past. The analysis unit can also predict the current nutritional status based on the history of past fertilizer use. Furthermore, the analysis unit can predict the current growth stage based on the past growth rate. This makes it possible to predict the current growth status by referring to the past growth data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the past growth data to the generation AI and cause the generation AI to execute a prediction of the current growth status.

[0079] The analysis unit can detect abnormalities in the growth condition and quickly propose countermeasures. The analysis unit can, for example, detect changes in the color of leaves and warn of nutrient deficiency. The analysis unit can also detect a decrease in growth rate and propose appropriate countermeasures. Furthermore, the analysis unit can detect abnormalities in moisture status and propose the timing of watering. This makes it possible to detect abnormalities in the growth condition and propose countermeasures early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input growth condition data to the generation AI and cause the generation AI to detect abnormalities and propose countermeasures.

[0080] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result according to the estimated emotion of the user. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis result according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The analysis unit can detect anomalies by comparing with the growth data of other plants. For example, the analysis unit can detect anomalies in the growth rate by comparing with other plants of the same variety. The analysis unit can also adjust the frequency of watering by comparing with the moisture status of other plants. Furthermore, the analysis unit can adjust the amount of fertilizer used by comparing with the nutritional status of other plants. In this way, anomalies can be detected by comparing with the growth data of other plants. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the growth data of other plants to the generation AI and cause the generation AI to detect anomalies.

[0082] The analysis unit can share the analysis result of the growth status with other users and obtain feedback in the community. For example, the analysis unit can post the analysis result of the growth status to the community and receive advice from other users. The analysis unit can also check the condition of one's own plant by comparing it with the growth status of other users. Furthermore, the analysis unit can improve the cultivation method based on the feedback in the community. In this way, by sharing the analysis result of the growth status with other users, feedback in the community can be obtained. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input feedback data of the community to the generation AI and cause the generation AI to execute analysis of the feedback.

[0083] The providing unit can estimate the user's emotion and adjust the frequency of providing the development method according to the estimated emotion of the user. For example, when the user is relaxed, the providing unit periodically provides the development method. In addition, when the user is in a hurry, the providing unit can also provide the minimum necessary development method. Furthermore, when the user is excited, the providing unit can frequently provide detailed development methods. This makes it possible to adjust the frequency of providing the development method according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The providing unit can provide optimal advice based on the user's past cultivation history when providing the cultivation method. The providing unit can, for example, suggest optimal fertilizer based on the type of fertilizer used by the user in the past. The providing unit can also suggest optimal watering timing based on the frequency of watering performed by the user in the past. Furthermore, the providing unit can also suggest optimal repotting timing based on the timing of repotting performed by the user in the past. In this way, optimal advice can be provided by referring to the user's past cultivation history. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past cultivation history data to the generating AI and cause the generating AI to provide optimal advice.

[0085] The providing unit can provide individual advice according to the growth stage of the plant when providing the cultivation method. For example, the providing unit can suggest appropriate watering and fertilizer use methods in the early growth stage. The providing unit can also suggest appropriate pruning methods in the middle growth stage. Furthermore, the providing unit can also suggest appropriate repotting times and methods in the later growth stage. This makes it possible to provide different advice according to the growth stage of the plant. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input plant growth stage data to the generating AI and cause the generating AI to provide individual advice.

[0086] The providing unit can estimate the user's emotion and adjust the display method of the training method according to the estimated emotion of the user. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the training method according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0087] The providing unit can provide optimal advice by comparing with the cultivation method of other users. For example, the providing unit can suggest optimal fertilizer by referring to the type of fertilizer used by other users. The providing unit can also suggest optimal watering timing by referring to the frequency of watering performed by other users. Furthermore, the providing unit can also suggest optimal repotting timing by referring to the timing of repotting performed by other users. This makes it possible to provide optimal advice by comparing with the cultivation method of other users. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input cultivation data of other users to the generating AI and cause the generating AI to provide optimal advice.

[0088] The providing unit can customize the cultivation method to suit the user's lifestyle when providing the cultivation method. For example, when the user is busy, the providing unit proposes an easy cultivation method. In addition, when the user is traveling, the providing unit can also propose a cultivation method that can be implemented even during the trip. Furthermore, when the user has a home garden, the providing unit can also propose a cultivation method suitable for the home garden. In this way, by customizing to suit the user's lifestyle, it is possible to provide an optimal cultivation method. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle data into the generating AI and cause the generating AI to provide the customized cultivation method.

[0089] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result according to the estimated user's emotion. For example, when the user is nervous, the prediction unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the prediction unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the prediction unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the prediction result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The prediction unit can perform more accurate prediction of sunlight and temperature based on past weather data. The prediction unit, for example, predicts a sunny time period based on past weather data. The prediction unit can also predict the optimal temperature based on past temperature data. Furthermore, the prediction unit can predict future weather based on past weather patterns. In this way, by referring to past weather data, more accurate prediction of sunlight and temperature can be performed. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past weather data to the generation AI and cause the generation AI to execute prediction of sunlight and temperature.

[0091] The prediction unit can suggest an appropriate placement location for the plant based on the prediction result. The prediction unit can suggest, for example, a sunny location and instruct the plant to be moved. The prediction unit can also suggest a location with stable temperature and instruct the plant to be moved. Furthermore, the prediction unit can also suggest a location with appropriate humidity and instruct the plant to be moved. In this way, it is possible to suggest an optimal placement location for the plant based on the prediction result. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the prediction result data to the generation AI and cause the generation AI to execute a proposal for an optimal placement location.

[0092] The prediction unit can estimate the user's emotion and determine the priority of the prediction results according to the estimated user's emotion. For example, when the user is nervous, the prediction unit preferentially displays important prediction results. In addition, when the user is relaxed, the prediction unit can also display detailed prediction results. Furthermore, when the user is in a hurry, the prediction unit can also display prediction results that focus on the main points. This makes it possible to determine the priority of the prediction results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The prediction unit can improve the prediction accuracy based on the weather data of other regions. The prediction unit, for example, refers to the weather data of other regions and improves the prediction accuracy. The prediction unit can also refer to the temperature data of other regions and improve the prediction accuracy. Furthermore, the prediction unit can also refer to the humidity data of other regions and improve the prediction accuracy. This makes it possible to improve the prediction accuracy by comparing with the weather data of other regions. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the weather data of other regions to the generation AI and cause the generation AI to improve the prediction accuracy.

[0094] The prediction unit can share the prediction result with other users and obtain feedback in the community. For example, the prediction unit posts the prediction result to the community and receives feedback from other users. The prediction unit can also compare its own prediction result with the prediction results of other users and confirm it. Furthermore, the prediction unit can improve the prediction method based on the feedback in the community. As a result, the prediction result can be shared with other users to obtain feedback in the community. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input feedback data of the community to the generation AI and cause the generation AI to execute an analysis of the feedback.

[0095] The monitoring unit can estimate the user's emotion and adjust the monitoring frequency according to the estimated user's emotion. For example, when the user is relaxed, the monitoring unit performs regular monitoring. Also, when the user is in a hurry, the monitoring unit can perform the minimum necessary monitoring. Furthermore, when the user is excited, the monitoring unit can perform detailed monitoring frequently. This makes it possible to adjust the monitoring frequency according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The monitoring unit can detect an abnormality in the environment based on the monitoring results and quickly propose countermeasures. For example, if the temperature in the room is too high, the monitoring unit can suggest adjusting the air conditioner. Also, if the humidity in the room is too low, the monitoring unit can suggest using a humidifier. Furthermore, if the light conditions in the room are inappropriate, the monitoring unit can suggest adjusting the lighting. This makes it possible to detect an abnormality in the environment based on the monitoring results and propose countermeasures early. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input environmental data to the generation AI and cause the generation AI to detect an abnormality and propose countermeasures.

[0097] The monitoring unit can compare the monitoring results with past data to analyze changes in the environment. The monitoring unit can compare the monitoring results with past temperature data to analyze changes in the current temperature. The monitoring unit can also compare the monitoring results with past humidity data to analyze changes in the current humidity. The monitoring unit can also compare the monitoring results with past light conditions to analyze changes in the current light. This makes it possible to analyze changes in the environment by comparing the monitoring results with past data. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or can be performed without using AI. For example, the monitoring unit can input past environmental data to the generation AI and cause the generation AI to analyze changes in the environment.

[0098] The monitoring unit can estimate the user's emotion and adjust the display method of the monitoring result according to the estimated emotion of the user. For example, when the user is nervous, the monitoring unit provides a simple and highly visible display method. In addition, when the user is relaxed, the monitoring unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the monitoring result according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The monitoring unit can adjust the environment in cooperation with other smart home appliances. The monitoring unit can, for example, work with an air conditioner to optimize the temperature in the room. The monitoring unit can also work with a humidifier to optimize the humidity in the room. Furthermore, the monitoring unit can also optimize the light conditions in the room in cooperation with lighting. This allows the environment to be optimized by working with other smart home appliances. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data of the smart home appliances to the generation AI and cause the generation AI to adjust the environment.

[0100] The monitoring unit can share the monitoring results with other users and obtain feedback in the community. For example, the monitoring unit posts the monitoring results to the community and receives feedback from other users. The monitoring unit can also check its own monitoring results by comparing them with the monitoring results of other users. Furthermore, the monitoring unit can improve the monitoring method based on the feedback in the community. This makes it possible to obtain feedback in the community by sharing the monitoring results with other users. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input feedback data of the community to the generation AI and cause the generation AI to analyze the feedback.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The discrimination unit can estimate the user's emotion and adjust the timing of taking a photo according to the estimated emotion of the user. For example, if the user is relaxed, it can suggest that the user wait for optimal lighting conditions before taking a photo. Also, if the user is in a hurry, it can be urged to take a photo immediately. Furthermore, if the user is excited, it can be suggested to take a photo from multiple angles. This makes it possible to provide the optimal timing of taking a photo according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The discrimination unit can evaluate the quality of the photograph taken and provide feedback to obtain a discrimination result. For example, if the photograph is dark, it suggests retaking the photograph in a bright place. Also, if the photograph is blurred, it can provide advice to prevent camera shake. Furthermore, if the composition of the photograph is inappropriate, it can instruct to retake the photograph with an appropriate composition. This makes it possible to evaluate the quality of the photograph and provide feedback to obtain an optimal discrimination result. Some or all of the above-mentioned processing in the discrimination unit may be performed, for example, using AI or may be performed without using AI. For example, the discrimination unit can input quality data of the photograph taken to the generation AI and cause the generation AI to perform quality evaluation.

[0104] The discrimination unit can integrate multiple photos to perform more accurate variety discrimination. For example, photos taken from multiple angles can be integrated to grasp the overall picture. Photos taken at different times can also be integrated to eliminate the influence of light. Furthermore, photos taken with different devices can also be integrated to improve the analysis accuracy. As a result, more accurate variety discrimination can be performed by integrating multiple photos. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input multiple photo data to the generation AI and cause the generation AI to execute the integration of the photos.

[0105] The discrimination unit can estimate the user's emotion and adjust the display method of the discrimination result according to the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the discrimination result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0106] The discrimination unit can analyze background information of the photographed photo to improve the accuracy of variety discrimination. For example, other plants in the background can be identified and used as a reference for variety discrimination. In addition, the light conditions of the background can be analyzed to correct the brightness of the photo. Furthermore, the color tone of the background can be analyzed to accurately discriminate the color of the plant. In this way, the accuracy of variety discrimination can be improved by analyzing the background information of the photo. Some or all of the above-mentioned processing in the discrimination unit can be performed, for example, using AI, or can be performed without using AI. For example, the discrimination unit can input background information data to the generation AI and cause the generation AI to execute the analysis of the background information.

[0107] The analysis unit can estimate the user's emotion and adjust the analysis frequency of the growth status according to the estimated emotion of the user. For example, when the user is relaxed, regular analysis is performed. Also, when the user is in a hurry, a minimum necessary analysis can be performed. Furthermore, when the user is excited, detailed analysis can be performed frequently. This makes it possible to adjust the analysis frequency of the growth status according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0108] The analysis unit can predict the current growth status based on past growth data. For example, the current moisture status can be predicted based on the frequency of watering in the past. The current nutritional status can also be predicted based on the history of fertilizer use in the past. Furthermore, the current growth stage can also be predicted based on the growth rate in the past. In this way, the current growth status can be predicted by referring to the past growth data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past growth data to the generation AI and cause the generation AI to execute a prediction of the current growth status.

[0109] The analysis unit can detect abnormalities in the growth conditions and quickly propose countermeasures. For example, it can detect changes in leaf color and warn of nutrient deficiency. It can also detect a decrease in growth rate and propose appropriate countermeasures. It can also detect abnormalities in moisture status and propose the timing of watering. This makes it possible to detect abnormalities in the growth conditions and propose countermeasures early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input growth condition data to the generation AI and cause the generation AI to detect abnormalities and propose countermeasures.

[0110] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result according to the estimated emotion of the user. For example, if the user is nervous, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the analysis result according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0111] The analysis unit can detect abnormalities by comparing with the growth data of other plants. For example, it can detect abnormalities in the growth rate by comparing with other plants of the same variety. It can also adjust the frequency of watering by comparing with the moisture status of other plants. It can also adjust the amount of fertilizer used by comparing with the nutritional status of other plants. In this way, it is possible to detect abnormalities by comparing with the growth data of other plants. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the growth data of other plants to the generation AI and cause the generation AI to detect abnormalities.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The discrimination unit analyzes photos of indoor plants taken by the user and discriminates the variety. The discrimination unit uses an image analysis algorithm to extract the features of the plant and identifies the variety based on the features such as the shape and color of the leaves and the thickness of the stem. In addition, the unit can discriminate the variety with high accuracy using a deep learning model. Step 2: The analysis unit analyzes the daily growth status based on the variety identified by the discrimination unit. The analysis unit analyzes the growth status based on the growth rate, leaf color, shape, etc. of the houseplant, and suggests appropriate measures if growth is slow. It can also use machine learning models to analyze growth patterns and detect abnormalities. Step 3: The provision unit provides an appropriate cultivation method based on the analysis results obtained by the analysis unit. The provision unit suggests watering frequency and type of fertilizer according to the variety of houseplant, and notifies the user of the optimal watering timing by reminder. In addition, the provision unit provides cultivation methods with high accuracy using a machine learning model. Step 4: The prediction unit uses weather forecasts and location information to predict sunlight and temperature. The prediction unit predicts the optimal sunlight and temperature for houseplants based on meteorological data, and advises users to move the houseplant to a window on sunny days based on the weather forecast. It also uses machine learning models to predict sunlight and temperature with high accuracy. Step 5: The monitoring unit monitors the environment in cooperation with the smart home appliance remote control. The monitoring unit monitors the temperature and humidity in the room and adjusts the air conditioner and humidifier as necessary. It uses temperature and humidity sensors to monitor the indoor environment in real time and uses machine learning models to suggest the optimal adjustment method.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the discrimination unit, analysis unit, provision unit, prediction unit, and monitoring unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the discrimination unit acquires a photo of an indoor houseplant taken by a user using the camera 42 of the smart device 14, and the specific processing unit 290 of the data processing device 12 uses an image analysis algorithm to determine the variety. The analysis unit analyzes the daily growth status based on the variety determined by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides an appropriate cultivation method based on the analysis result obtained by the specific processing unit 290 of the data processing device 12, for example. The prediction unit predicts sunlight and temperature using weather forecasts and location information by the specific processing unit 290 of the data processing device 12, for example. The monitoring unit cooperates with a smart home appliance remote control via the communication I / F 44 of the smart device 14, monitors the temperature and humidity in the room, and adjusts the air conditioner and humidifier as necessary. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the discrimination unit, analysis unit, provision unit, prediction unit, and monitoring unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the discrimination unit acquires a photo of an indoor houseplant taken by a user using the camera 42 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 uses an image analysis algorithm to determine the variety. The analysis unit analyzes the daily growth status based on the variety determined by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides an appropriate cultivation method based on the analysis result obtained by the specific processing unit 290 of the data processing device 12, for example. The prediction unit predicts sunlight and temperature using weather forecasts and location information by the specific processing unit 290 of the data processing device 12, for example. The monitoring unit cooperates with a smart home appliance remote control via the communication I / F 44 of the smart glasses 214, monitors the temperature and humidity in the room, and adjusts the air conditioner and humidifier as necessary. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements including the discrimination unit, analysis unit, provision unit, prediction unit, and monitoring unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the discrimination unit acquires a photo of an indoor houseplant taken by a user using the camera 42 of the headset type terminal 314, and the specific processing unit 290 of the data processing device 12 uses an image analysis algorithm to determine the variety. The analysis unit analyzes the daily growth status based on the variety determined by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides an appropriate cultivation method based on the analysis result obtained by the specific processing unit 290 of the data processing device 12, for example. The prediction unit predicts sunlight and temperature using weather forecasts and location information by the specific processing unit 290 of the data processing device 12, for example. The monitoring unit cooperates with a smart home appliance remote control via the communication I / F 44 of the headset type terminal 314, monitors the temperature and humidity in the room, and adjusts the air conditioner and humidifier as necessary. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a 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.

[0152] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] Each of the multiple elements including the discrimination unit, analysis unit, provision unit, prediction unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the discrimination unit acquires a photo of an indoor houseplant taken by a user using the camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 uses an image analysis algorithm to determine the variety. The analysis unit analyzes the daily growth status based on the variety determined by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides an appropriate cultivation method based on the analysis result obtained by the specific processing unit 290 of the data processing device 12, for example. The prediction unit predicts sunlight and temperature using weather forecasts and location information by the specific processing unit 290 of the data processing device 12, for example. The monitoring unit cooperates with a smart home appliance remote control via the communication I / F 44 of the robot 414, monitors the temperature and humidity in the room, and adjusts the air conditioner and humidifier as necessary. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] In addition, 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 upon request from the data processing device 12.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A discrimination unit that analyzes a photo of an indoor houseplant taken by a user and discriminates the species of the plant; an analysis unit that analyzes a growth condition based on the variety identified by the discrimination unit; a providing unit that provides an appropriate breeding method based on the analysis result obtained by the analysis unit; A prediction unit that predicts sunlight and temperature using weather forecasts or location information; A monitoring unit that monitors the environment in cooperation with a smart home appliance remote control. A system characterized by: (Appendix 2) The discrimination unit is Estimate the user's emotions and adjust the timing of taking photos according to the estimated user emotions 2. The system of claim 1, (Appendix 3) The discrimination unit is Evaluate the quality of the captured photos and provide feedback to help determine the results 2. The system of claim 1, (Appendix 4) The discrimination unit is Integrating multiple photos to identify varieties 2. The system of claim 1, (Appendix 5) The discrimination unit is Estimate the user's emotions and adjust the display method of the judgment results according to the estimated user emotions 2. The system of claim 1, (Appendix 6) The discrimination unit is Analyzing the background information of the photographs taken improves the accuracy of variety identification 2. The system of claim 1, (Appendix 7) The discrimination unit is Improve accuracy by comparing photos of plants of the same species with those of other users' photos 2. The system of claim 1, (Appendix 8) The analysis unit includes: The user's emotions are estimated, and the frequency of analysis of the growth status is adjusted according to the estimated user's emotions. 2. The system of claim 1, (Appendix 9) The analysis unit includes: Predicting current growth conditions based on past growth data 2. The system of claim 1, (Appendix 10) The analysis unit includes: Detect abnormalities in growth conditions and quickly propose countermeasures 2. The system of claim 1, (Appendix 11) The analysis unit includes: Infer user emotions and adjust the way analysis results are displayed according to the estimated user emotions 2. The system of claim 1, (Appendix 12) The analysis unit includes: Detect anomalies based on growth data from other plants 2. The system of claim 1, (Appendix 13) The analysis unit includes: Share your growth analysis with other users and get community feedback 2. The system of claim 1, (Appendix 14) The providing unit is The user's emotions are estimated, and the frequency of providing training methods is adjusted according to the estimated user's emotions. 2. The system of claim 1, (Appendix 15) The providing unit is When providing training methods, provide optimal advice based on the user's past training history 2. The system of claim 1, (Appendix 16) The providing unit is When providing growing instructions, we provide individualized advice for different stages of plant development. 2. The system of claim 1, (Appendix 17) The providing unit is Estimate the user's emotions and adjust the display method of the training method according to the estimated user's emotions 2. The system of claim 1, (Appendix 18) The providing unit is Compare with other users' training methods and provide appropriate advice 2. The system of claim 1, (Appendix 19) The providing unit is When providing training methods, customize them to suit the user's lifestyle. 2. The system of claim 1, (Appendix 20) The prediction unit is Infer the user's emotions and adjust the way prediction results are displayed according to the estimated user emotions 2. The system of claim 1, (Appendix 21) The prediction unit is Uses past weather data to provide more accurate forecasts of sunshine and temperature 2. The system of claim 1, (Appendix 22) The prediction unit is Suggests appropriate placement of plants based on prediction results 2. The system of claim 1, (Appendix 23) The prediction unit is Estimate the user's emotions and prioritize the prediction results according to the estimated user emotions. 2. The system of claim 1, (Appendix 24) The prediction unit is Improve forecast accuracy based on weather data from other regions 2. The system of claim 1, (Appendix 25) The prediction unit is Share your predictions with others and get community input 2. The system of claim 1, (Appendix 26) The monitoring unit is Estimate the user's emotions and adjust the monitoring frequency according to the estimated user emotions. 2. The system of claim 1, (Appendix 27) The monitoring unit is Detect environmental anomalies and quickly propose countermeasures based on monitoring results 2. The system of claim 1, (Appendix 28) The monitoring unit is Analyze environmental changes based on monitoring results and past data 2. The system of claim 1, (Appendix 29) The monitoring unit is Estimate the user's emotions and adjust the display method of the monitoring results according to the estimated user's emotions. 2. The system of claim 1, (Appendix 30) The monitoring unit is Link with other smart home appliances to adjust the environment 2. The system of claim 1, (Appendix 31) The monitoring unit is Share your monitoring results with other users and get community feedback 2. The system of claim 1, [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A discrimination unit that analyzes a photo of an indoor houseplant taken by a user and discriminates the species of the plant; an analysis unit that analyzes a growth condition based on the variety identified by the discrimination unit; a providing unit that provides an appropriate breeding method based on the analysis result obtained by the analysis unit; A prediction unit that predicts sunlight and temperature using weather forecasts or location information; A monitoring unit that monitors the environment in cooperation with a smart home appliance remote control. A system characterized by:

2. The discrimination unit is Estimate the user's emotions and adjust the timing of taking photos according to the estimated user emotions 2. The system of claim 1 .

3. The discrimination unit is Evaluate the quality of the captured photos and provide feedback to help determine the results 2. The system of claim 1 .

4. The discrimination unit is Integrating multiple photos to identify varieties 2. The system of claim 1 .

5. The discrimination unit is Estimate the user's emotions and adjust the display method of the judgment results according to the estimated user emotions 2. The system of claim 1 .

6. The discrimination unit is Analyzing the background information of the photographs taken improves the accuracy of variety identification 2. The system of claim 1 .

7. The discrimination unit is Improve accuracy by comparing photos of plants of the same species with those of other users' photos 2. The system of claim 1 .

8. The analysis unit includes: The user's emotions are estimated, and the frequency of analysis of the growth status is adjusted according to the estimated user's emotions.

2. The system of claim 1 .

Images