System

Abstract

An object of a system according to an embodiment is to provide a user with an appropriate maintenance method for a potted plant.SOLUTION: A system includes an imaging unit, a transmission unit, an analysis unit, and a provision unit. The imaging unit captures an image or a moving image of a potted plant. The transmission unit transmits the image captured by the imaging unit to the generation AI. The analysis unit analyzes the data transmitted by the transmission unit. The provision part provides care guidance on the basis of the result analyzed by the analysis part.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult for users who do not know how to properly care for potted plants to maintain their health.

[0005] The system according to the embodiment aims to provide a user with an appropriate method for caring for potted plants. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, a transmitting unit, an analyzing unit, and a providing unit. The photographing unit captures images and videos of the potted plant. The transmitting unit transmits the data photographed by the photographing unit to the generation AI. The analyzing unit analyzes the data transmitted by the transmitting unit. The providing unit provides care instructions based on the results of the analysis by the analyzing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a user with an appropriate method for caring for a potted plant. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 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 device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A cultivation support system according to an embodiment of the present invention uses a smartphone camera and a generation AI to support the cultivation of potted plants. This cultivation support system allows users to use their smartphone camera to take photos of the color, size, flowers, and fruit of potted plants, and then send the images and videos to the generation AI, which then provides care instructions tailored to the situation. This allows the cultivation support system to help users maintain the health of their plants and create an optimal cultivation environment while receiving expert-level advice.

[0029] A plant cultivation support system according to an embodiment includes a photographing unit, a transmitting unit, an analyzing unit, and a providing unit. The photographing unit captures images and videos of potted plants. For example, a user uses a smartphone camera to capture images of the color, size, flowers, and fruits of the potted plant's branches and leaves. The photographing unit can also record, for example, a time-lapse video of the plant's growth process. The transmitting unit transmits data captured by the photographing unit to the generating AI. For example, the transmitting unit transmits images and videos captured by the user to the generating AI. The transmitting unit can also simultaneously transmit, for example, environmental data such as temperature and humidity, in addition to the color of the plant's leaves and flowers. The analyzing unit analyzes the data transmitted by the transmitting unit using the generating AI. For example, the generating AI analyzes the transmitted images, videos, and text data to determine the plant's condition. The analyzing unit can also record, for example, a time-lapse video of the plant's growth process and analyze the changes to detect growth abnormalities. The providing unit provides care instructions based on the results of the analysis by the analyzing unit. For example, the generating AI provides the user with advice generated in the form of videos and documents. The providing unit can also use, for example, an emotion estimation function to analyze the user's emotions toward the plant and customize the tone and content of the advice based on those emotions. This allows the cultivation support system according to the embodiment to maintain the health of plants and create an optimal cultivation environment while receiving expert-like advice from the user. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students or teachers desire paper feedback, the results are printed using a printer. Sending the results via email provides quick feedback by directly sending the results to students and parents.

[0030] The camera unit records the plant's growth process as a time-lapse video, and the generation AI can analyze the changes to detect growth abnormalities. For example, the camera unit allows a user to take photos of the plant at the same time every day using a smartphone camera and record the images as a time-lapse video. The generation AI analyzes this time-lapse video to detect growth abnormalities and signs of disease. This allows the plant's growth process to be recorded as a time-lapse video, making it possible to detect growth abnormalities early on.

[0031] The transmitter not only transmits the color of the plant's leaves and flowers, but also environmental data such as temperature and humidity, allowing the generation AI to integrate and analyze this data. For example, when a user takes a photo of a plant, the transmitter simultaneously records environmental data using a thermometer and hygrometer. The generation AI then integrates and analyzes this data to evaluate the plant's health. This allows the transmitter to integrate and analyze not only the color of the plant's leaves and flowers, but also environmental data such as temperature and humidity.

[0032] The transmission unit uses voice input to record the user's questions and impressions when photographing a plant, and the generation AI can analyze the voice data. The transmission unit uses voice input to record the user's questions and impressions when photographing a plant, for example. The generation AI analyzes this voice data and provides appropriate advice. This allows the user's questions and impressions to be recorded using voice input, and the generation AI can analyze the voice data.

[0033] The analysis unit uses a dedicated soil sensor to collect data to check the condition of the plant's roots, and the generation AI can analyze that data. For example, when a user takes a photo of a plant, the analysis unit uses a dedicated soil sensor to check the condition of the roots. The generation AI analyzes this data and suggests appropriate care methods. This allows the generation AI to check the condition of the plant's roots using a dedicated soil sensor and analyze that data.

[0034] The analysis unit allows the generative AI to compare data with past data to identify abnormalities in order to detect signs of plant disease or pests at an early stage. For example, the analysis unit allows the generative AI to compare data with past data to identify abnormalities in plant disease or pests at an early stage. For example, the analysis unit analyzes changes in leaf color and shape to identify abnormalities. This allows for early detection of signs of plant disease or pests.

[0035] The analysis unit can analyze the growth pattern of the plant and generate customized advice for providing an optimal growth environment. For example, the generation AI analyzes the growth pattern of the plant and generates customized advice for providing an optimal growth environment. For example, adjusting the amount of light or water. This makes it possible to analyze the growth pattern of the plant and generate customized advice for providing an optimal growth environment.

[0036] The analysis unit can share the plant growth data with other users and generate advice and feedback on a community basis. For example, the generation AI of the analysis unit can share the plant growth data with other users and generate advice and feedback on a community basis. For example, users who grow the same plant can exchange information with each other. This allows the plant growth data to be shared with other users and generate advice and feedback on a community basis.

[0037] The analysis unit can generate advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, the generation AI generates advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, the analysis unit suggests seasonal care methods. This allows the generation of advice that takes into account external factors that affect plant growth.

[0038] The providing unit can visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, the providing unit uses a generating AI to visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, pruning methods and soil replacement procedures are displayed using 3D animations. This allows the plant care methods to be visually shown using 3D animations, allowing the user to intuitively understand.

[0039] The provision unit can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, the generation AI can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, the provision unit can generate a list of pruning shears, fertilizer, etc. This allows the automatic listing of tools and materials needed for plant care and support purchasing in cooperation with online shopping sites.

[0040] The provision unit customizes the plant care method according to the user's skill level, making it possible to accommodate users from beginners to advanced. For example, the provision unit customizes the plant care method according to the user's skill level using a generation AI, making it possible to accommodate users from beginners to advanced. For example, the provision unit provides basic care methods for beginners and advanced care methods for advanced users. This makes it possible to customize the care method according to the user's skill level.

[0041] The provision unit can provide plant care instructions with multilingual support that corresponds to different languages ​​and cultures. For example, the provision unit provides plant care instructions with multilingual support that corresponds to different languages ​​and cultures using a generation AI. For example, the care instructions are explained in multiple languages, such as English, French, and Chinese. This allows the care instructions to be provided with multilingual support that corresponds to different languages ​​and cultures.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can share plant growth data with other users and generate community-based advice and feedback. For example, the generation AI can share plant growth data with other users and generate community-based advice and feedback. For example, users who grow the same plant can exchange information with each other. This allows plant growth data to be shared with other users and generate community-based advice and feedback.

[0044] The providing unit can visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, the generating AI can visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, pruning methods and soil replacement procedures are displayed using 3D animations. This allows the plant care methods to be visually shown using 3D animations, allowing the user to intuitively understand.

[0045] The provision unit can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, the generation AI can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, it generates a list of pruning shears, fertilizer, etc. This allows the system to automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites.

[0046] The analysis unit can generate advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, the generation AI generates advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, it suggests seasonal care methods. This allows it to generate advice that takes into account external factors that affect plant growth.

[0047] The providing unit can provide plant care instructions with multilingual support corresponding to different languages ​​and cultures. For example, the generating AI provides plant care instructions with multilingual support corresponding to different languages ​​and cultures. For example, the care instructions are explained in multiple languages ​​such as English, French, and Chinese. This allows the care instructions to be provided with multilingual support corresponding to different languages ​​and cultures.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The camera captures images and videos of the potted plant. For example, a user can use a smartphone camera to capture images of the color, size, flowers, and fruit of the potted plant's branches and leaves. The camera can also record the plant's growth process as a time-lapse video. Step 2: The transmitter sends the data captured by the camera to the generation AI. For example, it sends images and videos captured by the user to the generation AI. The transmitter can also simultaneously transmit environmental data such as temperature and humidity, in addition to the color of the plant's leaves and flowers. Step 3: The analysis unit uses the generation AI to analyze the data sent by the transmission unit. For example, the generation AI analyzes the sent images, videos, and text data to determine the condition of the plant. The analysis unit can also record the plant's growth process as a time-lapse video and analyze the changes to detect growth abnormalities. Step 4: The provision unit provides care guidance based on the results of the analysis by the analysis unit. For example, the provision unit provides the user with advice generated by the generation AI in the form of video and text. The provision unit can also use an emotion estimation function to analyze the user's feelings toward the plant and customize the tone and content of the advice based on those feelings.

[0050] (Example 2) A cultivation support system according to an embodiment of the present invention uses a smartphone camera and a generation AI to support the cultivation of potted plants. This cultivation support system allows users to use their smartphone camera to take photos of the color, size, flowers, and fruit of potted plants, and then send the images and videos to the generation AI, which then provides care instructions tailored to the situation. This allows the cultivation support system to help users maintain the health of their plants and create an optimal cultivation environment while receiving expert-level advice.

[0051] A plant cultivation support system according to an embodiment includes a photographing unit, a transmitting unit, an analyzing unit, and a providing unit. The photographing unit captures images and videos of potted plants. For example, a user uses a smartphone camera to capture images of the color, size, flowers, and fruits of the potted plant's branches and leaves. The photographing unit can also record, for example, a time-lapse video of the plant's growth process. The transmitting unit transmits data captured by the photographing unit to the generating AI. For example, the transmitting unit transmits images and videos captured by the user to the generating AI. The transmitting unit can also simultaneously transmit, for example, environmental data such as temperature and humidity, in addition to the color of the plant's leaves and flowers. The analyzing unit analyzes the data transmitted by the transmitting unit using the generating AI. For example, the generating AI analyzes the transmitted images, videos, and text data to determine the plant's condition. The analyzing unit can also record, for example, a time-lapse video of the plant's growth process and analyze the changes to detect growth abnormalities. The providing unit provides care instructions based on the results of the analysis by the analyzing unit. For example, the generating AI provides the user with advice generated in the form of videos and documents. The providing unit can also use, for example, an emotion estimation function to analyze the user's emotions toward the plant and customize the tone and content of the advice based on those emotions. This allows the cultivation support system according to the embodiment to maintain the health of plants and create an optimal cultivation environment while receiving expert-like advice from the user. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students or teachers desire paper feedback, the results are printed using a printer. Sending the results via email provides quick feedback by directly sending the results to students and parents.

[0052] The camera unit records the plant's growth process as a time-lapse video, and the generation AI can analyze the changes to detect growth abnormalities. For example, the camera unit allows a user to take photos of the plant at the same time every day using a smartphone camera and record the images as a time-lapse video. The generation AI analyzes this time-lapse video to detect growth abnormalities and signs of disease. This allows the plant's growth process to be recorded as a time-lapse video, making it possible to detect growth abnormalities early on.

[0053] The transmitter not only transmits the color of the plant's leaves and flowers, but also environmental data such as temperature and humidity, allowing the generation AI to integrate and analyze this data. For example, when a user takes a photo of a plant, the transmitter simultaneously records environmental data using a thermometer and hygrometer. The generation AI then integrates and analyzes this data to evaluate the plant's health. This allows the transmitter to integrate and analyze not only the color of the plant's leaves and flowers, but also environmental data such as temperature and humidity.

[0054] The providing unit uses the emotion estimation function to analyze the emotions the user has toward the plant, and can customize the tone and content of the advice based on those emotions. For example, when the user takes a photo of the plant, the providing unit uses the emotion estimation function to analyze the user's emotions. The generation AI customizes the tone and content of the advice based on this emotion data. This makes it possible to customize the tone and content of the advice based on the user's emotions.

[0055] The transmission unit uses voice input to record the user's questions and impressions when photographing a plant, and the generation AI can analyze the voice data. The transmission unit uses voice input to record the user's questions and impressions when photographing a plant, for example. The generation AI analyzes this voice data and provides appropriate advice. This allows the user's questions and impressions to be recorded using voice input, and the generation AI can analyze the voice data.

[0056] The analysis unit uses a dedicated soil sensor to collect data to check the condition of the plant's roots, and the generation AI can analyze that data. For example, when a user takes a photo of a plant, the analysis unit uses a dedicated soil sensor to check the condition of the roots. The generation AI analyzes this data and suggests appropriate care methods. This allows the generation AI to check the condition of the plant's roots using a dedicated soil sensor and analyze that data.

[0057] The providing unit can use the emotion estimation function to provide relaxing music and a message to reduce stress and anxiety felt by the user when photographing a plant. For example, when the user takes a photo of a plant, the providing unit uses the emotion estimation function to analyze the user's stress and anxiety and provides relaxing music and a message. This makes it possible to provide relaxing music and a message to reduce stress and anxiety felt by the user when photographing a plant.

[0058] The analysis unit allows the generative AI to compare data with past data to identify abnormalities in order to detect signs of plant disease or pests at an early stage. For example, the analysis unit allows the generative AI to compare data with past data to identify abnormalities in plant disease or pests at an early stage. For example, the analysis unit analyzes changes in leaf color and shape to identify abnormalities. This allows for early detection of signs of plant disease or pests.

[0059] The analysis unit can analyze the growth pattern of the plant and generate customized advice for providing an optimal growth environment. For example, the generation AI analyzes the growth pattern of the plant and generates customized advice for providing an optimal growth environment. For example, adjusting the amount of light or water. This makes it possible to analyze the growth pattern of the plant and generate customized advice for providing an optimal growth environment.

[0060] The providing unit can use the emotion estimation function to analyze the emotion the user has about the state of the plant and adjust the content of the advice based on that emotion. For example, the generating AI can use the emotion estimation function to analyze the emotion the user has about the state of the plant and adjust the content of the advice based on that emotion. For example, if the user is feeling anxious, the providing unit can provide reassuring advice. This makes it possible to adjust the content of the advice based on the user's emotions.

[0061] The analysis unit can share the plant growth data with other users and generate advice and feedback on a community basis. For example, the generation AI of the analysis unit can share the plant growth data with other users and generate advice and feedback on a community basis. For example, users who grow the same plant can exchange information with each other. This allows the plant growth data to be shared with other users and generate advice and feedback on a community basis.

[0062] The analysis unit can generate advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, the generation AI generates advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, the analysis unit suggests seasonal care methods. This allows the generation of advice that takes into account external factors that affect plant growth.

[0063] The provision unit can use the emotion estimation function to analyze the expectations and hopes that the user has regarding the growth of the plant and provide advice that meets those expectations. For example, the provision unit can use the generation AI's emotion estimation function to analyze the expectations and hopes that the user has regarding the growth of the plant and provide advice that meets those expectations. For example, if the user wishes for the plant to bloom, the provision unit can suggest specific care methods to make the plant bloom. This makes it possible to provide advice that meets the user's expectations and hopes.

[0064] The providing unit can visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, the providing unit uses a generating AI to visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, pruning methods and soil replacement procedures are displayed using 3D animations. This allows the plant care methods to be visually shown using 3D animations, allowing the user to intuitively understand.

[0065] The provision unit can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, the generation AI can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, the provision unit can generate a list of pruning shears, fertilizer, etc. This allows the automatic listing of tools and materials needed for plant care and support purchasing in cooperation with online shopping sites.

[0066] The provision unit customizes the plant care method according to the user's skill level, making it possible to accommodate users from beginners to advanced. For example, the provision unit customizes the plant care method according to the user's skill level using a generation AI, making it possible to accommodate users from beginners to advanced. For example, the provision unit provides basic care methods for beginners and advanced care methods for advanced users. This makes it possible to customize the care method according to the user's skill level.

[0067] The provision unit can provide plant care instructions with multilingual support that corresponds to different languages ​​and cultures. For example, the provision unit provides plant care instructions with multilingual support that corresponds to different languages ​​and cultures using a generation AI. For example, the care instructions are explained in multiple languages, such as English, French, and Chinese. This allows the care instructions to be provided with multilingual support that corresponds to different languages ​​and cultures.

[0068] The providing unit can use the emotion estimation function to provide positive feedback to enhance the sense of accomplishment and satisfaction the user feels during the grooming work. For example, the generating AI uses the emotion estimation function to provide positive feedback to enhance the sense of accomplishment and satisfaction the user feels during the grooming work. For example, the providing unit displays words of praise when grooming is completed. This makes it possible to provide positive feedback to enhance the sense of accomplishment and satisfaction the user feels during the grooming work.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The analysis unit can share plant growth data with other users and generate community-based advice and feedback. For example, the generation AI can share plant growth data with other users and generate community-based advice and feedback. For example, users who grow the same plant can exchange information with each other. This allows plant growth data to be shared with other users and generate community-based advice and feedback.

[0071] The providing unit can visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, the generating AI can visually show plant care methods using 3D animations, allowing the user to intuitively understand. For example, pruning methods and soil replacement procedures are displayed using 3D animations. This allows the plant care methods to be visually shown using 3D animations, allowing the user to intuitively understand.

[0072] The provision unit can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, the generation AI can automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites. For example, it generates a list of pruning shears, fertilizer, etc. This allows the system to automatically list the tools and materials needed for plant care and support purchasing in cooperation with online shopping sites.

[0073] The analysis unit can generate advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, the generation AI generates advice that takes into account external factors that affect plant growth (weather, season, etc.). For example, it suggests seasonal care methods. This allows it to generate advice that takes into account external factors that affect plant growth.

[0074] The providing unit can provide plant care instructions with multilingual support corresponding to different languages ​​and cultures. For example, the generating AI provides plant care instructions with multilingual support corresponding to different languages ​​and cultures. For example, the care instructions are explained in multiple languages ​​such as English, French, and Chinese. This allows the care instructions to be provided with multilingual support corresponding to different languages ​​and cultures.

[0075] The providing unit can use the emotion estimation function to provide relaxing music and messages to reduce stress and anxiety felt by the user when photographing plants. For example, when the user takes a photo of a plant, the emotion estimation function is used to analyze the user's stress and anxiety and provide relaxing music and messages. This makes it possible to provide relaxing music and messages to reduce stress and anxiety felt by the user when photographing plants.

[0076] The provision unit can use the emotion estimation function to analyze the user's expectations and hopes for the growth of the plant and provide advice that meets those expectations. For example, the generation AI can use the emotion estimation function to analyze the user's expectations and hopes for the growth of the plant and provide advice that meets those expectations. For example, if the user wants the plant to bloom, the system will suggest specific care methods to make the plant bloom. This makes it possible to provide advice that meets the user's expectations and hopes.

[0077] The providing unit can use the emotion estimation function to provide positive feedback to increase the sense of accomplishment and satisfaction the user feels during the grooming work. For example, the generation AI can use the emotion estimation function to provide positive feedback to increase the sense of accomplishment and satisfaction the user feels during the grooming work. For example, it can display words of praise when grooming is completed. This can provide positive feedback to increase the sense of accomplishment and satisfaction the user feels during the grooming work.

[0078] The providing unit uses the emotion estimation function to analyze the emotions the user has toward plants and can customize the tone and content of advice based on those emotions. For example, when a user takes a photo of a plant, the emotion estimation function is used to analyze the user's emotions. The generating AI customizes the tone and content of advice based on this emotional data. This allows the tone and content of advice to be customized based on the user's emotions.

[0079] The providing unit can use the emotion estimation function to analyze the emotion the user feels about the state of the plant and adjust the content of the advice based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the emotion the user feels about the state of the plant and adjust the content of the advice based on that emotion. For example, if the user is feeling anxious, it can provide reassuring advice. This makes it possible to adjust the content of the advice based on the user's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The camera captures images and videos of the potted plant. For example, a user can use a smartphone camera to capture images of the color, size, flowers, and fruit of the potted plant's branches and leaves. The camera can also record the plant's growth process as a time-lapse video. Step 2: The transmitter sends the data captured by the camera to the generation AI. For example, it sends images and videos captured by the user to the generation AI. The transmitter can also simultaneously transmit environmental data such as temperature and humidity, in addition to the color of the plant's leaves and flowers. Step 3: The analysis unit uses the generation AI to analyze the data sent by the transmission unit. For example, the generation AI analyzes the sent images, videos, and text data to determine the condition of the plant. The analysis unit can also record the plant's growth process as a time-lapse video and analyze the changes to detect growth abnormalities. Step 4: The provision unit provides care guidance based on the results of the analysis by the analysis unit. For example, the provision unit provides the user with advice generated by the generation AI in the form of video and text. The provision unit can also use an emotion estimation function to analyze the user's feelings toward the plant and customize the tone and content of the advice based on those feelings.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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, RAM 30, and 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.

[0088] 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.

[0089] 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 audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] 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 carried out in a secure state.

[0092] Fig. 4 shows an example of the 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.

[0093] 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.

[0094] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0098] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and 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.

[0103] 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.

[0104] 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 audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] 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 carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0113] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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, RAM 30, and 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.

[0118] 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.

[0119] 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 audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] 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 carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0123] 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.

[0124] 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.

[0125] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 voice indicating a user input regarding 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.

[0129] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] 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.

[0139] 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 USB (Universal Serial Bus) 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.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0144] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 camera unit that takes images and videos of potted plants; a transmission unit that transmits the data captured by the imaging unit to a generation AI; an analysis unit that analyzes the data transmitted by the transmission unit; and a providing unit that provides care guidance based on the results of the analysis by the analyzing unit. A system characterized by:

2. The imaging unit is The growth process of the plant is recorded as a time-lapse video, and the generation AI analyzes the changes and detects growth abnormalities.

2. The system of claim 1.

3. The transmission unit Not only the color of the plant's leaves and flowers, but also environmental data such as temperature and humidity are transmitted at the same time, and the generating AI integrates and analyzes this data.

2. The system of claim 1.

4. The analysis unit The data is collected using a dedicated soil sensor to check the condition of the roots of the plant, and the generation AI analyzes the data.

2. The system of claim 1.

5. The providing unit The method of caring for the plant is visually shown in the 3D animation, allowing the user to intuitively understand it.

2. The system of claim 1.

6. The providing unit Using an emotion estimation function, the emotion the user feels toward the plant is analyzed, and the tone and content of the advice are customized based on the emotion.

2. The system of claim 1.

7. The providing unit Using an emotion estimation function, the emotion the user feels about the state of the plant is analyzed, and the content of the advice is adjusted based on the emotion.

2. The system of claim 1.

8. The providing unit Using the emotion estimation function, positive feedback is provided to enhance the sense of accomplishment and satisfaction felt by the user during the maintenance work.

2. The system of claim 1.

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