System

Abstract

An object of a system according to an embodiment is to accurately grasp the health condition of a plant and provide advice on how to grow the plant appropriately.SOLUTION: A system includes an image recognition unit, an advice providing unit, and an information providing unit. The image recognition unit analyzes the image of the plant. The advice providing unit provides advice on how to grow the plant based on the image of the plant analyzed by the image recognition unit. The information providing unit provides information according to the type and the health condition of the plant on the basis of the growing advice provided by the advice providing unit.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 technology has the problem of making it difficult to accurately grasp the health of plants and provide advice on how to grow them appropriately.

[0005] The system according to the embodiment aims to accurately grasp the health condition of a plant and provide advice on how to grow it appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes an image recognition unit, an advice providing unit, and an information providing unit. The image recognition unit analyzes an image of a plant. The advice providing unit provides advice on how to grow the plant based on the image of the plant analyzed by the image recognition unit. The information providing unit provides information according to the type and health condition of the plant based on the advice on how to grow the plant provided by the advice providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the health condition of a plant and provide advice on how to properly grow it. [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 non-volatile storage devices that store various programs, 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 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) The plant care support system according to an embodiment of the present invention is a system that automatically analyzes images of plants and uses a generation AI to provide advice and information on how to grow them. This allows the plant care support system to analyze images of plants taken by users and provide appropriate advice and information on how to grow them.

[0029] A plant care support system according to an embodiment includes an image recognition unit, an advice providing unit, and an information providing unit. The image recognition unit analyzes images of plants. For example, the generation AI analyzes the color and shape of the plant's leaves and the condition of its stem to determine the plant's health. The generation AI can also identify the plant's type. For example, the generation AI determines the plant's type based on the shape and color of its leaves. The advice providing unit provides plant care advice based on the plant image analyzed by the image recognition unit. For example, the generation AI can provide advice such as, "This plant is not getting enough sunlight, so move it to a sunnier location." The generation AI can also provide advice such as, "This plant is not getting enough water, so increase the amount of watering to once a week." The information providing unit provides information based on the plant care advice provided by the advice providing unit, depending on the plant's type and health condition. For example, the generation AI can provide information on how to care for a specific plant, how to prevent disease, and the nutrients it needs. The generation AI can also provide information based on the plant's health condition. For example, if a plant is sick, the generation AI can provide treatment methods for the disease. As a result, the plant care support system according to the embodiment can analyze images of plants taken by users and provide advice and information on appropriate ways to grow them. For example, users can constantly monitor the health of their plants and provide appropriate care. Furthermore, even if a plant becomes sick, it can be detected early and appropriate measures can be taken.

[0030] The image recognition unit detects minute discolorations and spots on plant leaves, enabling early detection of disease. For example, the image recognition unit uses an algorithm that enables the generative AI to detect minute discolorations and spots on plant leaves. For example, the generative AI uses an image processing algorithm to detect changes in leaf color and identify signs of disease. This enables early detection of plant diseases and the implementation of appropriate countermeasures.

[0031] The image recognition unit analyzes the growth patterns of plants and detects abnormal growth, making it possible to predict potential problems. For example, the image recognition unit uses an algorithm that allows the generative AI to analyze the growth patterns of plants and detect abnormal growth. For example, the generative AI analyzes changes in leaf growth rate and stem thickness to identify abnormal growth. The image recognition unit also uses an algorithm that allows the generative AI to analyze the growth patterns of plants. For example, the generative AI uses a machine learning algorithm to analyze growth patterns and detect abnormal growth. This makes it possible to predict potential problems and take appropriate measures by analyzing plant growth patterns and detecting abnormal growth early.

[0032] The image recognition unit can analyze images of the condition of plant roots and detect root rot and nutrient deficiencies. For example, the generation AI analyzes images of plant roots to detect root rot. For example, the image recognition unit analyzes the color and shape of the roots to identify signs of root rot. The image recognition unit also uses an algorithm that allows the generation AI to analyze the condition of plant roots. For example, the generation AI uses an image processing algorithm to detect changes in the color of the roots and identify signs of root rot. This allows the condition of plant roots to be analyzed and root rot and nutrient deficiencies to be detected early, allowing appropriate measures to be taken.

[0033] The image recognition unit can analyze the environment surrounding the plant, evaluate the soil quality and humidity, and make suggestions for improving the environment. For example, the image recognition unit allows the generation AI to analyze the environment surrounding the plant and evaluate the soil quality. For example, it analyzes the color and particle size of the soil to identify the soil quality. The image recognition unit also uses an algorithm that allows the generation AI to analyze the environment surrounding the plant. For example, the generation AI uses an image processing algorithm to evaluate the soil quality and make suggestions for improving the environment. This allows the health of the plant to be maintained by analyzing the environment surrounding the plant and making suggestions for appropriate environmental improvements.

[0034] The advice providing unit can analyze the growth history of a plant and compare past care methods with the current state to provide optimal advice. In the advice providing unit, for example, the generation AI analyzes the growth history of a plant and compares past care methods with the current state. For example, the current care method is suggested based on past watering and fertilizer use history. The advice providing unit also uses an algorithm for the generation AI to analyze the growth history of a plant. For example, the generation AI analyzes the growth history using a machine learning algorithm and provides optimal advice. In this way, optimal advice can be provided by analyzing the growth history of a plant and comparing past care methods with the current state.

[0035] The advice providing unit can learn different cultivation patterns for each type of plant and provide individually customized advice. For example, the generation AI can learn different cultivation patterns for each type of plant and provide individually customized advice. For example, the generation AI can suggest how to water and fertilize a specific plant. The advice providing unit also uses an algorithm that enables the generation AI to learn different cultivation patterns for each type of plant. For example, the generation AI can learn cultivation patterns using a machine learning algorithm and provide customized advice. This allows the generation AI to learn different cultivation patterns for each type of plant and provide individually customized advice.

[0036] The information providing unit can provide detailed growing guides for each type of plant, allowing users to easily search for the information they need. For example, the information providing unit allows the generation AI to provide detailed growing guides for each type of plant. For example, it provides detailed instructions on how to water and fertilize a specific plant. The information providing unit also uses an algorithm that enables the generation AI to easily search for the information it needs. For example, the generation AI uses keyword search and filtering functions to enable users to easily search for the information they need. This provides detailed growing guides for each type of plant, allowing users to easily search for the information they need.

[0037] The information provision unit can provide nutrient information according to the health state of the plant and recommend appropriate fertilizers and supplements. For example, the generation AI in the information provision unit analyzes the health state of the plant and identifies the nutrients it needs. For example, it recommends appropriate fertilizers and supplements based on the leaf color and growth rate. The information provision unit also uses an algorithm that enables the generation AI to provide nutrient information according to the health state of the plant. For example, the generation AI uses a machine learning algorithm to provide nutrient information and recommend appropriate fertilizers and supplements. This makes it possible to provide nutrient information according to the health state of the plant and recommend appropriate fertilizers and supplements.

[0038] The information provision unit can provide videos and tutorials on how to grow plants, providing information that is visually easy to understand. For example, the generation AI provides videos and tutorials on how to grow plants. For example, it uses videos to explain how to water plants and how to use fertilizer. The information provision unit also uses an algorithm that enables the generation AI to provide information that is visually easy to understand. For example, the generation AI uses illustrations and animations to provide information that is visually easy to understand. This allows the generation AI to provide videos and tutorials on how to grow plants, providing information that is visually easy to understand.

[0039] The information provision unit can provide the latest research results and news on how to grow plants, allowing the user to always be up to date with the latest information. For example, the generation AI provides the latest research results on how to grow plants. For example, it introduces research results on new fertilizers and nutrients. The information provision unit also uses an algorithm that enables the generation AI to always be up to date with the latest information. For example, the generation AI uses real-time updates and notification functions to always be up to date with the latest information. This provides the latest research results and news on how to grow plants, allowing the user to always be up to date with the latest information.

[0040] The advice providing unit can provide advice according to the growth stage of the plant and suggest care methods suitable for the growth period, flowering period, dormancy period, etc. The advice providing unit, for example, uses an algorithm that enables the generation AI to provide advice according to the growth stage of the plant. For example, it suggests an appropriate method of using fertilizer during the growth period. The advice providing unit also uses an algorithm that enables the generation AI to provide advice according to the growth stage of the plant. For example, the generation AI uses a machine learning algorithm to analyze the growth stage and suggest an appropriate care method. This allows the generation AI to provide advice according to the growth stage of the plant and suggest care methods suitable for the growth period, flowering period, dormancy period, etc.

[0041] The advice providing unit can predict the risk of plant disease and pest outbreaks and advise on preventive measures. For example, the generation AI in the advice providing unit predicts the risk of plant disease and pest outbreaks and advises on preventive measures. For example, it suggests preventive measures during periods when the risk of a particular disease outbreak is high. The advice providing unit also uses an algorithm that enables the generation AI to predict the risk of plant disease and pest outbreaks. For example, the generation AI predicts the risk of outbreaks based on past data and environmental conditions and suggests preventive measures. This makes it possible to predict the risk of plant disease and pest outbreaks and advise on preventive measures.

[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 plant care support system can further include a plant growth prediction unit. The growth prediction unit analyzes past growth data of the plant and predicts future growth. For example, it predicts the next growth stage based on past growth rate and environmental conditions. The growth prediction unit can also identify factors that affect plant growth and suggest optimal care methods. This allows the user to predict the future growth of the plant and provide appropriate care.

[0044] The plant care support system can further include a plant health diagnosis unit. The health diagnosis unit analyzes images of the plant and comprehensively evaluates its health condition. For example, it comprehensively analyzes the color and shape of the leaves, the condition of the stem, and the condition of the roots to calculate a health score. The health diagnosis unit can also suggest necessary care methods based on the health score. This allows the user to comprehensively understand the health condition of the plant and provide appropriate care.

[0045] The plant care support system may further include a plant growth simulation unit. The growth simulation unit simulates the growth of a plant and visually displays future growth. For example, it simulates the growth of a plant one month from its current state and visually displays this to the user. The growth simulation unit may also simulate different care methods and suggest the optimal care method. This allows the user to visually grasp the future growth of the plant and provide appropriate care.

[0046] The plant care support system can further include a plant nutrition management module. The nutrition management module analyzes the plant's nutritional status and identifies the nutrients it needs. For example, it can suggest the necessary fertilizers and supplements based on leaf color and growth rate. The nutrition management module can also evaluate the balance of nutrients and identify excess or deficiency of nutrients. This allows users to properly manage the nutritional status of their plants and promote healthy growth.

[0047] The plant care support system may further include a plant environment monitoring unit. The environment monitoring unit monitors the plant's surrounding environment in real time and evaluates the environmental conditions. For example, it monitors temperature, humidity, and light intensity and proposes optimal environmental conditions. The environment monitoring unit can also detect changes in the environmental conditions and propose appropriate measures. This allows the user to properly manage the plant's surrounding environment and promote healthy growth.

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

[0049] Step 1: The image recognition unit analyzes the image of the plant. For example, the generation AI analyzes the color and shape of the plant's leaves and the condition of the stem to determine the plant's health. The generation AI can also identify the plant's type. For example, the generation AI can determine the plant's type based on the shape and color of the leaves. Step 2: The advice provider provides advice on how to grow the plant based on the image of the plant analyzed by the image recognition unit. For example, the generator AI might provide advice such as, "This plant is not getting enough sunlight, so move it to a sunnier location." The generator AI might also provide advice such as, "This plant is not getting enough water, so increase the amount of watering to once a week." Step 3: The information providing unit provides information according to the type and health condition of the plant based on the growing advice provided by the advice providing unit. For example, the generating AI provides information such as how to grow a specific plant, how to prevent disease, and the nutrients it needs. The generating AI can also provide information according to the health condition of the plant. For example, if the plant is sick, the generating AI provides how to treat the disease.

[0050] (Example 2) The plant care support system according to an embodiment of the present invention is a system that automatically analyzes images of plants and uses a generation AI to provide advice and information on how to grow them. This allows the plant care support system to analyze images of plants taken by users and provide appropriate advice and information on how to grow them.

[0051] A plant care support system according to an embodiment includes an image recognition unit, an advice providing unit, and an information providing unit. The image recognition unit analyzes images of plants. For example, the generation AI analyzes the color and shape of the plant's leaves and the condition of its stem to determine the plant's health. The generation AI can also identify the plant's type. For example, the generation AI determines the plant's type based on the shape and color of its leaves. The advice providing unit provides plant care advice based on the plant image analyzed by the image recognition unit. For example, the generation AI can provide advice such as, "This plant is not getting enough sunlight, so move it to a sunnier location." The generation AI can also provide advice such as, "This plant is not getting enough water, so increase the amount of watering to once a week." The information providing unit provides information based on the plant care advice provided by the advice providing unit, depending on the plant's type and health condition. For example, the generation AI can provide information on how to care for a specific plant, how to prevent disease, and the nutrients it needs. The generation AI can also provide information based on the plant's health condition. For example, if a plant is sick, the generation AI can provide treatment methods for the disease. As a result, the plant care support system according to the embodiment can analyze images of plants taken by users and provide advice and information on appropriate ways to grow them. For example, users can constantly monitor the health of their plants and provide appropriate care. Furthermore, even if a plant becomes sick, it can be detected early and appropriate measures can be taken.

[0052] The image recognition unit detects minute discolorations and spots on plant leaves, enabling early detection of disease. For example, the image recognition unit uses an algorithm that enables the generative AI to detect minute discolorations and spots on plant leaves. For example, the generative AI uses an image processing algorithm to detect changes in leaf color and identify signs of disease. This enables early detection of plant diseases and the implementation of appropriate countermeasures.

[0053] The image recognition unit analyzes the growth patterns of plants and detects abnormal growth, making it possible to predict potential problems. For example, the image recognition unit uses an algorithm that allows the generative AI to analyze the growth patterns of plants and detect abnormal growth. For example, the generative AI analyzes changes in leaf growth rate and stem thickness to identify abnormal growth. The image recognition unit also uses an algorithm that allows the generative AI to analyze the growth patterns of plants. For example, the generative AI uses a machine learning algorithm to analyze growth patterns and detect abnormal growth. This makes it possible to predict potential problems and take appropriate measures by analyzing plant growth patterns and detecting abnormal growth early.

[0054] The image recognition unit can analyze images of the condition of plant roots and detect root rot and nutrient deficiencies. For example, the generation AI analyzes images of plant roots to detect root rot. For example, the image recognition unit analyzes the color and shape of the roots to identify signs of root rot. The image recognition unit also uses an algorithm that allows the generation AI to analyze the condition of plant roots. For example, the generation AI uses an image processing algorithm to detect changes in the color of the roots and identify signs of root rot. This allows the condition of plant roots to be analyzed and root rot and nutrient deficiencies to be detected early, allowing appropriate measures to be taken.

[0055] The image recognition unit can analyze the environment surrounding the plant, evaluate the soil quality and humidity, and make suggestions for improving the environment. For example, the image recognition unit allows the generation AI to analyze the environment surrounding the plant and evaluate the soil quality. For example, it analyzes the color and particle size of the soil to identify the soil quality. The image recognition unit also uses an algorithm that allows the generation AI to analyze the environment surrounding the plant. For example, the generation AI uses an image processing algorithm to evaluate the soil quality and make suggestions for improving the environment. This allows the health of the plant to be maintained by analyzing the environment surrounding the plant and making suggestions for appropriate environmental improvements.

[0056] The advice providing unit can analyze the growth history of a plant and compare past care methods with the current state to provide optimal advice. In the advice providing unit, for example, the generation AI analyzes the growth history of a plant and compares past care methods with the current state. For example, the current care method is suggested based on past watering and fertilizer use history. The advice providing unit also uses an algorithm for the generation AI to analyze the growth history of a plant. For example, the generation AI analyzes the growth history using a machine learning algorithm and provides optimal advice. In this way, optimal advice can be provided by analyzing the growth history of a plant and comparing past care methods with the current state.

[0057] The advice providing unit can learn different cultivation patterns for each type of plant and provide individually customized advice. For example, the generation AI can learn different cultivation patterns for each type of plant and provide individually customized advice. For example, the generation AI can suggest how to water and fertilize a specific plant. The advice providing unit also uses an algorithm that enables the generation AI to learn different cultivation patterns for each type of plant. For example, the generation AI can learn cultivation patterns using a machine learning algorithm and provide customized advice. This allows the generation AI to learn different cultivation patterns for each type of plant and provide individually customized advice.

[0058] The information providing unit can provide detailed growing guides for each type of plant, allowing users to easily search for the information they need. For example, the information providing unit allows the generation AI to provide detailed growing guides for each type of plant. For example, it provides detailed instructions on how to water and fertilize a specific plant. The information providing unit also uses an algorithm that enables the generation AI to easily search for the information it needs. For example, the generation AI uses keyword search and filtering functions to enable users to easily search for the information they need. This provides detailed growing guides for each type of plant, allowing users to easily search for the information they need.

[0059] The information provision unit can provide nutrient information according to the health state of the plant and recommend appropriate fertilizers and supplements. For example, the generation AI in the information provision unit analyzes the health state of the plant and identifies the nutrients it needs. For example, it recommends appropriate fertilizers and supplements based on the leaf color and growth rate. The information provision unit also uses an algorithm that enables the generation AI to provide nutrient information according to the health state of the plant. For example, the generation AI uses a machine learning algorithm to provide nutrient information and recommend appropriate fertilizers and supplements. This makes it possible to provide nutrient information according to the health state of the plant and recommend appropriate fertilizers and supplements.

[0060] The information provision unit can provide videos and tutorials on how to grow plants, providing information that is visually easy to understand. For example, the generation AI provides videos and tutorials on how to grow plants. For example, it uses videos to explain how to water plants and how to use fertilizer. The information provision unit also uses an algorithm that enables the generation AI to provide information that is visually easy to understand. For example, the generation AI uses illustrations and animations to provide information that is visually easy to understand. This allows the generation AI to provide videos and tutorials on how to grow plants, providing information that is visually easy to understand.

[0061] The information provision unit can provide the latest research results and news on how to grow plants, allowing the user to always be up to date with the latest information. For example, the generation AI provides the latest research results on how to grow plants. For example, it introduces research results on new fertilizers and nutrients. The information provision unit also uses an algorithm that enables the generation AI to always be up to date with the latest information. For example, the generation AI uses real-time updates and notification functions to always be up to date with the latest information. This provides the latest research results and news on how to grow plants, allowing the user to always be up to date with the latest information.

[0062] The advice providing unit can provide advice according to the growth stage of the plant and suggest care methods suitable for the growth period, flowering period, dormancy period, etc. The advice providing unit, for example, uses an algorithm that enables the generation AI to provide advice according to the growth stage of the plant. For example, it suggests an appropriate method of using fertilizer during the growth period. The advice providing unit also uses an algorithm that enables the generation AI to provide advice according to the growth stage of the plant. For example, the generation AI uses a machine learning algorithm to analyze the growth stage and suggest an appropriate care method. This allows the generation AI to provide advice according to the growth stage of the plant and suggest care methods suitable for the growth period, flowering period, dormancy period, etc.

[0063] The advice providing unit can predict the risk of plant disease and pest outbreaks and advise on preventive measures. For example, the generation AI in the advice providing unit predicts the risk of plant disease and pest outbreaks and advises on preventive measures. For example, it suggests preventive measures during periods when the risk of a particular disease outbreak is high. The advice providing unit also uses an algorithm that enables the generation AI to predict the risk of plant disease and pest outbreaks. For example, the generation AI predicts the risk of outbreaks based on past data and environmental conditions and suggests preventive measures. This makes it possible to predict the risk of plant disease and pest outbreaks and advise on preventive measures.

[0064] The advice providing unit uses the emotion estimation function to analyze the emotions of the user when receiving advice, and can provide advice to increase the user's motivation. The advice providing unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving advice. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The advice providing unit also provides advice to increase the user's motivation using the generation AI. For example, it provides words of encouragement or introduces success stories. In this way, the emotions of the user when receiving advice can be analyzed, and advice to increase motivation can be provided.

[0065] The information provision unit uses the emotion estimation function to analyze the emotion the user felt when receiving information, and can provide the information in a manner that attracts the user's interest. The information provision unit, for example, uses the emotion estimation function to analyze the emotion the user felt when receiving information. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information provision unit also uses the generation AI to provide information in a manner that attracts the user's interest. For example, it provides information using personalized content and interactive elements. This allows the information provision unit to analyze the emotion the user felt when receiving information, and can provide the information in a manner that attracts the user's interest.

[0066] The advice providing unit uses the emotion estimation function to analyze the emotion the user has when following the advice, and can provide the advice in a form that is easy to follow. The advice providing unit, for example, uses the emotion estimation function to analyze the emotion the user has when following the advice. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The advice providing unit also provides advice in a form that is easy for the generation AI to follow. For example, it provides a step-by-step guide or a simple task. This allows the emotion the user has when following the advice to be analyzed, and can provide the advice in a form that is easy to follow.

[0067] The information provision unit uses the emotion estimation function to analyze the emotions the user has when receiving information, and can provide information in a way that elicits positive emotions. The information provision unit, for example, uses the emotion estimation function to analyze the emotions the user has when receiving information. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information provision unit also provides information in a way that the generation AI elicits positive emotions. For example, it introduces success stories and provides positive feedback. This allows the information provision unit to analyze the emotions the user has when receiving information, and can provide information in a way that elicits positive emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The plant care support system can further include a plant growth prediction unit. The growth prediction unit analyzes past growth data of the plant and predicts future growth. For example, it predicts the next growth stage based on past growth rate and environmental conditions. The growth prediction unit can also identify factors that affect plant growth and suggest optimal care methods. This allows the user to predict the future growth of the plant and provide appropriate care.

[0070] The plant care support system can further include a plant health diagnosis unit. The health diagnosis unit analyzes images of the plant and comprehensively evaluates its health condition. For example, it comprehensively analyzes the color and shape of the leaves, the condition of the stem, and the condition of the roots to calculate a health score. The health diagnosis unit can also suggest necessary care methods based on the health score. This allows the user to comprehensively understand the health condition of the plant and provide appropriate care.

[0071] The plant care support system may further include a plant growth simulation unit. The growth simulation unit simulates the growth of a plant and visually displays future growth. For example, it simulates the growth of a plant one month from its current state and visually displays this to the user. The growth simulation unit may also simulate different care methods and suggest the optimal care method. This allows the user to visually grasp the future growth of the plant and provide appropriate care.

[0072] The plant care support system can further include a plant nutrition management module. The nutrition management module analyzes the plant's nutritional status and identifies the nutrients it needs. For example, it can suggest the necessary fertilizers and supplements based on leaf color and growth rate. The nutrition management module can also evaluate the balance of nutrients and identify excess or deficiency of nutrients. This allows users to properly manage the nutritional status of their plants and promote healthy growth.

[0073] The plant care support system may further include a plant environment monitoring unit. The environment monitoring unit monitors the plant's surrounding environment in real time and evaluates the environmental conditions. For example, it monitors temperature, humidity, and light intensity and proposes optimal environmental conditions. The environment monitoring unit can also detect changes in the environmental conditions and propose appropriate measures. This allows the user to properly manage the plant's surrounding environment and promote healthy growth.

[0074] The advice-providing unit uses the emotion estimation function to analyze the stress the user feels when caring for plants and can provide advice to reduce stress. For example, it can analyze the user's facial expressions and voice to calculate the stress level. The advice-providing unit also uses the generation AI to suggest relaxation methods and simplification methods for caring for plants to reduce stress. This reduces the stress the user feels when caring for plants and allows them to continue caring for them in an enjoyable way.

[0075] The information provision unit uses the emotion estimation function to analyze the emotions the user has when receiving plant information and can provide the information in a way that attracts the user's interest. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The information provision unit also uses the generation AI to provide information in a way that attracts the user's interest. For example, it provides information using personalized content and interactive elements. This allows the information provision unit to analyze the emotions the user has when receiving information and provide the information in a way that attracts the user's interest.

[0076] The advice providing unit uses the emotion estimation function to analyze the satisfaction the user feels with plant care and can provide advice to increase that satisfaction. For example, it can analyze the user's facial expressions and voice to calculate a satisfaction score. The advice providing unit also provides success stories and positive feedback that the generation AI uses to increase satisfaction. This helps increase the user's satisfaction with plant care and maintain their motivation.

[0077] The information provision unit uses the emotion estimation function to analyze the emotions felt by the user when receiving plant information, and can provide information in a way that elicits positive emotions. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The information provision unit also uses the generation AI to provide information in a way that elicits positive emotions. For example, it can introduce success stories or provide positive feedback. This allows the information provision unit to analyze the emotions felt by the user when receiving information, and can provide information in a way that elicits positive emotions.

[0078] The advice providing unit uses the emotion estimation function to analyze the emotions the user has when following the advice, and can provide the advice in a format that is easy to follow. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The advice providing unit also provides advice in a format that is easy for the generation AI to follow. For example, it can provide a step-by-step guide or a simple task. This allows the user's emotions when following the advice to be analyzed, and the advice can be provided in a format that is easy to follow.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The image recognition unit analyzes the image of the plant. For example, the generation AI analyzes the color and shape of the plant's leaves and the condition of the stem to determine the plant's health. The generation AI can also identify the plant's type. For example, the generation AI can determine the plant's type based on the shape and color of the leaves. Step 2: The advice provider provides advice on how to grow the plant based on the image of the plant analyzed by the image recognition unit. For example, the generator AI might provide advice such as, "This plant is not getting enough sunlight, so move it to a sunnier location." The generator AI might also provide advice such as, "This plant is not getting enough water, so increase the amount of watering to once a week." Step 3: The information providing unit provides information according to the type and health condition of the plant based on the growing advice provided by the advice providing unit. For example, the generating AI provides information such as how to grow a specific plant, how to prevent disease, and the nutrients it needs. The generating AI can also provide information according to the health condition of the plant. For example, if the plant is sick, the generating AI provides how to treat the disease.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. an image recognition unit that analyzes images of plants; an advice providing unit that provides advice on how to grow the plant based on the image of the plant analyzed by the image recognition unit; an information providing unit that provides information according to the type and health condition of the plant based on the growing advice provided by the advice providing unit. A system characterized by:

2. The image recognition unit Detects minute discolorations and spots on the leaves of the plant for early detection of disease.

2. The system of claim 1.

3. The image recognition unit Analyze the plant's surrounding environment, assess soil quality and humidity, and make recommendations for environmental improvements.

2. The system of claim 1.

4. The advice providing unit Analyze the growth history of the plant and compare past care methods with the current state to provide optimal advice 2. The system of claim 1.

5. The information providing unit A detailed guide on how to grow each type of plant is provided, allowing users to easily search for the information they need.

2. The system of claim 1.

6. The advice providing unit Using an emotion estimation function, the emotion of the user when receiving the advice is analyzed, and the advice is provided to increase the motivation of the user.

2. The system of claim 1.

Images