System

Abstract

An object of a system according to an embodiment is to enable gardening enthusiasts to efficiently select and grow plants and take measures against pests.SOLUTION: A system includes a plant selection part, a growth guide providing part, and a pest control part. The plant selection unit selects an optimal plant based on the gardening environment and preferences of the user. The growing guide providing part guides a growing method of the plant selected by the plant selecting part in detail. The pest countermeasure unit provides a countermeasure method against pests that may occur in plants.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 made it difficult for gardening enthusiasts to efficiently select and cultivate plants and take measures against pests and diseases.

[0005] The system according to the embodiment aims to enable gardening enthusiasts to efficiently select and cultivate plants and take measures against pests. [Means for solving the problem]

[0006] The system according to the embodiment includes a plant selection unit, a cultivation guide providing unit, and a pest control unit. The plant selection unit selects the most suitable plant based on the user's gardening environment and preferences. The cultivation guide providing unit provides detailed guidance on how to cultivate the plant selected by the plant selection unit. The pest control unit provides methods for controlling pests that may infest plants. [Effects of the Invention]

[0007] The system according to the embodiment can enable gardening enthusiasts to efficiently select and cultivate plants and take measures against pests. [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 gardening support system according to the embodiment of the present invention is a system that allows gardeners of all levels, from beginners to advanced gardeners, to grow plants and manage their gardens more easily and efficiently. As a result, the gardening support system can provide general support, from plant selection and cultivation to measures against pests and diseases.

[0029] A gardening support system according to an embodiment includes a plant selection unit, a cultivation guide providing unit, and a pest control unit. The plant selection unit selects optimal plants based on the user's gardening environment and preferences. For example, when a user inputs a prompt such as, "Tell me some flowers that can grow in the shade," the AI ​​analyzes the prompt and provides a list of flowers that can grow in the shade. The AI ​​can also analyze the user's past gardening history, learn patterns of successful plants, and suggest optimal plants. Furthermore, the AI ​​can acquire local weather data in real time and select optimal plants based on that data. The cultivation guide providing unit provides detailed guidance on how to cultivate the selected plants. For example, in response to a question such as, "What kind of soil is needed to grow this flower?", the AI ​​can suggest the appropriate type of soil, watering frequency, type of fertilizer, and so on. The AI ​​can also analyze the user's gardening skill level and provide customized cultivation guides for beginners to advanced gardeners. Furthermore, the AI ​​can monitor plant growth data in real time and update the cultivation guide as needed. The pest control unit provides methods for dealing with pests and diseases that may infest plants. For example, the generation AI can respond to a question such as, "Black spots have appeared on this leaf. What should I do?" by analyzing the symptoms and suggesting appropriate countermeasures. The generation AI can also analyze past pest and disease data, detect signs of outbreaks in real time, and suggest countermeasures. Furthermore, the generation AI can monitor the user's garden environment, predict the risk of pest and disease outbreaks, and suggest countermeasures. This allows gardening support systems of various levels, from beginners to advanced gardeners, to grow plants and manage their gardens more easily and efficiently. For example, even beginners can select appropriate plants, learn how to grow them, and take measures against pests and diseases. Even advanced gardeners can further deepen their enjoyment of gardening by receiving new designs and seasonal advice.

[0030] The plant selection unit can analyze the user's past gardening history, learn patterns of successful plants, and suggest the most suitable plants. For example, the plant selection unit uses a generative AI to analyze the user's past gardening history and learn patterns of successful plants. For example, it stores growth records and harvest yields of plants grown in the past in a database and suggests the most suitable plants based on that data. This improves the success rate of gardening by suggesting the most suitable plants based on the user's past successes.

[0031] The plant selection unit can acquire local weather data in real time and select the most suitable plants based on that data. For example, the generation AI acquires local weather data in real time and selects the most suitable plants based on that data. For example, it analyzes data such as temperature, precipitation, and hours of sunshine to suggest suitable plants. This improves the success rate of plant cultivation by selecting the most suitable plants based on local weather data.

[0032] The cultivation guide providing unit can analyze the user's gardening skill level and provide a customized cultivation guide for beginners to advanced users. For example, the generation AI analyzes the user's gardening skill level and provides a customized cultivation guide for beginners to advanced users. For example, it suggests basic cultivation methods for beginners and advanced techniques for advanced users. This improves the success rate of gardening by providing a cultivation guide that suits the user's skill level.

[0033] The cultivation guide providing unit can monitor plant growth data in real time and update the cultivation guide as necessary. For example, the generation AI monitors plant growth data in real time and updates the cultivation guide as necessary. For example, if growth delays or abnormalities are detected, the unit will suggest appropriate measures. In this way, the health of the plant can be maintained by updating the cultivation guide based on the plant growth data.

[0034] The pest control department can analyze past pest and disease data, detect signs of outbreaks in real time, and propose countermeasures. For example, the generative AI analyzes past pest and disease data and detects signs of outbreaks in real time. For example, it predicts the risk of pest and disease outbreaks based on past outbreak patterns and weather data. This allows it to detect signs of outbreaks based on past pest and disease data and propose appropriate countermeasures, thereby maintaining the health of plants.

[0035] The pest control unit monitors the user's garden environment, predicts the risk of pests and diseases occurring, and can propose countermeasures. For example, the pest control unit uses a generative AI to monitor the user's garden environment and predict the risk of pests and diseases occurring. For example, it analyzes changes in soil humidity and temperature in real time to predict the risk of occurrence. This allows the garden environment to be monitored and the risk of pests and diseases occurring to be predicted, allowing countermeasures to be taken early.

[0036] The garden design support unit can analyze photos of the user's garden and propose the optimal design plan. For example, the garden design support unit uses a generative AI to analyze photos of the user's garden and propose the optimal design plan. For example, it proposes a design plan based on the garden layout and plant placement. This increases the enjoyment of gardening by analyzing photos of the garden and proposing the optimal design plan.

[0037] The garden design support unit can analyze the user's preferences and lifestyle and provide a customized design based on that. For example, the garden design support unit uses a generation AI to analyze the user's preferences and lifestyle and provide a customized design based on that. For example, it can propose a design plan based on the user's favorite colors and design style. This increases the enjoyment of gardening by providing a customized design based on the user's preferences and lifestyle.

[0038] The seasonal advice providing unit can obtain local weather data in real time and provide optimal advice for each season based on that data. For example, the seasonal advice providing unit uses a generation AI to obtain local weather data in real time and provide optimal advice for each season based on that data. For example, it analyzes data such as temperature, precipitation, and hours of sunshine to suggest appropriate plant cultivation methods. This improves the success rate of gardening by providing optimal advice for each season based on local weather data.

[0039] The seasonal advice providing unit can analyze the user's gardening history and provide seasonal advice based on past successes. For example, the seasonal advice providing unit uses a generation AI to analyze the user's gardening history and provide seasonal advice based on past successes. For example, similar advice is provided based on a method for growing plants that was successful in the past. This improves the success rate of gardening by providing seasonal advice based on the user's past successes.

[0040] The seasonal advice providing unit can analyze gardening practices in different regions and cultural areas and provide seasonal advice based on that. For example, the generation AI analyzes gardening practices in different regions and cultural areas and provides seasonal advice based on that. For example, it suggests methods for growing plants according to the climate and culture of each region. This improves the success rate of gardening by providing seasonal advice based on gardening practices in different regions and cultural areas.

[0041] The seasonal advice providing unit can provide optimal seasonal advice taking into account the user's gardening goals. For example, the seasonal advice providing unit uses a generation AI to analyze the user's gardening goals and provide seasonal advice based on them. For example, advice is provided for ornamental plants on how to make them bloom beautifully, and advice is provided for edible plants on how to increase harvest yields. In this way, by providing seasonal advice based on the user's gardening goals, the success rate of gardening is improved.

[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 gardening support system further includes a plant growth prediction unit. The plant growth prediction unit can predict the growth of plants selected by the user and propose an optimal cultivation schedule. For example, it can predict the plant's growth rate and flowering period and suggest appropriate times for watering and fertilizing. The growth prediction unit can also analyze weather and soil data and provide advice on providing the optimal environment for plant growth. This allows the user to predict plant growth and enjoy gardening in a planned manner.

[0044] The gardening support system also includes a plant health check function. The plant health check function can diagnose the health of the plants the user is growing and suggest appropriate measures. For example, it can analyze changes in leaf color and shape to detect signs of disease or nutritional deficiencies. The health check function can also accumulate plant growth data and compare it with past data to detect abnormalities. This allows the user to constantly monitor the health of their plants and take early action.

[0045] The gardening support system further includes a plant growth recording unit. The plant growth recording unit can record and visually display the growth process of the plants being grown by the user. For example, it can periodically take photos of the plants and display the growth as a time-lapse video. The growth recording unit can also display plant growth data as graphs and charts, allowing the user to easily check the growth progress. This allows the user to enjoy recording the growth of the plants and look back on it later.

[0046] The gardening support system further includes a plant community linking unit. The plant community linking unit allows users to share information with other gardening enthusiasts and receive advice. For example, users can post photos of the plants they are growing and growth records to the community, and receive comments and advice from other users. The community linking unit can also promote interaction between users and provide a forum for sharing knowledge and experiences about gardening. This allows users to connect with other gardening enthusiasts and enjoy gardening more.

[0047] The gardening support system also includes an automatic plant irrigation unit. The automatic plant irrigation unit can automatically water plants based on a schedule set by the user and the plant's growth status. For example, it uses a soil humidity sensor to water plants at the appropriate time. The automatic irrigation unit can also analyze weather data and make adjustments, such as refraining from watering on rainy days. This allows the user to water plants appropriately without any effort.

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

[0049] Step 1: The plant selection unit selects the most suitable plants based on the user's gardening environment and preferences. For example, when a user inputs a prompt such as "Tell me some flowers that can grow in the shade," the generation AI analyzes the prompt and provides a list of flowers that can grow in the shade. The generation AI can also analyze the user's past gardening history, learn patterns of successful plants, and suggest the most suitable plants. Furthermore, the generation AI can obtain local weather data in real time and select the most suitable plants based on that data. Step 2: The cultivation guide provider provides detailed guidance on how to grow the selected plant. For example, in response to a question such as, "What kind of soil is needed to grow this flower?", the generation AI will suggest the appropriate type of soil, how often to water, and the type of fertilizer. The generation AI can also analyze the user's gardening skill level and provide customized cultivation guides for beginners to advanced users. Furthermore, the generation AI can monitor plant growth data in real time and update the cultivation guide as needed. Step 3: The pest control department provides countermeasures for pests and diseases that may occur in plants. For example, in response to a question such as, "Black spots have appeared on these leaves. What should I do?", the generation AI analyzes the symptoms and suggests appropriate countermeasures. The generation AI can also analyze past pest and disease data, detect signs of an outbreak in real time, and suggest countermeasures. Furthermore, the generation AI can monitor the user's garden environment, predict the risk of pest and disease outbreaks, and suggest countermeasures.

[0050] (Example 2) The gardening support system according to the embodiment of the present invention is a system that allows gardeners of all levels, from beginners to advanced gardeners, to grow plants and manage their gardens more easily and efficiently. As a result, the gardening support system can provide general support, from plant selection and cultivation to measures against pests and diseases.

[0051] A gardening support system according to an embodiment includes a plant selection unit, a cultivation guide providing unit, and a pest control unit. The plant selection unit selects optimal plants based on the user's gardening environment and preferences. For example, when a user inputs a prompt such as, "Tell me some flowers that can grow in the shade," the AI ​​analyzes the prompt and provides a list of flowers that can grow in the shade. The AI ​​can also analyze the user's past gardening history, learn patterns of successful plants, and suggest optimal plants. Furthermore, the AI ​​can acquire local weather data in real time and select optimal plants based on that data. The cultivation guide providing unit provides detailed guidance on how to cultivate the selected plants. For example, in response to a question such as, "What kind of soil is needed to grow this flower?", the AI ​​can suggest the appropriate type of soil, watering frequency, type of fertilizer, and so on. The AI ​​can also analyze the user's gardening skill level and provide customized cultivation guides for beginners to advanced gardeners. Furthermore, the AI ​​can monitor plant growth data in real time and update the cultivation guide as needed. The pest control unit provides methods for dealing with pests and diseases that may infest plants. For example, the generation AI can respond to a question such as, "Black spots have appeared on this leaf. What should I do?" by analyzing the symptoms and suggesting appropriate countermeasures. The generation AI can also analyze past pest and disease data, detect signs of outbreaks in real time, and suggest countermeasures. Furthermore, the generation AI can monitor the user's garden environment, predict the risk of pest and disease outbreaks, and suggest countermeasures. This allows gardening support systems of various levels, from beginners to advanced gardeners, to grow plants and manage their gardens more easily and efficiently. For example, even beginners can select appropriate plants, learn how to grow them, and take measures against pests and diseases. Even advanced gardeners can further deepen their enjoyment of gardening by receiving new designs and seasonal advice.

[0052] The plant selection unit can analyze the user's past gardening history, learn patterns of successful plants, and suggest the most suitable plants. For example, the plant selection unit uses a generative AI to analyze the user's past gardening history and learn patterns of successful plants. For example, it stores growth records and harvest yields of plants grown in the past in a database and suggests the most suitable plants based on that data. This improves the success rate of gardening by suggesting the most suitable plants based on the user's past successes.

[0053] The plant selection unit can acquire local weather data in real time and select the most suitable plants based on that data. For example, the generation AI acquires local weather data in real time and selects the most suitable plants based on that data. For example, it analyzes data such as temperature, precipitation, and hours of sunshine to suggest suitable plants. This improves the success rate of plant cultivation by selecting the most suitable plants based on local weather data.

[0054] The plant selection unit can use the emotion estimation function to select the plant that evokes the most positive emotion in the user. For example, the plant selection unit uses the emotion estimation function to select the plant that evokes the most positive emotion in the user. For example, the plant selection unit analyzes emotion data about plants that the user has grown in the past and suggests plants that evoke strong positive emotions. This increases the enjoyment of gardening by selecting plants based on the user's emotions.

[0055] The cultivation guide providing unit can analyze the user's gardening skill level and provide a customized cultivation guide for beginners to advanced users. For example, the generation AI analyzes the user's gardening skill level and provides a customized cultivation guide for beginners to advanced users. For example, it suggests basic cultivation methods for beginners and advanced techniques for advanced users. This improves the success rate of gardening by providing a cultivation guide that suits the user's skill level.

[0056] The cultivation guide providing unit can monitor plant growth data in real time and update the cultivation guide as necessary. For example, the generation AI monitors plant growth data in real time and updates the cultivation guide as necessary. For example, if growth delays or abnormalities are detected, the unit will suggest appropriate measures. In this way, the health of the plant can be maintained by updating the cultivation guide based on the plant growth data.

[0057] The cultivation guide providing unit can use the emotion estimation function to analyze the emotions of the user when reading the cultivation guide and use expressions that elicit positive emotions. For example, the cultivation guide providing unit can use the emotion estimation function to analyze the emotions of the user when reading the cultivation guide and use expressions that elicit positive emotions. For example, the cultivation guide providing unit can include many encouraging words and success stories. This can improve the enjoyment of gardening by providing a cultivation guide based on the user's emotions.

[0058] The pest control department can analyze past pest and disease data, detect signs of outbreaks in real time, and propose countermeasures. For example, the generative AI analyzes past pest and disease data and detects signs of outbreaks in real time. For example, it predicts the risk of pest and disease outbreaks based on past outbreak patterns and weather data. This allows it to detect signs of outbreaks based on past pest and disease data and propose appropriate countermeasures, thereby maintaining the health of plants.

[0059] The pest control unit monitors the user's garden environment, predicts the risk of pests and diseases occurring, and can propose countermeasures. For example, the pest control unit uses a generative AI to monitor the user's garden environment and predict the risk of pests and diseases occurring. For example, it analyzes changes in soil humidity and temperature in real time to predict the risk of occurrence. This allows the garden environment to be monitored and the risk of pests and diseases occurring to be predicted, allowing countermeasures to be taken early.

[0060] The pest control unit can use the emotion estimation function to analyze the emotions of the user when taking pest control measures and suggest measures to reduce stress. The pest control unit, for example, uses the emotion estimation function to analyze the emotions of the user when taking pest control measures and suggest measures to reduce stress. For example, the pest control unit analyzes the user's facial expressions and voice and suggests measures based on an emotion score. In this way, by suggesting pest control measures based on the user's emotions, stress is reduced and gardening is maintained as enjoyable.

[0061] The garden design support unit can analyze photos of the user's garden and propose the optimal design plan. For example, the garden design support unit uses a generative AI to analyze photos of the user's garden and propose the optimal design plan. For example, it proposes a design plan based on the garden layout and plant placement. This increases the enjoyment of gardening by analyzing photos of the garden and proposing the optimal design plan.

[0062] The garden design support unit can analyze the user's preferences and lifestyle and provide a customized design based on that. For example, the garden design support unit uses a generation AI to analyze the user's preferences and lifestyle and provide a customized design based on that. For example, it can propose a design plan based on the user's favorite colors and design style. This increases the enjoyment of gardening by providing a customized design based on the user's preferences and lifestyle.

[0063] The garden design support unit can use the emotion estimation function to analyze the emotions of the user when selecting a design plan and propose a design that elicits positive emotions. For example, the garden design support unit can use the emotion estimation function to analyze the emotions of the user when selecting a design plan and propose a design that elicits positive emotions. For example, the unit can analyze the user's facial expressions and voice and propose a design based on an emotion score. This improves the enjoyment of gardening by proposing a design plan based on the user's emotions.

[0064] The seasonal advice providing unit can obtain local weather data in real time and provide optimal advice for each season based on that data. For example, the seasonal advice providing unit uses a generation AI to obtain local weather data in real time and provide optimal advice for each season based on that data. For example, it analyzes data such as temperature, precipitation, and hours of sunshine to suggest appropriate plant cultivation methods. This improves the success rate of gardening by providing optimal advice for each season based on local weather data.

[0065] The seasonal advice providing unit can analyze the user's gardening history and provide seasonal advice based on past successes. For example, the seasonal advice providing unit uses a generation AI to analyze the user's gardening history and provide seasonal advice based on past successes. For example, similar advice is provided based on a method for growing plants that was successful in the past. This improves the success rate of gardening by providing seasonal advice based on the user's past successes.

[0066] The seasonal advice providing unit can use the emotion estimation function to analyze the emotions of the user when receiving seasonal advice and provide advice that elicits positive emotions. The seasonal advice providing unit can, for example, use the emotion estimation function to analyze the emotions of the user when receiving seasonal advice and provide advice that elicits positive emotions. For example, the unit can analyze the user's facial expressions and voice and provide advice based on an emotion score. This can improve the enjoyment of gardening by providing seasonal advice based on the user's emotions.

[0067] The seasonal advice providing unit can analyze gardening practices in different regions and cultural areas and provide seasonal advice based on that. For example, the generation AI analyzes gardening practices in different regions and cultural areas and provides seasonal advice based on that. For example, it suggests methods for growing plants according to the climate and culture of each region. This improves the success rate of gardening by providing seasonal advice based on gardening practices in different regions and cultural areas.

[0068] The seasonal advice providing unit can provide optimal seasonal advice taking into account the user's gardening goals. For example, the seasonal advice providing unit uses a generation AI to analyze the user's gardening goals and provide seasonal advice based on them. For example, advice is provided for ornamental plants on how to make them bloom beautifully, and advice is provided for edible plants on how to increase harvest yields. In this way, by providing seasonal advice based on the user's gardening goals, the success rate of gardening is improved.

[0069] The seasonal advice providing unit can use the emotion estimation function to analyze the emotions of the user when implementing seasonal advice in real time and provide advice that elicits positive emotions. The seasonal advice providing unit can, for example, use the emotion estimation function to analyze the emotions of the user when implementing seasonal advice in real time and provide advice that elicits positive emotions. For example, it can analyze the user's facial expressions and voice and provide advice based on an emotion score. In this way, seasonal advice can be provided based on the user's emotions, thereby making gardening more enjoyable.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The gardening support system further includes a plant growth prediction unit. The plant growth prediction unit can predict the growth of plants selected by the user and propose an optimal cultivation schedule. For example, it can predict the plant's growth rate and flowering period and suggest appropriate times for watering and fertilizing. The growth prediction unit can also analyze weather and soil data and provide advice on providing the optimal environment for plant growth. This allows the user to predict plant growth and enjoy gardening in a planned manner.

[0072] The gardening support system also includes a plant health check function. The plant health check function can diagnose the health of the plants the user is growing and suggest appropriate measures. For example, it can analyze changes in leaf color and shape to detect signs of disease or nutritional deficiencies. The health check function can also accumulate plant growth data and compare it with past data to detect abnormalities. This allows the user to constantly monitor the health of their plants and take early action.

[0073] The gardening support system further includes a plant growth recording unit. The plant growth recording unit can record and visually display the growth process of the plants being grown by the user. For example, it can periodically take photos of the plants and display the growth as a time-lapse video. The growth recording unit can also display plant growth data as graphs and charts, allowing the user to easily check the growth progress. This allows the user to enjoy recording the growth of the plants and look back on it later.

[0074] The gardening support system further includes a plant community linking unit. The plant community linking unit allows users to share information with other gardening enthusiasts and receive advice. For example, users can post photos of the plants they are growing and growth records to the community, and receive comments and advice from other users. The community linking unit can also promote interaction between users and provide a forum for sharing knowledge and experiences about gardening. This allows users to connect with other gardening enthusiasts and enjoy gardening more.

[0075] The gardening support system also includes an automatic plant irrigation unit. The automatic plant irrigation unit can automatically water plants based on a schedule set by the user and the plant's growth status. For example, it uses a soil humidity sensor to water plants at the appropriate time. The automatic irrigation unit can also analyze weather data and make adjustments, such as refraining from watering on rainy days. This allows the user to water plants appropriately without any effort.

[0076] The gardening support system further includes a plant emotion estimation unit. The plant emotion estimation unit can analyze the user's emotions toward plants and provide plant selection and cultivation guides based on those emotions. For example, if the user has positive emotions toward a particular plant, information and advice related to that plant will be provided preferentially. The emotion estimation unit can also accumulate the user's emotion data and analyze long-term changes in emotion. This allows the user to enjoy gardening based on their own emotions.

[0077] The gardening support system further includes a plant emotion feedback unit. The plant emotion feedback unit can provide real-time feedback on the user's emotions toward the plant and offer advice to elicit positive emotions. For example, if the user is feeling anxious about the growth of the plant, it can provide encouraging messages or success stories. The emotion feedback unit can also analyze the user's emotion data and offer advice according to changes in emotion. This allows the user to enjoy gardening while maintaining positive emotions.

[0078] The gardening support system further includes a plant emotion sharing unit. The plant emotion sharing unit allows users to share their feelings about plants with other users and receive sympathy and advice. For example, if a user feels joy in the growth of a plant, the user can post that emotion to the community and receive sympathy and comments from other users. The emotion sharing unit also encourages users to share emotions with each other and can increase motivation for gardening. This allows users to share their emotions with other gardening enthusiasts and enjoy gardening more.

[0079] The gardening support system also includes a plant emotion analysis unit. The plant emotion analysis unit can perform a detailed analysis of the user's feelings toward plants and provide plant selection and cultivation guidance based on the results. For example, if the user has negative feelings toward a particular plant, the system can analyze the cause and suggest solutions. The emotion analysis unit can also accumulate the user's emotion data over the long term and track changes in emotion. This allows the user to enjoy gardening based on their own emotions.

[0080] The gardening support system further includes a plant emotion prediction unit. The plant emotion prediction unit can predict the emotions a user will have toward plants and provide plant selection and cultivation guidance based on the prediction. For example, when a user is selecting a new plant, the unit can suggest plants that are likely to evoke positive emotions toward the user. The emotion prediction unit can also analyze the user's past emotion data and predict future changes in emotion. This allows the user to enjoy gardening based on their own emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The plant selection unit selects the most suitable plants based on the user's gardening environment and preferences. For example, when a user inputs a prompt such as "Tell me some flowers that can grow in the shade," the generation AI analyzes the prompt and provides a list of flowers that can grow in the shade. The generation AI can also analyze the user's past gardening history, learn patterns of successful plants, and suggest the most suitable plants. Furthermore, the generation AI can obtain local weather data in real time and select the most suitable plants based on that data. Step 2: The cultivation guide provider provides detailed guidance on how to grow the selected plant. For example, in response to a question such as, "What kind of soil is needed to grow this flower?", the generation AI will suggest the appropriate type of soil, how often to water, and the type of fertilizer. The generation AI can also analyze the user's gardening skill level and provide customized cultivation guides for beginners to advanced users. Furthermore, the generation AI can monitor plant growth data in real time and update the cultivation guide as needed. Step 3: The pest control department provides countermeasures for pests and diseases that may occur in plants. For example, in response to a question such as, "Black spots have appeared on these leaves. What should I do?", the generation AI analyzes the symptoms and suggests appropriate countermeasures. The generation AI can also analyze past pest and disease data, detect signs of an outbreak in real time, and suggest countermeasures. Furthermore, the generation AI can monitor the user's garden environment, predict the risk of pest and disease outbreaks, and suggest countermeasures.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] 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]

[0150] 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 plant selection unit that selects optimal plants based on the user's gardening environment and preferences; a cultivation guide providing unit that provides detailed guidance on how to cultivate the plant selected by the plant selecting unit; and a pest control unit that provides a method for controlling pests that may occur in the plant. A system characterized by:

2. The plant selection department Obtaining local weather data in real time and selecting the most suitable plants based on that data 2. The system of claim 1.

3. The training guide providing unit Analyze the user's gardening skill level and provide a customized gardening guide for beginners to advanced users.

2. The system of claim 1.

4. The pest control department Analyzing past pest and disease data, detecting signs of outbreaks in real time, and proposing countermeasures 2. The system of claim 1.

5. The Garden Design Support Department Analyze the photos of the user's garden and propose the optimal design plan 2. The system of claim 1.

6. Seasonal advice provided by Obtain local weather data in real time and provide optimal seasonal advice based on that data 2. The system of claim 1.

7. The plant selection department Using an emotion estimation function, the plant that the user feels the most positive about is selected.

2. The system of claim 1.

8. The pest control department Using the emotion estimation function, the emotions of the user when taking measures against pests are analyzed and measures to reduce stress are proposed.

2. The system of claim 1.

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