System

Abstract

An object of the system according to the embodiment is to maintain an optimal environmental condition in indoor vegetable cultivation and provide an appropriate cultivation method.SOLUTION: A system includes a cultivation unit, a AI management part, a monitoring part, and an advice providing part. The cultivating unit manages growth of the vegetable. The AI management part adjusts an environmental condition in the cultivation unit. The monitoring part monitors a cultivation situation in the cultivation unit in real time. The advice providing unit corrects the advice or the cultivation method as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to maintain optimal environmental conditions for indoor vegetable cultivation and provide appropriate cultivation methods.

[0005] The system according to the embodiment aims to maintain optimal environmental conditions for indoor vegetable cultivation and provide an appropriate cultivation method. [Means for solving the problem]

[0006] The system according to the embodiment includes a cultivation unit, an AI management unit, a monitoring unit, and an advice providing unit. The cultivation unit manages the growth of vegetables. The AI ​​management unit adjusts the environmental conditions within the cultivation unit. The monitoring unit monitors the cultivation status within the cultivation unit in real time. The advice providing unit provides advice or modifies the cultivation method as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can maintain optimal environmental conditions for indoor vegetable cultivation and provide an appropriate cultivation method. [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 automated cultivation system according to the embodiment of the present invention uses AI to manage the automated cultivation of vegetables indoors and provides optimal environmental conditions and cultivation methods. This allows users to easily grow vegetables indoors and maintain a healthy diet.

[0029] An automatic cultivation system according to an embodiment includes a cultivation unit, an AI management unit, a monitoring unit, and an advice providing unit. The cultivation unit manages the growth of vegetables. For example, the cultivation unit is equipped with sensors that measure temperature, humidity, light intensity, soil pH, and the like, and optimally manages the growth of vegetables. The cultivation unit also includes a cooling device and a humidifier, allowing it to adjust environmental conditions. The AI ​​management unit adjusts the environmental conditions within the cultivation unit. For example, the AI ​​management unit activates the cooling device if the temperature is too high, and the humidifier if the humidity is too low. The AI ​​management unit also adjusts the lighting if the light intensity is insufficient, and makes necessary adjustments if the soil pH is inappropriate. The monitoring unit monitors the cultivation status within the cultivation unit in real time. For example, the monitoring unit collects data from sensors and monitors the cultivation status. The monitoring unit can also display the cultivation status to the user via a dedicated app. The advice providing unit provides advice or modifies the cultivation method as needed. For example, the advice providing unit instructs the user to activate the humidifier because the current humidity is low. The advice providing unit also provides instructions on the appropriate frequency and amount of watering during the germination period and suggests the type and amount of fertilizer needed during the growth period. This allows the automatic cultivation system according to the embodiment to allow users to easily grow vegetables indoors and maintain a healthy diet.

[0030] Furthermore, the automatic cultivation system according to the embodiment is provided with an automatic cleaning function in the cultivation unit, and the cultivation unit periodically cleans the interior to prevent the occurrence of pests. For example, the cultivation unit is equipped with an automatic cleaning function, and periodically cleans the internal sensors and control devices. For example, the cultivation unit periodically cleans the interior using a water flow or air flow to prevent the occurrence of pests. This makes it possible to prevent the occurrence of pests.

[0031] Furthermore, the automatic cultivation system according to the embodiment includes a sensor with a self-diagnostic function in the cultivation unit, which automatically detects and notifies the user of a malfunction or abnormality. The cultivation unit, for example, is equipped with a sensor with a self-diagnostic function and periodically checks the sensor's operating status. For example, the system automatically calibrates the sensor and notifies the user if an abnormality is detected. This allows for early detection and response of malfunctions and abnormalities.

[0032] Furthermore, in the automatic cultivation system according to the embodiment, the cultivation units are designed modularly, allowing users to add or expand units as needed. For example, the cultivation units are designed modularly, allowing users to add or expand units as needed. For example, the cultivation area can be expanded by connecting additional modules to the basic unit. This allows users to add or expand units as needed.

[0033] Furthermore, the automated cultivation system according to the embodiment allows the design of the cultivation unit to be customized, allowing the user to choose an exterior that matches the interior. For example, the cultivation unit may have a customizable exterior design, allowing the user to choose a design that matches the interior. For example, options for selecting colors and materials may be provided. This allows the user to choose an exterior that matches the interior.

[0034] The AI ​​management unit can build predictive models based on past data and propose preventive measures for future environmental changes. For example, the AI ​​management unit analyzes past environmental data and builds predictive models. For example, it learns past patterns of temperature and humidity fluctuations and predicts future environmental changes. This makes it possible to propose preventive measures for future environmental changes.

[0035] The AI ​​management unit can introduce optimization algorithms to minimize energy consumption when adjusting environmental conditions. The AI ​​management unit can introduce optimization algorithms to minimize energy consumption when adjusting environmental conditions. For example, it can optimize the operating times of cooling devices and humidifiers. This can minimize energy consumption.

[0036] The AI ​​management unit can refer to external weather data and link it to the indoor environment. For example, if the outside temperature is high, the AI ​​management unit can activate the indoor cooling device earlier. This allows the AI ​​management unit to refer to external weather data and link it to the indoor environment.

[0037] The AI ​​management unit can coordinate the management of environmental conditions with other smart home devices to achieve integrated environmental control. The AI ​​management unit can, for example, coordinate with other smart home devices to achieve integrated environmental control. For example, it can coordinate with smart thermostats and smart lighting to adjust environmental conditions. This allows it to coordinate with other smart home devices to achieve integrated environmental control.

[0038] The AI ​​management unit can propose optimal cultivation conditions to maximize the nutritional value of various vegetables. For example, the AI ​​management unit can adjust the amount of light or the type of fertilizer to increase specific vitamins and minerals. This makes it possible to propose optimal cultivation conditions to maximize the nutritional value of various vegetables.

[0039] The AI ​​management unit can suggest the optimal type of vegetable or cultivation method based on the user's diet and health condition. For example, the AI ​​management unit analyzes the user's diet and health condition and suggests the optimal type of vegetable and cultivation method. For example, it suggests vitamin-rich vegetables to a user who is vitamin deficient. This makes it possible to suggest the optimal type of vegetable and cultivation method based on the user's diet and health condition.

[0040] The AI ​​management unit can refer to the success stories of other users and share the optimal methods.The AI ​​management unit can, for example, refer to the success stories of other users and share the optimal cultivation methods.For example, it can suggest the optimal method based on the data of other users who grow the same vegetables.This allows the success stories of other users to be referred to and the optimal methods to be shared.

[0041] The AI ​​management unit can customize cultivation methods to suit the characteristics of the season or region. The AI ​​management unit provides optimal cultivation methods, taking into account the characteristics of the season or region. For example, it can set the indoor temperature higher in winter and use cooling devices in summer. This allows cultivation methods to be customized to suit the characteristics of the season or region.

[0042] Furthermore, the automatic cultivation system according to the embodiment is equipped with a 3D visualization function in the monitoring unit, allowing the user to visually check the growth process. The monitoring unit, for example, uses the 3D visualization function to allow the user to visually check the cultivation status. For example, the growth process of vegetables is displayed as a 3D model. This allows the user to visually check the growth process.

[0043] Furthermore, the automatic cultivation system according to the embodiment has a voice assistant function in the monitoring unit, allowing the user to receive advice or instructions by voice. For example, the monitoring unit uses the voice assistant function to allow the user to receive advice or instructions by voice. For example, the monitoring unit answers questions such as "When is the next watering?" by voice. This allows the user to receive advice or instructions by voice.

[0044] Furthermore, the automatic cultivation system according to the embodiment has a community function in the monitoring unit, which promotes information exchange or advice sharing with other users. The monitoring unit, for example, uses the community function to promote information exchange or advice sharing with other users. For example, success stories and failure stories of cultivation can be shared. This can promote information exchange and advice sharing with other users.

[0045] Furthermore, the automatic cultivation system according to the embodiment has a cultivation diary function in the monitoring unit, allowing the user to keep a record of the cultivation. The monitoring unit, for example, uses the cultivation diary function to allow the user to keep a record of the cultivation. For example, the monitoring unit records daily watering and fertilizer use. This allows the user to keep a record of the cultivation.

[0046] When receiving feedback, the advice providing unit uses AI to automatically analyze the content of the feedback and immediately propose countermeasures. For example, the advice providing unit uses AI to automatically analyze feedback from users and immediately propose countermeasures. For example, in response to feedback such as "Growth is slow with this amount of fertilizer," the unit suggests the appropriate amount of fertilizer. This allows the content of feedback to be automatically analyzed and countermeasures to be immediately proposed.

[0047] The advice providing unit allows the AI ​​to learn areas for improvement in cultivation methods based on the content of the feedback and reflect these in future cultivations. For example, the advice providing unit allows the AI ​​to learn areas for improvement in cultivation methods based on feedback from users and reflect these in future cultivations. For example, based on feedback such as "it would be better to water more frequently," the next watering schedule is adjusted. This allows the AI ​​to learn areas for improvement in cultivation methods based on the content of the feedback and reflect these in future cultivations.

[0048] The advice providing unit can share feedback with other users and build a database for finding common problems or areas for improvement. The advice providing unit, for example, shares feedback with other users and builds a database for finding common problems or areas for improvement. For example, it allows users who have the same problem to share information with each other. This makes it possible to share feedback with other users and build a database for finding common problems or areas for improvement.

[0049] The advice providing unit allows the AI ​​to automatically generate new cultivation methods or adjustment proposals for environmental conditions based on the content of the feedback. For example, in response to feedback such as "humidity is too low," the advice providing unit can suggest an operating schedule for a humidifier. This makes it possible to automatically generate new cultivation methods or adjustment proposals for environmental conditions based on the content of the feedback.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The automatic cultivation system also has a music playback function in the cultivation unit, which can play music to promote plant growth. For example, music with a specific frequency is known to promote plant growth, and the cultivation unit can utilize this to optimize growth. It can also make the cultivation process more enjoyable by playing music that the user prefers. This can promote plant growth and improve the user's cultivation experience.

[0052] The automatic cultivation system also features a fragrance diffusion function in the cultivation unit, which can diffuse fragrances suitable for plant growth. For example, certain essential oils are known to promote plant growth, and the cultivation unit can utilize this to optimize growth. It can also diffuse relaxing fragrances to make the cultivation process more comfortable for the user. This promotes plant growth and improves the user's cultivation experience.

[0053] The automated cultivation system also equips the cultivation unit with an automatic harvesting function, allowing it to automatically harvest vegetables when it's time to harvest. For example, the AI ​​management unit monitors the growth of vegetables and determines the optimal time for harvesting. When harvesting is necessary, the automatic harvesting device activates and harvests the vegetables. This saves users the trouble of harvesting, allowing them to obtain fresh vegetables at the optimal time.

[0054] Automated cultivation systems also have water quality management functions in the cultivation units to optimize the quality of irrigation water. For example, they can be equipped with water quality sensors to measure the water's pH and mineral content. If necessary, they can use filters and additives to adjust the water quality. This helps maintain optimal water quality for plants and promote healthy growth.

[0055] The automatic cultivation system also has an automatic fertilizer supply function in the cultivation unit, which can automatically supply the nutrients necessary for plant growth. For example, the AI ​​management unit monitors the plant's growth status and determines the nutrients it needs. The automatic fertilizer supply device then activates and supplies the appropriate amount of fertilizer. This allows users to effortlessly manage fertilizer and support the healthy growth of their plants.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The cultivation unit manages the growth of the vegetables. For example, the cultivation unit is equipped with sensors that measure temperature, humidity, light intensity, soil pH, etc., to optimally manage the growth of the vegetables. The cultivation unit is also equipped with a cooling device and a humidifying device, allowing it to adjust environmental conditions. Step 2: The AI ​​controller adjusts the environmental conditions within the growing unit. For example, if the temperature is too high, the AI ​​controller activates the cooling system. If the humidity is too low, the AI ​​controller activates the humidifier. The AI ​​controller also adjusts the lighting if there is insufficient light, and makes necessary adjustments if the soil pH is not appropriate. Step 3: The monitoring unit monitors the cultivation status in the cultivation unit in real time. For example, the monitoring unit collects data from sensors and monitors the cultivation status. The monitoring unit can also display the cultivation status to the user through a dedicated app. Step 4: The advice providing unit provides advice or modifies cultivation methods as needed. For example, the advice providing unit may instruct the user to turn on a humidifier because the current humidity is low. The advice providing unit may also instruct the user on the appropriate frequency and amount of watering during the germination period, and suggest the type and amount of fertilizer needed during the growth period.

[0058] (Example 2) The automated cultivation system according to the embodiment of the present invention uses AI to manage the automated cultivation of vegetables indoors and provides optimal environmental conditions and cultivation methods. This allows users to easily grow vegetables indoors and maintain a healthy diet.

[0059] An automatic cultivation system according to an embodiment includes a cultivation unit, an AI management unit, a monitoring unit, and an advice providing unit. The cultivation unit manages the growth of vegetables. For example, the cultivation unit is equipped with sensors that measure temperature, humidity, light intensity, soil pH, and the like, and optimally manages the growth of vegetables. The cultivation unit also includes a cooling device and a humidifier, allowing it to adjust environmental conditions. The AI ​​management unit adjusts the environmental conditions within the cultivation unit. For example, the AI ​​management unit activates the cooling device if the temperature is too high, and the humidifier if the humidity is too low. The AI ​​management unit also adjusts the lighting if the light intensity is insufficient, and makes necessary adjustments if the soil pH is inappropriate. The monitoring unit monitors the cultivation status within the cultivation unit in real time. For example, the monitoring unit collects data from sensors and monitors the cultivation status. The monitoring unit can also display the cultivation status to the user via a dedicated app. The advice providing unit provides advice or modifies the cultivation method as needed. For example, the advice providing unit instructs the user to activate the humidifier because the current humidity is low. The advice providing unit also provides instructions on the appropriate frequency and amount of watering during the germination period and suggests the type and amount of fertilizer needed during the growth period. This allows the automatic cultivation system according to the embodiment to allow users to easily grow vegetables indoors and maintain a healthy diet.

[0060] Furthermore, the automatic cultivation system according to the embodiment is provided with an automatic cleaning function in the cultivation unit, and the cultivation unit periodically cleans the interior to prevent the occurrence of pests. For example, the cultivation unit is equipped with an automatic cleaning function, and periodically cleans the internal sensors and control devices. For example, the cultivation unit periodically cleans the interior using a water flow or air flow to prevent the occurrence of pests. This makes it possible to prevent the occurrence of pests.

[0061] Furthermore, the automatic cultivation system according to the embodiment includes a sensor with a self-diagnostic function in the cultivation unit, which automatically detects and notifies the user of a malfunction or abnormality. The cultivation unit, for example, is equipped with a sensor with a self-diagnostic function and periodically checks the sensor's operating status. For example, the system automatically calibrates the sensor and notifies the user if an abnormality is detected. This allows for early detection and response of malfunctions and abnormalities.

[0062] Furthermore, the automated cultivation system according to the embodiment includes an emotion estimation function in the cultivation unit, which estimates the stress level of the user when installing the unit and makes suggestions to simplify the installation procedure. For example, the cultivation unit uses the emotion estimation function to analyze the stress level of the user when installing the unit in real time. For example, the cultivation unit analyzes facial expressions and voice tones, and makes suggestions to simplify the installation procedure if the user's stress level is high. This reduces the user's stress and simplifies the installation procedure.

[0063] Furthermore, in the automatic cultivation system according to the embodiment, the cultivation units are designed modularly, allowing users to add or expand units as needed. For example, the cultivation units are designed modularly, allowing users to add or expand units as needed. For example, the cultivation area can be expanded by connecting additional modules to the basic unit. This allows users to add or expand units as needed.

[0064] Furthermore, the automated cultivation system according to the embodiment allows the design of the cultivation unit to be customized, allowing the user to choose an exterior that matches the interior. For example, the cultivation unit may have a customizable exterior design, allowing the user to choose a design that matches the interior. For example, options for selecting colors and materials may be provided. This allows the user to choose an exterior that matches the interior.

[0065] Furthermore, the automated cultivation system according to the embodiment includes an emotion estimation function in the cultivation unit, which monitors the user's emotions in real time when the cultivation unit is installed and provides positive feedback. For example, the cultivation unit uses the emotion estimation function to monitor the user's emotions in real time when the cultivation unit is installed. For example, the cultivation unit analyzes facial expressions and voice tones to provide positive feedback. This allows the user's emotions to be monitored and positive feedback to be provided.

[0066] The AI ​​management unit can build predictive models based on past data and propose preventive measures for future environmental changes. For example, the AI ​​management unit analyzes past environmental data and builds predictive models. For example, it learns past patterns of temperature and humidity fluctuations and predicts future environmental changes. This makes it possible to propose preventive measures for future environmental changes.

[0067] The AI ​​management unit can introduce optimization algorithms to minimize energy consumption when adjusting environmental conditions. The AI ​​management unit can introduce optimization algorithms to minimize energy consumption when adjusting environmental conditions. For example, it can optimize the operating times of cooling devices and humidifiers. This can minimize energy consumption.

[0068] The AI ​​management unit is equipped with an emotion estimation function and can adjust the environment to reduce stress by taking the user's emotional state into consideration. For example, the AI ​​management unit uses the emotion estimation function to analyze the user's emotional state in real time and adjust the environment to reduce stress. For example, if stress is high, the lighting can be adjusted to softer light. This can reduce the user's stress.

[0069] The AI ​​management unit can refer to external weather data and link it to the indoor environment. For example, if the outside temperature is high, the AI ​​management unit can activate the indoor cooling device earlier. This allows the AI ​​management unit to refer to external weather data and link it to the indoor environment.

[0070] The AI ​​management unit can coordinate the management of environmental conditions with other smart home devices to achieve integrated environmental control. The AI ​​management unit can, for example, coordinate with other smart home devices to achieve integrated environmental control. For example, it can coordinate with smart thermostats and smart lighting to adjust environmental conditions. This allows it to coordinate with other smart home devices to achieve integrated environmental control.

[0071] The AI ​​management unit is equipped with an emotion estimation function, which allows it to learn the environmental conditions that the user finds most comfortable and maintain those conditions as a priority. For example, the AI ​​management unit uses the emotion estimation function to learn the environmental conditions that the user finds most comfortable. For example, it analyzes the user's emotional reactions and identifies the optimal temperature and humidity. This allows it to maintain the environmental conditions that the user finds most comfortable.

[0072] The AI ​​management unit can propose optimal cultivation conditions to maximize the nutritional value of various vegetables. For example, the AI ​​management unit can adjust the amount of light or the type of fertilizer to increase specific vitamins and minerals. This makes it possible to propose optimal cultivation conditions to maximize the nutritional value of various vegetables.

[0073] The AI ​​management unit can suggest the optimal type of vegetable or cultivation method based on the user's diet and health condition. For example, the AI ​​management unit analyzes the user's diet and health condition and suggests the optimal type of vegetable and cultivation method. For example, it suggests vitamin-rich vegetables to a user who is vitamin deficient. This makes it possible to suggest the optimal type of vegetable and cultivation method based on the user's diet and health condition.

[0074] The AI ​​management unit is equipped with an emotion estimation function and can consider the user's emotional state and suggest cultivation methods to increase motivation. For example, the AI ​​management unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest cultivation methods to increase motivation. For example, if the user is feeling depressed, it will suggest a cultivation method that is easy and likely to be successful. This makes it possible to suggest cultivation methods to increase the user's motivation.

[0075] The AI ​​management unit can refer to the success stories of other users and share the optimal methods.The AI ​​management unit can, for example, refer to the success stories of other users and share the optimal cultivation methods.For example, it can suggest the optimal method based on the data of other users who grow the same vegetables.This allows the success stories of other users to be referred to and the optimal methods to be shared.

[0076] The AI ​​management unit can customize cultivation methods to suit the characteristics of the season or region. The AI ​​management unit provides optimal cultivation methods, taking into account the characteristics of the season or region. For example, it can set the indoor temperature higher in winter and use cooling devices in summer. This allows cultivation methods to be customized to suit the characteristics of the season or region.

[0077] The AI ​​management unit is equipped with an emotion estimation function, which allows it to learn which cultivation method the user will enjoy most and prioritize suggesting that method. For example, the AI ​​management unit uses the emotion estimation function to learn which cultivation method the user will enjoy most. For example, it analyzes the user's emotional response and identifies the cultivation method that the user finds enjoyable. This allows it to suggest the cultivation method that the user will enjoy most.

[0078] Furthermore, the automatic cultivation system according to the embodiment is equipped with a 3D visualization function in the monitoring unit, allowing the user to visually check the growth process. The monitoring unit, for example, uses the 3D visualization function to allow the user to visually check the cultivation status. For example, the growth process of vegetables is displayed as a 3D model. This allows the user to visually check the growth process.

[0079] Furthermore, the automatic cultivation system according to the embodiment has a voice assistant function in the monitoring unit, allowing the user to receive advice or instructions by voice. For example, the monitoring unit uses the voice assistant function to allow the user to receive advice or instructions by voice. For example, the monitoring unit answers questions such as "When is the next watering?" by voice. This allows the user to receive advice or instructions by voice.

[0080] Furthermore, the automatic cultivation system according to the embodiment includes a monitoring unit with an emotion estimation function, which monitors the user's emotional state in real time and provides advice to reduce stress. The monitoring unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and provides advice to reduce stress. For example, if stress is high, the monitoring unit suggests relaxation methods. This makes it possible to monitor the user's emotional state in real time and provide advice to reduce stress.

[0081] Furthermore, the automatic cultivation system according to the embodiment has a community function in the monitoring unit, which promotes information exchange or advice sharing with other users. The monitoring unit, for example, uses the community function to promote information exchange or advice sharing with other users. For example, success stories and failure stories of cultivation can be shared. This can promote information exchange and advice sharing with other users.

[0082] Furthermore, the automatic cultivation system according to the embodiment has a cultivation diary function in the monitoring unit, allowing the user to keep a record of the cultivation. The monitoring unit, for example, uses the cultivation diary function to allow the user to keep a record of the cultivation. For example, the monitoring unit records daily watering and fertilizer use. This allows the user to keep a record of the cultivation.

[0083] Furthermore, the automatic cultivation system according to the embodiment has an emotion estimation function in the monitoring unit, and provides advice at a timing when the user is most relaxed. For example, the monitoring unit uses the emotion estimation function to provide advice at a timing when the user is most relaxed. For example, the monitoring unit suggests a cultivation method when the user's emotions are stable. This allows advice to be provided at a timing when the user is most relaxed.

[0084] When receiving feedback, the advice providing unit uses AI to automatically analyze the content of the feedback and immediately propose countermeasures. For example, the advice providing unit uses AI to automatically analyze feedback from users and immediately propose countermeasures. For example, in response to feedback such as "Growth is slow with this amount of fertilizer," the unit suggests the appropriate amount of fertilizer. This allows the content of feedback to be automatically analyzed and countermeasures to be immediately proposed.

[0085] The advice providing unit allows the AI ​​to learn areas for improvement in cultivation methods based on the content of the feedback and reflect these in future cultivations. For example, the advice providing unit allows the AI ​​to learn areas for improvement in cultivation methods based on feedback from users and reflect these in future cultivations. For example, based on feedback such as "it would be better to water more frequently," the next watering schedule is adjusted. This allows the AI ​​to learn areas for improvement in cultivation methods based on the content of the feedback and reflect these in future cultivations.

[0086] The advice providing unit has an emotion estimation function and can analyze the user's emotion when providing feedback and preferentially reflect positive feedback. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotion when providing feedback and preferentially reflect positive feedback. For example, if the user is emotionally excited, that feedback is preferentially reflected. This makes it possible to analyze the user's emotion when providing feedback and preferentially reflect positive feedback.

[0087] The advice providing unit can share feedback with other users and build a database for finding common problems or areas for improvement. The advice providing unit, for example, shares feedback with other users and builds a database for finding common problems or areas for improvement. For example, it allows users who have the same problem to share information with each other. This makes it possible to share feedback with other users and build a database for finding common problems or areas for improvement.

[0088] The advice providing unit allows the AI ​​to automatically generate new cultivation methods or adjustment proposals for environmental conditions based on the content of the feedback. For example, in response to feedback such as "humidity is too low," the advice providing unit can suggest an operating schedule for a humidifier. This makes it possible to automatically generate new cultivation methods or adjustment proposals for environmental conditions based on the content of the feedback.

[0089] The advice providing unit has an emotion estimation function and can monitor the user's emotion when providing feedback in real time and propose optimal countermeasures. The advice providing unit, for example, uses the emotion estimation function to monitor the user's emotion when providing feedback in real time and propose optimal countermeasures. For example, if the user's emotion is elevated, that feedback is reflected preferentially. This makes it possible to monitor the user's emotion when providing feedback in real time and propose optimal countermeasures.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The automatic cultivation system also has a music playback function in the cultivation unit, which can play music to promote plant growth. For example, music with a specific frequency is known to promote plant growth, and the cultivation unit can utilize this to optimize growth. It can also make the cultivation process more enjoyable by playing music that the user prefers. This can promote plant growth and improve the user's cultivation experience.

[0092] The automatic cultivation system also features a fragrance diffusion function in the cultivation unit, which can diffuse fragrances suitable for plant growth. For example, certain essential oils are known to promote plant growth, and the cultivation unit can utilize this to optimize growth. It can also diffuse relaxing fragrances to make the cultivation process more comfortable for the user. This promotes plant growth and improves the user's cultivation experience.

[0093] The automated cultivation system also equips the cultivation unit with an automatic harvesting function, allowing it to automatically harvest vegetables when it's time to harvest. For example, the AI ​​management unit monitors the growth of vegetables and determines the optimal time for harvesting. When harvesting is necessary, the automatic harvesting device activates and harvests the vegetables. This saves users the trouble of harvesting, allowing them to obtain fresh vegetables at the optimal time.

[0094] Automated cultivation systems also have water quality management functions in the cultivation units to optimize the quality of irrigation water. For example, they can be equipped with water quality sensors to measure the water's pH and mineral content. If necessary, they can use filters and additives to adjust the water quality. This helps maintain optimal water quality for plants and promote healthy growth.

[0095] The automatic cultivation system also has an automatic fertilizer supply function in the cultivation unit, which can automatically supply the nutrients necessary for plant growth. For example, the AI ​​management unit monitors the plant's growth status and determines the nutrients it needs. The automatic fertilizer supply device then activates and supplies the appropriate amount of fertilizer. This allows users to effortlessly manage fertilizer and support the healthy growth of their plants.

[0096] The automatic cultivation system can further use emotion estimation to customize cultivation advice based on the user's emotional state. For example, if the user is stressed, it can suggest an easy and successful cultivation method. On the other hand, if the user is relaxed, it can suggest a more challenging cultivation method. This allows the system to provide optimal cultivation advice according to the user's emotional state and improve the cultivation experience.

[0097] The automatic cultivation system can further use emotion estimation to adjust the environment of the cultivation unit based on the user's emotional state. For example, if the user is relaxed, the system can adjust the lighting to softer light and play music. If the user is stressed, the system can keep the environment quiet and diffuse a relaxing fragrance. This provides an optimal environment according to the user's emotional state, improving the cultivation experience.

[0098] The automatic cultivation system can further use the emotion estimation function to guide the operation of the cultivation unit based on the user's emotional state. For example, if the user feels anxious, the system can provide detailed explanations and support for the operation procedures. On the other hand, if the user feels confident, the system can provide simplified operation procedures. This allows the system to provide optimal operation guidance according to the user's emotional state and improve the cultivation experience.

[0099] The automatic cultivation system can further use its emotion estimation function to suggest maintenance for the cultivation unit based on the user's emotional state. For example, if the user is tired, it will suggest simple maintenance tasks, and if the user is energetic, it will suggest more detailed maintenance tasks. This allows it to provide optimal maintenance suggestions based on the user's emotional state and support the maintenance of the cultivation unit.

[0100] The automated cultivation system can further use emotion estimation to customize the feedback of the cultivation unit based on the user's emotional state. For example, if the user has positive emotions, it can highlight success stories and positive feedback. If the user has negative emotions, it can provide suggestions for improvement and support. This allows the system to provide optimal feedback according to the user's emotional state and improve the cultivation experience.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The cultivation unit manages the growth of the vegetables. For example, the cultivation unit is equipped with sensors that measure temperature, humidity, light intensity, soil pH, etc., to optimally manage the growth of the vegetables. The cultivation unit is also equipped with a cooling device and a humidifying device, allowing it to adjust environmental conditions. Step 2: The AI ​​controller adjusts the environmental conditions within the growing unit. For example, if the temperature is too high, the AI ​​controller activates the cooling system. If the humidity is too low, the AI ​​controller activates the humidifier. The AI ​​controller also adjusts the lighting if there is insufficient light, and makes necessary adjustments if the soil pH is not appropriate. Step 3: The monitoring unit monitors the cultivation status in the cultivation unit in real time. For example, the monitoring unit collects data from sensors and monitors the cultivation status. The monitoring unit can also display the cultivation status to the user through a dedicated app. Step 4: The advice providing unit provides advice or modifies cultivation methods as needed. For example, the advice providing unit may instruct the user to turn on a humidifier because the current humidity is low. The advice providing unit may also instruct the user on the appropriate frequency and amount of watering during the germination period, and suggest the type and amount of fertilizer needed during the growth period.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 cultivation unit; AI Management Department and A monitoring department; an advice providing unit; The cultivation unit comprises: Controlling the growth of vegetables The AI ​​management department: adjusting the environmental conditions within the cultivation unit; The monitoring unit Monitoring the cultivation status in the cultivation unit in real time; The advice providing unit A system that provides advice or corrects cultivation methods as needed.

2. The AI ​​management department: The system according to claim 1, characterized in that it constructs a predictive model based on past data and proposes preventive measures against future environmental changes.

3. The AI ​​management department:

2. The system according to claim 1, wherein the system suggests optimal cultivation conditions for maximizing the nutritional value of various vegetables.

4. The monitoring unit The system according to claim 1, characterized in that it has a 3D visualization function, allowing the user to visually confirm the growth process.

5. The advice providing unit The system according to claim 1, further comprising an emotion estimation function, which analyzes the user's emotions when providing feedback and preferentially reflects positive feedback.

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