system

Abstract

An object of a system according to an embodiment is to provide an appropriate environment and support for performing home gardening remotely.SOLUTION: A system includes a land providing part, an information providing part, a caring part, a consulting part, and a feedback part. The land providing part provides land of a plurality of climates. The information providing unit may provide real-time information using a camera or IoT. The maintenance unit performs maintenance using an AGV. The consultation unit performs consultation of the crop according to the generation AI. The feedback unit generates feedback based on the image analysis.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 made it difficult to grow crops when there is not enough land or a suitable environment for home gardening, or when there is a lack of know-how to grow crops.

[0005] The system according to the embodiment aims to provide an appropriate environment and support for remote home gardening. [Means for solving the problem]

[0006] The system according to the embodiment includes a land providing unit, an information providing unit, a maintenance unit, a consultation unit, and a feedback unit. The land providing unit provides land with multiple climates. The information providing unit provides real-time information using a camera or IoT. The maintenance unit performs maintenance using an AGV. The consultation unit provides consultation about agricultural crops using a generation AI. The feedback unit generates feedback using image analysis. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate environment and support for remote home gardening. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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 remote vegetable gardening system according to an embodiment of the present invention allows users to enjoy remote vegetable gardening. This system provides land with various climates, provides real-time information using cameras and IoT, cares for the land using AGVs, provides crop consultations using AI, and generates feedback through image analysis. This allows users to enjoy remote vegetable gardening.

[0029] A remote vegetable gardening system according to an embodiment includes a land provision unit, an information provision unit, a maintenance unit, a consultation unit, and a feedback unit. The land provision unit provides land in various climates. For example, if a user living in a cold region wants to grow tropical crops, they can select land in a tropical region and cultivate the crops remotely. The information provision unit provides real-time information using cameras and IoT. For example, it can monitor the growth status of crops through cameras and measure soil humidity and temperature using IoT sensors. The maintenance unit uses AGVs to perform maintenance. For example, the AGVs can automatically water the crops and remove weeds. The consultation unit provides consultation about crops using a generation AI. For example, in response to a question such as, "My tomato leaves are turning yellow. What should I do?", the generation AI can provide advice such as, "Try watering them less frequently." The feedback unit generates feedback using image analysis. For example, it can analyze the color and shape of crop leaves to detect the occurrence of pests and diseases and suggest countermeasures. This allows the remote vegetable gardening system to allow users to enjoy vegetable gardening remotely.

[0030] The land provision unit can collect climate data in real time and automatically suggest the most suitable land for the user's crops. The land provision unit, for example, develops a system that collects climate data in real time and automatically suggests the most suitable land for the crops that the user wants to grow. For example, the most suitable land is selected based on data such as temperature, humidity, and precipitation. The land provision unit also analyzes climate data and builds a system that suggests land suitable for the crops that the user wants to grow. For example, land that meets the climatic conditions required for a specific crop is automatically selected. The land provision unit also collects climate data in real time and develops a system that suggests the most suitable land for the crops that the user wants to grow. For example, the most suitable land is automatically selected based on the climate data. This allows the user to select the most suitable land.

[0031] The information providing unit can analyze data obtained from cameras and IoT sensors and predict crop growth. The information providing unit, for example, analyzes data obtained from cameras and IoT sensors and develops a system for predicting crop growth. For example, growth predictions are made based on image data and sensor data. The information providing unit also builds a system for analyzing data obtained from cameras and IoT sensors in real time and predicting crop growth. For example, growth predictions are made based on temperature and humidity data. The information providing unit also analyzes data obtained from cameras and IoT sensors and develops a system for predicting crop growth. For example, growth predictions are made using image analysis technology and machine learning. This makes it possible to predict crop growth and provide appropriate care.

[0032] The care department will equip AGVs with AI to automatically determine the condition of crops and provide optimal care. The care department will, for example, develop a system that equips AGVs with AI to automatically determine the condition of crops and provide optimal care. For example, it will use image analysis technology to determine the condition of crops. The care department will also equip AGVs with AI to build a system that determines the condition of crops in real time and provides optimal care. For example, it will determine the condition of crops based on sensor data. The care department will also develop a system that equips AGVs with AI to add a function that automatically determines the condition of crops and provides optimal care. For example, it will use a machine learning algorithm to determine the condition of crops. This will enable optimal care according to the condition of the crops.

[0033] The consultation department has the generation AI learn the consultation history and is able to provide highly accurate advice. For example, the consultation department develops a system that has the generation AI learn past consultation history and provides more accurate advice. For example, optimal advice is generated based on past data. The consultation department also has the generation AI learn past consultation history in real time and builds a system that provides more accurate advice. For example, advice is automatically generated based on consultation history data. The consultation department also has the generation AI learn past consultation history and develops a system that provides more accurate advice. For example, advice is generated using a machine learning algorithm. This makes it possible to provide highly accurate advice based on past consultation history.

[0034] The feedback unit can automatically determine the growth stage of a crop using image analysis technology and suggest an appropriate care method. The feedback unit, for example, uses image analysis technology to develop a system that automatically determines the growth stage of a crop and suggests an appropriate care method. For example, the growth stage is determined based on image data. The feedback unit also uses image analysis technology in real time to build a system that determines the growth stage of a crop and suggests an appropriate care method. For example, a care method according to the growth stage is automatically suggested. The feedback unit also uses image analysis technology to develop a system that automatically determines the growth stage of a crop and suggests an appropriate care method. For example, the growth stage is determined using a machine learning algorithm. This makes it possible to provide appropriate care according to the crop's growth stage.

[0035] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0036] The remote vegetable gardening system may further include a health monitoring unit that monitors the user's health condition and provides advice on selecting crops and care based on the health condition. For example, if the user has allergies, it can suggest crops that do not cause allergies. Also, if the user needs a specific nutrient, it can suggest crops that contain a lot of that nutrient. Furthermore, if the user is not getting enough exercise, it can suggest crops that require good care. This makes it possible to select and care for the most suitable crops according to the user's health condition.

[0037] The remote vegetable gardening system may further include a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm and suggests the timing and method of care based on that lifestyle rhythm. For example, if the user is a nocturnal person, crops that can be cared for at night can be suggested. Also, if the user has time on the weekend, crops that can be cared for intensively on the weekend can be suggested. Furthermore, if the user has an irregular lifestyle, crops that can be cared for flexibly can be suggested. This makes it possible to suggest optimal care that suits the user's lifestyle rhythm.

[0038] The remote vegetable gardening system may further include a preference learning unit that learns the user's preferences and provides advice on selecting crops and caring for them based on the preferences. For example, if the user likes a particular flower, advice on how to grow that flower can be provided. If the user likes a particular dish, crops that can be used in that dish can be suggested. Furthermore, if the user likes a particular color, flowers and crops of that color can be suggested. This makes it possible to select and care for crops that are optimal for the user's preferences.

[0039] The remote vegetable gardening system may further include a regional adaptation unit that takes into account the culture and customs of the user's region and provides advice on selecting crops and care suitable for the region. For example, it can suggest crops that are traditionally grown in a particular region. It can also suggest crops that are suitable for the local climate and soil. It can also suggest crops that match local festivals and events. This allows the user to select and care for crops that are optimal for the user's region.

[0040] The remote vegetable gardening system may further include a cultivation history analysis unit that analyzes the user's past cultivation history and provides advice on crop selection and care based on past successes and failures. For example, it may be possible to suggest crops that have been successful in the past. It may also be possible to analyze the causes of past crop failures and propose improvements. It may also be possible to suggest trying new crops based on the past cultivation history. This allows for optimal crop selection and care based on the user's past experience.

[0041] The processing flow of the first embodiment will be briefly explained below.

[0042] Step 1: The land provider provides land in various climates. For example, if a user living in a cold region wants to grow tropical crops, they can select land in a tropical region and cultivate it remotely. Step 2: The information provider uses cameras and IoT to provide real-time information. For example, the growing status of crops can be checked through cameras, and soil humidity and temperature can be measured using IoT sensors. Step 3: The maintenance section uses AGVs to perform maintenance. For example, the AGVs can automatically water the plants and remove weeds. Step 4: The consultation section uses the generated AI to provide advice about agricultural crops. For example, in response to a question such as, "My tomato leaves are turning yellow. What should I do?", the generated AI will provide advice such as, "Try watering them less frequently." Step 5: The feedback unit generates feedback based on image analysis. For example, it analyzes the color and shape of crop leaves to detect the occurrence of pests and diseases and proposes countermeasures.

[0043] (Example 2) The remote vegetable gardening system according to an embodiment of the present invention allows users to enjoy remote vegetable gardening. This system provides land with various climates, provides real-time information using cameras and IoT, cares for the land using AGVs, provides crop consultations using AI, and generates feedback through image analysis. This allows users to enjoy remote vegetable gardening.

[0044] A remote vegetable gardening system according to an embodiment includes a land provision unit, an information provision unit, a maintenance unit, a consultation unit, and a feedback unit. The land provision unit provides land in various climates. For example, if a user living in a cold region wants to grow tropical crops, they can select land in a tropical region and cultivate the crops remotely. The information provision unit provides real-time information using cameras and IoT. For example, it can monitor the growth status of crops through cameras and measure soil humidity and temperature using IoT sensors. The maintenance unit uses AGVs to perform maintenance. For example, the AGVs can automatically water the crops and remove weeds. The consultation unit provides consultation about crops using a generation AI. For example, in response to a question such as, "My tomato leaves are turning yellow. What should I do?", the generation AI can provide advice such as, "Try watering them less frequently." The feedback unit generates feedback using image analysis. For example, it can analyze the color and shape of crop leaves to detect the occurrence of pests and diseases and suggest countermeasures. This allows the remote vegetable gardening system to allow users to enjoy vegetable gardening remotely.

[0045] The land provision unit can collect climate data in real time and automatically suggest the most suitable land for the user's crops. The land provision unit, for example, develops a system that collects climate data in real time and automatically suggests the most suitable land for the crops that the user wants to grow. For example, the most suitable land is selected based on data such as temperature, humidity, and precipitation. The land provision unit also analyzes climate data and builds a system that suggests land suitable for the crops that the user wants to grow. For example, land that meets the climatic conditions required for a specific crop is automatically selected. The land provision unit also collects climate data in real time and develops a system that suggests the most suitable land for the crops that the user wants to grow. For example, the most suitable land is automatically selected based on the climate data. This allows the user to select the most suitable land.

[0046] The information providing unit can analyze data obtained from cameras and IoT sensors and predict crop growth. The information providing unit, for example, analyzes data obtained from cameras and IoT sensors and develops a system for predicting crop growth. For example, growth predictions are made based on image data and sensor data. The information providing unit also builds a system for analyzing data obtained from cameras and IoT sensors in real time and predicting crop growth. For example, growth predictions are made based on temperature and humidity data. The information providing unit also analyzes data obtained from cameras and IoT sensors and develops a system for predicting crop growth. For example, growth predictions are made using image analysis technology and machine learning. This makes it possible to predict crop growth and provide appropriate care.

[0047] The care department will equip AGVs with AI to automatically determine the condition of crops and provide optimal care. The care department will, for example, develop a system that equips AGVs with AI to automatically determine the condition of crops and provide optimal care. For example, it will use image analysis technology to determine the condition of crops. The care department will also equip AGVs with AI to build a system that determines the condition of crops in real time and provides optimal care. For example, it will determine the condition of crops based on sensor data. The care department will also develop a system that equips AGVs with AI to add a function that automatically determines the condition of crops and provides optimal care. For example, it will use a machine learning algorithm to determine the condition of crops. This will enable optimal care according to the condition of the crops.

[0048] The consultation department has the generation AI learn the consultation history and is able to provide highly accurate advice. For example, the consultation department develops a system that has the generation AI learn past consultation history and provides more accurate advice. For example, optimal advice is generated based on past data. The consultation department also has the generation AI learn past consultation history in real time and builds a system that provides more accurate advice. For example, advice is automatically generated based on consultation history data. The consultation department also has the generation AI learn past consultation history and develops a system that provides more accurate advice. For example, advice is generated using a machine learning algorithm. This makes it possible to provide highly accurate advice based on past consultation history.

[0049] The feedback unit can automatically determine the growth stage of a crop using image analysis technology and suggest an appropriate care method. The feedback unit, for example, uses image analysis technology to develop a system that automatically determines the growth stage of a crop and suggests an appropriate care method. For example, the growth stage is determined based on image data. The feedback unit also uses image analysis technology in real time to build a system that determines the growth stage of a crop and suggests an appropriate care method. For example, a care method according to the growth stage is automatically suggested. The feedback unit also uses image analysis technology to develop a system that automatically determines the growth stage of a crop and suggests an appropriate care method. For example, the growth stage is determined using a machine learning algorithm. This makes it possible to provide appropriate care according to the crop's growth stage.

[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 remote vegetable gardening system may further include an emotion estimation unit that estimates the user's emotions and provides advice on selecting crops and caring for them based on the estimated emotions. For example, if the user is feeling stressed, it may suggest growing herbs that have a relaxing effect. If the user is feeling happy, it may suggest crops that grow quickly and are fun to harvest. If the user is feeling anxious, it may suggest crops that are easy to care for and unlikely to fail. This makes it possible to select and care for optimal crops according to the user's emotions.

[0052] The remote vegetable gardening system may further include an emotion estimation unit that estimates the user's emotion and provides feedback regarding the crop growth status based on the estimated emotion. For example, if the user is depressed, feedback emphasizing that the crops are growing well can be provided. If the user is excited, detailed data and predictions regarding the crop growth can be provided. Furthermore, if the user is tired, concise and easy-to-understand feedback can be provided. This makes it possible to provide appropriate feedback according to the user's emotion.

[0053] The remote vegetable gardening system may further include an emotion estimation unit that estimates the user's emotion and suggests the timing and method of care based on the estimated emotion. For example, if the user feels busy, a suggestion can be made to reduce the frequency of care. If the user feels like relaxing, a suggestion can be made to make the care work more enjoyable. Furthermore, if the user feels anxious, a suggestion can be made that explains the care method in detail. This makes it possible to suggest optimal care according to the user's emotion.

[0054] The remote vegetable gardening system may further include an emotion estimation unit that estimates the user's emotion and customizes the crop consultation content based on the estimated emotion. For example, if the user is confused, concise and specific advice can be provided. If the user is excited, detailed background information and additional advice can be provided. Furthermore, if the user is depressed, advice that includes words of encouragement can be provided. This makes it possible to customize the consultation content optimally according to the user's emotion.

[0055] The remote vegetable gardening system may further include an emotion estimation unit that estimates the user's emotion and provides a crop growth forecast based on the estimated emotion. For example, if the user is excited, a detailed growth forecast can be provided. If the user is anxious, a concise growth forecast can be provided, with additional information to provide a sense of security. Furthermore, if the user is excited, in addition to the growth forecast, suggestions on how to enjoy the growth process can be provided. This makes it possible to provide an optimal growth forecast according to the user's emotion.

[0056] The remote vegetable gardening system may further include a health monitoring unit that monitors the user's health condition and provides advice on selecting crops and care based on the health condition. For example, if the user has allergies, it can suggest crops that do not cause allergies. Also, if the user needs a specific nutrient, it can suggest crops that contain a lot of that nutrient. Furthermore, if the user is not getting enough exercise, it can suggest crops that require good care. This makes it possible to select and care for the most suitable crops according to the user's health condition.

[0057] The remote vegetable gardening system may further include a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm and suggests the timing and method of care based on that lifestyle rhythm. For example, if the user is a nocturnal person, crops that can be cared for at night can be suggested. Also, if the user has time on the weekend, crops that can be cared for intensively on the weekend can be suggested. Furthermore, if the user has an irregular lifestyle, crops that can be cared for flexibly can be suggested. This makes it possible to suggest optimal care that suits the user's lifestyle rhythm.

[0058] The remote vegetable gardening system may further include a preference learning unit that learns the user's preferences and provides advice on selecting crops and caring for them based on the preferences. For example, if the user likes a particular flower, advice on how to grow that flower can be provided. If the user likes a particular dish, crops that can be used in that dish can be suggested. Furthermore, if the user likes a particular color, flowers and crops of that color can be suggested. This makes it possible to select and care for crops that are optimal for the user's preferences.

[0059] The remote vegetable gardening system may further include a regional adaptation unit that takes into account the culture and customs of the user's region and provides advice on selecting crops and care suitable for the region. For example, it can suggest crops that are traditionally grown in a particular region. It can also suggest crops that are suitable for the local climate and soil. It can also suggest crops that match local festivals and events. This allows the user to select and care for crops that are optimal for the user's region.

[0060] The remote vegetable gardening system may further include a cultivation history analysis unit that analyzes the user's past cultivation history and provides advice on crop selection and care based on past successes and failures. For example, it may be possible to suggest crops that have been successful in the past. It may also be possible to analyze the causes of past crop failures and propose improvements. It may also be possible to suggest trying new crops based on the past cultivation history. This allows for optimal crop selection and care based on the user's past experience.

[0061] The processing flow of the second embodiment will be briefly explained below.

[0062] Step 1: The land provider provides land in various climates. For example, if a user living in a cold region wants to grow tropical crops, they can select land in a tropical region and cultivate it remotely. Step 2: The information provider uses cameras and IoT to provide real-time information. For example, the growing status of crops can be checked through cameras, and soil humidity and temperature can be measured using IoT sensors. Step 3: The maintenance section uses AGVs to perform maintenance. For example, the AGVs can automatically water the plants and remove weeds. Step 4: The consultation section uses the generated AI to provide advice about agricultural crops. For example, in response to a question such as, "My tomato leaves are turning yellow. What should I do?", the generated AI will provide advice such as, "Try watering them less frequently." Step 5: The feedback unit generates feedback based on image analysis. For example, it analyzes the color and shape of crop leaves to detect the occurrence of pests and diseases and proposes countermeasures.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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).

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0076] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0091] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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."

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0129] 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]

[0130] 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 land provision unit that provides land in multiple climates; an information providing unit that provides real-time information using a camera or IoT; The maintenance unit performs maintenance using an AGV; A consultation department that provides advice on agricultural products using generative AI, a feedback unit that generates feedback based on image analysis. A system characterized by:

2. The land providing unit Collects real-time weather data and automatically suggests the best land for users' crops 2. The system of claim 1.

3. The information providing unit Analyze data obtained from the camera and IoT sensor to predict crop growth.

2. The system of claim 1.

4. The maintenance section includes: The AGV is equipped with the AI, which automatically determines the condition of the crops and provides optimal care.

2. The system of claim 1.

5. The consultation department: The AI ​​generation system learns consultation history and provides more accurate advice 2. The system of claim 1.

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