System

Abstract

An object of a system according to an embodiment is to efficiently acquire information necessary for starting home gardening and provide appropriate advice.SOLUTION: A system according to an embodiment includes an image analysis unit, an advice providing unit, and a planning support unit. The image analysis unit analyzes the image of the plant uploaded by the user. The advice providing unit provides advice in response to a question from the user. The plan creation support unit creates a plan of the home garden based on information provided by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to efficiently obtain the information needed to start a home vegetable garden and receive appropriate advice.

[0005] The system according to the embodiment aims to efficiently acquire information necessary for starting a home vegetable garden and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, an advice providing unit, and a planning support unit. The image analysis unit analyzes images of plants uploaded by a user. The advice providing unit provides advice in response to questions from the user. The planning support unit creates a home vegetable garden plan based on information provided by the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently acquire information necessary for starting a home vegetable garden and provide appropriate advice. [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 smart gardening system according to an embodiment of the present invention provides comprehensive support to people who want to start a home vegetable garden or who are having trouble with their home vegetable garden. This system uses image analysis and a generative AI based on Retrieval-Augmented Generation (RAG) to flexibly respond to the user's concerns. This allows the smart gardening system to solve problems such as having too many things to consider when it comes to home vegetable gardening, and not being able to find a solution even after researching because responses vary depending on the situation.

[0029] A smart gardening system according to an embodiment includes an image analysis unit, an advice providing unit, and a planning support unit. The image analysis unit analyzes images of plants uploaded by a user. For example, the image analysis unit analyzes the color and shape of the plant's leaves, the presence or absence of disease spots, and the like to determine whether the plant is healthy, diseased, or malnourished. The image analysis unit can analyze images using, for example, deep learning or neural networks. The advice providing unit provides advice in response to a user's question. For example, when a user inputs a question such as, "My tomato leaves are turning yellow. What should I do?", the advice providing unit searches for related information and suggests appropriate measures. The advice providing unit can provide advice by, for example, referring to an answer database. The planning support unit creates a home garden plan based on information provided by the user. For example, when the planning support unit inputs the type of plant the user wants to grow, the space for the home garden, and the weather conditions, the generation AI suggests the optimal planting time and cultivation method. The planning support unit can create a plan by, for example, referring to a database of past success stories. As a result, the smart gardening system according to the embodiment can provide comprehensive support, including image analysis, advice provision, and planning assistance, when a user starts a home vegetable garden or encounters problems.

[0030] The image analysis unit can reference plant growth history data and compare it with growth patterns to detect abnormalities. For example, the image analysis unit analyzes images of plants uploaded by users and compares them with past growth history data to detect growth delays or abnormalities. For example, it identifies abnormalities by comparing changes in leaf color or shape with past data. The image analysis unit also allows the generation AI to reference the plant's growth history data and compare past growth patterns with the current state to detect signs of disease or nutritional deficiencies early. For example, it detects changes in leaf color or slow growth rates. The image analysis unit also allows the generation AI to evaluate the current growth state based on the plant's growth history data and notify the user if an abnormality is detected. For example, if growth is slower than past data, it will suggest appropriate measures. This allows abnormalities to be detected early based on the plant's growth history data.

[0031] The image analysis unit can simultaneously analyze environmental data such as soil quality, humidity, and temperature to perform a comprehensive health check. For example, the image analysis unit uses environmental data provided by the user, such as soil quality, humidity, and temperature, to have the generation AI analyze images of plants and perform a comprehensive health check. For example, it evaluates the impact of soil pH and humidity on plant health. The image analysis unit also simultaneously analyzes plant images and environmental data to comprehensively diagnose the plant's health. For example, it evaluates the impact of temperature and humidity fluctuations on plant growth and suggests appropriate countermeasures. The image analysis unit also builds a system in which the generation AI analyzes plant images based on environmental data to perform a comprehensive health check. For example, it evaluates the impact of soil quality and humidity on plant health and notifies the user. This allows for a more accurate understanding of the plant's health by performing a comprehensive health check based on environmental data.

[0032] The image analysis unit can integrate and analyze multiple images taken from different angles to make an accurate diagnosis. For example, a user can upload multiple plant images taken from different angles, and the generation AI can integrate and analyze them. For example, a comprehensive diagnosis can be made, including the underside of the leaves and the condition of the stem. The image analysis unit also integrates multiple images taken from different angles to more accurately diagnose the condition of the plant. For example, it can evaluate changes in leaf color and shape from multiple perspectives and identify abnormalities. The image analysis unit also integrates and analyzes multiple images to build a system in which the generation AI can more accurately diagnose the condition of the plant. For example, it can detect signs of disease or nutritional deficiencies early based on images from different angles. This allows the generation AI to more accurately diagnose the condition of the plant by integrating and analyzing images from multiple angles.

[0033] The advice providing unit can provide effective advice by referring to a database of past user questions and answers. For example, the generation AI in the advice providing unit refers to a database of past user questions and their answers to provide the most effective advice. For example, the generation AI suggests the optimal solution based on past answers to the same question. The advice providing unit also analyzes the database of past user questions and answers to build a system in which the generation AI provides the most effective advice. For example, it compares multiple answers to the same problem and selects the optimal solution. The advice providing unit also refers to a database of past questions and answers to provide the most effective advice for the user's question. For example, advice is given based on past success stories for a question about the same type of plant. In this way, the most effective advice can be provided by referring to the database of past questions and answers.

[0034] The advice providing unit can provide customized advice based on background information of the question. For example, the generation AI provides customized advice in response to a user's question by taking into account background information such as the local climate and season. For example, the advice providing unit proposes the optimal cultivation method for a specific region. The advice providing unit also builds a system in which the generation AI provides customized advice based on the background information of the question. For example, the advice providing unit proposes a cultivation method that corresponds to seasonal climate changes. The advice providing unit also provides optimal advice in response to a user's question by taking into account background information such as the local climate and season. For example, the advice providing unit proposes a fertilization method that is suitable for a specific season. In this way, more appropriate advice can be provided by taking into account the background information of the question.

[0035] The plan creation support unit can refer to a database of past success stories and propose plans with a high success rate. In the plan creation support unit, for example, the generation AI refers to the database of past success stories and proposes a home vegetable garden plan with the highest success rate. For example, it proposes the optimal planting time and cultivation method based on past success stories of growing the same type of plant. The plan creation support unit also analyzes the database of past success stories and builds a system in which the generation AI proposes a plan with the highest success rate. For example, it extracts common points between the success stories and creates a plan based on that. In addition, the plan creation support unit also refers to the database of past success stories and proposes the optimal home vegetable garden plan to the user. For example, it proposes the optimal cultivation method based on success stories under the same climatic conditions. In this way, it is possible to propose a plan with the highest success rate based on past success stories.

[0036] The plan creation support unit can propose a reasonable plan based on the user's lifestyle and schedule. In the plan creation support unit, for example, the generation AI takes into account the user's lifestyle and schedule to propose a reasonable home garden plan. For example, for a user who can only find time on weekends, the generation AI proposes a plan that allows the user to concentrate on work on the weekend. The plan creation support unit also builds a system in which the generation AI proposes a reasonable plan based on the user's lifestyle and schedule. For example, for a user who is busy on weekdays, the generation AI proposes plants that are easy to manage. In addition, the plan creation support unit proposes an optimal home garden plan by taking into account the user's lifestyle and schedule. For example, for a user who wants to work a little at a time every day, the generation AI proposes daily tasks. In this way, a reasonable plan can be proposed by taking into account the user's lifestyle and schedule.

[0037] The plan creation support unit can compare the plan with those of other users, extract common success factors, and make suggestions. For example, the generation AI in the plan creation support unit compares the plan with those of other users' home gardens, extracts common success factors, and makes suggestions. For example, the plan creation support unit proposes the optimal cultivation method based on success stories of users who have grown the same type of plant. The plan creation support unit also analyzes other users' plans, and the generation AI builds a system in which it extracts and proposes common success factors. For example, it extracts common points in the success stories and creates a plan based on those. The plan creation support unit also compares the plan with those of other users, extracts common success factors, and makes suggestions to the user. For example, it proposes the optimal cultivation method based on success stories under the same climatic conditions. In this way, by comparing with other users' plans, common success factors can be extracted, and plans with a higher success rate can be proposed.

[0038] The plan creation support unit can refer to data on different climatic conditions and regions and propose plans that are specialized for the region. For example, the generation AI in the plan creation support unit refers to data on different climatic conditions and regions and proposes a home vegetable garden plan that is specialized for the region. For example, it proposes the optimal planting time and cultivation method for a specific region. The plan creation support unit also builds a system in which the generation AI proposes a region-specific plan based on data on different climatic conditions and regions. For example, it proposes cultivation methods that are suited to climate change in each region. The plan creation support unit also builds a system in which the generation AI refers to data on different climatic conditions and regions and proposes the optimal home vegetable garden plan for the user. For example, it proposes the optimal cultivation method based on successful examples in a specific region. In this way, by referring to data on different climatic conditions and regions, it is possible to propose a plan that is specialized for the region.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The smart gardening system may further include a voice recognition unit. The voice recognition unit allows the user to input questions and instructions by voice. For example, if the user asks, "My tomato leaves are turning yellow. What should I do?", the voice recognition unit converts the question into text, and the advice providing unit suggests appropriate measures. The voice recognition unit also allows the user to operate the system hands-free, making it easy to use even while gardening. For example, if the user asks, "What should I do next?", the planning support unit suggests the next step. Furthermore, the voice recognition unit can learn the user's pronunciation and accent and recognize voices more accurately. This allows the user to operate the system more intuitively using voice recognition.

[0041] The smart gardening system may further include a community collaboration unit. The community collaboration unit allows users to share information and receive advice from other home gardeners. For example, a user can upload photos of their home garden and receive comments and advice from other users. The community collaboration unit also supports users in asking other users questions about specific problems. For example, a user can post a question such as, "My tomato leaves are turning yellow. Has anyone had the same experience?" The community collaboration unit also encourages users to share success stories and failure stories and learn from each other. This allows users to share information with each other and manage their home gardens more effectively.

[0042] The smart gardening system can further include an automatic irrigation control unit. The automatic irrigation control unit automatically sets an optimal irrigation schedule based on soil moisture and weather data. For example, it automatically supplies water when soil moisture drops. The automatic irrigation control unit can also refer to the weather forecast and refrain from irrigating if rain is expected. For example, it will suspend irrigation if rain is forecast for the next day. The automatic irrigation control unit also allows the user to manually adjust the irrigation schedule. For example, it can be set to supply water at specific times. This allows for efficient water use while maintaining plant health.

[0043] The smart gardening system can further include a pest detection unit. The pest detection unit analyzes images of plants to detect the presence of pests. For example, it identifies small insects attached to leaves or signs of feeding damage. The pest detection unit can also suggest appropriate countermeasures depending on the type of pest detected. For example, it can notify the user of effective extermination methods for specific pests. Furthermore, the pest detection unit can record the occurrence of pests and suggest preventive measures based on past data. For example, it can suggest preventive measures in advance for pests that are more likely to occur in a particular season. This makes it possible to minimize pest damage and maintain the health of plants.

[0044] The smart gardening system can further include a harvest prediction unit. The harvest prediction unit predicts the harvest time based on plant growth data. For example, it references the plant's growth rate and past data and notifies the user of the optimal harvest time. The harvest prediction unit can also set reminders so that the user does not miss the harvest time. For example, it sends a notification when harvest is approaching. Furthermore, the harvest prediction unit can also predict the harvest yield. For example, it can notify the user of the expected harvest yield based on the current growth state. This allows the user to harvest at the optimal time and obtain the maximum harvest.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The image analysis unit analyzes the plant images uploaded by the user. For example, the image analysis unit analyzes the color and shape of the plant's leaves, the presence or absence of disease spots, and other factors to determine whether the plant is healthy, diseased, or nutrient-deficient. The image analysis unit can also analyze images using deep learning and neural networks. Step 2: The advice provider provides advice in response to the user's question. For example, if a user inputs a question such as, "My tomato leaves are turning yellow. What should I do?", the AI ​​generator searches for related information and suggests appropriate measures. The advice provider can also provide advice by referring to the answer database. Step 3: The planning support unit creates a home garden plan based on the information provided by the user. For example, if the user inputs the type of plants they want to grow, the space available for the home garden, and the weather conditions, the generating AI will suggest the optimal planting time and cultivation method. The planning support unit can also create a plan by referencing a database of past success stories.

[0047] (Example 2) The smart gardening system according to an embodiment of the present invention provides comprehensive support to people who want to start a home vegetable garden or who are having trouble with their home vegetable garden. This system uses image analysis and a generative AI based on Retrieval-Augmented Generation (RAG) to flexibly respond to the user's concerns. This allows the smart gardening system to solve problems such as having too many things to consider when it comes to home vegetable gardening, and not being able to find a solution even after researching because responses vary depending on the situation.

[0048] A smart gardening system according to an embodiment includes an image analysis unit, an advice providing unit, and a planning support unit. The image analysis unit analyzes images of plants uploaded by a user. For example, the image analysis unit analyzes the color and shape of the plant's leaves, the presence or absence of disease spots, and the like to determine whether the plant is healthy, diseased, or malnourished. The image analysis unit can analyze images using, for example, deep learning or neural networks. The advice providing unit provides advice in response to a user's question. For example, when a user inputs a question such as, "My tomato leaves are turning yellow. What should I do?", the advice providing unit searches for related information and suggests appropriate measures. The advice providing unit can provide advice by, for example, referring to an answer database. The planning support unit creates a home garden plan based on information provided by the user. For example, when the planning support unit inputs the type of plant the user wants to grow, the space for the home garden, and the weather conditions, the generation AI suggests the optimal planting time and cultivation method. The planning support unit can create a plan by, for example, referring to a database of past success stories. As a result, the smart gardening system according to the embodiment can provide comprehensive support, including image analysis, advice provision, and planning assistance, when a user starts a home vegetable garden or encounters problems.

[0049] The image analysis unit can reference plant growth history data and compare it with growth patterns to detect abnormalities. For example, the image analysis unit analyzes images of plants uploaded by users and compares them with past growth history data to detect growth delays or abnormalities. For example, it identifies abnormalities by comparing changes in leaf color or shape with past data. The image analysis unit also allows the generation AI to reference the plant's growth history data and compare past growth patterns with the current state to detect signs of disease or nutritional deficiencies early. For example, it detects changes in leaf color or slow growth rates. The image analysis unit also allows the generation AI to evaluate the current growth state based on the plant's growth history data and notify the user if an abnormality is detected. For example, if growth is slower than past data, it will suggest appropriate measures. This allows abnormalities to be detected early based on the plant's growth history data.

[0050] The image analysis unit can simultaneously analyze environmental data such as soil quality, humidity, and temperature to perform a comprehensive health check. For example, the image analysis unit uses environmental data provided by the user, such as soil quality, humidity, and temperature, to have the generation AI analyze images of plants and perform a comprehensive health check. For example, it evaluates the impact of soil pH and humidity on plant health. The image analysis unit also simultaneously analyzes plant images and environmental data to comprehensively diagnose the plant's health. For example, it evaluates the impact of temperature and humidity fluctuations on plant growth and suggests appropriate countermeasures. The image analysis unit also builds a system in which the generation AI analyzes plant images based on environmental data to perform a comprehensive health check. For example, it evaluates the impact of soil quality and humidity on plant health and notifies the user. This allows for a more accurate understanding of the plant's health by performing a comprehensive health check based on environmental data.

[0051] The image analysis unit can integrate and analyze multiple images taken from different angles to make an accurate diagnosis. For example, a user can upload multiple plant images taken from different angles, and the generation AI can integrate and analyze them. For example, a comprehensive diagnosis can be made, including the underside of the leaves and the condition of the stem. The image analysis unit also integrates multiple images taken from different angles to more accurately diagnose the condition of the plant. For example, it can evaluate changes in leaf color and shape from multiple perspectives and identify abnormalities. The image analysis unit also integrates and analyzes multiple images to build a system in which the generation AI can more accurately diagnose the condition of the plant. For example, it can detect signs of disease or nutritional deficiencies early based on images from different angles. This allows the generation AI to more accurately diagnose the condition of the plant by integrating and analyzing images from multiple angles.

[0052] The advice providing unit can provide effective advice by referring to a database of past user questions and answers. For example, the generation AI in the advice providing unit refers to a database of past user questions and their answers to provide the most effective advice. For example, the generation AI suggests the optimal solution based on past answers to the same question. The advice providing unit also analyzes the database of past user questions and answers to build a system in which the generation AI provides the most effective advice. For example, it compares multiple answers to the same problem and selects the optimal solution. The advice providing unit also refers to a database of past questions and answers to provide the most effective advice for the user's question. For example, advice is given based on past success stories for a question about the same type of plant. In this way, the most effective advice can be provided by referring to the database of past questions and answers.

[0053] The advice providing unit can provide customized advice based on background information of the question. For example, the generation AI provides customized advice in response to a user's question by taking into account background information such as the local climate and season. For example, the advice providing unit proposes the optimal cultivation method for a specific region. The advice providing unit also builds a system in which the generation AI provides customized advice based on the background information of the question. For example, the advice providing unit proposes a cultivation method that corresponds to seasonal climate changes. The advice providing unit also provides optimal advice in response to a user's question by taking into account background information such as the local climate and season. For example, the advice providing unit proposes a fertilization method that is suitable for a specific season. In this way, more appropriate advice can be provided by taking into account the background information of the question.

[0054] The plan creation support unit can refer to a database of past success stories and propose plans with a high success rate. In the plan creation support unit, for example, the generation AI refers to the database of past success stories and proposes a home vegetable garden plan with the highest success rate. For example, it proposes the optimal planting time and cultivation method based on past success stories of growing the same type of plant. The plan creation support unit also analyzes the database of past success stories and builds a system in which the generation AI proposes a plan with the highest success rate. For example, it extracts common points between the success stories and creates a plan based on that. In addition, the plan creation support unit also refers to the database of past success stories and proposes the optimal home vegetable garden plan to the user. For example, it proposes the optimal cultivation method based on success stories under the same climatic conditions. In this way, it is possible to propose a plan with the highest success rate based on past success stories.

[0055] The plan creation support unit can propose a reasonable plan based on the user's lifestyle and schedule. In the plan creation support unit, for example, the generation AI takes into account the user's lifestyle and schedule to propose a reasonable home garden plan. For example, for a user who can only find time on weekends, the generation AI proposes a plan that allows the user to concentrate on work on the weekend. The plan creation support unit also builds a system in which the generation AI proposes a reasonable plan based on the user's lifestyle and schedule. For example, for a user who is busy on weekdays, the generation AI proposes plants that are easy to manage. In addition, the plan creation support unit proposes an optimal home garden plan by taking into account the user's lifestyle and schedule. For example, for a user who wants to work a little at a time every day, the generation AI proposes daily tasks. In this way, a reasonable plan can be proposed by taking into account the user's lifestyle and schedule.

[0056] The plan creation support unit can compare the plan with those of other users, extract common success factors, and make suggestions. For example, the generation AI in the plan creation support unit compares the plan with those of other users' home gardens, extracts common success factors, and makes suggestions. For example, the plan creation support unit proposes the optimal cultivation method based on success stories of users who have grown the same type of plant. The plan creation support unit also analyzes other users' plans, and the generation AI builds a system in which it extracts and proposes common success factors. For example, it extracts common points in the success stories and creates a plan based on those. The plan creation support unit also compares the plan with those of other users, extracts common success factors, and makes suggestions to the user. For example, it proposes the optimal cultivation method based on success stories under the same climatic conditions. In this way, by comparing with other users' plans, common success factors can be extracted, and plans with a higher success rate can be proposed.

[0057] The plan creation support unit can refer to data on different climatic conditions and regions and propose plans that are specialized for the region. For example, the generation AI in the plan creation support unit refers to data on different climatic conditions and regions and proposes a home vegetable garden plan that is specialized for the region. For example, it proposes the optimal planting time and cultivation method for a specific region. The plan creation support unit also builds a system in which the generation AI proposes a region-specific plan based on data on different climatic conditions and regions. For example, it proposes cultivation methods that are suited to climate change in each region. The plan creation support unit also builds a system in which the generation AI refers to data on different climatic conditions and regions and proposes the optimal home vegetable garden plan for the user. For example, it proposes the optimal cultivation method based on successful examples in a specific region. In this way, by referring to data on different climatic conditions and regions, it is possible to propose a plan that is specialized for the region.

[0058] The advice providing unit can use the emotion estimation function to analyze the user's emotion in response to the question and provide advice according to the user's emotion. For example, the advice providing unit uses the emotion estimation function to analyze the user's emotion in response to the question and provide advice according to the emotion. For example, if the user is feeling anxious, the advice providing unit provides advice that gives a sense of security. The advice providing unit also builds a system that analyzes the user's emotion in response to the question and provides advice according to the emotion. For example, if the user is feeling impatient, the advice providing unit suggests specific measures. The advice providing unit also uses the emotion estimation function to analyze the user's emotion in response to the question in real time and provide advice according to the emotion. For example, if the user is feeling happy, the advice providing unit sends a message sharing a successful experience. In this way, by providing advice according to the user's emotion, user satisfaction is improved.

[0059] The plan creation support unit can use the emotion estimation function to analyze the user's emotions regarding the plan and propose a plan to increase the user's motivation. For example, the plan creation support unit uses the emotion estimation function to analyze the user's emotions regarding a home gardening plan and propose a plan to increase the user's motivation. For example, the plan creation support unit proposes a plan that includes many tasks that the user finds enjoyable. The plan creation support unit also analyzes the user's emotions and builds a system that proposes a plan based on the user's emotions. For example, if the user is feeling anxious, the plan creation support unit proposes a simple plan that is likely to be successful. The plan creation support unit also uses the emotion estimation function to analyze the user's emotions in real time and propose a plan to increase the user's motivation. For example, the plan creation support unit proposes a plan that includes tasks that give the user a sense of accomplishment. In this way, the user's motivation is increased by proposing a plan that matches the user's emotions.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The smart gardening system may further include a voice recognition unit. The voice recognition unit allows the user to input questions and instructions by voice. For example, if the user asks, "My tomato leaves are turning yellow. What should I do?", the voice recognition unit converts the question into text, and the advice providing unit suggests appropriate measures. The voice recognition unit also allows the user to operate the system hands-free, making it easy to use even while gardening. For example, if the user asks, "What should I do next?", the planning support unit suggests the next step. Furthermore, the voice recognition unit can learn the user's pronunciation and accent and recognize voices more accurately. This allows the user to operate the system more intuitively using voice recognition.

[0062] The smart gardening system may further include a community collaboration unit. The community collaboration unit allows users to share information and receive advice from other home gardeners. For example, a user can upload photos of their home garden and receive comments and advice from other users. The community collaboration unit also supports users in asking other users questions about specific problems. For example, a user can post a question such as, "My tomato leaves are turning yellow. Has anyone had the same experience?" The community collaboration unit also encourages users to share success stories and failure stories and learn from each other. This allows users to share information with each other and manage their home gardens more effectively.

[0063] The smart gardening system can further include an automatic irrigation control unit. The automatic irrigation control unit automatically sets an optimal irrigation schedule based on soil moisture and weather data. For example, it automatically supplies water when soil moisture drops. The automatic irrigation control unit can also refer to the weather forecast and refrain from irrigating if rain is expected. For example, it will suspend irrigation if rain is forecast for the next day. The automatic irrigation control unit also allows the user to manually adjust the irrigation schedule. For example, it can be set to supply water at specific times. This allows for efficient water use while maintaining plant health.

[0064] The smart gardening system can further include a pest detection unit. The pest detection unit analyzes images of plants to detect the presence of pests. For example, it identifies small insects attached to leaves or signs of feeding damage. The pest detection unit can also suggest appropriate countermeasures depending on the type of pest detected. For example, it can notify the user of effective extermination methods for specific pests. Furthermore, the pest detection unit can record the occurrence of pests and suggest preventive measures based on past data. For example, it can suggest preventive measures in advance for pests that are more likely to occur in a particular season. This makes it possible to minimize pest damage and maintain the health of plants.

[0065] The smart gardening system can further include a harvest prediction unit. The harvest prediction unit predicts the harvest time based on plant growth data. For example, it references the plant's growth rate and past data and notifies the user of the optimal harvest time. The harvest prediction unit can also set reminders so that the user does not miss the harvest time. For example, it sends a notification when harvest is approaching. Furthermore, the harvest prediction unit can also predict the harvest yield. For example, it can notify the user of the expected harvest yield based on the current growth state. This allows the user to harvest at the optimal time and obtain the maximum harvest.

[0066] The advice providing unit can estimate the user's emotions and adjust the tone of the advice to the user based on the estimated emotions. For example, if the user is feeling anxious, the advice can be provided in a gentle tone that gives the user a sense of security. If the user is excited, the advice providing unit can provide the advice in an encouraging tone. Furthermore, the advice providing unit can also customize the content of the advice according to the user's emotions. For example, if the user is feeling depressed, the advice providing unit can suggest a simple and likely to be successful solution. In this way, by providing advice according to the user's emotions, it is possible to improve user satisfaction.

[0067] The plan creation support unit can estimate the user's emotions and propose a plan that the user can enjoy based on the estimated emotions. For example, it can propose a plan that includes many tasks that the user finds enjoyable. Also, if the user is feeling stressed, it can propose a plan that includes easy and relaxing tasks. Furthermore, the plan creation support unit can adjust the progress of the plan according to the user's emotions. For example, if the user is tired, it can suggest reducing the amount of work. In this way, by proposing a plan that corresponds to the user's emotions, the user can maintain their motivation and enjoy home gardening.

[0068] The smart gardening system may further include an emotion sharing unit. The emotion sharing unit allows users to share their emotions about home gardening with other users. For example, users can share their success stories or difficult situations with other users and receive empathy and encouragement. The emotion sharing unit also provides a function that allows users to record their emotions and look back on them later. For example, it is possible to record changes in emotions along with the progress of the home garden. Furthermore, the emotion sharing unit allows users to refer to the emotions of other users and use this information to help manage their own home gardens. This allows users to share emotions with each other and feel even more enjoyment in home gardening.

[0069] The advice providing unit can estimate the user's emotions and adjust the frequency of advice to the user based on the estimated emotions. For example, if the user is feeling stressed, the advice providing unit can reduce the frequency of advice to allow the user to relax. Also, if the user is highly motivated, the advice providing unit can increase the frequency of advice to actively support the user. Furthermore, the advice providing unit can also adjust the timing of advice according to the user's emotions. For example, if the user is busy, the advice providing unit can suggest refraining from providing advice. In this way, by adjusting the frequency and timing of advice according to the user's emotions, user satisfaction can be improved.

[0070] The plan creation support unit can estimate the user's emotions and, based on the estimated emotions, propose a plan that will give the user a sense of accomplishment. For example, it can propose a plan that includes many tasks that will give the user a sense of accomplishment. Also, if the user is feeling anxious, it can propose a plan that includes simple tasks that are likely to be successful. Furthermore, the plan creation support unit can adjust the progress of the plan according to the user's emotions. For example, if the user is tired, it can suggest reducing the amount of work. In this way, by proposing a plan that corresponds to the user's emotions, the user can maintain their motivation and enjoy home gardening.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The image analysis unit analyzes the plant images uploaded by the user. For example, the image analysis unit analyzes the color and shape of the plant's leaves, the presence or absence of disease spots, and other factors to determine whether the plant is healthy, diseased, or nutrient-deficient. The image analysis unit can also analyze images using deep learning and neural networks. Step 2: The advice provider provides advice in response to the user's question. For example, if a user inputs a question such as, "My tomato leaves are turning yellow. What should I do?", the AI ​​generator searches for related information and suggests appropriate measures. The advice provider can also provide advice by referring to the answer database. Step 3: The planning support unit creates a home garden plan based on the information provided by the user. For example, if the user inputs the type of plants they want to grow, the space available for the home garden, and the weather conditions, the generating AI will suggest the optimal planting time and cultivation method. The planning support unit can also create a plan by referencing a database of past success stories.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[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 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.

[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 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).

[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] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 system equipped with a generative AI, The generated AI is an image analysis unit that analyzes plant images uploaded by users; an advice providing unit that provides advice in response to the user's question; a plan creation support unit that creates a home vegetable garden plan based on the information provided by the user. A system characterized by:

2. The image analysis unit The growth history data of the plant is referenced and compared with the growth pattern to detect abnormalities.

2. The system of claim 1.

3. The image analysis unit Environmental data such as soil quality, humidity, and temperature are also analyzed simultaneously to provide a comprehensive health check.

2. The system of claim 1.

4. The image analysis unit The multiple images taken from different angles are integrated and analyzed to make an accurate diagnosis.

2. The system of claim 1.

5. The advice providing unit Refer to a database of past user questions and answers to provide effective advice 2. The system of claim 1.

Images