System

Abstract

An object of a system according to an embodiment is to identify a problem in a home garden and provide an appropriate solution.SOLUTION: A system includes a reception unit, an analysis unit, a provision unit, and a learning unit. A reception part allows a user to photograph the situation of a home garden and uploads the image. The analysis unit analyzes the image uploaded by the reception unit and identifies a state of the plant or a problem. The providing unit provides a specific solution based on the problem identified by the analyzing unit. The learning unit learns past questions and situations of the user based on the solution provided by the providing unit, and provides individualized advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to identify problems in home gardens and provide appropriate solutions.

[0005] The system according to the embodiment aims to identify problems in home gardens and provide appropriate solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit allows a user to take photos of the state of their home vegetable garden and upload the images. The analysis unit analyzes the images uploaded by the reception unit and identifies the condition of the plants or problems. The provision unit provides specific solutions based on the problems identified by the analysis unit. The learning unit learns the user's past questions and situations based on the solutions provided by the provision unit and provides personalized advice. [Effects of the Invention]

[0007] The system according to the embodiment can identify problems in home gardens and provide appropriate solutions. [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) In a home gardening support system according to an embodiment of the present invention, a user takes photos of their home garden and uploads the images. A generating AI analyzes the images, identifies the condition and problems of the plants, and provides appropriate solutions. In this system, a user takes photos of their home garden with a smartphone and uploads the images to a service. The generating AI then analyzes the images to identify the condition and problems of the plants. For example, it can detect disease and nutrient deficiencies based on the color and shape of the leaves. The generating AI then uses enhanced information retrieval generation to provide solutions to the user's concerns. For example, if a plant is diseased, it can suggest treatment and preventative measures. If a plant is nutrient-deficient, it can advise on appropriate fertilizer and cultivation methods. Furthermore, the generating AI learns from the user's past questions and situations to provide more personalized advice. This makes home gardening easier for users and improves their success rate. For example, in a home gardening support system, a user takes photos of their home garden with a smartphone and uploads the images to a service. The generating AI then analyzes the images to identify the condition and problems of the plants. For example, it can detect disease and nutrient deficiencies based on the color and shape of the leaves. Then, using information retrieval-enhanced generation, the generative AI provides solutions to the user's concerns. For example, if a plant is sick, it will suggest treatment and preventative measures for the disease. Also, if the plant is nutrient-deficient, it will advise on appropriate fertilizer and cultivation methods. Furthermore, the generative AI learns from the user's past questions and situations to provide more personalized advice. This makes it easier for users to manage their home gardens and improves their success rate. In this way, the home gardening support system can make it easier for users to manage their home gardens and improve their success rate. For example, users can enjoy their home gardens with the support of the generative AI.

[0029] A home gardening support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit allows a user to take photos of their home garden and upload the images. Users can take photos of their home garden using a smartphone and upload the images to the service. For example, the reception unit specifies the image format and size and guides the user through the uploading process. The analysis unit uses a generation AI to analyze the images uploaded by the reception unit and identify the plant's condition and problems. For example, the analysis unit uses an algorithm that analyzes leaf color and shape to detect disease and nutrient deficiencies. The analysis unit can also use the generation AI to evaluate the plant's health and growth. For example, the analysis unit uses an image analysis algorithm to detect abnormalities in the color and shape of plant leaves. The provision unit provides specific solutions based on the problems identified by the analysis unit. For example, the provision unit suggests treatments and preventive measures based on the type of disease or specific symptoms of nutrient deficiency. The provision unit can also use the generation AI to provide advice on appropriate fertilizers and cultivation methods. For example, the provision unit suggests treatments and preventive measures for plant diseases. The learning unit learns the user's past questions and situations based on the solutions provided by the providing unit, and provides personalized advice. For example, the learning unit learns the user's past questions and history of solutions, and provides more personalized advice. This allows the home gardening support system according to the embodiment to facilitate the management of the user's home garden and improve the success rate. For example, the user can enjoy home gardening with the support of the generation AI.

[0030] The analysis unit can detect disease or nutritional deficiency using an algorithm that analyzes leaf color and shape. Leaf color and shape include, but are not limited to, color changes, abnormal shapes, and signs of disease. The analysis unit can use an image analysis algorithm to detect leaf color changes. For example, the analysis unit can detect leaf color changes as fluctuations in RGB values. The analysis unit can also use a shape analysis algorithm to detect abnormal leaf shapes. For example, the analysis unit can detect abnormal leaf shapes using contour extraction technology. Furthermore, the analysis unit can use a machine learning algorithm to detect signs of disease. For example, the analysis unit can detect signs of disease using a trained model. This allows the plant's condition to be accurately understood and appropriate measures to be taken. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input leaf color and shape data into the generation AI and cause the generation AI to detect disease or nutritional deficiency.

[0031] The providing unit can suggest a treatment method or preventive measure based on the type of disease or specific symptoms of nutritional deficiency. Examples of disease types include, but are not limited to, fungi, viruses, bacteria, etc. The providing unit can suggest a treatment method for, for example, a fungal disease. For example, the providing unit can suggest the use of a specific fungicide for a fungal disease. The providing unit can also suggest preventive measures for a viral disease. For example, the providing unit can suggest a specific cultivation method as a preventive measure for a viral disease. Furthermore, the providing unit can suggest the use of an appropriate fertilizer based on specific symptoms of nutritional deficiency. For example, the providing unit can suggest the use of a specific fertilizer for yellowing leaves. This allows the user to quickly obtain appropriate treatment methods or preventive measures. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data on the type of disease or symptoms of nutritional deficiency into the generating AI and cause the generating AI to suggest treatment methods or preventive measures.

[0032] The learning unit can learn the user's past questions and situations and provide personalized advice. Examples of personalized advice include, but are not limited to, advice based on the user's past data and real-time feedback. For example, the learning unit can learn the content of the user's past questions and provide advice for similar questions. For example, the learning unit can provide advice for illnesses that the user has previously inquired about. The learning unit can also learn the user's past situations and provide advice tailored to the user's current situation. For example, the learning unit can learn the user's past cultivation history and provide advice tailored to the user's current cultivation situation. Furthermore, the learning unit can learn the user's past feedback and provide more appropriate advice. For example, the learning unit can adjust the content of the advice based on the user's past feedback. This allows the user to receive more appropriate and personalized advice. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input data on the user's past questions and situations into the generation AI and cause the generation AI to provide personalized advice.

[0033] The providing unit can provide advice on specific fertilizers or cultivation methods. Examples of fertilizers include, but are not limited to, organic fertilizers and chemical fertilizers. The providing unit can provide advice on how to use organic fertilizers. For example, the providing unit can provide advice on the timing and amount of organic fertilizer application. The providing unit can also provide advice on how to use chemical fertilizers. For example, the providing unit can provide advice on the type of chemical fertilizer and how to apply it. The providing unit can also provide advice on cultivation methods. For example, the providing unit can provide advice on how to prepare soil and how to irrigate. This allows the user to learn appropriate fertilizers and cultivation methods, improving the success rate of home gardening. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can input data on fertilizers and cultivation methods into the generating AI and cause the generating AI to provide advice.

[0034] The analysis unit can perform image analysis to identify the condition or problems of the plant. Image analysis includes, but is not limited to, the algorithm used, the accuracy of the analysis, and details of the target of analysis. The analysis unit can identify the condition of the plant using, for example, an image analysis algorithm. For example, the analysis unit can use an image analysis algorithm to detect abnormalities in the color or shape of plant leaves. The analysis unit can also use a machine learning algorithm to detect signs of plant disease. For example, the analysis unit can detect signs of disease using a trained model. Furthermore, the analysis unit can perform image analysis to evaluate the growth condition of the plant. For example, the analysis unit can identify the growth stage of the plant through image analysis. This allows the plant's condition or problems to be accurately identified. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the plant into a generation AI and have the generation AI identify the condition or problems.

[0035] The reception unit can analyze the user's past image upload history and select a specific upload method. The image upload history includes, for example, past upload dates and times, image content, and frequency, but is not limited to these examples. The reception unit, for example, preferentially suggests upload methods that the user has frequently used in the past (e.g., specific time periods or days of the week). For example, the reception unit can analyze the user's past upload history and select an optimal upload method. The reception unit can also analyze upload methods that the user has used successfully in the past and suggest an optimal method. For example, the reception unit can select and suggest the most efficient method from the user's past upload history. Furthermore, the reception unit can customize the optimal upload method based on the user's past upload history. For example, the reception unit can analyze the user's past upload history and customize the upload method. This allows the optimal upload method to be suggested based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into a generation AI and have the generation AI select an upload method.

[0036] When uploading images, the reception unit can filter the images based on the user's current home garden status and areas of interest. Home garden status includes, but is not limited to, the type of plant, growth stage, and environmental conditions. For example, if the user is interested in a particular plant, the reception unit can prioritize uploading images related to that plant. For example, the reception unit can analyze the user's areas of interest and filter and upload related images. The reception unit can also select optimal images based on the user's home garden status (e.g., season and weather). For example, the reception unit can analyze the user's home garden status and select and upload optimal images. Furthermore, the reception unit can analyze the user's past areas of interest and filter and upload related images. For example, the reception unit can analyze the user's past areas of interest and filter and upload related images. This allows images that correspond to the user's interests and status to be prioritized for upload. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input data on the user's areas of interest and the status of their home garden into the generation AI and have the generation AI perform filtering.

[0037] When uploading an image, the reception unit can select a specific upload means according to the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit can enable the user to instruct the upload of an image by voice. For example, the reception unit can use voice recognition technology to analyze the user's voice input and instruct the user to upload an image. Furthermore, if the user prefers text input, the reception unit can also enable the user to instruct the user to upload an image by text. For example, the reception unit can use text analysis technology to analyze the user's text input and instruct the user to upload an image. Furthermore, if the user prefers image input, the reception unit can also enable the user to directly upload an image. For example, the reception unit can use image analysis technology to analyze the user's image input and instruct the user to upload an image. This makes it possible to provide an optimal upload means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's input method into a generation AI and have the generation AI select an upload means.

[0038] When uploading images, the reception unit may prioritize uploading relevant images taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit may prioritize uploading images related to that area. For example, the reception unit may select and upload optimal images based on the user's geographical location information. The reception unit may also filter and upload highly relevant images based on the user's current location. For example, the reception unit may select and upload highly relevant images based on the user's current location. The reception unit may also select and upload optimal images based on the user's geographical location information. For example, the reception unit may select and upload optimal images based on the user's geographical location information. This allows the optimal images to be uploaded based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's geographical location information into a generation AI and cause the generation AI to select relevant images.

[0039] When uploading an image, the reception unit can analyze the user's social media activity and upload related images. Social media activity includes, but is not limited to, post content, the number of likes, and comments. The reception unit uploads related images based on, for example, images shared by the user on social media. For example, the reception unit can analyze the user's social media activity and select and upload an optimal image. The reception unit can also analyze the user's social media activity and select and upload an optimal image. For example, the reception unit can upload related images based on the activity of the user's friends on social media. Furthermore, the reception unit can analyze the user's social media activity and upload related images. For example, the reception unit can analyze the user's social media activity and upload related images. This allows the optimal image to be uploaded based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to select related images.

[0040] When uploading an image, the reception unit can adjust the upload method by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. The reception unit can propose an optimal upload method based on, for example, feedback provided by the user in the past. For example, the reception unit can analyze the user's past feedback and customize the upload method. The reception unit can also optimize the upload method by reflecting the user's past feedback. For example, the reception unit can optimize the upload method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and adjust the upload method. For example, the reception unit can adjust the upload method based on the user's past feedback. This makes it possible to provide an optimal upload method based on the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback data into a generation AI and cause the generation AI to adjust the upload method.

[0041] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the plant. Examples of the importance of a plant include, but are not limited to, the type, growth stage, and economic value of the plant. For example, the analysis unit performs a detailed analysis of an important plant. For example, the analysis unit can perform a detailed image analysis of an important plant. Furthermore, the analysis unit can perform a standard analysis of a common plant. For example, the analysis unit can perform a standard image analysis of a common plant. Furthermore, the analysis unit can perform a simplified analysis of a plant of low importance. For example, the analysis unit can perform a simplified image analysis of a plant of low importance. This allows for providing an optimal analysis according to the importance of the plant. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input plant importance data into the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0042] During analysis, the analysis unit can apply a specific analysis algorithm depending on the type of plant. Examples of plant types include, but are not limited to, vegetables, fruits, and flowers. The analysis unit can apply a dedicated analysis algorithm to, for example, a specific plant. For example, the analysis unit can apply a dedicated image analysis algorithm to specific vegetables. The analysis unit can also apply a standard analysis algorithm to general plants. For example, the analysis unit can apply a standard image analysis algorithm to general fruits. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the type of plant. For example, the analysis unit can select and apply an optimal machine learning algorithm depending on the type of plant. This makes it possible to provide an optimal analysis depending on the type of plant. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input plant type data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data, analysis accuracy, and result reliability. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis method. For example, the analysis unit can improve the analysis method based on the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the order of analysis based on the growth stage of the plant. Examples of plant growth stages include, but are not limited to, the germination stage, growth stage, and harvest stage. For example, the analysis unit can prioritize analysis of plants in the early growth stage. For example, the analysis unit can prioritize image analysis of plants in the early growth stage. The analysis unit can also perform regular analysis of plants in the middle growth stage. For example, the analysis unit can perform standard image analysis of plants in the middle growth stage. Furthermore, the analysis unit can also perform simplified analysis of plants in the late growth stage. For example, the analysis unit can perform simplified image analysis of plants in the late growth stage. This allows for optimal analysis according to the plant's growth stage. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input plant growth stage data to the generation AI and have the generation AI determine the order of analysis.

[0045] During analysis, the analysis unit can change the analysis order based on the relevance of the plants. Examples of plant relevance include, but are not limited to, plants of the same family, common pests and diseases, and cultivation conditions. The analysis unit, for example, prioritizes analysis of highly related plants. For example, the analysis unit can prioritize image analysis of highly related plants. The analysis unit can also perform regular analysis on less related plants. For example, the analysis unit can perform standard image analysis on less related plants. Furthermore, the analysis unit can determine an optimal analysis order based on the relevance of the plants. For example, the analysis unit can determine an optimal analysis order based on the relevance of the plants. This makes it possible to provide an optimal analysis order according to the relevance of the plants. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input plant relevance data to the generation AI and cause the generation AI to change the analysis order.

[0046] During analysis, the analysis unit can change the terminology used in the analysis depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can determine that the user is a beginner based on survey results or past questions and provide the analysis results in simple language. Furthermore, if the user is an intermediate user, the analysis unit can provide the analysis results using appropriate terminology. For example, the analysis unit can determine that the user is an intermediate user based on usage history and provide the analysis results using appropriate terminology. Furthermore, if the user is an advanced user, the analysis unit can provide the analysis results using detailed terminology. For example, the analysis unit can determine that the user is an advanced user based on past questions and provide the analysis results using detailed terminology. This allows the provision of optimal analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's expertise level data into the generation AI and have the generation AI execute the use of technical terms.

[0047] The providing unit can adjust the accuracy of the solution based on the importance of the problem when providing the solution. The importance of the problem includes, but is not limited to, the health of the plant, the scope of the impact, and the urgency of the problem. The providing unit can provide a detailed solution for an important problem. For example, if the health of the plant is deteriorating, the providing unit can provide a detailed treatment method. The providing unit can also provide a standard solution for a general problem. For example, if the plant's growth is slowing, the providing unit can provide a standard cultivation method. The providing unit can also provide a simplified solution for a less important problem. For example, if the plant's leaves are slightly yellowing, the providing unit can provide a simplified fertilization method. This allows the provision of an optimal solution according to the importance of the problem. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input problem importance data into the generating AI and cause the generating AI to adjust the accuracy of the solution.

[0048] When providing the solution, the providing unit can apply a specific solution algorithm depending on the type of problem. Examples of problem types include, but are not limited to, diseases, nutritional deficiencies, and pest damage. For example, the providing unit can apply a dedicated solution algorithm to a specific disease. For example, the providing unit can apply a dedicated treatment algorithm to a disease caused by a specific fungus. The providing unit can also apply a standard solution algorithm to a general problem. For example, the providing unit can apply a standard fertilization algorithm to a general nutritional deficiency. Furthermore, the providing unit can select and apply an optimal solution algorithm depending on the type of problem. For example, the providing unit can select and apply an optimal machine learning algorithm depending on the type of problem. This allows the optimal solution to be provided depending on the type of problem. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input problem type data to the generating AI and cause the generating AI to apply the solution algorithm.

[0049] When providing a solution, the providing unit can improve the accuracy of the solution by referring to the user's past solution provision results. Past solution provision results include, but are not limited to, user feedback, success rates, and execution results. The providing unit, for example, optimizes a solution algorithm based on the user's past solution provision results. For example, the providing unit can optimize the solution algorithm based on the user's past feedback. The providing unit can also improve the accuracy of the solution by referring to the user's past solution provision results. For example, the providing unit can improve the accuracy of the solution based on the user's past success rates. Furthermore, the providing unit can analyze the user's past solution provision results and improve the solution method. For example, the providing unit can improve the solution method based on the user's past execution results. This can improve the accuracy of the solution based on the user's past solution provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past solution provision result data into the generation AI and cause the generation AI to improve the accuracy of the solution.

[0050] When providing the solutions, the providing unit can determine the order of solutions based on the time when the problem occurred. Examples of the time when the problem occurred include, but are not limited to, the season, the growth stage, and environmental conditions. For example, the providing unit can prioritize providing solutions for urgent problems. For example, the providing unit can prioritize providing a treatment method when a plant disease is rapidly progressing. The providing unit can also provide solutions with normal priority for general problems. For example, the providing unit can recommend normal cultivation methods when a plant's growth is slow. Furthermore, the providing unit can determine optimal priorities and provide solutions based on the time when the problem occurred. For example, the providing unit can determine optimal priorities and provide solutions based on the plant's growth stage. This allows the provision of optimal solutions according to the time when the problem occurred. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input data on the time when the problem occurred to the generating AI and cause the generating AI to determine the order of solutions.

[0051] When providing solutions, the providing unit can change the order of solutions based on the relevance of the problems. Examples of problem relevance include, but are not limited to, the same cause, common symptoms, and scope of impact. For example, the providing unit can prioritize providing solutions to highly related problems. For example, the providing unit can prioritize providing solutions to multiple problems caused by the same cause. The providing unit can also provide solutions in a normal order for less related problems. For example, the providing unit can provide solutions in a standard order for less related problems. Furthermore, the providing unit can determine an optimal order based on the relevance of the problems and provide the solutions. For example, the providing unit can determine an optimal order based on the relevance of the problems and provide the solutions. This makes it possible to provide an optimal solution according to the relevance of the problems. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input problem relevance data to the generation AI and cause the generation AI to change the order of the solutions.

[0052] When providing a solution, the providing unit can change the use of technical terminology in the solution depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the providing unit can provide the solution in simple language. For example, the providing unit can determine that the user is a beginner based on survey results or past questions and provide the solution in simple language. Furthermore, if the user is an intermediate user, the providing unit can provide the solution using appropriate technical terminology. For example, the providing unit can determine that the user is an intermediate user based on usage history and provide the solution using appropriate technical terminology. Furthermore, if the user is an advanced user, the providing unit can provide the solution using detailed technical terminology. For example, the providing unit can determine that the user is an advanced user based on past questions and provide the solution using detailed technical terminology. This allows the provision of an optimal solution according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's expertise level data into the generating AI and cause the generating AI to use specialized terminology.

[0053] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, past question content, solutions, and feedback. The learning unit, for example, optimizes the learning algorithm based on past learning data. For example, the learning unit can optimize the learning algorithm based on past question content. The learning unit can also improve the accuracy of learning by referring to past solutions. For example, the learning unit can improve the accuracy of learning based on past solutions. Furthermore, the learning unit can analyze past feedback and improve the learning method. For example, the learning unit can improve the learning method based on past feedback. This allows the learning algorithm to be optimized based on the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to the generation AI and cause the generation AI to adjust the learning algorithm.

[0054] During learning, the learning unit can analyze the user's past questions and situations and change the update frequency of the learning data. Past questions and situations include, but are not limited to, the frequency of questions, content, and solution results. The learning unit adjusts the update frequency of the learning data based on, for example, the user's past questions. For example, the learning unit can adjust the update frequency of the learning data based on the frequency of questions asked by the user in the past. The learning unit can also analyze the user's past situations and determine an optimal update frequency. For example, the learning unit can determine an optimal update frequency based on the user's past situations. Furthermore, the learning unit can optimize the update frequency of the learning data by referring to the user's past questions and situations. For example, the learning unit can optimize the update frequency of the learning data based on the user's past questions and situations. This allows the update frequency of the learning data to be optimized based on the user's past questions and situations. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input data on the user's past questions and situations into the generation AI and cause the generation AI to change the update frequency of the learning data.

[0055] During learning, the learning unit can adjust the weighting of the learning data based on the growth stage of the plant. Examples of plant growth stages include, but are not limited to, the germination stage, growth stage, and harvest stage. For example, the learning unit can assign a higher weighting to the learning data for plants in the early growth stage. Furthermore, the learning unit can assign a normal weighting to plants in the middle growth stage. For example, the learning unit can assign a normal weighting to plants in the middle growth stage. Furthermore, the learning unit can assign a lower weighting to the learning data for plants in the late growth stage. For example, the learning unit can assign a lower weighting to the learning data for plants in the late growth stage. This allows optimal weighting of the learning data according to the plant's growth stage to be provided. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input plant growth stage data to the generation AI and cause the generation AI to adjust the weighting of the learning data.

[0056] During learning, the learning unit can change the learning algorithm to reflect user feedback. User feedback includes, but is not limited to, ratings, comments, and usage history. The learning unit, for example, optimizes the learning algorithm based on user feedback. For example, the learning unit can optimize the learning algorithm based on user ratings. The learning unit can also improve the accuracy of learning by referring to user feedback. For example, the learning unit can improve the accuracy of learning based on user comments. Furthermore, the learning unit can analyze user feedback and improve the learning method. For example, the learning unit can improve the learning method based on the user usage history. This allows the learning algorithm to be optimized based on user feedback. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to change the learning algorithm.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The home gardening support system may further include a notification unit. The notification unit can send real-time notifications to the user based on the condition or problems of the plants identified by the analysis unit. For example, if a plant is diseased, the notification unit can immediately send an alert to the user to urge them to take immediate action. Also, if a nutrient deficiency is detected, the notification unit can send a notification suggesting the use of appropriate fertilizer. Furthermore, the notification unit can periodically send advice according to the growth stage of the plant. This allows the user to manage their home garden more efficiently.

[0059] The analysis unit can also perform voice analysis to identify the condition of the plant or any problems. For example, if a user describes the condition of a plant by voice, the analysis unit can analyze the voice data and identify the condition or problems of the plant. Furthermore, the analysis unit can perform a more accurate diagnosis by combining and analyzing voice data and image data. This allows the user to report the condition of the plant through voice input, and the analysis unit can provide appropriate advice based on that information.

[0060] The learning unit can learn the location information of the user's home garden and provide advice based on the local climate and soil conditions. For example, if the user's home garden is in a dry region, the learning unit can suggest an appropriate irrigation method. If the user's home garden is in a cold region, the learning unit can advise on the selection of cold-tolerant plants. Furthermore, the learning unit can suggest seasonal cultivation schedules based on local climate data. This allows the user to receive advice optimized for the local conditions.

[0061] The analysis unit can acquire environmental data such as temperature and humidity and use it for analysis to identify the condition of the plant and any problems. For example, the analysis unit can acquire the temperature and humidity around the plant using a sensor and evaluate the condition of the plant based on that data. Furthermore, the analysis unit can perform a more accurate diagnosis by combining and analyzing environmental data and image data. This allows the user to receive detailed advice based on the environmental data.

[0062] The analysis unit can acquire and use the plant's growth history data for analysis in order to identify the plant's condition and problems. For example, the analysis unit can evaluate the plant's current condition based on past growth data. Furthermore, the analysis unit can perform a more accurate diagnosis by combining and analyzing the growth history data and image data. This allows the user to receive detailed advice based on the growth history data.

[0063] The providing unit can adjust the content of the advice depending on the size of the user's home garden. For example, if the home garden is small, the providing unit can provide simple and easy-to-follow advice. If the home garden is medium-sized, the providing unit can provide detailed cultivation and management methods. Furthermore, if the home garden is large, the providing unit can provide advice using advanced techniques and equipment. This makes it possible to provide optimal advice depending on the size of the user's home garden.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit allows the user to take a photo of the state of their home vegetable garden and upload the image. The user can use their smartphone to take a photo of the state of their home vegetable garden and upload the image to the service. The reception unit specifies the format and size of the image and guides the user through the upload procedure. Step 2: The analysis unit uses generative AI to analyze the images uploaded by the reception unit and identify the plant's condition and problems. For example, the analysis unit uses algorithms that analyze leaf color and shape to detect disease and nutritional deficiencies. The analysis unit can also evaluate the plant's health and growth status. Step 3: The provisioning unit provides specific solutions based on the problems identified by the analysis unit. For example, the provisioning unit can suggest treatments and preventative measures based on the type of disease or specific symptoms of nutritional deficiency. The provisioning unit can also use generative AI to advise on appropriate fertilizers and cultivation methods. Step 4: The learning unit learns the user's past questions and situations based on the solutions provided by the providing unit, and provides personalized advice. For example, the learning unit learns the user's past question content and solution history to provide more personalized advice.

[0066] (Example 2) In a home gardening support system according to an embodiment of the present invention, a user takes photos of their home garden and uploads the images. A generating AI analyzes the images, identifies the condition and problems of the plants, and provides appropriate solutions. In this system, a user takes photos of their home garden with a smartphone and uploads the images to a service. The generating AI then analyzes the images to identify the condition and problems of the plants. For example, it can detect disease and nutrient deficiencies based on the color and shape of the leaves. The generating AI then uses enhanced information retrieval generation to provide solutions to the user's concerns. For example, if a plant is diseased, it can suggest treatment and preventative measures. If a plant is nutrient-deficient, it can advise on appropriate fertilizer and cultivation methods. Furthermore, the generating AI learns from the user's past questions and situations to provide more personalized advice. This makes home gardening easier for users and improves their success rate. For example, in a home gardening support system, a user takes photos of their home garden with a smartphone and uploads the images to a service. The generating AI then analyzes the images to identify the condition and problems of the plants. For example, it can detect disease and nutrient deficiencies based on the color and shape of the leaves. Then, using information retrieval-enhanced generation, the generative AI provides solutions to the user's concerns. For example, if a plant is sick, it will suggest treatment and preventative measures for the disease. Also, if the plant is nutrient-deficient, it will advise on appropriate fertilizer and cultivation methods. Furthermore, the generative AI learns from the user's past questions and situations to provide more personalized advice. This makes it easier for users to manage their home gardens and improves their success rate. In this way, the home gardening support system can make it easier for users to manage their home gardens and improve their success rate. For example, users can enjoy their home gardens with the support of the generative AI.

[0067] A home gardening support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit allows a user to take photos of their home garden and upload the images. Users can take photos of their home garden using a smartphone and upload the images to the service. For example, the reception unit specifies the image format and size and guides the user through the uploading process. The analysis unit uses a generation AI to analyze the images uploaded by the reception unit and identify the plant's condition and problems. For example, the analysis unit uses an algorithm that analyzes leaf color and shape to detect disease and nutrient deficiencies. The analysis unit can also use the generation AI to evaluate the plant's health and growth. For example, the analysis unit uses an image analysis algorithm to detect abnormalities in the color and shape of plant leaves. The provision unit provides specific solutions based on the problems identified by the analysis unit. For example, the provision unit suggests treatments and preventive measures based on the type of disease or specific symptoms of nutrient deficiency. The provision unit can also use the generation AI to provide advice on appropriate fertilizers and cultivation methods. For example, the provision unit suggests treatments and preventive measures for plant diseases. The learning unit learns the user's past questions and situations based on the solutions provided by the providing unit, and provides personalized advice. For example, the learning unit learns the user's past questions and history of solutions, and provides more personalized advice. This allows the home gardening support system according to the embodiment to facilitate the management of the user's home garden and improve the success rate. For example, the user can enjoy home gardening with the support of the generation AI.

[0068] The analysis unit can detect disease or nutritional deficiency using an algorithm that analyzes leaf color and shape. Leaf color and shape include, but are not limited to, color changes, abnormal shapes, and signs of disease. The analysis unit can use an image analysis algorithm to detect leaf color changes. For example, the analysis unit can detect leaf color changes as fluctuations in RGB values. The analysis unit can also use a shape analysis algorithm to detect abnormal leaf shapes. For example, the analysis unit can detect abnormal leaf shapes using contour extraction technology. Furthermore, the analysis unit can use a machine learning algorithm to detect signs of disease. For example, the analysis unit can detect signs of disease using a trained model. This allows the plant's condition to be accurately understood and appropriate measures to be taken. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input leaf color and shape data into the generation AI and cause the generation AI to detect disease or nutritional deficiency.

[0069] The providing unit can suggest a treatment method or preventive measure based on the type of disease or specific symptoms of nutritional deficiency. Examples of disease types include, but are not limited to, fungi, viruses, bacteria, etc. The providing unit can suggest a treatment method for, for example, a fungal disease. For example, the providing unit can suggest the use of a specific fungicide for a fungal disease. The providing unit can also suggest preventive measures for a viral disease. For example, the providing unit can suggest a specific cultivation method as a preventive measure for a viral disease. Furthermore, the providing unit can suggest the use of an appropriate fertilizer based on specific symptoms of nutritional deficiency. For example, the providing unit can suggest the use of a specific fertilizer for yellowing leaves. This allows the user to quickly obtain appropriate treatment methods or preventive measures. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data on the type of disease or symptoms of nutritional deficiency into the generating AI and cause the generating AI to suggest treatment methods or preventive measures.

[0070] The learning unit can learn the user's past questions and situations and provide personalized advice. Examples of personalized advice include, but are not limited to, advice based on the user's past data and real-time feedback. For example, the learning unit can learn the content of the user's past questions and provide advice for similar questions. For example, the learning unit can provide advice for illnesses that the user has previously inquired about. The learning unit can also learn the user's past situations and provide advice tailored to the user's current situation. For example, the learning unit can learn the user's past cultivation history and provide advice tailored to the user's current cultivation situation. Furthermore, the learning unit can learn the user's past feedback and provide more appropriate advice. For example, the learning unit can adjust the content of the advice based on the user's past feedback. This allows the user to receive more appropriate and personalized advice. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input data on the user's past questions and situations into the generation AI and cause the generation AI to provide personalized advice.

[0071] The providing unit can provide advice on specific fertilizers or cultivation methods. Examples of fertilizers include, but are not limited to, organic fertilizers and chemical fertilizers. The providing unit can provide advice on how to use organic fertilizers. For example, the providing unit can provide advice on the timing and amount of organic fertilizer application. The providing unit can also provide advice on how to use chemical fertilizers. For example, the providing unit can provide advice on the type of chemical fertilizer and how to apply it. The providing unit can also provide advice on cultivation methods. For example, the providing unit can provide advice on how to prepare soil and how to irrigate. This allows the user to learn appropriate fertilizers and cultivation methods, improving the success rate of home gardening. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can input data on fertilizers and cultivation methods into the generating AI and cause the generating AI to provide advice.

[0072] The analysis unit can perform image analysis to identify the condition or problems of the plant. Image analysis includes, but is not limited to, the algorithm used, the accuracy of the analysis, and details of the target of analysis. The analysis unit can identify the condition of the plant using, for example, an image analysis algorithm. For example, the analysis unit can use an image analysis algorithm to detect abnormalities in the color or shape of plant leaves. The analysis unit can also use a machine learning algorithm to detect signs of plant disease. For example, the analysis unit can detect signs of disease using a trained model. Furthermore, the analysis unit can perform image analysis to evaluate the growth condition of the plant. For example, the analysis unit can identify the growth stage of the plant through image analysis. This allows the plant's condition or problems to be accurately identified. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the plant into a generation AI and have the generation AI identify the condition or problems.

[0073] The reception unit can analyze the user's emotions and adjust the timing of image uploads based on the analyzed user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and busyness. For example, if the user is feeling stressed, the reception unit can send a reminder to prompt the user to upload images. For example, the reception unit can detect that the user is feeling stressed through facial expression analysis and send a reminder. Furthermore, if the user is relaxed, the reception unit can adjust the upload timing to match the user's pace. For example, the reception unit can detect that the user is relaxed through voice analysis and adjust the upload timing. Furthermore, if the user is busy, the reception unit can suggest the optimal timing to prompt the user to upload images. For example, the reception unit can detect that the user is busy through behavioral pattern analysis and suggest the optimal timing. This allows images to be uploaded at the optimal timing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI adjust the upload timing.

[0074] The reception unit can analyze the user's past image upload history and select a specific upload method. The image upload history includes, for example, past upload dates and times, image content, and frequency, but is not limited to these examples. The reception unit, for example, preferentially suggests upload methods that the user has frequently used in the past (e.g., specific time periods or days of the week). For example, the reception unit can analyze the user's past upload history and select an optimal upload method. The reception unit can also analyze upload methods that the user has used successfully in the past and suggest an optimal method. For example, the reception unit can select and suggest the most efficient method from the user's past upload history. Furthermore, the reception unit can customize the optimal upload method based on the user's past upload history. For example, the reception unit can analyze the user's past upload history and customize the upload method. This allows the optimal upload method to be suggested based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into a generation AI and have the generation AI select an upload method.

[0075] When uploading images, the reception unit can filter the images based on the user's current home garden status and areas of interest. Home garden status includes, but is not limited to, the type of plant, growth stage, and environmental conditions. For example, if the user is interested in a particular plant, the reception unit can prioritize uploading images related to that plant. For example, the reception unit can analyze the user's areas of interest and filter and upload related images. The reception unit can also select optimal images based on the user's home garden status (e.g., season and weather). For example, the reception unit can analyze the user's home garden status and select and upload optimal images. Furthermore, the reception unit can analyze the user's past areas of interest and filter and upload related images. For example, the reception unit can analyze the user's past areas of interest and filter and upload related images. This allows images that correspond to the user's interests and status to be prioritized for upload. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input data on the user's areas of interest and the status of their home garden into the generation AI and have the generation AI perform filtering.

[0076] When uploading an image, the reception unit can select a specific upload means according to the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit can enable the user to instruct the upload of an image by voice. For example, the reception unit can use voice recognition technology to analyze the user's voice input and instruct the user to upload an image. Furthermore, if the user prefers text input, the reception unit can also enable the user to instruct the user to upload an image by text. For example, the reception unit can use text analysis technology to analyze the user's text input and instruct the user to upload an image. Furthermore, if the user prefers image input, the reception unit can also enable the user to directly upload an image. For example, the reception unit can use image analysis technology to analyze the user's image input and instruct the user to upload an image. This makes it possible to provide an optimal upload means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's input method into a generation AI and have the generation AI select an upload means.

[0077] The reception unit can analyze the user's emotions and determine the priority of images to be uploaded based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, excitement, relaxation, and stress. For example, when the user is excited, the reception unit prioritizes uploading important images. For example, the reception unit can detect that the user is excited through facial expression analysis and prioritize uploading important images. Furthermore, when the user is relaxed, the reception unit can prioritize uploading normal images. For example, the reception unit can detect that the user is relaxed through voice analysis and prioritize uploading normal images. Furthermore, when the user is stressed, the reception unit can prioritize uploading images with high urgency. For example, the reception unit can detect that the user is stressed through behavioral pattern analysis and prioritize uploading images with high urgency. This allows images optimal for the user's emotions to be prioritized for upload. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI determine the priority of images.

[0078] When uploading images, the reception unit may prioritize uploading relevant images taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit may prioritize uploading images related to that area. For example, the reception unit may select and upload optimal images based on the user's geographical location information. The reception unit may also filter and upload highly relevant images based on the user's current location. For example, the reception unit may select and upload highly relevant images based on the user's current location. The reception unit may also select and upload optimal images based on the user's geographical location information. For example, the reception unit may select and upload optimal images based on the user's geographical location information. This allows the optimal images to be uploaded based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's geographical location information into a generation AI and cause the generation AI to select relevant images.

[0079] When uploading an image, the reception unit can analyze the user's social media activity and upload related images. Social media activity includes, but is not limited to, post content, the number of likes, and comments. The reception unit uploads related images based on, for example, images shared by the user on social media. For example, the reception unit can analyze the user's social media activity and select and upload an optimal image. The reception unit can also analyze the user's social media activity and select and upload an optimal image. For example, the reception unit can upload related images based on the activity of the user's friends on social media. Furthermore, the reception unit can analyze the user's social media activity and upload related images. For example, the reception unit can analyze the user's social media activity and upload related images. This allows the optimal image to be uploaded based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to select related images.

[0080] When uploading an image, the reception unit can adjust the upload method by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. The reception unit can propose an optimal upload method based on, for example, feedback provided by the user in the past. For example, the reception unit can analyze the user's past feedback and customize the upload method. The reception unit can also optimize the upload method by reflecting the user's past feedback. For example, the reception unit can optimize the upload method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and adjust the upload method. For example, the reception unit can adjust the upload method based on the user's past feedback. This makes it possible to provide an optimal upload method based on the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback data into a generation AI and cause the generation AI to adjust the upload method.

[0081] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, tension, relaxation, and excitement. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. For example, the analysis unit can detect that the user is nervous through facial expression analysis and provide a simple analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can detect that the user is relaxed through voice analysis and provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can detect that the user is excited through behavioral pattern analysis and provide a visually stimulating analysis result. This allows for providing an optimal analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expression of the analysis.

[0082] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the plant. Examples of the importance of a plant include, but are not limited to, the type, growth stage, and economic value of the plant. For example, the analysis unit performs a detailed analysis of an important plant. For example, the analysis unit can perform a detailed image analysis of an important plant. Furthermore, the analysis unit can perform a standard analysis of a common plant. For example, the analysis unit can perform a standard image analysis of a common plant. Furthermore, the analysis unit can perform a simplified analysis of a plant of low importance. For example, the analysis unit can perform a simplified image analysis of a plant of low importance. This allows for providing an optimal analysis according to the importance of the plant. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input plant importance data into the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0083] During analysis, the analysis unit can apply a specific analysis algorithm depending on the type of plant. Examples of plant types include, but are not limited to, vegetables, fruits, and flowers. The analysis unit can apply a dedicated analysis algorithm to, for example, a specific plant. For example, the analysis unit can apply a dedicated image analysis algorithm to specific vegetables. The analysis unit can also apply a standard analysis algorithm to general plants. For example, the analysis unit can apply a standard image analysis algorithm to general fruits. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the type of plant. For example, the analysis unit can select and apply an optimal machine learning algorithm depending on the type of plant. This makes it possible to provide an optimal analysis depending on the type of plant. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input plant type data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data, analysis accuracy, and result reliability. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis method. For example, the analysis unit can improve the analysis method based on the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, being in a hurry, being relaxed, and being excited. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, the analysis unit can detect that the user is in a hurry through facial expression analysis and provide a short analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can detect that the user is relaxed through voice analysis and provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can detect that the user is excited through behavioral pattern analysis and provide a visually stimulating analysis result. This allows for providing an optimal analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the order of analysis based on the growth stage of the plant. Examples of plant growth stages include, but are not limited to, the germination stage, growth stage, and harvest stage. For example, the analysis unit can prioritize analysis of plants in the early growth stage. For example, the analysis unit can prioritize image analysis of plants in the early growth stage. The analysis unit can also perform regular analysis of plants in the middle growth stage. For example, the analysis unit can perform standard image analysis of plants in the middle growth stage. Furthermore, the analysis unit can also perform simplified analysis of plants in the late growth stage. For example, the analysis unit can perform simplified image analysis of plants in the late growth stage. This allows for optimal analysis according to the plant's growth stage. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input plant growth stage data to the generation AI and have the generation AI determine the order of analysis.

[0087] During analysis, the analysis unit can change the analysis order based on the relevance of the plants. Examples of plant relevance include, but are not limited to, plants of the same family, common pests and diseases, and cultivation conditions. The analysis unit, for example, prioritizes analysis of highly related plants. For example, the analysis unit can prioritize image analysis of highly related plants. The analysis unit can also perform regular analysis on less related plants. For example, the analysis unit can perform standard image analysis on less related plants. Furthermore, the analysis unit can determine an optimal analysis order based on the relevance of the plants. For example, the analysis unit can determine an optimal analysis order based on the relevance of the plants. This makes it possible to provide an optimal analysis order according to the relevance of the plants. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input plant relevance data to the generation AI and cause the generation AI to change the analysis order.

[0088] During analysis, the analysis unit can change the terminology used in the analysis depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can determine that the user is a beginner based on survey results or past questions and provide the analysis results in simple language. Furthermore, if the user is an intermediate user, the analysis unit can provide the analysis results using appropriate terminology. For example, the analysis unit can determine that the user is an intermediate user based on usage history and provide the analysis results using appropriate terminology. Furthermore, if the user is an advanced user, the analysis unit can provide the analysis results using detailed terminology. For example, the analysis unit can determine that the user is an advanced user based on past questions and provide the analysis results using detailed terminology. This allows the provision of optimal analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's expertise level data into the generation AI and have the generation AI execute the use of technical terms.

[0089] The providing unit can analyze the user's emotions and adjust the way the solution is presented based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, tension, relaxation, and excitement. For example, if the user is nervous, the providing unit can provide a simple, highly visible solution. For example, the providing unit can detect that the user is nervous through facial expression analysis and provide a simple solution. Furthermore, if the user is relaxed, the providing unit can provide a detailed solution. For example, the providing unit can detect that the user is relaxed through voice analysis and provide a detailed solution. Furthermore, if the user is excited, the providing unit can provide a visually stimulating solution. For example, the providing unit can detect that the user is excited through behavioral pattern analysis and provide a visually stimulating solution. This allows the provision of an optimal solution according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the way the solution is expressed.

[0090] The providing unit can adjust the accuracy of the solution based on the importance of the problem when providing the solution. The importance of the problem includes, but is not limited to, the health of the plant, the scope of the impact, and the urgency of the problem. The providing unit can provide a detailed solution for an important problem. For example, if the health of the plant is deteriorating, the providing unit can provide a detailed treatment method. The providing unit can also provide a standard solution for a general problem. For example, if the plant's growth is slowing, the providing unit can provide a standard cultivation method. The providing unit can also provide a simplified solution for a less important problem. For example, if the plant's leaves are slightly yellowing, the providing unit can provide a simplified fertilization method. This allows the provision of an optimal solution according to the importance of the problem. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input problem importance data into the generating AI and cause the generating AI to adjust the accuracy of the solution.

[0091] When providing the solution, the providing unit can apply a specific solution algorithm depending on the type of problem. Examples of problem types include, but are not limited to, diseases, nutritional deficiencies, and pest damage. For example, the providing unit can apply a dedicated solution algorithm to a specific disease. For example, the providing unit can apply a dedicated treatment algorithm to a disease caused by a specific fungus. The providing unit can also apply a standard solution algorithm to a general problem. For example, the providing unit can apply a standard fertilization algorithm to a general nutritional deficiency. Furthermore, the providing unit can select and apply an optimal solution algorithm depending on the type of problem. For example, the providing unit can select and apply an optimal machine learning algorithm depending on the type of problem. This allows the optimal solution to be provided depending on the type of problem. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input problem type data to the generating AI and cause the generating AI to apply the solution algorithm.

[0092] When providing a solution, the providing unit can improve the accuracy of the solution by referring to the user's past solution provision results. Past solution provision results include, but are not limited to, user feedback, success rates, and execution results. The providing unit, for example, optimizes a solution algorithm based on the user's past solution provision results. For example, the providing unit can optimize the solution algorithm based on the user's past feedback. The providing unit can also improve the accuracy of the solution by referring to the user's past solution provision results. For example, the providing unit can improve the accuracy of the solution based on the user's past success rates. Furthermore, the providing unit can analyze the user's past solution provision results and improve the solution method. For example, the providing unit can improve the solution method based on the user's past execution results. This can improve the accuracy of the solution based on the user's past solution provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past solution provision result data into the generation AI and cause the generation AI to improve the accuracy of the solution.

[0093] The providing unit can analyze the user's emotions and adjust the length of the solution to be provided based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, being in a hurry, being relaxed, and being excited. For example, if the user is in a hurry, the providing unit can provide a short and concise solution. For example, the providing unit can detect that the user is in a hurry through facial expression analysis and provide a short solution. Furthermore, if the user is relaxed, the providing unit can provide a detailed solution. For example, the providing unit can detect that the user is relaxed through voice analysis and provide a detailed solution. Furthermore, if the user is excited, the providing unit can provide a visually stimulating solution. For example, the providing unit can detect that the user is excited through behavioral pattern analysis and provide a visually stimulating solution. This allows the provision of an optimal solution according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the length of the solution.

[0094] When providing the solutions, the providing unit can determine the order of solutions based on the time when the problem occurred. Examples of the time when the problem occurred include, but are not limited to, the season, the growth stage, and environmental conditions. For example, the providing unit can prioritize providing solutions for urgent problems. For example, the providing unit can prioritize providing a treatment method when a plant disease is rapidly progressing. The providing unit can also provide solutions with normal priority for general problems. For example, the providing unit can recommend normal cultivation methods when a plant's growth is slow. Furthermore, the providing unit can determine optimal priorities and provide solutions based on the time when the problem occurred. For example, the providing unit can determine optimal priorities and provide solutions based on the plant's growth stage. This allows the provision of optimal solutions according to the time when the problem occurred. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input data on the time when the problem occurred to the generating AI and cause the generating AI to determine the order of solutions.

[0095] When providing solutions, the providing unit can change the order of solutions based on the relevance of the problems. Examples of problem relevance include, but are not limited to, the same cause, common symptoms, and scope of impact. For example, the providing unit can prioritize providing solutions to highly related problems. For example, the providing unit can prioritize providing solutions to multiple problems caused by the same cause. The providing unit can also provide solutions in a normal order for less related problems. For example, the providing unit can provide solutions in a standard order for less related problems. Furthermore, the providing unit can determine an optimal order based on the relevance of the problems and provide the solutions. For example, the providing unit can determine an optimal order based on the relevance of the problems and provide the solutions. This makes it possible to provide an optimal solution according to the relevance of the problems. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input problem relevance data to the generation AI and cause the generation AI to change the order of the solutions.

[0096] When providing a solution, the providing unit can change the use of technical terminology in the solution depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the providing unit can provide the solution in simple language. For example, the providing unit can determine that the user is a beginner based on survey results or past questions and provide the solution in simple language. Furthermore, if the user is an intermediate user, the providing unit can provide the solution using appropriate technical terminology. For example, the providing unit can determine that the user is an intermediate user based on usage history and provide the solution using appropriate technical terminology. Furthermore, if the user is an advanced user, the providing unit can provide the solution using detailed technical terminology. For example, the providing unit can determine that the user is an advanced user based on past questions and provide the solution using detailed technical terminology. This allows the provision of an optimal solution according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's expertise level data into the generating AI and cause the generating AI to use specialized terminology.

[0097] The learning unit can analyze the user's emotions and select training data based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, relaxed, rushed, and excited. For example, when the user is relaxed, the learning unit selects detailed training data. For example, the learning unit can detect that the user is relaxed through facial expression analysis and select detailed training data. Furthermore, when the user is in a hurry, the learning unit can select training data that emphasizes the key points. For example, the learning unit can detect that the user is in a hurry through voice analysis and select training data that emphasizes the key points. Furthermore, when the user is excited, the learning unit can select visually stimulating training data. For example, the learning unit can detect that the user is excited through behavioral pattern analysis and select visually stimulating training data. This allows optimal training data to be selected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input user emotion data to the generation AI and cause the generation AI to select learning data.

[0098] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, past question content, solutions, and feedback. The learning unit, for example, optimizes the learning algorithm based on past learning data. For example, the learning unit can optimize the learning algorithm based on past question content. The learning unit can also improve the accuracy of learning by referring to past solutions. For example, the learning unit can improve the accuracy of learning based on past solutions. Furthermore, the learning unit can analyze past feedback and improve the learning method. For example, the learning unit can improve the learning method based on past feedback. This allows the learning algorithm to be optimized based on the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to the generation AI and cause the generation AI to adjust the learning algorithm.

[0099] During learning, the learning unit can analyze the user's past questions and situations and change the update frequency of the learning data. Past questions and situations include, but are not limited to, the frequency of questions, content, and solution results. The learning unit adjusts the update frequency of the learning data based on, for example, the user's past questions. For example, the learning unit can adjust the update frequency of the learning data based on the frequency of questions asked by the user in the past. The learning unit can also analyze the user's past situations and determine an optimal update frequency. For example, the learning unit can determine an optimal update frequency based on the user's past situations. Furthermore, the learning unit can optimize the update frequency of the learning data by referring to the user's past questions and situations. For example, the learning unit can optimize the update frequency of the learning data based on the user's past questions and situations. This allows the update frequency of the learning data to be optimized based on the user's past questions and situations. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input data on the user's past questions and situations into the generation AI and cause the generation AI to change the update frequency of the learning data.

[0100] The learning unit can analyze the user's emotions and adjust the frequency of learning based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, relaxed, rushed, and excited. For example, the learning unit can increase the frequency of learning when the user is relaxed. For example, the learning unit can detect that the user is relaxed through facial expression analysis and increase the frequency of learning. Furthermore, the learning unit can also decrease the frequency of learning when the user is rushed. For example, the learning unit can detect that the user is rushed through voice analysis and decrease the frequency of learning. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. For example, the learning unit can detect that the user is excited through behavioral pattern analysis and adjust the frequency of learning. This allows for an optimal learning frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input user emotion data to the generation AI and cause the generation AI to adjust the frequency of learning.

[0101] During learning, the learning unit can adjust the weighting of the learning data based on the growth stage of the plant. Examples of plant growth stages include, but are not limited to, the germination stage, growth stage, and harvest stage. For example, the learning unit can assign a higher weighting to the learning data for plants in the early growth stage. Furthermore, the learning unit can assign a normal weighting to plants in the middle growth stage. For example, the learning unit can assign a normal weighting to plants in the middle growth stage. Furthermore, the learning unit can assign a lower weighting to the learning data for plants in the late growth stage. For example, the learning unit can assign a lower weighting to the learning data for plants in the late growth stage. This allows optimal weighting of the learning data according to the plant's growth stage to be provided. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input plant growth stage data to the generation AI and cause the generation AI to adjust the weighting of the learning data.

[0102] During learning, the learning unit can change the learning algorithm to reflect user feedback. User feedback includes, but is not limited to, ratings, comments, and usage history. The learning unit, for example, optimizes the learning algorithm based on user feedback. For example, the learning unit can optimize the learning algorithm based on user ratings. The learning unit can also improve the accuracy of learning by referring to user feedback. For example, the learning unit can improve the accuracy of learning based on user comments. Furthermore, the learning unit can analyze user feedback and improve the learning method. For example, the learning unit can improve the learning method based on the user usage history. This allows the learning algorithm to be optimized based on user feedback. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to change the learning algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and learning unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit uses the camera 42 of the smart device 14 to take pictures of the home vegetable garden and uploads the images to the data processing device 12 via the communication I / F 44. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded images using a generative AI to identify the condition of the plants and any problems. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides specific solutions based on the analysis results. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past questions and situations and provides personalized advice. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and learning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit uses the camera 42 of the smart glasses 214 to capture images of the home vegetable garden and uploads the images to the data processing device 12 via the communication I / F 44. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded images using a generative AI to identify the condition of the plants and any problems. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides specific solutions based on the analysis results. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past questions and situations and provides personalized advice. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and learning unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit takes a picture of the state of the home vegetable garden using the camera 42 of the headset-type terminal 314 and uploads the image to the data processing device 12 via the communication I / F 44. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded image using a generative AI to identify the condition of the plants and any problems. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides specific solutions based on the analysis results. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past questions and situations and provides personalized advice. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and learning unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit uses the camera 42 of the robot 414 to take pictures of the home vegetable garden and uploads the images to the data processing device 12 via the communication I / F 44. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded images using a generative AI to identify the condition of the plants and any problems. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides specific solutions based on the analysis results. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's past questions and situations and provides personalized advice.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The home gardening support system may further include a notification unit. The notification unit can send real-time notifications to the user based on the condition or problems of the plants identified by the analysis unit. For example, if a plant is diseased, the notification unit can immediately send an alert to the user to urge them to take immediate action. Also, if a nutrient deficiency is detected, the notification unit can send a notification suggesting the use of appropriate fertilizer. Furthermore, the notification unit can periodically send advice according to the growth stage of the plant. This allows the user to manage their home garden more efficiently.

[0105] The analysis unit can also perform voice analysis to identify the condition of the plant or any problems. For example, if a user describes the condition of a plant by voice, the analysis unit can analyze the voice data and identify the condition or problems of the plant. Furthermore, the analysis unit can perform a more accurate diagnosis by combining and analyzing voice data and image data. This allows the user to report the condition of the plant through voice input, and the analysis unit can provide appropriate advice based on that information.

[0106] The providing unit can analyze the user's emotions and adjust the tone of the advice to be provided based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a gentle tone. If the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is excited, the providing unit can provide advice that will increase motivation. In this way, it is possible to provide optimal advice according to the user's emotions.

[0107] The learning unit can learn the location information of the user's home garden and provide advice based on the local climate and soil conditions. For example, if the user's home garden is in a dry region, the learning unit can suggest an appropriate irrigation method. If the user's home garden is in a cold region, the learning unit can advise on the selection of cold-tolerant plants. Furthermore, the learning unit can suggest seasonal cultivation schedules based on local climate data. This allows the user to receive advice optimized for the local conditions.

[0108] The providing unit can analyze the user's emotions and adjust the form of advice to be provided based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise, visually easy-to-understand advice. If the user is relaxed, the providing unit can provide detailed text-based advice. Furthermore, if the user is excited, the providing unit can provide interactive advice. In this way, it is possible to provide the optimal form of advice according to the user's emotions.

[0109] The analysis unit can acquire environmental data such as temperature and humidity and use it for analysis to identify the condition of the plant and any problems. For example, the analysis unit can acquire the temperature and humidity around the plant using a sensor and evaluate the condition of the plant based on that data. Furthermore, the analysis unit can perform a more accurate diagnosis by combining and analyzing environmental data and image data. This allows the user to receive detailed advice based on the environmental data.

[0110] The reception unit can analyze the user's emotions and suggest an image upload method based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can suggest a method that allows the user to upload images with simple operations. If the user is relaxed, the reception unit can provide detailed instructions. Furthermore, if the user is excited, the reception unit can suggest an interactive upload method. In this way, it is possible to provide the optimal upload method according to the user's emotions.

[0111] The analysis unit can acquire and use the plant's growth history data for analysis in order to identify the plant's condition and problems. For example, the analysis unit can evaluate the plant's current condition based on past growth data. Furthermore, the analysis unit can perform a more accurate diagnosis by combining and analyzing the growth history data and image data. This allows the user to receive detailed advice based on the growth history data.

[0112] The providing unit can analyze the user's emotions and adjust the frequency of advice provided based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can reduce the frequency of advice to allow the user to relax. Also, if the user is relaxed, the providing unit can increase the frequency of advice and provide detailed support. Furthermore, if the user is excited, the providing unit can adjust the frequency of advice to maintain the user's motivation. In this way, it is possible to provide an optimal frequency of advice according to the user's emotions.

[0113] The providing unit can adjust the content of the advice depending on the size of the user's home garden. For example, if the home garden is small, the providing unit can provide simple and easy-to-follow advice. If the home garden is medium-sized, the providing unit can provide detailed cultivation and management methods. Furthermore, if the home garden is large, the providing unit can provide advice using advanced techniques and equipment. This makes it possible to provide optimal advice depending on the size of the user's home garden.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit allows the user to take a photo of the state of their home vegetable garden and upload the image. The user can use their smartphone to take a photo of the state of their home vegetable garden and upload the image to the service. The reception unit specifies the format and size of the image and guides the user through the upload procedure. Step 2: The analysis unit uses generative AI to analyze the images uploaded by the reception unit and identify the plant's condition and problems. For example, the analysis unit uses algorithms that analyze leaf color and shape to detect disease and nutritional deficiencies. The analysis unit can also evaluate the plant's health and growth status. Step 3: The provisioning unit provides specific solutions based on the problems identified by the analysis unit. For example, the provisioning unit can suggest treatments and preventative measures based on the type of disease or specific symptoms of nutritional deficiency. The provisioning unit can also use generative AI to advise on appropriate fertilizers and cultivation methods. Step 4: The learning unit learns the user's past questions and situations based on the solutions provided by the providing unit, and provides personalized advice. For example, the learning unit learns the user's past question content and solution history to provide more personalized advice.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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 reception unit where users take pictures of their vegetable gardens and upload the pictures; an analysis unit that analyzes the image uploaded by the reception unit and identifies the state or problem of the plant; a providing unit that provides a specific solution based on the problem identified by the analysis unit; a learning unit that learns the user's past questions and situations based on the solutions provided by the providing unit and provides personalized advice. A system characterized by:

2. The analysis unit Algorithms that analyze leaf color and shape are used to detect disease or nutrient deficiencies The system of claim 1 .

3. The providing unit Suggest treatments or preventative measures based on the type of illness or specific symptoms of nutritional deficiencies The system of claim 1 .

4. The learning unit Learn from users' past questions and situations to provide personalized advice The system of claim 1 .

5. The providing unit Advise on specific fertilizers or cultivation methods The system of claim 1 .

6. The analysis unit Perform image analysis to identify plant conditions or problems The system of claim 1 .

7. The reception unit Analyze user emotions and adjust the timing of image uploads based on the analyzed user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past image upload history and select a specific upload method The system of claim 1 .

Images