system

Abstract

The system according to this embodiment aims to clarify the evaluation of bonsai trees and provide an appropriate cultivation plan. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a price presentation unit, an advice unit, and a calendar creation unit. The reception unit receives images of bonsai trees. The analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai trees. The price presentation unit presents market prices based on the evaluation results obtained by the analysis unit. The advice unit provides advice based on the evaluation results obtained by the analysis unit. The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the evaluation criteria for potted plants are ambiguous and it is difficult to obtain a consistent evaluation.

[0005] The system according to the embodiment aims to clarify the evaluation of potted plants and provide an appropriate cultivation plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a price presentation unit, an advice unit, and a calendar creation unit. The reception unit receives images of bonsai trees. The analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai trees. The price presentation unit presents market prices based on the evaluation results obtained by the analysis unit. The advice unit provides advice based on the evaluation results obtained by the analysis unit. The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can clarify the evaluation of bonsai and provide an appropriate cultivation plan. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The bonsai evaluation support system according to an embodiment of the present invention is a system that supports the evaluation and cultivation of bonsai using a generating AI. In this bonsai evaluation support system, an image of a bonsai is input to the generating AI, which then analyzes the image. The generating AI analyzes characteristic points such as the shape, balance, and health of the bonsai and calculates evaluation points. Next, the generating AI presents a market price by comparing the analysis results with current market data. Furthermore, based on the current evaluation points, the generating AI specifically points out which aspects should be improved to increase the value. For example, it provides advice on pruning, fertilization, and shape adjustment. The generating AI also creates a cultivation calendar according to the local climate and season and suggests the timing of specific tasks such as pruning and fertilization. This mechanism makes the evaluation of bonsai clearer and allows for a proper understanding of its value. Furthermore, individualized cultivation plans enable the cultivation of healthy bonsai, and specific measures to improve the value of bonsai can be easily implemented. For example, a user inputs an image of a bonsai into the generating AI. In this case, an image taken so that the overall appearance of the bonsai is visible is used. The generating AI analyzes the input image and analyzes characteristic points such as the shape, balance, and health of the bonsai. For example, the AI ​​calculates evaluation points based on the arrangement of bonsai branches, leaf color, and trunk thickness. Next, the generating AI presents a market price by comparing the analysis results with current market data. The market data is based on past transaction history and current market trends, and the generating AI analyzes this data to calculate an appropriate market price. This allows users to understand the fair price of their bonsai. Furthermore, based on the current evaluation points, the generating AI specifically points out what aspects can be improved to increase the value. For example, it advises on how to prune the bonsai branches, the timing of fertilization, and how to adjust the shape. This allows users to implement specific measures to improve the value of their bonsai. The generating AI also creates a cultivation calendar that is appropriate for the local climate and season. For example, it suggests the timing of specific tasks, such as pruning in spring and fertilizing in summer. This allows users to support the healthy growth of their bonsai. This system makes the evaluation of bonsai clearer and allows for a proper understanding of their value.Furthermore, personalized cultivation plans enable the growth of healthy bonsai trees, and concrete measures to enhance the value of bonsai can be easily implemented. For example, a higher evaluation score for a bonsai increases its market value, allowing users to cultivate high-quality bonsai. As a result, the bonsai evaluation support system clarifies the evaluation of bonsai and allows for a proper understanding of their value.

[0029] The bonsai evaluation support system according to this embodiment comprises a reception unit, an analysis unit, a price presentation unit, an advice unit, and a calendar creation unit. The reception unit receives images of bonsai. The reception unit can, for example, receive images of bonsai taken by a user. The reception unit can, for example, receive images taken with a smartphone or digital camera. The reception unit can, for example, automatically adjust the image resolution and shooting angle before receiving the image. The analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai. The analysis unit calculates evaluation points, for example, the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk. The analysis unit extracts characteristic points of the bonsai using image analysis technology and calculates evaluation points. The analysis unit can, for example, evaluate the shape and balance of the bonsai using generative AI. The price presentation unit presents market prices based on the evaluation results obtained by the analysis unit. The price presentation unit calculates market prices based on, for example, past transaction history and current market trends. The price presentation unit can, for example, calculate market prices using generative AI. The price-setting unit can, for example, collect market data in real time and suggest an appropriate market price. The advice unit provides advice based on the evaluation results obtained by the analysis unit. The advice unit can, for example, advise on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape. The advice unit can, for example, provide specific advice using generative AI. The advice unit can, for example, provide customized advice according to the user's cultivation status. The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. The calendar creation unit can, for example, create a cultivation calendar according to the local climate and season. The calendar creation unit can, for example, create a cultivation calendar using generative AI. The calendar creation unit can, for example, suggest the timing of specific tasks such as pruning and fertilization. As a result, the bonsai evaluation support system according to the embodiment can consistently perform bonsai evaluation, price-setting, advice, and cultivation calendar creation.Some or all of the above-described processes in the reception unit, analysis unit, price quote unit, advice unit, and calendar creation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the reception unit can input an image of a bonsai taken by the user into the generation AI and have the generation AI perform image reception. The analysis unit can input the image received by the reception unit into the generation AI and have the generation AI perform an evaluation of the bonsai's shape, balance, and health. The price quote unit can input the evaluation results obtained by the analysis unit into the generation AI and have the generation AI perform a market price calculation. The advice unit can input the evaluation results obtained by the analysis unit into the generation AI and have the generation AI perform the provision of advice. The calendar creation unit can input the advice provided by the advice unit into the generation AI and have the generation AI perform the creation of a cultivation calendar.

[0030] The reception unit accepts images of bonsai trees. For example, the reception unit can accept images of bonsai trees taken by users. For example, the reception unit can accept images taken with smartphones or digital cameras. For example, the reception unit can automatically adjust the resolution and shooting angle of the images before accepting them. Specifically, when a user uploads an image of a bonsai tree taken with a smartphone or digital camera, the reception unit checks the resolution of the image and automatically adjusts the resolution as needed. In addition, if the shooting angle is not appropriate, it can display a message to the user prompting them to retake the photo. Furthermore, the reception unit can automatically recognize the background of the image and crop out only the bonsai tree. This prepares the analysis unit to accurately evaluate the shape and balance of the bonsai tree. The reception unit can also accept multiple images from users and integrate images taken from different angles to provide data for a more detailed evaluation. For example, it can accept images of the front, side, and back of a bonsai tree and integrate them to generate a 3D model. This allows the analysis unit to grasp the overall appearance of the bonsai tree and perform a more accurate evaluation. The reception desk provides an intuitive user interface to simplify the image upload process for users. For example, it includes features such as drag-and-drop image uploading and the ability to upload images directly taken using a smartphone camera app. This allows users to easily upload images and begin using the system.

[0031] The analysis unit analyzes images received by the reception unit and evaluates the shape, balance, and health of the bonsai. For example, the analysis unit calculates evaluation points such as the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk. For example, the analysis unit extracts feature points of the bonsai using image analysis technology and calculates evaluation points. For example, the analysis unit can evaluate the shape and balance of the bonsai using generative AI. Specifically, in order to evaluate the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk using image analysis technology, feature points are first extracted from the image. Feature points include branching points of the bonsai branches, points of change in leaf color, and points of change in trunk thickness, and the shape and balance of the bonsai are evaluated based on these. The generative AI builds a model for evaluating the shape and balance of the bonsai based on these feature points. For example, the AI ​​learns from past bonsai image data and their evaluation results, and performs a similar evaluation on newly received images. The AI ​​evaluates whether the arrangement of the bonsai branches is natural, whether the leaf color is healthy, and whether the trunk thickness is appropriate, and calculates an overall evaluation score. Furthermore, the analysis unit performs a detailed analysis of leaf color and shape, trunk surface condition, and other factors to assess the bonsai's health. For example, if the leaves are yellowing, it may indicate nutrient deficiency or pest / disease infestation, and the AI ​​uses this information to assess the bonsai's health. Similarly, if cracks or discoloration are observed on the trunk surface, it may indicate dryness or pest / disease infestation, and the AI ​​uses this information to assess the bonsai's health. In this way, the analysis unit can comprehensively evaluate the bonsai's shape, balance, and health, and provide the user with detailed evaluation results.

[0032] The price quotation unit presents market prices based on evaluation results obtained by the analysis unit. The price quotation unit calculates market prices based on, for example, past transaction history and current market trends. The price quotation unit can calculate market prices using, for example, generative AI. The price quotation unit can, for example, collect market data in real time and present appropriate market prices. Specifically, the price quotation unit stores past transaction history in a database and collects current market trends in real time. This allows it to integrate past transaction history and current market trends to calculate appropriate market prices. The generative AI builds a model for predicting market prices based on this data. For example, the AI ​​learns from past transaction data and understands the relationship between bonsai evaluation points and market prices. This allows it to present appropriate market prices for newly evaluated bonsai. Furthermore, the price quotation unit obtains data from online bonsai trading platforms and auction sites to collect market data in real time. This allows it to accurately grasp current market trends and present appropriate market prices. For example, if a particular type of bonsai is trading at a high price in the market, the price quotation unit can adjust the market price based on that information. This allows the pricing unit to always display appropriate market prices based on the latest market information.

[0033] The advice unit provides advice based on the evaluation results obtained by the analysis unit. For example, the advice unit advises on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape. The advice unit can provide specific advice using, for example, generative AI. The advice unit can provide customized advice tailored to the user's cultivation status. Specifically, the advice unit provides detailed advice on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape, based on the evaluation results obtained by the analysis unit. The generative AI learns from past cultivation data and expert knowledge to provide specific advice to the user. For example, the AI ​​advises which branches to prune, when to fertilize, and how to adjust the shape, based on the arrangement of bonsai branches, leaf color, and trunk thickness. Furthermore, the advice unit provides customized advice tailored to the user's cultivation status. For example, if the user is a beginner, it will explain basic cultivation methods and points to note in detail, while experienced users will be given more advanced cultivation techniques and expert advice. This allows the advice unit to provide appropriate advice tailored to the user's bonsai cultivation progress, supporting the growth of the bonsai.

[0034] The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. For example, the calendar creation unit creates a cultivation calendar tailored to the local climate and season. The calendar creation unit can also create a cultivation calendar using, for example, generative AI. The calendar creation unit can suggest the timing of specific tasks such as pruning and fertilizing. Specifically, the calendar creation unit creates a cultivation calendar tailored to the local climate and season based on the advice provided by the advice unit. The generative AI learns past weather data and local climate characteristics to suggest an optimal cultivation schedule. For example, the AI ​​calculates the optimal timing for pruning and fertilizing based on local temperature, precipitation, and sunshine hours. This allows users to understand an appropriate cultivation schedule tailored to the local climate and season. Furthermore, the calendar creation unit provides a customized cultivation calendar tailored to the user's cultivation status and bonsai type. For example, if a particular type of bonsai requires special cultivation methods, the calendar is created based on that information. It can also suggest the next task to be performed, taking into account the user's past cultivation history. In this way, the calendar creation unit can provide users with a concrete and practical cultivation schedule, supporting their bonsai cultivation.

[0035] The reception unit can accept images taken in a way that shows the overall appearance of the bonsai. The reception unit can, for example, accept images taken in a way that shows the overall appearance of the bonsai. The reception unit can, for example, automatically adjust the shooting angle and resolution before accepting the images. The reception unit can, for example, process the background and accept images in a way that clearly shows the overall appearance of the bonsai. This makes accurate evaluation possible by accepting images that show the overall appearance of the bonsai. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the reception unit can input an image taken in a way that shows the overall appearance of the bonsai into a generation AI and have the generation AI perform the image acceptance.

[0036] The analysis unit can calculate evaluation points for the arrangement of bonsai branches, the color of the leaves, the thickness of the trunk, and so on. For example, the analysis unit can calculate the arrangement of the bonsai branches as an evaluation point. For example, the analysis unit can calculate the color of the bonsai leaves as an evaluation point. For example, the analysis unit can calculate the thickness of the bonsai trunk as an evaluation point. This allows for accurate evaluation by calculating the detailed characteristics of the bonsai as evaluation points. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data such as the arrangement of bonsai branches, the color of the leaves, and the thickness of the trunk into a generation AI and have the generation AI perform the calculation of evaluation points.

[0037] The price quotation unit can calculate market prices based on past transaction history and current market trends. For example, the price quotation unit calculates market prices based on past transaction history. For example, the price quotation unit calculates market prices based on current market trends. For example, the price quotation unit calculates market prices by combining past transaction history and current market trends. This makes it possible to quote appropriate prices by calculating market prices based on past transaction history and current market trends. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit can input data on past transaction history and current market trends into a generation AI and have the generation AI perform the calculation of market prices.

[0038] The advice unit can provide advice on bonsai branch pruning methods, fertilization timing, and shape adjustment methods. For example, the advice unit can advise on bonsai branch pruning methods. For example, the advice unit can advise on fertilization timing. For example, the advice unit can advise on shape adjustment methods. By providing specific advice, it becomes possible to improve the value of the bonsai. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input data on bonsai branch pruning methods, fertilization timing, and shape adjustment methods into a generation AI, and have the generation AI provide the advice.

[0039] The calendar creation unit can suggest the timing of specific tasks, such as pruning in spring and fertilizing in summer. For example, the calendar creation unit can suggest the timing of pruning in spring. For example, the calendar creation unit can suggest the timing of fertilizing in summer. For example, the calendar creation unit can suggest the timing of pest and disease control in autumn. By suggesting the timing of specific tasks, it becomes possible to cultivate healthy bonsai. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input the timing of spring pruning, summer fertilization, and autumn pest and disease control into a generation AI, and have the generation AI create a cultivation calendar.

[0040] The reception unit can analyze the user's past image submission history when receiving an image and select the optimal reception method. For example, the reception unit can analyze the frequency of images previously submitted by the user and suggest the optimal reception timing. For example, the reception unit can analyze the quality of images previously submitted by the user and suggest the optimal reception method. For example, the reception unit can analyze the content of images previously submitted by the user and suggest the optimal reception method. In this way, the optimal reception method can be selected by analyzing the past image submission history. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input the user's past image submission history into a generation AI and have the generation AI select the optimal reception method.

[0041] The reception unit can filter images upon receipt based on the user's current projects and areas of interest. For example, the reception unit may prioritize images related to the user's current projects. For example, the reception unit may prioritize images related to the user's areas of interest. For example, the reception unit may prioritize images related to areas the user has shown interest in in the past. This allows the reception unit to prioritize the receipt of highly relevant images by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input data on the user's current projects and areas of interest into a generative AI and have the generative AI perform the filtering.

[0042] The reception unit can prioritize receiving images that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can prioritize receiving images related to the user's current location. For example, the reception unit can prioritize receiving images related to places the user has visited in the past. For example, the reception unit can prioritize receiving images related to places the user plans to visit in the future. In this way, by considering geographical location information, highly relevant images can be prioritized. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input the user's geographical location information into a generation AI and have the generation AI select highly relevant images.

[0043] The reception unit can analyze the user's social media activity when receiving images and accept relevant images. For example, the reception unit may prioritize images related to images the user has shared on social media. For example, the reception unit may prioritize images related to images the user has shown interest in on social media. For example, the reception unit may prioritize images related to accounts the user follows on social media. In this way, by analyzing social media activity, it is possible to prioritize the acceptance of relevant images. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit may input data on the user's social media activity into a generative AI and have the generative AI select relevant images.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the bonsai during the analysis. For example, the analysis unit performs a detailed analysis for bonsai of high importance. For example, the analysis unit performs a simplified analysis for bonsai of low importance. For example, the analysis unit performs an analysis with an appropriate level of detail for bonsai of medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the bonsai, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input bonsai importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the bonsai category during analysis. For example, the analysis unit selects the optimal analysis algorithm depending on the type of bonsai. For example, the analysis unit applies an appropriate analysis algorithm depending on the growth stage of the bonsai. For example, the analysis unit uses different analysis algorithms depending on the health status of the bonsai. By applying different analysis algorithms depending on the bonsai category, the optimal analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input bonsai category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the bonsai were photographed. For example, if the bonsai were photographed recently, the analysis unit will prioritize the analysis. If the bonsai were photographed a long time ago, the analysis unit will postpone the analysis. If the bonsai were photographed during a specific season, the analysis unit will perform an analysis appropriate to that season. By determining the priority of analysis based on when the bonsai were photographed, the analysis results can be provided at an appropriate time. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the bonsai photography date data into a generation AI and have the generation AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relationships between bonsai trees during the analysis process. For example, the analysis unit prioritizes analysis of bonsai trees with high relationships. For example, the analysis unit postpones analysis of bonsai trees with low relationships. For example, the analysis unit performs analysis in an appropriate order when the relationships between bonsai trees are moderate. In this way, by adjusting the order of analysis based on the relationships between bonsai trees, highly related bonsai trees can be analyzed preferentially. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input bonsai relationship data into a generation AI and have the generation AI perform the adjustment of the analysis order.

[0048] The price quotation unit can improve the accuracy of its price quotations by referring to past transaction history when quoting prices. For example, the price quotation unit quotes an appropriate price based on past transaction history. For example, the price quotation unit analyzes past transaction history and quotes a price considering price fluctuations. For example, the price quotation unit refers to past transaction history and quotes a price in comparison to the current market price. This improves the accuracy of price quotations by referring to past transaction history. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit can input past transaction history data into a generation AI and have the generation AI perform the task of improving the accuracy of price quotations.

[0049] The price quotation unit can customize its pricing method based on current market trends when quoting prices. For example, the price quotation unit may quote an appropriate price based on current market trends. For example, the price quotation unit may analyze current market trends and quote a price considering price fluctuations. For example, the price quotation unit may refer to current market trends and quote a price in comparison to past transaction history. This makes it possible to quote an appropriate price by customizing the pricing method based on current market trends. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit may input current market trend data into a generation AI and have the generation AI perform the customization of the pricing method.

[0050] The pricing unit can select the optimal pricing method when providing pricing information, taking into account the user's geographical location. For example, the pricing unit may provide pricing information related to the user's current location, places the user has visited in the past, or places the user plans to visit in the future. This allows the system to select the optimal pricing method by considering the user's geographical location. Some or all of the above processing in the pricing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the pricing unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal pricing method.

[0051] The pricing unit can analyze the user's social media activity and propose pricing methods when providing pricing information. For example, the pricing unit may provide pricing information based on information shared by the user on social media. For example, the pricing unit may provide pricing information based on information the user has shown interest in on social media. For example, the pricing unit may provide pricing information related to accounts the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to propose appropriate pricing methods. Some or all of the above processing in the pricing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the pricing unit may input the user's social media activity data into a generative AI and have the generative AI propose pricing methods.

[0052] The advice unit can adjust the level of detail in its advice based on the bonsai's evaluation points. For example, the advice unit provides detailed advice for high-importance evaluation points. For example, it provides concise advice for low-importance evaluation points. For example, it provides advice with an appropriate level of detail for medium-importance evaluation points. In this way, by adjusting the level of detail in the advice based on the bonsai's evaluation points, it can provide appropriate advice. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input bonsai evaluation point data into a generation AI and have the generation AI perform the adjustment of the level of detail in the advice.

[0053] The advice unit can apply different advice algorithms depending on the bonsai category when providing advice. For example, the advice unit can select the optimal advice algorithm depending on the type of bonsai. For example, the advice unit can apply an appropriate advice algorithm depending on the growth stage of the bonsai. For example, the advice unit can use different advice algorithms depending on the health status of the bonsai. In this way, by applying different advice algorithms depending on the bonsai category, the optimal advice can be provided. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input bonsai category data into a generative AI and have the generative AI execute the application of the advice algorithm.

[0054] The advice unit can determine the priority of advice based on when the bonsai was photographed. For example, if the bonsai was photographed recently, the advice unit will prioritize giving advice. If the bonsai was photographed a long time ago, the advice unit will postpone giving advice. If the bonsai was photographed during a specific season, the advice unit will provide advice appropriate to that season. By determining the priority of advice based on when the bonsai was photographed, advice can be provided at the appropriate time. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input bonsai photographing date data into a generative AI and have the generative AI determine the priority of advice.

[0055] The advice unit can adjust the order of advice based on the relevance of the bonsai trees. For example, the advice unit will prioritize advice if the bonsai trees are highly relevant. For example, it will postpone advice if the bonsai trees are less relevant. For example, if the bonsai trees are moderately relevant, it will provide advice in an appropriate order. In this way, by adjusting the order of advice based on the relevance of the bonsai trees, advice can be given preferentially to highly relevant bonsai trees. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input bonsai relevance data into a generative AI and have the generative AI perform the adjustment of the order of advice.

[0056] The calendar creation unit can improve the accuracy of the calendar by referring to past training data when creating it. For example, the calendar creation unit creates an appropriate calendar based on past training data. For example, the calendar creation unit analyzes past training data and creates a calendar considering fluctuations in training. For example, the calendar creation unit refers to past training data and creates a calendar by comparing it with the current training status. In this way, the accuracy of the calendar is improved by referring to past training data. Some or all of the above processes in the calendar creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the calendar creation unit can input past training data into a generation AI and have the generation AI perform the calendar accuracy improvement.

[0057] The calendar creation unit can customize the calendar method based on the local climate and season when creating a calendar. For example, the calendar creation unit can create an appropriate calendar based on the local climate. For example, the calendar creation unit can analyze the local season and create a calendar considering variations in growth. For example, the calendar creation unit can refer to the local climate and season and create a calendar by comparing it with past growth data. In this way, by customizing the calendar method based on the local climate and season, the optimal calendar can be provided. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input local climate and seasonal data into a generation AI and have the generation AI perform the customization of the calendar method.

[0058] The calendar creation unit can select the optimal calendar creation method by considering the user's geographical location information when creating a calendar. For example, the calendar creation unit can create a calendar related to the user's current location. For example, the calendar creation unit can create a calendar related to places the user has visited in the past. For example, the calendar creation unit can create a calendar related to places the user plans to visit in the future. In this way, the optimal calendar creation method can be selected by considering the user's geographical location information. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the selection of the optimal calendar creation method.

[0059] The calendar creation unit can analyze the user's social media activity and suggest methods for creating a calendar when creating one. For example, the calendar creation unit can create a calendar based on information shared by the user on social media. For example, the calendar creation unit can create a calendar based on information the user has shown interest in on social media. For example, the calendar creation unit can create a calendar related to accounts the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to suggest appropriate methods for creating a calendar. Some or all of the above processing in the calendar creation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the calendar creation unit can input the user's social media activity data into a generative AI and have the generative AI perform the task of suggesting methods for creating a calendar.

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

[0061] The analysis unit can calculate evaluation points based on the arrangement of bonsai branches, the color of the leaves, the thickness of the trunk, and so on. For example, the arrangement of bonsai branches can be calculated as an evaluation point. The color of bonsai leaves can be calculated as an evaluation point. The thickness of bonsai trunk can be calculated as an evaluation point. This allows for accurate evaluation by calculating detailed characteristics of the bonsai as evaluation points. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data such as the arrangement of bonsai branches, the color of the leaves, and the thickness of the trunk into a generation AI and have the generation AI perform the calculation of evaluation points.

[0062] The price quotation unit can calculate market prices based on past transaction history and current market trends. For example, it can calculate market prices based on past transaction history. It can calculate market prices based on current market trends. It can calculate market prices by combining past transaction history and current market trends. This makes it possible to quote appropriate prices by calculating market prices based on past transaction history and current market trends. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit can input data on past transaction history and current market trends into a generation AI and have the generation AI calculate the market price.

[0063] The advice unit can provide advice on bonsai branch pruning methods, fertilization timing, and shape adjustment methods. For example, it can advise on bonsai branch pruning methods, fertilization timing, and shape adjustment methods. By providing specific advice, it is possible to improve the value of the bonsai. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input data on bonsai branch pruning methods, fertilization timing, and shape adjustment methods into a generation AI, and have the generation AI provide the advice.

[0064] The calendar creation unit can suggest the timing of specific tasks, such as pruning in spring and fertilizing in summer. For example, it can suggest the timing of pruning in spring, the timing of fertilizing in summer, and the timing of pest and disease control in autumn. By suggesting the timing of specific tasks, it becomes possible to cultivate healthy bonsai. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input the timing of spring pruning, summer fertilization, and autumn pest and disease control into a generation AI, and have the generation AI create a cultivation calendar.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk accepts images of bonsai trees. For example, it can accept images of bonsai trees taken by users with their smartphones or digital cameras. The reception desk can automatically adjust the image resolution and shooting angle before accepting the images. Step 2: The analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai. For example, the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk are used as evaluation points. Image analysis technology and generative AI are used to extract the characteristic features of the bonsai and calculate the evaluation points. Step 3: The price presentation unit presents the market price based on the evaluation results obtained by the analysis unit. For example, it calculates the market price based on past transaction history and current market trends. By using a generation AI to calculate the market price and collecting market data in real time, it is possible to present an appropriate market price. Step 4: The advice unit provides advice based on the evaluation results obtained by the analysis unit. For example, it provides advice on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape. By using generation AI, it can provide specific advice and customized advice tailored to the user's cultivation status. Step 5: The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. For example, it creates a cultivation calendar that is appropriate for the local climate and season. Using generation AI, it can create a cultivation calendar and suggest the timing of specific tasks such as pruning and fertilizing.

[0067] (Example of form 2) The bonsai evaluation support system according to an embodiment of the present invention is a system that supports the evaluation and cultivation of bonsai using a generating AI. In this bonsai evaluation support system, an image of a bonsai is input to the generating AI, which then analyzes the image. The generating AI analyzes characteristic points such as the shape, balance, and health of the bonsai and calculates evaluation points. Next, the generating AI presents a market price by comparing the analysis results with current market data. Furthermore, based on the current evaluation points, the generating AI specifically points out which aspects should be improved to increase the value. For example, it provides advice on pruning, fertilization, and shape adjustment. The generating AI also creates a cultivation calendar according to the local climate and season and suggests the timing of specific tasks such as pruning and fertilization. This mechanism makes the evaluation of bonsai clearer and allows for a proper understanding of its value. Furthermore, individualized cultivation plans enable the cultivation of healthy bonsai, and specific measures to improve the value of bonsai can be easily implemented. For example, a user inputs an image of a bonsai into the generating AI. In this case, an image taken so that the overall appearance of the bonsai is visible is used. The generating AI analyzes the input image and analyzes characteristic points such as the shape, balance, and health of the bonsai. For example, the AI ​​calculates evaluation points based on the arrangement of bonsai branches, leaf color, and trunk thickness. Next, the generating AI presents a market price by comparing the analysis results with current market data. The market data is based on past transaction history and current market trends, and the generating AI analyzes this data to calculate an appropriate market price. This allows users to understand the fair price of their bonsai. Furthermore, based on the current evaluation points, the generating AI specifically points out what aspects can be improved to increase the value. For example, it advises on how to prune the bonsai branches, the timing of fertilization, and how to adjust the shape. This allows users to implement specific measures to improve the value of their bonsai. The generating AI also creates a cultivation calendar that is appropriate for the local climate and season. For example, it suggests the timing of specific tasks, such as pruning in spring and fertilizing in summer. This allows users to support the healthy growth of their bonsai. This system makes the evaluation of bonsai clearer and allows for a proper understanding of their value.Furthermore, personalized cultivation plans enable the growth of healthy bonsai trees, and concrete measures to enhance the value of bonsai can be easily implemented. For example, a higher evaluation score for a bonsai increases its market value, allowing users to cultivate high-quality bonsai. As a result, the bonsai evaluation support system clarifies the evaluation of bonsai and allows for a proper understanding of their value.

[0068] The bonsai evaluation support system according to this embodiment comprises a reception unit, an analysis unit, a price presentation unit, an advice unit, and a calendar creation unit. The reception unit receives images of bonsai. The reception unit can, for example, receive images of bonsai taken by a user. The reception unit can, for example, receive images taken with a smartphone or digital camera. The reception unit can, for example, automatically adjust the image resolution and shooting angle before receiving the image. The analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai. The analysis unit calculates evaluation points, for example, the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk. The analysis unit extracts characteristic points of the bonsai using image analysis technology and calculates evaluation points. The analysis unit can, for example, evaluate the shape and balance of the bonsai using generative AI. The price presentation unit presents market prices based on the evaluation results obtained by the analysis unit. The price presentation unit calculates market prices based on, for example, past transaction history and current market trends. The price presentation unit can, for example, calculate market prices using generative AI. The price-setting unit can, for example, collect market data in real time and suggest an appropriate market price. The advice unit provides advice based on the evaluation results obtained by the analysis unit. The advice unit can, for example, advise on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape. The advice unit can, for example, provide specific advice using generative AI. The advice unit can, for example, provide customized advice according to the user's cultivation status. The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. The calendar creation unit can, for example, create a cultivation calendar according to the local climate and season. The calendar creation unit can, for example, create a cultivation calendar using generative AI. The calendar creation unit can, for example, suggest the timing of specific tasks such as pruning and fertilization. As a result, the bonsai evaluation support system according to the embodiment can consistently perform bonsai evaluation, price-setting, advice, and cultivation calendar creation.Some or all of the above-described processes in the reception unit, analysis unit, price quote unit, advice unit, and calendar creation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the reception unit can input an image of a bonsai taken by the user into the generation AI and have the generation AI perform image reception. The analysis unit can input the image received by the reception unit into the generation AI and have the generation AI perform an evaluation of the bonsai's shape, balance, and health. The price quote unit can input the evaluation results obtained by the analysis unit into the generation AI and have the generation AI perform a market price calculation. The advice unit can input the evaluation results obtained by the analysis unit into the generation AI and have the generation AI perform the provision of advice. The calendar creation unit can input the advice provided by the advice unit into the generation AI and have the generation AI perform the creation of a cultivation calendar.

[0069] The reception unit accepts images of bonsai trees. For example, the reception unit can accept images of bonsai trees taken by users. For example, the reception unit can accept images taken with smartphones or digital cameras. For example, the reception unit can automatically adjust the resolution and shooting angle of the images before accepting them. Specifically, when a user uploads an image of a bonsai tree taken with a smartphone or digital camera, the reception unit checks the resolution of the image and automatically adjusts the resolution as needed. In addition, if the shooting angle is not appropriate, it can display a message to the user prompting them to retake the photo. Furthermore, the reception unit can automatically recognize the background of the image and crop out only the bonsai tree. This prepares the analysis unit to accurately evaluate the shape and balance of the bonsai tree. The reception unit can also accept multiple images from users and integrate images taken from different angles to provide data for a more detailed evaluation. For example, it can accept images of the front, side, and back of a bonsai tree and integrate them to generate a 3D model. This allows the analysis unit to grasp the overall appearance of the bonsai tree and perform a more accurate evaluation. The reception desk provides an intuitive user interface to simplify the image upload process for users. For example, it includes features such as drag-and-drop image uploading and the ability to upload images directly taken using a smartphone camera app. This allows users to easily upload images and begin using the system.

[0070] The analysis unit analyzes images received by the reception unit and evaluates the shape, balance, and health of the bonsai. For example, the analysis unit calculates evaluation points such as the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk. For example, the analysis unit extracts feature points of the bonsai using image analysis technology and calculates evaluation points. For example, the analysis unit can evaluate the shape and balance of the bonsai using generative AI. Specifically, in order to evaluate the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk using image analysis technology, feature points are first extracted from the image. Feature points include branching points of the bonsai branches, points of change in leaf color, and points of change in trunk thickness, and the shape and balance of the bonsai are evaluated based on these. The generative AI builds a model for evaluating the shape and balance of the bonsai based on these feature points. For example, the AI ​​learns from past bonsai image data and their evaluation results, and performs a similar evaluation on newly received images. The AI ​​evaluates whether the arrangement of the bonsai branches is natural, whether the leaf color is healthy, and whether the trunk thickness is appropriate, and calculates an overall evaluation score. Furthermore, the analysis unit performs a detailed analysis of leaf color and shape, trunk surface condition, and other factors to assess the bonsai's health. For example, if the leaves are yellowing, it may indicate nutrient deficiency or pest / disease infestation, and the AI ​​uses this information to assess the bonsai's health. Similarly, if cracks or discoloration are observed on the trunk surface, it may indicate dryness or pest / disease infestation, and the AI ​​uses this information to assess the bonsai's health. In this way, the analysis unit can comprehensively evaluate the bonsai's shape, balance, and health, and provide the user with detailed evaluation results.

[0071] The price quotation unit presents market prices based on evaluation results obtained by the analysis unit. The price quotation unit calculates market prices based on, for example, past transaction history and current market trends. The price quotation unit can calculate market prices using, for example, generative AI. The price quotation unit can, for example, collect market data in real time and present appropriate market prices. Specifically, the price quotation unit stores past transaction history in a database and collects current market trends in real time. This allows it to integrate past transaction history and current market trends to calculate appropriate market prices. The generative AI builds a model for predicting market prices based on this data. For example, the AI ​​learns from past transaction data and understands the relationship between bonsai evaluation points and market prices. This allows it to present appropriate market prices for newly evaluated bonsai. Furthermore, the price quotation unit obtains data from online bonsai trading platforms and auction sites to collect market data in real time. This allows it to accurately grasp current market trends and present appropriate market prices. For example, if a particular type of bonsai is trading at a high price in the market, the price quotation unit can adjust the market price based on that information. This allows the pricing unit to always display appropriate market prices based on the latest market information.

[0072] The advice unit provides advice based on the evaluation results obtained by the analysis unit. For example, the advice unit advises on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape. The advice unit can provide specific advice using, for example, generative AI. The advice unit can provide customized advice tailored to the user's cultivation status. Specifically, the advice unit provides detailed advice on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape, based on the evaluation results obtained by the analysis unit. The generative AI learns from past cultivation data and expert knowledge to provide specific advice to the user. For example, the AI ​​advises which branches to prune, when to fertilize, and how to adjust the shape, based on the arrangement of bonsai branches, leaf color, and trunk thickness. Furthermore, the advice unit provides customized advice tailored to the user's cultivation status. For example, if the user is a beginner, it will explain basic cultivation methods and points to note in detail, while experienced users will be given more advanced cultivation techniques and expert advice. This allows the advice unit to provide appropriate advice tailored to the user's bonsai cultivation progress, supporting the growth of the bonsai.

[0073] The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. For example, the calendar creation unit creates a cultivation calendar tailored to the local climate and season. The calendar creation unit can also create a cultivation calendar using, for example, generative AI. The calendar creation unit can suggest the timing of specific tasks such as pruning and fertilizing. Specifically, the calendar creation unit creates a cultivation calendar tailored to the local climate and season based on the advice provided by the advice unit. The generative AI learns past weather data and local climate characteristics to suggest an optimal cultivation schedule. For example, the AI ​​calculates the optimal timing for pruning and fertilizing based on local temperature, precipitation, and sunshine hours. This allows users to understand an appropriate cultivation schedule tailored to the local climate and season. Furthermore, the calendar creation unit provides a customized cultivation calendar tailored to the user's cultivation status and bonsai type. For example, if a particular type of bonsai requires special cultivation methods, the calendar is created based on that information. It can also suggest the next task to be performed, taking into account the user's past cultivation history. In this way, the calendar creation unit can provide users with a concrete and practical cultivation schedule, supporting their bonsai cultivation.

[0074] The reception unit can accept images taken in a way that shows the overall appearance of the bonsai. The reception unit can, for example, accept images taken in a way that shows the overall appearance of the bonsai. The reception unit can, for example, automatically adjust the shooting angle and resolution before accepting the images. The reception unit can, for example, process the background and accept images in a way that clearly shows the overall appearance of the bonsai. This makes accurate evaluation possible by accepting images that show the overall appearance of the bonsai. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the reception unit can input an image taken in a way that shows the overall appearance of the bonsai into a generation AI and have the generation AI perform the image acceptance.

[0075] The analysis unit can calculate evaluation points for the arrangement of bonsai branches, the color of the leaves, the thickness of the trunk, and so on. For example, the analysis unit can calculate the arrangement of the bonsai branches as an evaluation point. For example, the analysis unit can calculate the color of the bonsai leaves as an evaluation point. For example, the analysis unit can calculate the thickness of the bonsai trunk as an evaluation point. This allows for accurate evaluation by calculating the detailed characteristics of the bonsai as evaluation points. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data such as the arrangement of bonsai branches, the color of the leaves, and the thickness of the trunk into a generation AI and have the generation AI perform the calculation of evaluation points.

[0076] The price quotation unit can calculate market prices based on past transaction history and current market trends. For example, the price quotation unit calculates market prices based on past transaction history. For example, the price quotation unit calculates market prices based on current market trends. For example, the price quotation unit calculates market prices by combining past transaction history and current market trends. This makes it possible to quote appropriate prices by calculating market prices based on past transaction history and current market trends. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit can input data on past transaction history and current market trends into a generation AI and have the generation AI perform the calculation of market prices.

[0077] The advice unit can provide advice on bonsai branch pruning methods, fertilization timing, and shape adjustment methods. For example, the advice unit can advise on bonsai branch pruning methods. For example, the advice unit can advise on fertilization timing. For example, the advice unit can advise on shape adjustment methods. By providing specific advice, it becomes possible to improve the value of the bonsai. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input data on bonsai branch pruning methods, fertilization timing, and shape adjustment methods into a generation AI, and have the generation AI provide the advice.

[0078] The calendar creation unit can suggest the timing of specific tasks, such as pruning in spring and fertilizing in summer. For example, the calendar creation unit can suggest the timing of pruning in spring. For example, the calendar creation unit can suggest the timing of fertilizing in summer. For example, the calendar creation unit can suggest the timing of pest and disease control in autumn. By suggesting the timing of specific tasks, it becomes possible to cultivate healthy bonsai. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input the timing of spring pruning, summer fertilization, and autumn pest and disease control into a generation AI, and have the generation AI create a cultivation calendar.

[0079] The reception unit can estimate the user's emotions and adjust the timing of image submission based on the estimated emotions. For example, if the user is relaxed, the reception unit will immediately submit the image. If the user is busy, the reception unit will postpone submitting the image. If the user is stressed, the reception unit will temporarily stop submitting the image. By adjusting the timing of image submission according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception unit can analyze the user's past image submission history when receiving an image and select the optimal reception method. For example, the reception unit can analyze the frequency of images previously submitted by the user and suggest the optimal reception timing. For example, the reception unit can analyze the quality of images previously submitted by the user and suggest the optimal reception method. For example, the reception unit can analyze the content of images previously submitted by the user and suggest the optimal reception method. In this way, the optimal reception method can be selected by analyzing the past image submission history. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input the user's past image submission history into a generation AI and have the generation AI select the optimal reception method.

[0081] The reception unit can filter images upon receipt based on the user's current projects and areas of interest. For example, the reception unit may prioritize images related to the user's current projects. For example, the reception unit may prioritize images related to the user's areas of interest. For example, the reception unit may prioritize images related to areas the user has shown interest in in the past. This allows the reception unit to prioritize the receipt of highly relevant images by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input data on the user's current projects and areas of interest into a generative AI and have the generative AI perform the filtering.

[0082] The reception unit can estimate the user's emotions and determine the priority of images to accept based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize accepting high-importance images. If the user is busy, for example, the reception unit will postpone accepting low-importance images. If the user is stressed, for example, the reception unit will temporarily hold high-importance images. This allows for prioritizing important images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception unit can prioritize receiving images that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can prioritize receiving images related to the user's current location. For example, the reception unit can prioritize receiving images related to places the user has visited in the past. For example, the reception unit can prioritize receiving images related to places the user plans to visit in the future. In this way, by considering geographical location information, highly relevant images can be prioritized. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input the user's geographical location information into a generation AI and have the generation AI select highly relevant images.

[0084] The reception unit can analyze the user's social media activity when receiving images and accept relevant images. For example, the reception unit may prioritize images related to images the user has shared on social media. For example, the reception unit may prioritize images related to images the user has shown interest in on social media. For example, the reception unit may prioritize images related to accounts the user follows on social media. In this way, by analyzing social media activity, it is possible to prioritize the acceptance of relevant images. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit may input data on the user's social media activity into a generative AI and have the generative AI select relevant images.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is stressed, the analysis unit provides visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the bonsai during the analysis. For example, the analysis unit performs a detailed analysis for bonsai of high importance. For example, the analysis unit performs a simplified analysis for bonsai of low importance. For example, the analysis unit performs an analysis with an appropriate level of detail for bonsai of medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the bonsai, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input bonsai importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the bonsai category during analysis. For example, the analysis unit selects the optimal analysis algorithm depending on the type of bonsai. For example, the analysis unit applies an appropriate analysis algorithm depending on the growth stage of the bonsai. For example, the analysis unit uses different analysis algorithms depending on the health status of the bonsai. By applying different analysis algorithms depending on the bonsai category, the optimal analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input bonsai category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is in a hurry, the analysis unit provides a concise analysis. For example, if the user is stressed, the analysis unit provides a visually easy-to-understand analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit can determine the priority of analysis based on when the bonsai were photographed. For example, if the bonsai were photographed recently, the analysis unit will prioritize the analysis. If the bonsai were photographed a long time ago, the analysis unit will postpone the analysis. If the bonsai were photographed during a specific season, the analysis unit will perform an analysis appropriate to that season. By determining the priority of analysis based on when the bonsai were photographed, the analysis results can be provided at an appropriate time. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the bonsai photography date data into a generation AI and have the generation AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relationships between bonsai trees during the analysis process. For example, the analysis unit prioritizes analysis of bonsai trees with high relationships. For example, the analysis unit postpones analysis of bonsai trees with low relationships. For example, the analysis unit performs analysis in an appropriate order when the relationships between bonsai trees are moderate. In this way, by adjusting the order of analysis based on the relationships between bonsai trees, highly related bonsai trees can be analyzed preferentially. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input bonsai relationship data into a generation AI and have the generation AI perform the adjustment of the analysis order.

[0091] The pricing unit can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, if the user is relaxed, the pricing unit will provide a detailed price quote. If the user is in a hurry, the pricing unit will provide a concise price quote. If the user is stressed, the pricing unit will provide a visually easy-to-understand price quote. By adjusting the pricing method according to the user's emotions, it becomes possible to provide pricing that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit may be performed using or without a generative AI. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The price quotation unit can improve the accuracy of its price quotations by referring to past transaction history when quoting prices. For example, the price quotation unit quotes an appropriate price based on past transaction history. For example, the price quotation unit analyzes past transaction history and quotes a price considering price fluctuations. For example, the price quotation unit refers to past transaction history and quotes a price in comparison to the current market price. This improves the accuracy of price quotations by referring to past transaction history. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit can input past transaction history data into a generation AI and have the generation AI perform the task of improving the accuracy of price quotations.

[0093] The price quotation unit can customize its pricing method based on current market trends when quoting prices. For example, the price quotation unit may quote an appropriate price based on current market trends. For example, the price quotation unit may analyze current market trends and quote a price considering price fluctuations. For example, the price quotation unit may refer to current market trends and quote a price in comparison to past transaction history. This makes it possible to quote an appropriate price by customizing the pricing method based on current market trends. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit may input current market trend data into a generation AI and have the generation AI perform the customization of the pricing method.

[0094] The pricing unit can estimate the user's emotions and prioritize pricing based on those emotions. For example, if the user is relaxed, the pricing unit will prioritize high-priority pricing. If the user is in a hurry, for example, the pricing unit will postpone low-priority pricing. If the user is stressed, for example, the pricing unit will temporarily hold high-priority pricing. This allows for prioritizing important pricing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit may be performed using or without a generative AI. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The pricing unit can select the optimal pricing method when providing pricing information, taking into account the user's geographical location. For example, the pricing unit may provide pricing information related to the user's current location, places the user has visited in the past, or places the user plans to visit in the future. This allows the system to select the optimal pricing method by considering the user's geographical location. Some or all of the above processing in the pricing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the pricing unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal pricing method.

[0096] The pricing unit can analyze the user's social media activity and propose pricing methods when providing pricing information. For example, the pricing unit may provide pricing information based on information shared by the user on social media. For example, the pricing unit may provide pricing information based on information the user has shown interest in on social media. For example, the pricing unit may provide pricing information related to accounts the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to propose appropriate pricing methods. Some or all of the above processing in the pricing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the pricing unit may input the user's social media activity data into a generative AI and have the generative AI propose pricing methods.

[0097] The advice unit can estimate the user's emotions and adjust the way it presents advice based on the estimated emotions. For example, if the user is relaxed, the advice unit will provide detailed advice. If the user is in a hurry, the advice unit will provide concise advice that gets straight to the point. If the user is stressed, the advice unit will provide visually easy-to-understand advice. By adjusting the way it presents advice according to the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using or without a generative AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The advice unit can adjust the level of detail in its advice based on the bonsai's evaluation points. For example, the advice unit provides detailed advice for high-importance evaluation points. For example, it provides concise advice for low-importance evaluation points. For example, it provides advice with an appropriate level of detail for medium-importance evaluation points. In this way, by adjusting the level of detail in the advice based on the bonsai's evaluation points, it can provide appropriate advice. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input bonsai evaluation point data into a generation AI and have the generation AI perform the adjustment of the level of detail in the advice.

[0099] The advice unit can apply different advice algorithms depending on the bonsai category when providing advice. For example, the advice unit can select the optimal advice algorithm depending on the type of bonsai. For example, the advice unit can apply an appropriate advice algorithm depending on the growth stage of the bonsai. For example, the advice unit can use different advice algorithms depending on the health status of the bonsai. In this way, by applying different advice algorithms depending on the bonsai category, the optimal advice can be provided. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input bonsai category data into a generative AI and have the generative AI execute the application of the advice algorithm.

[0100] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the advice unit will provide detailed advice. For example, if the user is in a hurry, the advice unit will provide concise advice. For example, if the user is stressed, the advice unit will provide visually easy-to-understand advice. By adjusting the length of the advice according to the user's emotions, the advice unit can provide appropriate advice to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using or without a generative AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The advice unit can determine the priority of advice based on when the bonsai was photographed. For example, if the bonsai was photographed recently, the advice unit will prioritize giving advice. If the bonsai was photographed a long time ago, the advice unit will postpone giving advice. If the bonsai was photographed during a specific season, the advice unit will provide advice appropriate to that season. By determining the priority of advice based on when the bonsai was photographed, advice can be provided at the appropriate time. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input bonsai photographing date data into a generative AI and have the generative AI determine the priority of advice.

[0102] The advice unit can adjust the order of advice based on the relevance of the bonsai trees. For example, the advice unit will prioritize advice if the bonsai trees are highly relevant. For example, it will postpone advice if the bonsai trees are less relevant. For example, if the bonsai trees are moderately relevant, it will provide advice in an appropriate order. In this way, by adjusting the order of advice based on the relevance of the bonsai trees, advice can be given preferentially to highly relevant bonsai trees. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input bonsai relevance data into a generative AI and have the generative AI perform the adjustment of the order of advice.

[0103] The calendar creation unit can estimate the user's emotions and adjust the calendar creation method based on the estimated emotions. For example, if the user is relaxed, the calendar creation unit provides a detailed calendar. For example, if the user is in a hurry, the calendar creation unit provides a concise calendar. For example, if the user is stressed, the calendar creation unit provides a visually easy-to-understand calendar. In this way, by adjusting the calendar creation method according to the user's emotions, a calendar that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calendar creation unit may be performed using generative AI or not using generative AI. For example, the calendar creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The calendar creation unit can improve the accuracy of the calendar by referring to past training data when creating it. For example, the calendar creation unit creates an appropriate calendar based on past training data. For example, the calendar creation unit analyzes past training data and creates a calendar considering fluctuations in training. For example, the calendar creation unit refers to past training data and creates a calendar by comparing it with the current training status. In this way, the accuracy of the calendar is improved by referring to past training data. Some or all of the above processes in the calendar creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the calendar creation unit can input past training data into a generation AI and have the generation AI perform the calendar accuracy improvement.

[0105] The calendar creation unit can customize the calendar method based on the local climate and season when creating a calendar. For example, the calendar creation unit can create an appropriate calendar based on the local climate. For example, the calendar creation unit can analyze the local season and create a calendar considering variations in growth. For example, the calendar creation unit can refer to the local climate and season and create a calendar by comparing it with past growth data. In this way, by customizing the calendar method based on the local climate and season, the optimal calendar can be provided. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input local climate and seasonal data into a generation AI and have the generation AI perform the customization of the calendar method.

[0106] The calendar creation unit can estimate the user's emotions and determine calendar priorities based on those emotions. For example, if the user is relaxed, the calendar creation unit will prioritize creating high-priority calendars. If the user is in a hurry, the calendar creation unit will postpone less important calendars. If the user is stressed, the calendar creation unit will temporarily hold off on creating high-priority calendars. This allows for the prioritization of important calendars by determining calendar priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the calendar creation unit may be performed using or without a generative AI. For example, the calendar creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The calendar creation unit can select the optimal calendar creation method by considering the user's geographical location information when creating a calendar. For example, the calendar creation unit can create a calendar related to the user's current location. For example, the calendar creation unit can create a calendar related to places the user has visited in the past. For example, the calendar creation unit can create a calendar related to places the user plans to visit in the future. In this way, the optimal calendar creation method can be selected by considering the user's geographical location information. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the selection of the optimal calendar creation method.

[0108] The calendar creation unit can analyze the user's social media activity and suggest methods for creating a calendar when creating one. For example, the calendar creation unit can create a calendar based on information shared by the user on social media. For example, the calendar creation unit can create a calendar based on information the user has shown interest in on social media. For example, the calendar creation unit can create a calendar related to accounts the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to suggest appropriate methods for creating a calendar. Some or all of the above processing in the calendar creation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the calendar creation unit can input the user's social media activity data into a generative AI and have the generative AI perform the task of suggesting methods for creating a calendar.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The reception unit can estimate the user's emotions and adjust the timing of image submission based on the estimated emotions. For example, if the user is relaxed, image submission can be processed immediately. If the user is busy, image submission can be postponed. If the user is stressed, image submission can be temporarily suspended. This improves user convenience by adjusting the timing of image submission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The analysis unit can calculate evaluation points based on the arrangement of bonsai branches, the color of the leaves, the thickness of the trunk, and so on. For example, the arrangement of bonsai branches can be calculated as an evaluation point. The color of bonsai leaves can be calculated as an evaluation point. The thickness of bonsai trunk can be calculated as an evaluation point. This allows for accurate evaluation by calculating detailed characteristics of the bonsai as evaluation points. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data such as the arrangement of bonsai branches, the color of the leaves, and the thickness of the trunk into a generation AI and have the generation AI perform the calculation of evaluation points.

[0112] The price quotation unit can calculate market prices based on past transaction history and current market trends. For example, it can calculate market prices based on past transaction history. It can calculate market prices based on current market trends. It can calculate market prices by combining past transaction history and current market trends. This makes it possible to quote appropriate prices by calculating market prices based on past transaction history and current market trends. Some or all of the above processing in the price quotation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the price quotation unit can input data on past transaction history and current market trends into a generation AI and have the generation AI calculate the market price.

[0113] The advice unit can provide advice on bonsai branch pruning methods, fertilization timing, and shape adjustment methods. For example, it can advise on bonsai branch pruning methods, fertilization timing, and shape adjustment methods. By providing specific advice, it is possible to improve the value of the bonsai. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input data on bonsai branch pruning methods, fertilization timing, and shape adjustment methods into a generation AI, and have the generation AI provide the advice.

[0114] The calendar creation unit can suggest the timing of specific tasks, such as pruning in spring and fertilizing in summer. For example, it can suggest the timing of pruning in spring, the timing of fertilizing in summer, and the timing of pest and disease control in autumn. By suggesting the timing of specific tasks, it becomes possible to cultivate healthy bonsai. Some or all of the above processing in the calendar creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calendar creation unit can input the timing of spring pruning, summer fertilization, and autumn pest and disease control into a generation AI, and have the generation AI create a cultivation calendar.

[0115] The reception unit can estimate the user's emotions and determine the priority of images to accept based on the estimated emotions. For example, if the user is relaxed, high-importance images can be prioritized. If the user is busy, less important images can be postponed. If the user is stressed, high-importance images can be temporarily held back. This allows for prioritizing important images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. If the user is stressed, it can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The pricing unit can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, if the user is relaxed, a detailed pricing offer can be provided. If the user is in a hurry, a concise pricing offer can be provided. If the user is stressed, a visually easy-to-understand pricing offer can be provided. By adjusting the pricing method according to the user's emotions, it becomes possible to provide pricing that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit may be performed using generative AI or not. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is relaxed, it can provide detailed advice. If the user is in a hurry, it can provide concise advice that gets straight to the point. If the user is stressed, it can provide visually easy-to-understand advice. By adjusting the way advice is presented according to the user's emotions, it can provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using or without a generative AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The calendar creation unit can estimate the user's emotions and adjust the calendar creation method based on the estimated emotions. For example, if the user is relaxed, a detailed calendar can be provided. If the user is in a hurry, a concise calendar can be provided. If the user is stressed, a visually easy-to-understand calendar can be provided. In this way, by adjusting the calendar creation method according to the user's emotions, a calendar that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the calendar creation unit may be performed using a generative AI or not using a generative AI. For example, the calendar creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk accepts images of bonsai trees. For example, it can accept images of bonsai trees taken by users with their smartphones or digital cameras. The reception desk can automatically adjust the image resolution and shooting angle before accepting the images. Step 2: The analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai. For example, the arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk are used as evaluation points. Image analysis technology and generative AI are used to extract the characteristic features of the bonsai and calculate the evaluation points. Step 3: The price presentation unit presents the market price based on the evaluation results obtained by the analysis unit. For example, it calculates the market price based on past transaction history and current market trends. By using a generation AI to calculate the market price and collecting market data in real time, it is possible to present an appropriate market price. Step 4: The advice unit provides advice based on the evaluation results obtained by the analysis unit. For example, it provides advice on how to prune bonsai branches, the timing of fertilization, and how to adjust the shape. By using generation AI, it can provide specific advice and customized advice tailored to the user's cultivation status. Step 5: The calendar creation unit creates a cultivation calendar based on the advice provided by the advice unit. For example, it creates a cultivation calendar that is appropriate for the local climate and season. Using generation AI, it can create a cultivation calendar and suggest the timing of specific tasks such as pruning and fertilizing.

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, price presentation unit, advice unit, and calendar creation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives images of bonsai trees taken by the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the received images to evaluate the shape, balance, and health of the bonsai trees. The price presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and presents market prices based on the evaluation results. The advice unit is implemented, for example, by the control unit 46A of the smart device 14 and provides specific advice based on the evaluation results. The calendar creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a cultivation calendar that is appropriate to the local climate and season. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, price presentation unit, advice unit, and calendar creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives an image of a bonsai tree taken by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received image to evaluate the shape, balance, and health of the bonsai tree. The price presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents a market price based on the evaluation results. The advice unit is implemented by the control unit 46A of the smart glasses 214 and provides specific advice based on the evaluation results. The calendar creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a cultivation calendar according to the local climate and season. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, price presentation unit, advice unit, and calendar creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives images of bonsai trees taken by the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received images to evaluate the shape, balance, and health of the bonsai trees. The price presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents market prices based on the evaluation results. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides specific advice based on the evaluation results. The calendar creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a cultivation calendar that is appropriate to the local climate and season. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, analysis unit, price presentation unit, advice unit, and calendar creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives images of bonsai trees taken by the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received images to evaluate the shape, balance, and health of the bonsai trees. The price presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents market prices based on the evaluation results. The advice unit is implemented by, for example, the control unit 46A of the robot 414 and provides specific advice based on the evaluation results. The calendar creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a cultivation calendar that is appropriate to the local climate and season. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A reception desk that accepts images of bonsai trees, An analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai, A price presentation unit presents a market price based on the evaluation results obtained by the analysis unit, An advice unit provides advice based on the evaluation results obtained by the analysis unit, The system includes a calendar creation unit that creates a training calendar based on the advice provided by the aforementioned advice unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept images that show the overall appearance of the bonsai. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk are used as evaluation points to calculate the score. The system described in Appendix 1, characterized by the features described herein. (Note 4) The price display unit is, The market price is calculated based on past transaction history and current market trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, We provide advice on bonsai branch pruning methods, fertilization timing, and shaping techniques. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned calendar creation unit, We suggest specific timings for tasks such as pruning in the spring and fertilizing in the summer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of image requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving an image, the system analyzes the user's past image submission history and selects the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When images are submitted, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of images to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving images, the system prioritizes accepting images that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving images, the system analyzes the user's social media activity and accepts relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of each bonsai. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the bonsai category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the bonsai were photographed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the bonsai trees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The price display unit is, It estimates user sentiment and adjusts pricing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The price display unit is, When providing price quotes, we refer to past transaction history to improve the accuracy of the quotes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The price display unit is, When providing pricing information, customize the pricing method based on current market trends. The system described in Appendix 1, characterized by the features described herein. (Note 22) The price display unit is, The system estimates user sentiment and prioritizes price offers based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The price display unit is, When presenting prices, the system selects the optimal pricing method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The price display unit is, When presenting prices, we analyze users' social media activity and suggest methods for presenting prices. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When giving advice, adjust the level of detail based on the evaluation points of the bonsai. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the bonsai category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When giving advice, prioritize the advice based on when the bonsai was photographed. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When giving advice, adjust the order of advice based on the relevance of the bonsai. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned calendar creation unit, It estimates the user's emotions and adjusts how the calendar is created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned calendar creation unit, When creating a calendar, we refer to past training data to improve the accuracy of the calendar. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned calendar creation unit, When creating a calendar, customize the calendar elements based on the local climate and season. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned calendar creation unit, It estimates the user's emotions and determines calendar priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned calendar creation unit, When creating a calendar, the system selects the optimal calendar creation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned calendar creation unit, When creating a calendar, we analyze the user's social media activity and suggest methods for creating the calendar. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts images of bonsai trees, An analysis unit analyzes the images received by the reception unit and evaluates the shape, balance, and health of the bonsai, A price presentation unit presents a market price based on the evaluation results obtained by the analysis unit, An advice unit provides advice based on the evaluation results obtained by the analysis unit, The system includes a calendar creation unit that creates a training calendar based on the advice provided by the aforementioned advice unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept images that show the overall appearance of the bonsai. The system according to feature 1.

3. The aforementioned analysis unit, The arrangement of the bonsai branches, the color of the leaves, and the thickness of the trunk are used as evaluation points to calculate the score. The system according to feature 1.

4. The price display unit is, The market price is calculated based on past transaction history and current market trends. The system according to feature 1.

5. The aforementioned advice section, We provide advice on bonsai branch pruning methods, fertilization timing, and shaping techniques. The system according to feature 1.

6. The aforementioned calendar creation unit, We suggest specific timings for tasks such as pruning in the spring and fertilizing in the summer. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of image requests based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving an image, the system analyzes the user's past image submission history and selects the most suitable submission method. The system according to feature 1.

9. The aforementioned reception unit is When images are submitted, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of images to accept based on the estimated user emotions. The system according to feature 1.

Images