system

Abstract

To provide a system according to an embodiment that grasps a mental state of a user in real time, and properly intervenes.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a generation unit; a providing unit; a recording unit; and an intervention unit. The collection unit is configured to collect information on a weather or a previous day's mental state. The analysis unit is configured to analyze the information collected by the collection unit. The generation unit is configured to generate a painting on the basis of the information analyzed by the analysis unit. The providing unit is configured to provide the painting generated by the generation unit for the user. The recording unit is configured to record a change in emotion of the user relative to the painting provided by the providing unit. The intervention unit is configured to early intervene on the basis of information recorded by the recording unit.SELECTED DRAWING: Figure 1

Description

Technical Field

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

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance responsive to the user utterance. Prior Art Literature Patent Documents

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

[0004] In conventional technology, there has been a problem that it is difficult to grasp changes in a user's mental state in real time and perform appropriate intervention.

[0005] An object of the system according to an embodiment is to grasp a user's mental state in real time and perform appropriate intervention. Means for Solving the Problem

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a recording unit, and an intervention unit. The collection unit collects information on the weather or the mental state of the previous day. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a painting based on the information analyzed by the analysis unit. The provision unit provides the painting generated by the generation unit to the user. The recording unit records the user's emotional changes in response to the painting provided by the provision unit. The intervention unit provides early intervention based on the information recorded by the recording unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the user's mental state in real time and provide appropriate intervention. [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 embodiment, a signed communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like.

[0015] In the following embodiment, "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, may be only B, or may be a combination of A and B. In addition, in the present specification, when three or more matters are expressed by connecting with "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 apparatus 12 and a smart device 14. A server is an example of the data processing apparatus 12.

[0018] The data processing apparatus 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include WAN (Wide Area Network) and / or LAN (Local Area Network), and the like.

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving 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 mental state stabilization system according to an embodiment of the present invention is a system that promotes mental state stabilization and mood improvement by providing a new painting automatically generated by AI every day and collecting and recording the user's emotional changes. This system generates and provides a painting that the user will like, taking into account factors such as the weather and the previous day's mental state. If a period of no improvement in mood is observed, early intervention is performed to contribute to the prevention of depression and improvement of dementia. This system makes it easier and more convenient to maintain daily mental health than conventional painting exchanges. For example, the mental state stabilization system obtains weather information from the internet, and the previous day's mental state is based on data entered by the user. Next, the AI ​​analyzes this information and generates a painting that the user will like. For example, it can generate a painting with bright colors on a sunny day and a painting with calm colors on a rainy day. The generated painting is provided to the user's device. The user records changes in emotion after viewing the provided painting. For example, after viewing the painting, the user enters comments such as "I feel better" or "No change." These emotional changes are collected and recorded by the AI. If a period of no improvement in mood is observed, the AI ​​suggests early intervention. For example, it can display messages encouraging users to consult with experts. This is expected to contribute to the prevention of depression and the improvement of dementia. This system makes it easy and convenient for users to maintain their mental health on a daily basis. In conventional painting exchange systems, users had to choose paintings themselves, but in this system, AI automatically generates and provides paintings, saving users time and effort. In addition, by collecting and recording changes in the user's emotions, more personalized support becomes possible. As a result, the mental state stabilization system can stabilize the user's mental state and promote mood improvement.

[0029] The mental state stabilization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a recording unit, and an intervention unit. The collection unit collects information on the weather and the mental state of the previous day. The collection unit can, for example, collect weather information from the internet. The collection unit can also collect data on the mental state of the previous day entered by the user. For example, the collection unit can obtain weather information using a specific API and collect data on the mental state of the previous day from the user in the form of a questionnaire. The analysis unit analyzes the collected weather information and the data on the mental state of the previous day. The analysis unit can analyze the collected data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze the relationship between weather information and mental state using correlation analysis and construct a predictive model using regression analysis. The generation unit generates paintings that the user likes based on the analysis results. The generation unit can generate paintings based on the analysis results using, for example, a generation AI. For example, the generation unit can generate paintings according to the user's preferences using a text generation AI (e.g., LLM). The generation unit can also generate paintings according to the user's preferences using a multimodal generation AI. For example, the generation unit generates paintings with bright colors on sunny days and paintings with calm colors on rainy days. The delivery unit provides the generated paintings to the user's device. The delivery unit can provide the generated paintings to the user's device using, for example, app notifications or email. For example, the delivery unit can display the painting on the user's smartphone using app notifications. The delivery unit can also send the painting to the user's email address using email. The recording unit records the user's emotional changes after viewing the painting. The recording unit can record the user's emotional changes using, for example, a diary format or by inputting an emotional score. For example, the recording unit can input comments such as "I feel better" or "No change" after the user views the painting. The recording unit can also quantify and record the user's emotional changes using an emotional score. The intervention unit displays a message encouraging consultation with a professional if no improvement in mood is observed for an extended period. The intervention unit can provide early intervention by, for example, displaying a message encouraging consultation with a professional.For example, the intervention unit displays a message such as "Please consult a specialist" on the user's device. This allows the mental state stabilization system according to the embodiment to stabilize the user's mental state and promote mood improvement.

[0030] The data collection unit collects information on weather and the user's mental state from the previous day. For example, the data collection unit can collect weather information from the internet. Specifically, the data collection unit uses the API of a weather data provision service to obtain current weather and forecasts. This allows the data collection unit to understand weather information in the user's place of residence or activity area in real time. The data collection unit can also collect data on the user's mental state from the previous day. For example, the data collection unit provides users with a daily questionnaire to input their mood, stress level, sleep quality, etc. from the previous day. The questionnaire is designed to be easily completed through a smartphone app or web interface, minimizing the burden on the user. Furthermore, the data collection unit uses encryption technology to securely store user input data and protect privacy. This allows the data collection unit to efficiently collect weather information and user mental state data and provide it to the analysis unit.

[0031] The analysis unit analyzes collected weather information and data on the previous day's mental state. The analysis unit can analyze the collected data using, for example, statistical analysis and machine learning algorithms. Specifically, it uses correlation analysis to analyze the relationship between weather information and mental state, and regression analysis to build predictive models. For example, it identifies patterns such as whether users tend to feel better on sunny days or whether stress increases on rainy days. Furthermore, the analysis unit uses machine learning algorithms to learn individual user tendencies and make more accurate predictions. For example, it builds a model that predicts under what weather conditions a particular user's mood will fluctuate, based on past data. This allows the analysis unit to identify factors influencing users' mental states and provide countermeasures tailored to individual needs. Additionally, the analysis unit improves the reliability of the analysis results by detecting outliers and missing values ​​in the data and performing appropriate imputation. This enables the analysis unit to quickly and accurately analyze collected data and provide a foundation for understanding users' mental states.

[0032] The generation unit generates paintings that the user will like based on the analysis results. The generation unit can generate paintings based on the analysis results, for example, using a generation AI. Specifically, the generation unit uses a text generation AI (e.g., LLM) to generate paintings that match the user's preferences. For example, if the analysis results show that the user prefers paintings with bright colors, the generation unit will generate paintings with bright colors. The generation unit can also use a multimodal generation AI to generate paintings that match the user's preferences. For example, the generation unit will generate paintings with bright colors on sunny days and paintings with calm colors on rainy days. Furthermore, the generation unit can learn the user's past preference data and generate more personalized paintings. For example, it can generate new paintings based on the style and theme of paintings the user has liked in the past. This allows the generation unit to provide the optimal painting to stabilize the user's mental state. In addition, the generation unit can always provide high-quality paintings by evaluating the quality of the generated paintings and making corrections or regenerations as needed. This allows the generation unit to provide an effective means of stabilizing the user's mental state and promoting improved mood.

[0033] The service provider delivers the generated paintings to the user's device. The service provider can deliver the generated paintings to the user's device using methods such as app notifications or email. Specifically, the service provider can display the painting on the user's smartphone using app notifications. When the user opens the app, the generated painting will be displayed, and the user can view it. The service provider can also send the painting to the user's email address using email. This allows the user to view the painting on various devices, such as smartphones and computers. Furthermore, the service provider can provide a function to save the painting to the user's device, allowing the user to view it again later. For example, the service provider can save the painting to the user's smartphone gallery, making it accessible at any time. This allows the service provider to deliver the generated paintings to the user quickly and effectively, supporting the user's mental well-being. Additionally, the service provider can collect user feedback to improve its delivery methods. For example, it can collect feedback in the form of surveys on how users felt about the paintings and which delivery method was most effective, and use this information to improve future delivery methods. This allows the service provider to implement the optimal delivery method tailored to user needs and maximize the overall effectiveness of the system.

[0034] The recording unit records the changes in a user's emotions after viewing a painting. The recording unit can record these changes using methods such as a diary format or emotion score input. Specifically, the recording unit provides an interface for users to input their feelings after viewing a painting, such as "I feel better" or "No change." Users can easily input their feelings through a smartphone app or web interface. The recording unit can also quantify and record the user's emotional changes using emotion scores. For example, it can record the user's mood after viewing a painting on a scale of 1 to 10. Furthermore, the recording unit can store the user's emotional data chronologically and analyze long-term changes. This allows the recording unit to gain a detailed understanding of changes in the user's mental state and provide feedback to the analysis and generation units. Additionally, the recording unit implements security measures to protect user privacy and ensures the safe storage and management of data. This allows the recording unit to accurately record changes in the user's emotions and provide crucial data for evaluating the overall system's effectiveness.

[0035] The intervention unit displays a message encouraging users to consult a professional if their mood does not improve for an extended period. For example, by displaying a message prompting the user to consult a professional, the intervention unit can provide early intervention. Specifically, the intervention unit analyzes the user's emotional data and issues an alert if there is no improvement in mood within a certain period. For example, if a user records a low emotional score for more than a week, the intervention unit displays a message on the user's device such as "Please consult a professional." The intervention unit can also provide links and contact information to facilitate professional consultation. For example, it can include professional contact information and online consultation links in the message, allowing users to seek help immediately. Furthermore, the intervention unit can build a system to provide necessary information to professionals while protecting user privacy. This allows the intervention unit to ensure users receive professional support at the appropriate time and provide support to prevent a deterioration of their mental state. Additionally, the intervention unit can continuously improve the timing and methods of intervention based on user feedback. This allows the intervention unit to implement effective interventions to stabilize the user's mental state and improve the overall reliability and effectiveness of the system.

[0036] The data collection unit can collect weather information from the internet. For example, the data collection unit can collect weather information from the internet using a specific API. For example, the data collection unit can obtain current weather information using an API of a weather information service. The data collection unit can also collect weather information using web scraping technology. For example, the data collection unit can automatically obtain weather information from a weather information website. By automatically collecting weather information, it becomes possible to generate paintings based on the user's mental state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the collection of weather information.

[0037] The data collection unit can collect data on the user's mental state from the previous day. For example, the data collection unit can collect data on the user's mental state from the previous day in the form of a questionnaire. For example, the data collection unit can ask the user a question such as, "How did you feel yesterday?" and collect data by having the user choose an answer from a set of options. The data collection unit can also collect data on the user's mental state from the previous day in the form of a scale. For example, the data collection unit can ask the user a question such as, "Please rate how you felt yesterday on a scale from 1 to 10," and collect data by having the user input a numerical value. This makes it possible to generate paintings based on the user's mental state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's input data.

[0038] The analysis unit can analyze collected weather information and data on the previous day's mental state. For example, the analysis unit can analyze the collected weather information and the previous day's mental state data using correlation analysis. For example, the analysis unit can evaluate the relationship between weather information and mental state using a correlation coefficient. The analysis unit can also analyze the collected data using regression analysis. For example, the analysis unit can construct a predictive model of weather information and mental state to predict future mental state. This makes it possible to generate paintings based on the user's mental state. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the collected data.

[0039] The generation unit can generate paintings that the user will like based on the analysis results. For example, the generation unit can use a generation AI to generate paintings that the user will like based on the analysis results. For example, the generation unit can use a text generation AI (e.g., LLM) to generate paintings that suit the user's preferences. The generation unit can also use a multimodal generation AI to generate paintings that suit the user's preferences. For example, the generation unit can generate paintings with bright colors on sunny days and paintings with calm colors on rainy days. This promotes mood improvement by generating paintings that suit the user's preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have AI perform the painting generation.

[0040] The service provider can provide the generated painting to the user's device. For example, the service provider can provide the generated painting to the user's device using an app notification. For example, the service provider can send an app notification to the user's smartphone and display the painting. The service provider can also provide the generated painting to the user's device using email. For example, the service provider can send the painting to the user's email address, allowing the user to view the painting by opening the email. This makes it easy for the user to receive the painting. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI select the method of providing the painting.

[0041] The recording unit can record changes in a user's emotions after viewing a painting. For example, the recording unit can record changes in a user's emotions after viewing a painting in a diary format. For example, the recording unit can ask the user a question such as, "How did you feel after viewing the painting?" and record changes in emotions by allowing the user to freely write down their impressions. Alternatively, the recording unit can record changes in emotions after viewing a painting using the input of an emotion score. For example, the recording unit can ask the user a question such as, "Please rate how you felt after viewing the painting on a scale from 1 to 10," and record changes in emotions by allowing the user to input a numerical value. This allows for personalized support by recording changes in the user's emotions. Some or all of the above processing in the recording unit may be performed using AI, for example, or not. For example, the recording unit can have AI analyze the user's changes in emotions.

[0042] The intervention unit can display a message encouraging the user to consult a professional if there is a prolonged period without improvement in mood. For example, the intervention unit can display a message such as "Please consult a professional" on the user's device. The intervention unit can also monitor the user's emotional changes and automatically display a message if there is a prolonged period without improvement in mood. For example, the intervention unit can display a message encouraging the user to consult a professional if the user's emotional score remains low for a certain period. This is expected to contribute to the prevention of depression and the improvement of dementia through early intervention. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's emotional data and display a message at the appropriate time.

[0043] The data collection unit can analyze the user's past mental state data and select the optimal data collection method. For example, the data collection unit can analyze the user's past mental state data and select a data collection method appropriate for that period. For example, the data collection unit can analyze data from periods when the user experienced stress in the past and select a data collection method appropriate for that period. The data collection unit can also analyze data from periods when the user was relaxed in the past and select a data collection method appropriate for that period. For example, the data collection unit can determine the optimal data collection timing based on the user's past mental state data. By selecting the optimal data collection method based on the user's past mental state, it becomes possible to generate more appropriate images. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's past mental state data and select the optimal data collection method.

[0044] The data collection unit can filter weather information based on the user's current activity status and lifestyle. For example, if the user is out, the data collection unit will prioritize collecting weather information for the user's destination. For example, if the user is at home, the data collection unit will prioritize collecting weather information for the area around the user's home. The data collection unit can also collect weather information at the necessary times in accordance with the user's lifestyle. For example, the data collection unit will adjust the collection of weather information based on the user's activity status and lifestyle. This makes it possible to generate more appropriate images by collecting weather information that matches the user's activity status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's activity status and lifestyle and adjust the collection of weather information accordingly.

[0045] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when collecting weather information. For example, if the user is traveling, the data collection unit will prioritize collecting weather information for their travel destination. For example, if the user is commuting, the data collection unit will prioritize collecting weather information for their commute route. Furthermore, if the user is at home, the data collection unit can prioritize collecting weather information for the area around their home. This allows for the generation of more appropriate images by collecting weather information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's geographical location and prioritize collecting highly relevant weather information.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information when collecting weather information. For example, if the user has posted about the weather on social media, the data collection unit will collect weather information based on that information. For example, if the user has posted on social media that they plan to attend a specific event, the data collection unit will collect weather information for that event. The data collection unit can also analyze the user's social media activity and collect relevant weather information. This allows for the generation of more appropriate images by collecting weather information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's social media activity and collect relevant weather information.

[0047] The analysis unit can adjust the level of detail of its analysis based on the importance of weather information and mental state data during the analysis. For example, if weather information is important, the analysis unit performs a detailed weather analysis and provides it to the user. For example, if mental state data is important, the analysis unit performs a detailed mental state analysis and provides it to the user. Furthermore, if both weather information and mental state data are important, the analysis unit can perform a balanced analysis and provide it to the user. This makes it possible to generate more appropriate images by performing analysis based on important information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the importance of weather information and mental state data and adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to weather information to provide a detailed weather forecast. For example, the analysis unit can apply a psychological analysis algorithm to mental state data to analyze the user's mental state in detail. The analysis unit can also apply appropriate analysis algorithms to other data to provide the user with the most suitable information. This makes it possible to generate more appropriate images by performing analysis according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI apply analysis algorithms according to the data category.

[0049] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize analyzing the latest weather information and provide it to the user. For example, the analysis unit may prioritize analyzing the latest mental state data and provide it to the user. The analysis unit can also prioritize analyzing the most important information based on the data collection timing. This enables the generation of more appropriate images by performing analysis based on the latest information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may have AI analyze the data collection timing and determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit considers the relevance of weather information and mental state data and prioritizes the analysis of the most relevant data. For example, the analysis unit prioritizes the analysis of the most important information based on the relevance of the data. The analysis unit can also analyze the relevance of the data and determine the optimal analysis order. This makes it possible to generate more appropriate images by performing analysis based on highly relevant data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the relevance of the data and adjust the order of analysis.

[0051] The generation unit can adjust the level of detail of the generated image based on the importance of the analysis results. For example, if the analysis results are important, the generation unit will generate a detailed image. For example, if the analysis results are not very important, the generation unit will generate a simple image. The generation unit can also generate an image with the optimal level of detail based on the importance of the analysis results. This improves user satisfaction by generating detailed images according to the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the importance of the analysis results and adjust the level of detail of the generated image.

[0052] The generation unit can apply different generation algorithms to paintings according to the user's past preferences. For example, the generation unit can apply an algorithm to generate paintings with a color scheme the user has liked in the past. For example, the generation unit can apply an algorithm to generate paintings with a theme the user has liked in the past. The generation unit can also apply the optimal generation algorithm based on the user's past preferences. This improves user satisfaction by generating paintings based on the user's past preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the user's past preferences and apply the optimal generation algorithm.

[0053] The generation unit can determine the generation priority based on the timing of analysis result collection when generating paintings. For example, the generation unit generates the most relevant painting based on the latest analysis results. For example, the generation unit prioritizes generating the most important painting based on the timing of analysis result collection. The generation unit can also determine the optimal generation order by considering the timing of analysis result collection. This improves user satisfaction by generating paintings based on the latest analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the timing of analysis result collection and determine the generation priority.

[0054] The generation unit can adjust the generation order based on the relevance of the analysis results when generating paintings. For example, the generation unit can prioritize generating the most relevant paintings by considering the relevance of the analysis results. For example, the generation unit can prioritize generating the most important paintings based on the relevance of the analysis results. The generation unit can also analyze the relevance of the analysis results and determine the optimal generation order. This improves user satisfaction by generating paintings based on highly relevant analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the relevance of the analysis results and adjust the generation order.

[0055] The delivery unit can select the optimal delivery method when delivering paintings by referring to the user's past reactions. For example, the delivery unit may prioritize delivery methods that the user has preferred in the past. For example, the delivery unit may analyze the user's past reactions and determine the optimal delivery timing. The delivery unit may also select the optimal means of delivery based on the user's past reactions. This improves user satisfaction by selecting a delivery method based on the user's past reactions. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may have AI analyze the user's past reactions and select the optimal delivery method.

[0056] The service provider can customize the method of providing artwork based on the user's current living situation. For example, if the user is at home, the service provider can provide the artwork in a way that is best suited for viewing at home. For example, if the user is out, the service provider can provide the artwork in a way that is best suited for a mobile device. The service provider can also customize the optimal method of provision according to the user's living situation. By customizing the method of provision according to the user's living situation, the service provider can improve user satisfaction. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI analyze the user's living situation and customize the method of provision.

[0057] The service provider can select the optimal delivery method when providing paintings, taking into account the user's geographical location information. For example, if the user is traveling, the service provider can provide paintings related to the culture or scenery of the travel destination. For example, if the user is commuting, the service provider can provide paintings related to the commute route. The service provider can also select the optimal delivery method based on the user's geographical location information. By selecting a delivery method based on the user's geographical location information, user satisfaction can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI analyze the user's geographical location information and select the optimal delivery method.

[0058] The service provider can analyze the user's social media activity and propose a means of delivery when providing paintings. For example, if the user has posted about paintings on social media, the service provider will provide paintings related to those posts. For example, if the user has posted about attending a specific event on social media, the service provider will provide paintings related to that event. The service provider can also analyze the user's social media activity and propose the most suitable means of delivery. This improves user satisfaction by proposing a means of delivery based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI analyze the user's social media activity and propose the most suitable means of delivery.

[0059] The recording unit can select the optimal recording method when recording changes in emotion by referring to the user's past responses. For example, the recording unit may prioritize recording methods that the user has preferred in the past. For example, the recording unit may analyze the user's past responses and determine the optimal recording timing. The recording unit may also select the optimal recording means based on the user's past responses. By selecting a recording method based on the user's past responses, it is possible to record changes in emotion more accurately. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may have AI analyze the user's past responses and select the optimal recording method.

[0060] The recording unit can customize the recording method based on the user's current living situation when recording emotional changes. For example, if the user is at home, the recording unit will record emotional changes in a way that is best suited for recording at home. For example, if the user is out, the recording unit will record emotional changes in a way that is best suited for a mobile device. The recording unit can also customize the optimal recording method according to the user's living situation. By customizing the recording method according to the user's living situation, it is possible to record emotional changes more accurately. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's living situation and customize the recording method.

[0061] The recording unit can select the optimal recording method when recording changes in emotions, taking into account the user's geographical location information. For example, if the user is traveling, the recording unit can record emotions related to the culture and scenery of the travel destination. For example, if the user is commuting, the recording unit can record emotions related to the commute route. The recording unit can also select the optimal recording method based on the user's geographical location information. By selecting a recording method based on the user's geographical location information, it is possible to record changes in emotions more accurately. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's geographical location information and select the optimal recording method.

[0062] The recording unit can analyze the user's social media activity and suggest recording methods when recording changes in emotions. For example, if the user makes emotional posts on social media, the recording unit will record the emotions associated with those posts. For example, if the user posts on social media about attending a specific event, the recording unit will record the emotions associated with that event. The recording unit can also analyze the user's social media activity and suggest the most suitable recording method. This allows for more accurate recording of emotional changes by suggesting recording methods based on the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's social media activity and suggest the most suitable recording method.

[0063] The intervention unit can select the optimal intervention method by referring to the user's past responses during an intervention. For example, the intervention unit may prioritize intervention methods that the user has preferred in the past. For example, the intervention unit may analyze the user's past responses and determine the optimal timing for intervention. The intervention unit may also select the optimal intervention method based on the user's past responses. This allows for more effective intervention by selecting an intervention method based on the user's past responses. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit may have AI analyze the user's past responses and select the optimal intervention method.

[0064] The intervention unit can customize the intervention methods based on the user's current living situation at the time of intervention. For example, if the user is at home, the intervention unit will select the most suitable method for intervention at home. For example, if the user is out, the intervention unit will perform the intervention in a way that is best suited to the mobile device. The intervention unit can also customize the optimal intervention methods according to the user's living situation. By customizing the intervention methods according to the user's living situation, more effective intervention becomes possible. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's living situation and customize the intervention methods.

[0065] The intervention unit can select the optimal intervention method by considering the user's geographical location information at the time of intervention. For example, if the user is traveling, the intervention unit will perform an intervention related to the culture and scenery of the travel destination. For example, if the user is commuting, the intervention unit will perform an intervention related to the commuting route. The intervention unit can also select the optimal intervention method based on the user's geographical location information. This allows for more effective intervention by selecting an intervention method based on the user's geographical location information. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's geographical location information and select the optimal intervention method.

[0066] The intervention unit can analyze the user's social media activity and propose intervention methods at the time of intervention. For example, if the user has made emotional posts on social media, the intervention unit will take action related to those posts. For example, if the user has posted on social media that they plan to attend a specific event, the intervention unit will take action related to that event. The intervention unit can also analyze the user's social media activity and propose the most suitable intervention method. This allows for more effective intervention by proposing intervention methods based on the user's social media activity. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's social media activity and propose the most suitable intervention method.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The data collection unit can also collect the user's biometric information. For example, it can collect biometric information such as the user's heart rate, blood pressure, and body temperature. This makes it possible to generate paintings that take the user's physical condition into consideration. For instance, if the user's heart rate is high, a painting with a relaxing effect can be generated, and if the heart rate is stable, a painting tailored to the user's preferences can be generated. This enables more personalized support.

[0069] The analysis unit can analyze a user's past behavioral history. For example, it can analyze what kind of paintings a user has liked in the past and at what times of day they viewed them. This makes it possible to generate paintings based on the user's behavioral patterns. For example, if a user often views paintings at night to relax, the system can generate paintings that have a relaxing effect at night. This makes it possible to provide support tailored to the user's behavioral patterns.

[0070] The generation unit can generate paintings based on the user's hobbies and interests. For example, the generation unit can generate paintings considering the user's favorite animals, landscapes, colors, etc. This can help improve the user's mood by providing paintings that match their hobbies and interests. For example, if the user likes cats, the unit can generate paintings with cats as the motif. Also, if the user likes ocean scenery, the unit can generate paintings depicting ocean scenery. This can lead to a higher level of satisfaction by providing paintings that match the user's hobbies and interests.

[0071] The service provider can deliver artwork not only to the user's device but also to smart home devices. For example, the service provider can display artwork on smart displays and smart TVs. This allows users to enjoy artwork in various locations around their home and experience a relaxing effect. For instance, artwork can be displayed on a smart TV in the living room for the whole family to enjoy. Alternatively, artwork can be displayed on a smart display in the bedroom for relaxation before going to sleep. This allows users to experience a relaxing effect throughout their entire living space.

[0072] The data collection unit can collect user meal information. For example, it can collect what the user ate and at what time of day they ate. This makes it possible to generate paintings based on the user's meal patterns. For instance, it can generate a relaxing painting after the user has breakfast and a mood-enhancing painting after dinner. This allows for support tailored to the user's meal patterns.

[0073] The analysis unit can analyze the user's sleep data. For example, it can analyze how long the user slept and the quality of their sleep. This makes it possible to generate paintings based on the user's sleep patterns. For instance, if the user gets enough sleep, it can generate an energizing painting, and if they are sleep-deprived, it can generate a relaxing painting. This allows for support tailored to the user's sleep patterns.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit collects information on weather and the user's mental state from the previous day. The data collection unit can, for example, collect weather information from the internet. It can also collect data on the user's mental state from the previous day. For example, the data collection unit can obtain weather information using a specific API and collect data on the user's mental state from the previous day in the form of a questionnaire. Step 2: The analysis unit analyzes the collected weather information and the previous day's mental state data. The analysis unit can analyze the collected data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze the relationship between weather information and mental state using correlation analysis and construct a predictive model using regression analysis. Step 3: The generation unit generates a painting that the user will like based on the analysis results. The generation unit can generate a painting based on the analysis results, for example, using a generation AI. For example, the generation unit can generate a painting that suits the user's preferences using a text generation AI (e.g., LLM). The generation unit can also generate a painting that suits the user's preferences using a multimodal generation AI. For example, the generation unit can generate a painting with bright colors on a sunny day and a painting with calm colors on a rainy day. Step 4: The provider delivers the generated painting to the user's device. The provider can deliver the generated painting to the user's device, for example, by using app notifications or email. For example, the provider can display the painting on the user's smartphone using app notifications. Alternatively, the provider can send the painting to the user's email address using email. Step 5: The recording unit records the user's emotional changes after viewing the painting. The recording unit can record the user's emotional changes using methods such as a diary format or emotional score input. For example, the recording unit can input the user's feelings after viewing the painting, such as "I feel better" or "No change." The recording unit can also quantify and record the user's emotional changes using an emotional score. Step 6: The intervention unit displays a message encouraging the user to consult a professional if mood improvement persists. The intervention unit can intervene early, for example, by displaying a message encouraging the user to consult a professional. For example, the intervention unit may display a message such as "Consult a professional" on the user's device.

[0076] (Example of form 2) The mental state stabilization system according to an embodiment of the present invention is a system that promotes mental state stabilization and mood improvement by providing a new painting automatically generated by AI every day and collecting and recording the user's emotional changes. This system generates and provides a painting that the user will like, taking into account factors such as the weather and the previous day's mental state. If a period of no improvement in mood is observed, early intervention is performed to contribute to the prevention of depression and improvement of dementia. This system makes it easier and more convenient to maintain daily mental health than conventional painting exchanges. For example, the mental state stabilization system obtains weather information from the internet, and the previous day's mental state is based on data entered by the user. Next, the AI ​​analyzes this information and generates a painting that the user will like. For example, it can generate a painting with bright colors on a sunny day and a painting with calm colors on a rainy day. The generated painting is provided to the user's device. The user records changes in emotion after viewing the provided painting. For example, after viewing the painting, the user enters comments such as "I feel better" or "No change." These emotional changes are collected and recorded by the AI. If a period of no improvement in mood is observed, the AI ​​suggests early intervention. For example, it can display messages encouraging users to consult with experts. This is expected to contribute to the prevention of depression and the improvement of dementia. This system makes it easy and convenient for users to maintain their mental health on a daily basis. In conventional painting exchange systems, users had to choose paintings themselves, but in this system, AI automatically generates and provides paintings, saving users time and effort. In addition, by collecting and recording changes in the user's emotions, more personalized support becomes possible. As a result, the mental state stabilization system can stabilize the user's mental state and promote mood improvement.

[0077] The mental state stabilization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a recording unit, and an intervention unit. The collection unit collects information on the weather and the mental state of the previous day. The collection unit can, for example, collect weather information from the internet. The collection unit can also collect data on the mental state of the previous day entered by the user. For example, the collection unit can obtain weather information using a specific API and collect data on the mental state of the previous day from the user in the form of a questionnaire. The analysis unit analyzes the collected weather information and the data on the mental state of the previous day. The analysis unit can analyze the collected data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze the relationship between weather information and mental state using correlation analysis and construct a predictive model using regression analysis. The generation unit generates paintings that the user likes based on the analysis results. The generation unit can generate paintings based on the analysis results using, for example, a generation AI. For example, the generation unit can generate paintings according to the user's preferences using a text generation AI (e.g., LLM). The generation unit can also generate paintings according to the user's preferences using a multimodal generation AI. For example, the generation unit generates paintings with bright colors on sunny days and paintings with calm colors on rainy days. The delivery unit provides the generated paintings to the user's device. The delivery unit can provide the generated paintings to the user's device using, for example, app notifications or email. For example, the delivery unit can display the painting on the user's smartphone using app notifications. The delivery unit can also send the painting to the user's email address using email. The recording unit records the user's emotional changes after viewing the painting. The recording unit can record the user's emotional changes using, for example, a diary format or by inputting an emotional score. For example, the recording unit can input comments such as "I feel better" or "No change" after the user views the painting. The recording unit can also quantify and record the user's emotional changes using an emotional score. The intervention unit displays a message encouraging consultation with a professional if no improvement in mood is observed for an extended period. The intervention unit can provide early intervention by, for example, displaying a message encouraging consultation with a professional.For example, the intervention unit displays a message such as "Please consult a specialist" on the user's device. This allows the mental state stabilization system according to the embodiment to stabilize the user's mental state and promote mood improvement.

[0078] The data collection unit collects information on weather and the user's mental state from the previous day. For example, the data collection unit can collect weather information from the internet. Specifically, the data collection unit uses the API of a weather data provision service to obtain current weather and forecasts. This allows the data collection unit to understand weather information in the user's place of residence or activity area in real time. The data collection unit can also collect data on the user's mental state from the previous day. For example, the data collection unit provides users with a daily questionnaire to input their mood, stress level, sleep quality, etc. from the previous day. The questionnaire is designed to be easily completed through a smartphone app or web interface, minimizing the burden on the user. Furthermore, the data collection unit uses encryption technology to securely store user input data and protect privacy. This allows the data collection unit to efficiently collect weather information and user mental state data and provide it to the analysis unit.

[0079] The analysis unit analyzes collected weather information and data on the previous day's mental state. The analysis unit can analyze the collected data using, for example, statistical analysis and machine learning algorithms. Specifically, it uses correlation analysis to analyze the relationship between weather information and mental state, and regression analysis to build predictive models. For example, it identifies patterns such as whether users tend to feel better on sunny days or whether stress increases on rainy days. Furthermore, the analysis unit uses machine learning algorithms to learn individual user tendencies and make more accurate predictions. For example, it builds a model that predicts under what weather conditions a particular user's mood will fluctuate, based on past data. This allows the analysis unit to identify factors influencing users' mental states and provide countermeasures tailored to individual needs. Additionally, the analysis unit improves the reliability of the analysis results by detecting outliers and missing values ​​in the data and performing appropriate imputation. This enables the analysis unit to quickly and accurately analyze collected data and provide a foundation for understanding users' mental states.

[0080] The generation unit generates paintings that the user will like based on the analysis results. The generation unit can generate paintings based on the analysis results, for example, using a generation AI. Specifically, the generation unit uses a text generation AI (e.g., LLM) to generate paintings that match the user's preferences. For example, if the analysis results show that the user prefers paintings with bright colors, the generation unit will generate paintings with bright colors. The generation unit can also use a multimodal generation AI to generate paintings that match the user's preferences. For example, the generation unit will generate paintings with bright colors on sunny days and paintings with calm colors on rainy days. Furthermore, the generation unit can learn the user's past preference data and generate more personalized paintings. For example, it can generate new paintings based on the style and theme of paintings the user has liked in the past. This allows the generation unit to provide the optimal painting to stabilize the user's mental state. In addition, the generation unit can always provide high-quality paintings by evaluating the quality of the generated paintings and making corrections or regenerations as needed. This allows the generation unit to provide an effective means of stabilizing the user's mental state and promoting improved mood.

[0081] The service provider delivers the generated paintings to the user's device. The service provider can deliver the generated paintings to the user's device using methods such as app notifications or email. Specifically, the service provider can display the painting on the user's smartphone using app notifications. When the user opens the app, the generated painting will be displayed, and the user can view it. The service provider can also send the painting to the user's email address using email. This allows the user to view the painting on various devices, such as smartphones and computers. Furthermore, the service provider can provide a function to save the painting to the user's device, allowing the user to view it again later. For example, the service provider can save the painting to the user's smartphone gallery, making it accessible at any time. This allows the service provider to deliver the generated paintings to the user quickly and effectively, supporting the user's mental well-being. Additionally, the service provider can collect user feedback to improve its delivery methods. For example, it can collect feedback in the form of surveys on how users felt about the paintings and which delivery method was most effective, and use this information to improve future delivery methods. This allows the service provider to implement the optimal delivery method tailored to user needs and maximize the overall effectiveness of the system.

[0082] The recording unit records the changes in a user's emotions after viewing a painting. The recording unit can record these changes using methods such as a diary format or emotion score input. Specifically, the recording unit provides an interface for users to input their feelings after viewing a painting, such as "I feel better" or "No change." Users can easily input their feelings through a smartphone app or web interface. The recording unit can also quantify and record the user's emotional changes using emotion scores. For example, it can record the user's mood after viewing a painting on a scale of 1 to 10. Furthermore, the recording unit can store the user's emotional data chronologically and analyze long-term changes. This allows the recording unit to gain a detailed understanding of changes in the user's mental state and provide feedback to the analysis and generation units. Additionally, the recording unit implements security measures to protect user privacy and ensures the safe storage and management of data. This allows the recording unit to accurately record changes in the user's emotions and provide crucial data for evaluating the overall system's effectiveness.

[0083] The intervention unit displays a message encouraging users to consult a professional if their mood does not improve for an extended period. For example, by displaying a message prompting the user to consult a professional, the intervention unit can provide early intervention. Specifically, the intervention unit analyzes the user's emotional data and issues an alert if there is no improvement in mood within a certain period. For example, if a user records a low emotional score for more than a week, the intervention unit displays a message on the user's device such as "Please consult a professional." The intervention unit can also provide links and contact information to facilitate professional consultation. For example, it can include professional contact information and online consultation links in the message, allowing users to seek help immediately. Furthermore, the intervention unit can build a system to provide necessary information to professionals while protecting user privacy. This allows the intervention unit to ensure users receive professional support at the appropriate time and provide support to prevent a deterioration of their mental state. Additionally, the intervention unit can continuously improve the timing and methods of intervention based on user feedback. This allows the intervention unit to implement effective interventions to stabilize the user's mental state and improve the overall reliability and effectiveness of the system.

[0084] The data collection unit can collect weather information from the internet. For example, the data collection unit can collect weather information from the internet using a specific API. For example, the data collection unit can obtain current weather information using an API of a weather information service. The data collection unit can also collect weather information using web scraping technology. For example, the data collection unit can automatically obtain weather information from a weather information website. By automatically collecting weather information, it becomes possible to generate paintings based on the user's mental state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the collection of weather information.

[0085] The data collection unit can collect data on the user's mental state from the previous day. For example, the data collection unit can collect data on the user's mental state from the previous day in the form of a questionnaire. For example, the data collection unit can ask the user a question such as, "How did you feel yesterday?" and collect data by having the user choose an answer from a set of options. The data collection unit can also collect data on the user's mental state from the previous day in the form of a scale. For example, the data collection unit can ask the user a question such as, "Please rate how you felt yesterday on a scale from 1 to 10," and collect data by having the user input a numerical value. This makes it possible to generate paintings based on the user's mental state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's input data.

[0086] The analysis unit can analyze collected weather information and data on the previous day's mental state. For example, the analysis unit can analyze the collected weather information and the previous day's mental state data using correlation analysis. For example, the analysis unit can evaluate the relationship between weather information and mental state using a correlation coefficient. The analysis unit can also analyze the collected data using regression analysis. For example, the analysis unit can construct a predictive model of weather information and mental state to predict future mental state. This makes it possible to generate paintings based on the user's mental state. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the collected data.

[0087] The generation unit can generate paintings that the user will like based on the analysis results. For example, the generation unit can use a generation AI to generate paintings that the user will like based on the analysis results. For example, the generation unit can use a text generation AI (e.g., LLM) to generate paintings that suit the user's preferences. The generation unit can also use a multimodal generation AI to generate paintings that suit the user's preferences. For example, the generation unit can generate paintings with bright colors on sunny days and paintings with calm colors on rainy days. This promotes mood improvement by generating paintings that suit the user's preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have AI perform the painting generation.

[0088] The service provider can provide the generated painting to the user's device. For example, the service provider can provide the generated painting to the user's device using an app notification. For example, the service provider can send an app notification to the user's smartphone and display the painting. The service provider can also provide the generated painting to the user's device using email. For example, the service provider can send the painting to the user's email address, allowing the user to view the painting by opening the email. This makes it easy for the user to receive the painting. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI select the method of providing the painting.

[0089] The recording unit can record changes in a user's emotions after viewing a painting. For example, the recording unit can record changes in a user's emotions after viewing a painting in a diary format. For example, the recording unit can ask the user a question such as, "How did you feel after viewing the painting?" and record changes in emotions by allowing the user to freely write down their impressions. Alternatively, the recording unit can record changes in emotions after viewing a painting using the input of an emotion score. For example, the recording unit can ask the user a question such as, "Please rate how you felt after viewing the painting on a scale from 1 to 10," and record changes in emotions by allowing the user to input a numerical value. This allows for personalized support by recording changes in the user's emotions. Some or all of the above processing in the recording unit may be performed using AI, for example, or not. For example, the recording unit can have AI analyze the user's changes in emotions.

[0090] The intervention unit can display a message encouraging the user to consult a professional if there is a prolonged period without improvement in mood. For example, the intervention unit can display a message such as "Please consult a professional" on the user's device. The intervention unit can also monitor the user's emotional changes and automatically display a message if there is a prolonged period without improvement in mood. For example, the intervention unit can display a message encouraging the user to consult a professional if the user's emotional score remains low for a certain period. This is expected to contribute to the prevention of depression and the improvement of dementia through early intervention. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's emotional data and display a message at the appropriate time.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of weather information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect weather information more frequently and provide the latest information. Conversely, if the user is relaxed, the data collection unit can reduce the frequency of weather information collection and collect it only when necessary. For example, if the user is in a hurry, the data collection unit will collect weather information quickly and provide it immediately. This allows for more appropriate picture generation by collecting weather information at a timing that matches the user's emotions. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's emotion data and adjust the timing of weather information collection.

[0092] The data collection unit can analyze the user's past mental state data and select the optimal data collection method. For example, the data collection unit can analyze the user's past mental state data and select a data collection method appropriate for that period. For example, the data collection unit can analyze data from periods when the user experienced stress in the past and select a data collection method appropriate for that period. The data collection unit can also analyze data from periods when the user was relaxed in the past and select a data collection method appropriate for that period. For example, the data collection unit can determine the optimal data collection timing based on the user's past mental state data. By selecting the optimal data collection method based on the user's past mental state, it becomes possible to generate more appropriate images. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's past mental state data and select the optimal data collection method.

[0093] The data collection unit can filter weather information based on the user's current activity status and lifestyle. For example, if the user is out, the data collection unit will prioritize collecting weather information for the user's destination. For example, if the user is at home, the data collection unit will prioritize collecting weather information for the area around the user's home. The data collection unit can also collect weather information at the necessary times in accordance with the user's lifestyle. For example, the data collection unit will adjust the collection of weather information based on the user's activity status and lifestyle. This makes it possible to generate more appropriate images by collecting weather information that matches the user's activity status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's activity status and lifestyle and adjust the collection of weather information accordingly.

[0094] The data collection unit can estimate the user's emotions and determine the priority of weather information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information on temperature and humidity. For example, if the user is relaxed, the data collection unit will prioritize collecting information on sunny or cloudy conditions. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information on precipitation probability and wind speed. This allows for the generation of more appropriate images by prioritizing the collection of weather information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have the AI ​​analyze the user's emotion data to determine the priority of weather information.

[0095] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when collecting weather information. For example, if the user is traveling, the data collection unit will prioritize collecting weather information for their travel destination. For example, if the user is commuting, the data collection unit will prioritize collecting weather information for their commute route. Furthermore, if the user is at home, the data collection unit can prioritize collecting weather information for the area around their home. This allows for the generation of more appropriate images by collecting weather information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's geographical location and prioritize collecting highly relevant weather information.

[0096] The data collection unit can analyze the user's social media activity and collect relevant information when collecting weather information. For example, if the user has posted about the weather on social media, the data collection unit will collect weather information based on that information. For example, if the user has posted on social media that they plan to attend a specific event, the data collection unit will collect weather information for that event. The data collection unit can also analyze the user's social media activity and collect relevant weather information. This allows for the generation of more appropriate images by collecting weather information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze the user's social media activity and collect relevant weather information.

[0097] 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 stressed, the analysis unit displays the analysis results in a simple and easy-to-understand format. For example, if the user is relaxed, the analysis unit displays the analysis results in detail to capture the user's interest. The analysis unit can also quickly display the analysis results and provide only the necessary information if the user is in a hurry. This enables more appropriate picture generation by providing analysis results that match the user's emotions. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the user's emotion data and adjust the presentation of the analysis.

[0098] The analysis unit can adjust the level of detail of its analysis based on the importance of weather information and mental state data during the analysis. For example, if weather information is important, the analysis unit performs a detailed weather analysis and provides it to the user. For example, if mental state data is important, the analysis unit performs a detailed mental state analysis and provides it to the user. Furthermore, if both weather information and mental state data are important, the analysis unit can perform a balanced analysis and provide it to the user. This makes it possible to generate more appropriate images by performing analysis based on important information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the importance of weather information and mental state data and adjust the level of detail of the analysis.

[0099] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to weather information to provide a detailed weather forecast. For example, the analysis unit can apply a psychological analysis algorithm to mental state data to analyze the user's mental state in detail. The analysis unit can also apply appropriate analysis algorithms to other data to provide the user with the most suitable information. This makes it possible to generate more appropriate images by performing analysis according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI apply analysis algorithms according to the data category.

[0100] 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 stressed, the analysis unit can summarize the analysis results briefly and provide only the necessary information. For example, if the user is relaxed, the analysis unit can display the analysis results in detail to capture the user's interest. The analysis unit can also quickly display the analysis results and provide only the necessary information if the user is in a hurry. This allows for more appropriate picture generation by providing analysis results that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have the AI ​​analyze the user's emotion data and adjust the length of the analysis.

[0101] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize analyzing the latest weather information and provide it to the user. For example, the analysis unit may prioritize analyzing the latest mental state data and provide it to the user. The analysis unit can also prioritize analyzing the most important information based on the data collection timing. This enables the generation of more appropriate images by performing analysis based on the latest information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may have AI analyze the data collection timing and determine the priority of analysis.

[0102] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit considers the relevance of weather information and mental state data and prioritizes the analysis of the most relevant data. For example, the analysis unit prioritizes the analysis of the most important information based on the relevance of the data. The analysis unit can also analyze the relevance of the data and determine the optimal analysis order. This makes it possible to generate more appropriate images by performing analysis based on highly relevant data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the relevance of the data and adjust the order of analysis.

[0103] The generation unit can estimate the user's emotions and adjust the expression of the generated painting based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a painting with calming colors. For example, if the user is stressed, the generation unit will generate a painting with calming colors. The generation unit can also generate a painting with vibrant colors if the user is excited. This promotes mood improvement by generating paintings that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have the AI ​​analyze the user's emotion data and adjust the expression of the painting.

[0104] The generation unit can adjust the level of detail of the generated image based on the importance of the analysis results. For example, if the analysis results are important, the generation unit will generate a detailed image. For example, if the analysis results are not very important, the generation unit will generate a simple image. The generation unit can also generate an image with the optimal level of detail based on the importance of the analysis results. This improves user satisfaction by generating detailed images according to the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the importance of the analysis results and adjust the level of detail of the generated image.

[0105] The generation unit can apply different generation algorithms to paintings according to the user's past preferences. For example, the generation unit can apply an algorithm to generate paintings with a color scheme the user has liked in the past. For example, the generation unit can apply an algorithm to generate paintings with a theme the user has liked in the past. The generation unit can also apply the optimal generation algorithm based on the user's past preferences. This improves user satisfaction by generating paintings based on the user's past preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the user's past preferences and apply the optimal generation algorithm.

[0106] The generation unit can estimate the user's emotions and adjust the length of the generated painting based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer painting. For example, if the user is stressed, the generation unit will generate a shorter painting. The generation unit can also generate a short, concise painting if the user is in a hurry. By adjusting the length of the painting according to the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can have the AI ​​analyze the user's emotion data and adjust the length of the painting.

[0107] The generation unit can determine the generation priority based on the timing of analysis result collection when generating paintings. For example, the generation unit generates the most relevant painting based on the latest analysis results. For example, the generation unit prioritizes generating the most important painting based on the timing of analysis result collection. The generation unit can also determine the optimal generation order by considering the timing of analysis result collection. This improves user satisfaction by generating paintings based on the latest analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the timing of analysis result collection and determine the generation priority.

[0108] The generation unit can adjust the generation order based on the relevance of the analysis results when generating paintings. For example, the generation unit can prioritize generating the most relevant paintings by considering the relevance of the analysis results. For example, the generation unit can prioritize generating the most important paintings based on the relevance of the analysis results. The generation unit can also analyze the relevance of the analysis results and determine the optimal generation order. This improves user satisfaction by generating paintings based on highly relevant analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the relevance of the analysis results and adjust the generation order.

[0109] The service provider can estimate the user's emotions and adjust the method of delivering the paintings based on the estimated emotions. For example, if the user is relaxed, the service provider will deliver the paintings at a leisurely pace. For example, if the user is stressed, the service provider will deliver the paintings quickly. The service provider can also deliver the paintings immediately if the user is in a hurry. This improves user satisfaction by adjusting the delivery method according to the user's emotions. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI analyze the user's emotion data and adjust the method of delivering the paintings.

[0110] The delivery unit can select the optimal delivery method when delivering paintings by referring to the user's past reactions. For example, the delivery unit may prioritize delivery methods that the user has preferred in the past. For example, the delivery unit may analyze the user's past reactions and determine the optimal delivery timing. The delivery unit may also select the optimal means of delivery based on the user's past reactions. This improves user satisfaction by selecting a delivery method based on the user's past reactions. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may have AI analyze the user's past reactions and select the optimal delivery method.

[0111] The service provider can customize the method of providing artwork based on the user's current living situation. For example, if the user is at home, the service provider can provide the artwork in a way that is best suited for viewing at home. For example, if the user is out, the service provider can provide the artwork in a way that is best suited for a mobile device. The service provider can also customize the optimal method of provision according to the user's living situation. By customizing the method of provision according to the user's living situation, the service provider can improve user satisfaction. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI analyze the user's living situation and customize the method of provision.

[0112] The service provider can estimate the user's emotions and determine the priority of the paintings to offer based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize offering paintings with a high relaxation effect. For example, if the user is stressed, the service provider will prioritize offering paintings with a high stress reduction effect. Furthermore, if the user is in a hurry, the service provider can prioritize offering paintings that can provide an effect in a short time. In this way, by prioritizing the provision of paintings that match the user's emotions, user satisfaction is improved. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI analyze the user's emotion data and determine the priority of the paintings to offer.

[0113] The service provider can select the optimal delivery method when providing paintings, taking into account the user's geographical location information. For example, if the user is traveling, the service provider can provide paintings related to the culture or scenery of the travel destination. For example, if the user is commuting, the service provider can provide paintings related to the commute route. The service provider can also select the optimal delivery method based on the user's geographical location information. By selecting a delivery method based on the user's geographical location information, user satisfaction can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI analyze the user's geographical location information and select the optimal delivery method.

[0114] The service provider can analyze the user's social media activity and propose a means of delivery when providing paintings. For example, if the user has posted about paintings on social media, the service provider will provide paintings related to those posts. For example, if the user has posted about attending a specific event on social media, the service provider will provide paintings related to that event. The service provider can also analyze the user's social media activity and propose the most suitable means of delivery. This improves user satisfaction by proposing a means of delivery based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI analyze the user's social media activity and propose the most suitable means of delivery.

[0115] The recording unit can estimate the user's emotions and adjust the method of recording emotional changes based on the estimated user emotions. For example, if the user is relaxed, the recording unit will record detailed emotional changes. For example, if the user is stressed, the recording unit will record concise emotional changes. The recording unit can also quickly record emotional changes if the user is in a hurry. By adjusting the recording method according to the user's emotions, more accurate emotional changes can be recorded. 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 recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's emotional data and adjust the recording method.

[0116] The recording unit can select the optimal recording method when recording changes in emotion by referring to the user's past responses. For example, the recording unit may prioritize recording methods that the user has preferred in the past. For example, the recording unit may analyze the user's past responses and determine the optimal recording timing. The recording unit may also select the optimal recording means based on the user's past responses. By selecting a recording method based on the user's past responses, it is possible to record changes in emotion more accurately. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may have AI analyze the user's past responses and select the optimal recording method.

[0117] The recording unit can customize the recording method based on the user's current living situation when recording emotional changes. For example, if the user is at home, the recording unit will record emotional changes in a way that is best suited for recording at home. For example, if the user is out, the recording unit will record emotional changes in a way that is best suited for a mobile device. The recording unit can also customize the optimal recording method according to the user's living situation. By customizing the recording method according to the user's living situation, it is possible to record emotional changes more accurately. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's living situation and customize the recording method.

[0118] The recording unit can estimate the user's emotions and determine the priority of emotions to record based on the estimated emotions. For example, if the user is relaxed, the recording unit will prioritize recording emotions that have a high relaxation effect. For example, if the user is stressed, the recording unit will prioritize recording emotions that have a high stress reduction effect. Also, if the user is in a hurry, the recording unit can prioritize recording emotions that can provide an effect in a short time. In this way, by determining the priority of emotions according to the user's emotions, it is possible to record emotional changes more accurately. 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 recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can have AI analyze the user's emotional data and determine the priority of emotions to record.

[0119] The recording unit can select the optimal recording method when recording changes in emotions, taking into account the user's geographical location information. For example, if the user is traveling, the recording unit can record emotions related to the culture and scenery of the travel destination. For example, if the user is commuting, the recording unit can record emotions related to the commute route. The recording unit can also select the optimal recording method based on the user's geographical location information. By selecting a recording method based on the user's geographical location information, it is possible to record changes in emotions more accurately. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's geographical location information and select the optimal recording method.

[0120] The recording unit can analyze the user's social media activity and suggest recording methods when recording changes in emotions. For example, if the user makes emotional posts on social media, the recording unit will record the emotions associated with those posts. For example, if the user posts on social media about attending a specific event, the recording unit will record the emotions associated with that event. The recording unit can also analyze the user's social media activity and suggest the most suitable recording method. This allows for more accurate recording of emotional changes by suggesting recording methods based on the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can have AI analyze the user's social media activity and suggest the most suitable recording method.

[0121] The intervention unit can estimate the user's emotions and adjust the intervention method based on the estimated emotions. For example, if the user is relaxed, the intervention unit will select a gentle intervention method. For example, if the user is stressed, the intervention unit will select a rapid intervention method. The intervention unit can also select an immediate intervention method if the user is in a hurry. By adjusting the intervention method according to the user's emotions, more effective intervention becomes possible. 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 intervention unit may be performed using AI, for example, or not using AI. For example, the intervention unit can have AI analyze the user's emotion data and adjust the intervention method.

[0122] The intervention unit can select the optimal intervention method by referring to the user's past responses during an intervention. For example, the intervention unit may prioritize intervention methods that the user has preferred in the past. For example, the intervention unit may analyze the user's past responses and determine the optimal timing for intervention. The intervention unit may also select the optimal intervention method based on the user's past responses. This allows for more effective intervention by selecting an intervention method based on the user's past responses. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit may have AI analyze the user's past responses and select the optimal intervention method.

[0123] The intervention unit can customize the intervention methods based on the user's current living situation at the time of intervention. For example, if the user is at home, the intervention unit will select the most suitable method for intervention at home. For example, if the user is out, the intervention unit will perform the intervention in a way that is best suited to the mobile device. The intervention unit can also customize the optimal intervention methods according to the user's living situation. By customizing the intervention methods according to the user's living situation, more effective intervention becomes possible. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's living situation and customize the intervention methods.

[0124] The intervention unit can estimate the user's emotions and determine the priority of interventions based on the estimated emotions. For example, if the user is relaxed, the intervention unit will prioritize interventions that have a high relaxation effect. For example, if the user is stressed, the intervention unit will prioritize interventions that have a high stress reduction effect. Also, if the user is in a hurry, the intervention unit can prioritize interventions that can produce results in a short time. By determining the priority of interventions according to the user's emotions, more effective interventions become possible. 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 intervention unit may be performed using AI, for example, or not using AI. For example, the intervention unit can have AI analyze the user's emotion data and determine the priority of interventions.

[0125] The intervention unit can select the optimal intervention method by considering the user's geographical location information at the time of intervention. For example, if the user is traveling, the intervention unit will perform an intervention related to the culture and scenery of the travel destination. For example, if the user is commuting, the intervention unit will perform an intervention related to the commuting route. The intervention unit can also select the optimal intervention method based on the user's geographical location information. This allows for more effective intervention by selecting an intervention method based on the user's geographical location information. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's geographical location information and select the optimal intervention method.

[0126] The intervention unit can analyze the user's social media activity and propose intervention methods at the time of intervention. For example, if the user has made emotional posts on social media, the intervention unit will take action related to those posts. For example, if the user has posted on social media that they plan to attend a specific event, the intervention unit will take action related to that event. The intervention unit can also analyze the user's social media activity and propose the most suitable intervention method. This allows for more effective intervention by proposing intervention methods based on the user's social media activity. Some or all of the above processing in the intervention unit may be performed using AI, for example, or without AI. For example, the intervention unit can have AI analyze the user's social media activity and propose the most suitable intervention method.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The mental state stabilization system may also include an audio guide. This audio guide can provide relaxing audio guidance while the user is viewing a painting. For example, it can provide explanations about the painting's background or play relaxing music. Furthermore, it can offer words of encouragement or advice for relaxation, depending on the user's emotions. This allows the user to experience relaxation not only visually but also aurally, contributing to a more stable mental state.

[0129] The data collection unit can also collect the user's biometric information. For example, it can collect biometric information such as the user's heart rate, blood pressure, and body temperature. This makes it possible to generate paintings that take the user's physical condition into consideration. For instance, if the user's heart rate is high, a painting with a relaxing effect can be generated, and if the heart rate is stable, a painting tailored to the user's preferences can be generated. This enables more personalized support.

[0130] The analysis unit can analyze a user's past behavioral history. For example, it can analyze what kind of paintings a user has liked in the past and at what times of day they viewed them. This makes it possible to generate paintings based on the user's behavioral patterns. For example, if a user often views paintings at night to relax, the system can generate paintings that have a relaxing effect at night. This makes it possible to provide support tailored to the user's behavioral patterns.

[0131] The generation unit can generate paintings based on the user's hobbies and interests. For example, the generation unit can generate paintings considering the user's favorite animals, landscapes, colors, etc. This can help improve the user's mood by providing paintings that match their hobbies and interests. For example, if the user likes cats, the unit can generate paintings with cats as the motif. Also, if the user likes ocean scenery, the unit can generate paintings depicting ocean scenery. This can lead to a higher level of satisfaction by providing paintings that match the user's hobbies and interests.

[0132] The service provider can deliver artwork not only to the user's device but also to smart home devices. For example, the service provider can display artwork on smart displays and smart TVs. This allows users to enjoy artwork in various locations around their home and experience a relaxing effect. For instance, artwork can be displayed on a smart TV in the living room for the whole family to enjoy. Alternatively, artwork can be displayed on a smart display in the bedroom for relaxation before going to sleep. This allows users to experience a relaxing effect throughout their entire living space.

[0133] The recording unit can utilize voice input to record changes in the user's emotions. For example, the recording unit can record changes in emotions when the user voices their impressions after viewing a painting. This allows users to record changes in their emotions without any effort. For instance, if the user voice-inputs, "Looking at this painting made me feel relaxed," the recording unit can record that change in emotion. Similarly, if the user voice-inputs, "I feel better," the recording unit can record that change in emotion. This allows for a more accurate recording of the user's emotional changes.

[0134] The intervention unit can suggest relaxation techniques according to the user's emotions. For example, if the user is feeling stressed, the intervention unit can suggest relaxation techniques such as deep breathing, meditation, or yoga. This allows the user to more effectively improve their mood by practicing relaxation techniques in conjunction with art appreciation. For example, the intervention unit can display a message to the user such as, "Take a deep breath and relax." It can also display a message such as, "Meditate to calm your mind." By suggesting relaxation techniques that are appropriate to the user's emotions, it can promote mood improvement.

[0135] The data collection unit can collect user meal information. For example, it can collect what the user ate and at what time of day they ate. This makes it possible to generate paintings based on the user's meal patterns. For instance, it can generate a relaxing painting after the user has breakfast and a mood-enhancing painting after dinner. This allows for support tailored to the user's meal patterns.

[0136] The analysis unit can analyze the user's sleep data. For example, it can analyze how long the user slept and the quality of their sleep. This makes it possible to generate paintings based on the user's sleep patterns. For instance, if the user gets enough sleep, it can generate an energizing painting, and if they are sleep-deprived, it can generate a relaxing painting. This allows for support tailored to the user's sleep patterns.

[0137] The generation unit can estimate the user's emotions and select a painting theme based on those emotions. For example, if the user is sad, the generation unit can generate a painting with a comforting theme. For example, if the user is happy, the generation unit can generate a painting with a celebratory theme. Furthermore, if the user is feeling anxious, the generation unit can generate a painting with a reassuring theme. In this way, by providing paintings with themes that match the user's emotions, it is possible to promote mood improvement.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The data collection unit collects information on weather and the user's mental state from the previous day. The data collection unit can, for example, collect weather information from the internet. It can also collect data on the user's mental state from the previous day. For example, the data collection unit can obtain weather information using a specific API and collect data on the user's mental state from the previous day in the form of a questionnaire. Step 2: The analysis unit analyzes the collected weather information and the previous day's mental state data. The analysis unit can analyze the collected data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze the relationship between weather information and mental state using correlation analysis and construct a predictive model using regression analysis. Step 3: The generation unit generates a painting that the user will like based on the analysis results. The generation unit can generate a painting based on the analysis results, for example, using a generation AI. For example, the generation unit can generate a painting that suits the user's preferences using a text generation AI (e.g., LLM). The generation unit can also generate a painting that suits the user's preferences using a multimodal generation AI. For example, the generation unit can generate a painting with bright colors on a sunny day and a painting with calm colors on a rainy day. Step 4: The provider delivers the generated painting to the user's device. The provider can deliver the generated painting to the user's device, for example, by using app notifications or email. For example, the provider can display the painting on the user's smartphone using app notifications. Alternatively, the provider can send the painting to the user's email address using email. Step 5: The recording unit records the user's emotional changes after viewing the painting. The recording unit can record the user's emotional changes using methods such as a diary format or emotional score input. For example, the recording unit can input the user's feelings after viewing the painting, such as "I feel better" or "No change." The recording unit can also quantify and record the user's emotional changes using an emotional score. Step 6: The intervention unit displays a message encouraging the user to consult a professional if mood improvement persists. The intervention unit can intervene early, for example, by displaying a message encouraging the user to consult a professional. For example, the intervention unit may display a message such as "Consult a professional" on the user's device.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires weather information from the internet using the communication I / F 44 of the smart device 14 and collects data on the user's mental state from the previous day. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a painting based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the generated painting to the user's device. The recording unit is implemented in the specific processing unit 46A of the smart device 14 and records changes in the user's emotions. The intervention unit is implemented in the specific processing unit 290 of the data processing unit 12 and displays a message prompting the user to consult a professional if there is a prolonged period without improvement in mood. 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.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires weather information from the internet using the communication I / F 44 of the smart glasses 214 and collects data on the user's mental state from the previous day. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a painting based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated painting to the user's device. The recording unit is implemented, for example, by the control unit 46A of the smart glasses 214 and records changes in the user's emotions. The intervention unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and displays a message prompting the user to consult a professional if a period of no improvement in mood continues. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires weather information from the internet using the communication I / F 44 of the headset terminal 314 and collects data on the user's mental state from the previous day. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a painting based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the generated painting to the user's device. The recording unit is implemented, for example, by the control unit 46A of the headset terminal 314 and records changes in the user's emotions. The intervention unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and displays a message prompting the user to consult a professional if a period of no improvement in mood continues. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires weather information from the internet using the robot 414's communication I / F 44 and collects data on the user's mental state from the previous day. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a painting based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated painting to the user's device. The recording unit is implemented, for example, by the control unit 46A of the robot 414 and records changes in the user's emotions. The intervention unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and displays a message prompting the user to consult a professional if there is a prolonged period without improvement in mood. 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] (Note 1) A collection unit that collects information on weather or mental state from the previous day, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates a painting based on the information analyzed by the analysis unit, A providing unit that provides the user with the painting generated by the generation unit, A recording unit that records the user's emotional changes regarding the painting provided by the aforementioned providing unit, The system includes an intervention unit that performs early intervention based on the information recorded by the recording unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather weather information from the internet The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system collects data on the user's mental state from the previous day. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected weather information and data on the previous day's mental state are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generation unit is Based on the analysis results, the user will generate paintings they like. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provides the generated painting to the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is Users record the changes in their emotions after viewing a painting. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned intervention unit is If no improvement in mood is observed for an extended period, a message prompting consultation with a professional will be displayed. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of weather information collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past mental state data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting weather information, filtering is performed based on the user's current activity status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of weather information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting weather information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting weather information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of weather information and mental state data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generation unit is It estimates the user's emotions and adjusts the way the generated paintings are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generation unit is When generating images, the level of detail is adjusted based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generation unit is When generating paintings, different generation algorithms are applied depending on the user's past preferences. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generation unit is It estimates the user's emotions and adjusts the length of the generated painting based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generation unit is When generating images, the generation priority is determined based on when the analysis results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generation unit is When generating images, the generation order is adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way the paintings are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing artwork, the optimal delivery method is selected by referring to the user's past reactions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing artwork, the method of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of the paintings to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing artwork, the optimal delivery method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing artwork, we analyze the user's social media activity and propose methods for providing the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned recording unit is We estimate the user's emotions and adjust the method of recording emotional changes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned recording unit is When recording emotional changes, the system selects the optimal recording method by referring to the user's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned recording unit is When recording emotional changes, the recording method is customized based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned recording unit is It estimates the user's emotions and determines the priority of emotions to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned recording unit is When recording emotional changes, the optimal recording method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned recording unit is When recording emotional changes, we analyze the user's social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned intervention unit is It estimates the user's emotions and adjusts the intervention method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned intervention unit is During intervention, the optimal intervention method is selected by referring to the user's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned intervention unit is During intervention, the intervention methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned intervention unit is The system estimates the user's emotions and determines the priority of interventions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned intervention unit is During intervention, the optimal intervention method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned intervention unit is During intervention, we analyze the user's social media activity and propose intervention methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0212] 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 collection unit that collects information on weather or the user's mental state from the previous day, estimates the user's emotions, determines the priority of weather information to collect based on the estimated user's emotions, and, if the user is feeling stressed, prioritizes the collection of temperature and humidity information from the weather information, An analysis unit uses a machine learning algorithm to learn individual user trends based on the weather information and the mental state information from the previous day collected by the collection unit, and constructs a model to predict under what weather conditions a particular user's mood will fluctuate. A generation unit that uses multimodal generation AI to generate paintings with bright colors on sunny days and paintings with subdued colors on rainy days, according to the analysis results based on the model constructed by the analysis unit, A providing unit that provides the user with the painting generated by the generation unit, A recording unit that records the user's emotional changes regarding the painting provided by the aforementioned providing unit, The system includes an intervention unit that analyzes the fluctuations in the user's emotions recorded by the recording unit and displays a message prompting the user to consult a professional if no improvement in mood is observed within a certain period of time. A system characterized by the following features.

2. The aforementioned collection unit is Gather weather information from the internet The system according to feature 1.

3. The aforementioned collection unit is The system collects information about the user's mental state from the previous day. The system according to feature 1.

4. The aforementioned supply unit is, Provides the generated painting to the user's device. The system according to feature 1.

5. The aforementioned recording unit is Users record the changes in their emotions after viewing a painting. The system according to feature 1.

Images