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 technology]

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to grasp changes in a user's mental state in real time and provide appropriate intervention.

[0005] The system according to the embodiment aims to grasp the mental state of the user in real time and provide appropriate intervention. [Means for solving the problem]

[0006] The system according to the embodiment includes 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 previous day's mental state. 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 a user. The recording unit records the user's emotional fluctuations regarding the painting provided by the provision unit. The intervention unit performs early intervention based on the information recorded by the recording unit. Effect of the Invention

[0007] The system according to the embodiment is capable of grasping the mental state of the user in real time and providing appropriate intervention. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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 a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The mental state stabilization system according to the embodiment of the present invention is a system that provides new paintings automatically generated by AI every day and collects and records changes in the user's emotions, thereby promoting mental state stabilization and mood improvement. This system generates and provides paintings that the user likes, taking into account the weather and the previous day's mental state. If a period of time continues in which the mood does not improve, early intervention is performed, contributing 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 likes. For example, it is possible to generate paintings with bright colors on sunny days and paintings with calm colors on rainy days. The generated painting is provided to the user's device. The user looks at the provided painting and records changes in emotions. For example, the user inputs impressions such as "I feel better" or "No change" after looking at the painting. This change in emotion is collected and recorded by the AI. If a period of time continues in which the mood does not improve, the AI ​​suggests early intervention. For example, a message encouraging the user to consult with a specialist can be displayed. This is expected to contribute to the prevention of depression and the improvement of dementia. This system allows users to easily and conveniently maintain their daily mental health. In conventional art exchanges, users had to choose their own art, but in this system, AI automatically generates and provides art, saving the user time. In addition, by collecting and recording changes in the user's emotions, more personalized support is possible. As a result, the mental state stabilization system can stabilize the user's mental state and promote improved mood.

[0029] The mental state stabilization system according to the embodiment includes 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 collect weather information from the Internet, for example. The collection unit can also collect data on the mental state of the previous day input by the user. For example, the collection unit obtains weather information using a specific API, and collects 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 a machine learning algorithm. For example, the analysis unit analyzes the relationship between the weather information and the mental state using correlation analysis, and builds a prediction model using regression analysis. The generation unit generates a painting that the user likes based on the analysis result. The generation unit can generate a painting based on the analysis result using, for example, a generation AI. For example, the generation unit generates a painting according to the user's preference using a text generation AI (for example, LLM). The generation unit can also generate a painting according to the user's preference using a multimodal generation AI. For example, the generating unit generates a painting with bright colors on a sunny day and generates a painting with calm colors on a rainy day. The providing unit provides the generated painting to the user's device. The providing unit can provide the generated painting to the user's device, for example, by using an app notification or email transmission. For example, the providing unit displays the painting on the user's smartphone using an app notification. The providing unit can also send the painting to the user's email address using email transmission. The recording unit records the change in the user's emotions after the user has viewed the painting. The recording unit can record the change in the user's emotions, for example, by using a diary format or input of an emotion score. For example, the recording unit inputs the user's impressions such as "I feel better" or "No change" after viewing the painting. The recording unit can also use the emotion score to quantify and record the change in the user's emotions. The intervention unit displays a message encouraging the user to consult with an expert if a period of time continues in which the mood does not improve. The intervention unit can perform early intervention, for example, by displaying a message encouraging the user to consult with an expert.For example, the intervention unit displays a message such as "Consult a specialist" on the user's device. In this way, the mental state stabilization system according to the embodiment can stabilize the user's mental state and promote improvement of the user's mood.

[0030] The collection unit collects information on the weather and the previous day's mental state. The collection unit can collect weather information from the Internet, for example. Specifically, the collection unit acquires the current weather and forecast using an API of a weather data providing service. This allows the collection unit to grasp weather information in the user's place of residence and area of ​​activity in real time. The collection unit can also collect data on the previous day's mental state entered by the user. For example, the collection unit provides the user with a daily questionnaire, and has the user enter the previous day's mood, stress level, sleep quality, and the like. The questionnaire is designed to be easily entered through a smartphone app or web interface, minimizing the burden on the user. Furthermore, the collection unit uses encryption technology to safely store the user's input data and protect privacy. This allows the collection unit to efficiently collect weather information and the user's mental state data and provide them to the analysis unit.

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

[0032] The generation unit generates a painting that the user likes based on the analysis result. The generation unit can generate a painting based on the analysis result, for example, using a generation AI. Specifically, the generation unit generates a painting according to the user's preference using a text generation AI (for example, LLM). For example, if the analysis result shows that the user likes paintings in bright tones, the generation unit generates a painting in bright tones. The generation unit can also generate a painting according to the user's preference using a multimodal generation AI. For example, the generation unit generates a painting in bright tones on a sunny day and generates a painting in calm tones on a rainy day. Furthermore, the generation unit can learn the user's past preference data and generate a more individualized painting. For example, a new painting is generated based on the style or theme of a painting that the user liked in the past. This allows the generation unit to provide an optimal painting for stabilizing the user's mental state. Furthermore, the generation unit can always provide a high-quality painting by evaluating the quality of the generated painting and correcting or regenerating it as necessary. This allows the generation unit to provide an effective means for stabilizing the user's mental state and promoting mood improvement.

[0033] The providing unit provides the generated painting to the user's device. The providing unit can provide the generated painting to the user's device, for example, by using an app notification or email transmission. Specifically, the providing unit displays the painting on the user's smartphone using the app notification. When the user opens the app, the generated painting is displayed, and the user can appreciate the painting. The providing unit can also send the painting to the user's email address using email transmission. This allows the user to appreciate the painting on various devices, such as a smartphone or a personal computer. Furthermore, the providing unit can provide a function for saving the painting on the user's device so that the user can appreciate the painting again later. For example, the providing unit can save the painting in the gallery of the user's smartphone and make it accessible at any time. This allows the providing unit to quickly and effectively provide the generated painting to the user and provide support to stabilize the user's mental state. Furthermore, the providing unit can collect user feedback and use it to improve the providing method. For example, how the user felt about the painting and which providing method was most effective are collected in a questionnaire format, and the collected information is reflected in future improvements to the providing method. This enables the providing unit to realize the optimal providing method according to the user's needs, maximizing the effectiveness of the entire system.

[0034] The recording unit records the change in the user's emotions after viewing the painting. The recording unit can record the change in the user's emotions, for example, by using a diary format or input of an emotion score. Specifically, the recording unit provides an interface for the user to input impressions such as "I feel better" or "No change" after viewing the painting. The user can easily input their impressions through a smartphone app or a web interface. The recording unit can also quantify and record the change in the user's emotions using the emotion score. For example, the user evaluates the mood after viewing the painting on a scale of 1 to 10 and records the score. Furthermore, the recording unit can store the user's emotion data in chronological order and analyze long-term changes. This allows the recording unit to grasp changes in the user's mental state in detail and provide feedback to the analysis unit and generation unit. Furthermore, the recording unit takes security measures to protect the user's privacy and safely stores and manages data. This allows the recording unit to accurately record changes in the user's emotions and provide important data for evaluating the effectiveness of the entire system.

[0035] The intervention unit displays a message encouraging the user to consult with a specialist if the period of time during which the mood does not improve continues. The intervention unit can perform early intervention, for example, by displaying a message encouraging the user to consult with a specialist. Specifically, the intervention unit analyzes the user's emotion data and issues an alert if the user does not see improvement in the mood within a certain period of time. For example, if the user has recorded a low emotion score for more than one week, the intervention unit displays a message such as "Consult a specialist" on the user's device. The intervention unit can also provide links and contact information to facilitate consultation with a specialist. For example, the message can include the contact information of the specialist and a link to online consultation, so that the user can consult immediately. Furthermore, the intervention unit can build a mechanism for providing necessary information to a specialist while protecting the user's privacy. This allows the intervention unit to enable the user to receive assistance from a specialist at an appropriate time and provide support to prevent the deterioration of the user's mental state. Furthermore, the intervention unit can continuously improve the timing and method of intervention based on the user's feedback. This allows the intervention unit to realize effective intervention to stabilize the user's mental state and improve the reliability and effectiveness of the entire system.

[0036] The collection unit can collect weather information from the Internet. The collection unit, for example, uses a specific API to collect weather information from the Internet. For example, the collection unit uses an API of a weather information service to obtain current weather information. The collection unit can also collect weather information using web scraping technology. For example, the collection unit automatically obtains weather information from a weather information site. This makes it possible to generate a painting based on the user's mental state by automatically collecting weather information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause AI to collect weather information.

[0037] The collection unit can collect data on the mental state of the previous day input by the user. The collection unit collects data on the mental state of the previous day input by the user in, for example, a questionnaire format. For example, the collection unit asks the user a question such as "How did you feel yesterday?" and collects data in a format in which the user selects an answer from a selection. The collection unit can also collect data on the mental state of the previous day input by the user in a scale format. For example, the collection unit asks the user a question such as "Please rate your mood yesterday on a scale of 1 to 10" and collects data in a format in which the user inputs a numerical value. This makes it possible to generate a painting based on the mental state of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the input data of the user.

[0038] The analysis unit can analyze the collected weather information and the data of the mental state of the previous day. The analysis unit, for example, analyzes the collected weather information and the data of the mental state of the previous day using correlation analysis. For example, the analysis unit evaluates the relationship between the weather information and the mental state by a correlation coefficient. The analysis unit can also analyze the collected data using regression analysis. For example, the analysis unit constructs a prediction model of the weather information and the mental state and predicts the future mental state. This makes it possible to generate a painting based on the user's mental state. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the collected data.

[0039] The generation unit can generate a painting that the user likes based on the analysis result. The generation unit generates a painting that the user likes based on the analysis result, for example, using a generation AI. For example, the generation unit generates a painting according to the user's preference using a text generation AI (for example, LLM). The generation unit can also generate a painting according to the user's preference using a multimodal generation AI. For example, the generation unit generates a painting in bright colors on a sunny day and generates a painting in calm colors on a rainy day. This promotes mood improvement by generating a painting according to the user's preference. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the analysis result to the AI ​​and cause the AI ​​to generate the painting.

[0040] The providing unit can provide the generated painting to the user's device. The providing unit provides the generated painting to the user's device using, for example, an app notification. For example, the providing unit sends an app notification to the user's smartphone and displays the painting. The providing unit can also provide the generated painting to the user's device using email transmission. For example, the providing unit sends the painting to the user's email address so that the user can view the painting by opening the email. This allows the user to easily receive the painting. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to select a method for providing the painting.

[0041] The recording unit can record the change in the user's emotions after viewing the painting. The recording unit records the change in the user's emotions after viewing the painting in a diary format, for example. For example, the recording unit asks the user a question such as "How did you feel after viewing the painting?" and records the change in emotions in a format in which the user freely describes their impressions. The recording unit can also record the change in the user's emotions after viewing the painting by using the input of an emotion score. For example, the recording unit asks the user a question such as "Please rate how you felt after viewing the painting on a scale of 1 to 10" and records the change in emotions in a format in which the user inputs a numerical value. This enables individualized support by recording the change in the user's emotions. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can cause AI to analyze the change in the user's emotions.

[0042] The intervention unit can display a message encouraging the user to consult with a specialist when a period of time continues in which the user's mood does not improve. The intervention unit, for example, displays a message encouraging the user to consult with a specialist. For example, the intervention unit displays a message such as "Consult a specialist" on the user's device. The intervention unit can also monitor changes in the user's emotions and automatically display a message when a period of time continues in which the user's mood does not improve. For example, the intervention unit displays a message encouraging the user to consult with a specialist when the user's emotion score remains low for a certain period of time. This is expected to contribute to the prevention of depression and the improvement of dementia through early intervention. A part or all of the above-mentioned processing in the intervention unit may be performed, for example, using AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's emotion data and display a message at an appropriate time.

[0043] The collection unit can analyze the user's past mental state data and select the optimal collection method. The collection unit, for example, analyzes the user's past mental state data and selects a collection method suited to that time period. For example, the collection unit analyzes data from a time period when the user felt stressed in the past and selects a collection method suited to that time period. The collection unit can also analyze data from a time period when the user was relaxed in the past and select a collection method suited to that time period. For example, the collection unit determines the optimal collection timing based on the user's past mental state data. This allows for more appropriate painting generation by selecting the optimal collection method based on the user's past mental state. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can have AI analyze the user's past mental state data and select the optimal collection method.

[0044] The collection unit can perform filtering based on the user's current activity status and life rhythm when collecting weather information. For example, when the user is out, the collection unit prioritizes collecting weather information at the user's destination. For example, when the user is at home, the collection unit prioritizes collecting weather information around the user's home. The collection unit can also collect weather information at a necessary timing in accordance with the user's life rhythm. For example, the collection unit adjusts the collection of weather information based on the user's activity status and life rhythm. This makes it possible to generate a more appropriate painting by collecting weather information according to the user's activity status and life rhythm. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the user's activity status and life rhythm and adjust the collection of weather information.

[0045] When collecting weather information, the collection unit can preferentially collect highly relevant information in consideration of the user's geographical location information. For example, when the user is traveling, the collection unit preferentially collects weather information for the travel destination. For example, when the user is commuting, the collection unit preferentially collects weather information for the commuting route. In addition, when the user is at home, the collection unit can also preferentially collect weather information around the home. This makes it possible to generate more appropriate paintings by collecting weather information based on the user's geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the user's geographical location information and preferentially collect highly relevant weather information.

[0046] The collection unit can analyze the user's social media activities and collect related information when collecting weather information. For example, when a user posts about the weather on social media, the collection unit collects weather information based on the information. For example, when a user posts a plan to participate in a specific event on social media, the collection unit collects weather information for the event. The collection unit can also analyze the user's social media activities and collect related weather information. This makes it possible to generate more appropriate paintings by collecting weather information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can have AI analyze the user's social media activities and collect related weather information.

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

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

[0049] The analysis unit can determine the priority of the analysis based on the time of data collection during the analysis. For example, the analysis unit prioritizes the analysis of the latest weather information and provides it to the user. For example, the analysis unit prioritizes the analysis of the latest mental state data and provides it to the user. The analysis unit can also prioritize the analysis of the most important information based on the time of data collection. This makes it possible to generate a more appropriate painting by performing an analysis based on the latest information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the time of data collection and determine the priority of the analysis.

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

[0051] The generation unit can adjust the level of detail of the generation based on the importance of the analysis result when generating a painting. For example, when the analysis result is important, the generation unit generates a detailed painting. For example, when the analysis result is not so important, the generation unit generates a simple painting. The generation unit can also generate a painting with an optimal level of detail based on the importance of the analysis result. This improves user satisfaction by generating a detailed painting according to the importance of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to analyze the importance of the analysis result and adjust the level of detail of the generation.

[0052] The generation unit can apply different generation algorithms according to the user's past preferences when generating a painting. The generation unit applies, for example, an algorithm that generates a painting with a color tone that the user previously liked. For example, the generation unit applies an algorithm that generates a painting with a theme that the user previously liked. The generation unit can also apply an optimal generation algorithm based on the user's past preferences. This improves the user's satisfaction by generating a painting based on the user's past preferences. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI analyze the user's past preferences and apply an optimal generation algorithm.

[0053] The generation unit can determine the priority of generation based on the time of collection of the analysis results when generating the painting. The generation unit generates the most relevant painting based on, for example, the latest analysis result. For example, the generation unit generates the most important painting preferentially based on the time of collection of the analysis result. The generation unit can also determine the optimal generation order taking into account the time of collection of the analysis result. This improves user satisfaction by generating a painting based on the latest analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI analyze the time of collection of the analysis result to determine the priority of generation.

[0054] The generation unit can adjust the order of generation based on the relevance of the analysis results when generating paintings. The generation unit, for example, takes into consideration the relevance of the analysis results and generates the most relevant painting with priority. For example, the generation unit generates the most important painting with priority 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. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to analyze the relevance of the analysis results and adjust the order of generation.

[0055] The providing unit can select the optimal providing method by referring to the user's past reactions when providing a painting. For example, the providing unit preferentially selects a providing method that the user has preferred in the past. For example, the providing unit analyzes the user's past reactions and determines the optimal providing timing. The providing unit can also select the optimal providing means based on the user's past reactions. This improves the user's satisfaction by selecting a providing method based on the user's past reactions. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the user's past reactions and select the optimal providing method.

[0056] The providing unit can customize the means of providing the painting based on the user's current living situation when providing the painting. For example, when the user is at home, the providing unit provides the painting in an optimal manner for viewing at home. For example, when the user is out, the providing unit provides the painting to a mobile device in an optimal manner. The providing unit can also customize the optimal means of providing according to the user's living situation. This improves the user's satisfaction by customizing the means of providing according to the user's living situation. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI analyze the user's living situation and customize the means of providing.

[0057] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing a painting. For example, when the user is traveling, the providing unit provides a painting related to the culture or scenery of the travel destination. For example, when the user is commuting, the providing unit provides a painting related to the commuting route. The providing unit can also select the optimal providing method based on the geographical location information of the user. This improves the satisfaction of the user by selecting a providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI analyze the geographical location information of the user and select the optimal providing method.

[0058] The providing unit can analyze the user's social media activity and suggest a means of providing the painting when providing the painting. For example, when the user posts on social media about a painting, the providing unit provides a painting related to the post. For example, when the user posts on social media that he / she plans to participate in a specific event, the providing unit provides a painting related to the event. The providing unit can also analyze the user's social media activity and suggest an optimal means of providing. This improves the user's satisfaction by suggesting a means of providing based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI analyze the user's social media activity and suggest the optimal means of providing.

[0059] The recording unit can select the optimal recording method by referring to the user's past reactions when recording changes in emotions. For example, the recording unit preferentially selects a recording method that the user has preferred in the past. For example, the recording unit analyzes the user's past reactions and determines the optimal recording timing. The recording unit can also select the optimal recording means based on the user's past reactions. This allows more accurate recording of changes in emotions by selecting a recording method based on the user's past reactions. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can have AI analyze the user's past reactions and select the optimal recording method.

[0060] The recording unit can customize the recording means based on the user's current living situation when recording changes in emotions. For example, when the user is at home, the recording unit records changes in emotions in a manner optimal for recording at home. For example, when the user is out, the recording unit records changes in emotions in a manner optimal for a mobile device. The recording unit can also customize the optimal recording means according to the user's living situation. This allows more accurate recording of changes in emotions by customizing the recording means according to the user's living situation. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can have AI analyze the user's living situation and customize the recording means.

[0061] The recording unit can select the optimal recording method in consideration of the geographical location information of the user when recording changes in emotion. For example, when the user is traveling, the recording unit records emotions related to the culture and scenery of the travel destination. For example, when the user is commuting, the recording unit records emotions related to the commuting route. The recording unit can also select the optimal recording method based on the geographical location information of the user. As a result, by selecting a recording method based on the geographical location information of the user, it is possible to record changes in emotion more accurately. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can have AI analyze the geographical location information of the user and select the optimal recording method.

[0062] The recording unit can analyze the user's social media activity and suggest a means of recording when recording changes in emotions. For example, when a user posts on social media about emotions, the recording unit records emotions related to the post. For example, when a user posts on social media that he or she plans to participate in a specific event, the recording unit records emotions related to the event. The recording unit can also analyze the user's social media activity and suggest an optimal recording means. This makes it possible to record changes in emotions more accurately by suggesting a recording means based on the user's social media activity. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can have AI analyze the user's social media activity and suggest an optimal recording means.

[0063] The intervention unit can select the optimal intervention method by referring to the user's past reactions at the time of intervention. For example, the intervention unit preferentially selects an intervention method that the user has preferred in the past. For example, the intervention unit analyzes the user's past reactions and determines the optimal intervention timing. The intervention unit can also select the optimal intervention means based on the user's past reactions. This enables more effective intervention by selecting an intervention method based on the user's past reactions. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's past reactions and select the optimal intervention method.

[0064] The intervention unit can customize the intervention means based on the user's current living situation at the time of intervention. For example, when the user is at home, the intervention unit selects the optimal method for intervention at home. For example, when the user is out, the intervention unit performs intervention in the optimal method for the mobile device. The intervention unit can also customize the optimal intervention means according to the user's living situation. This enables more effective intervention by customizing the intervention means according to the user's living situation. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's living situation and customize the intervention means.

[0065] The intervention unit can select the optimal intervention method in consideration of the geographical location information of the user at the time of intervention. For example, when the user is traveling, the intervention unit performs intervention related to the culture and scenery of the travel destination. For example, when the user is commuting, the intervention unit performs intervention related to the commuting route. The intervention unit can also select the optimal intervention method based on the geographical location information of the user. As a result, more effective intervention is possible by selecting an intervention method based on the geographical location information of the user. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the geographical location information of the user and select the optimal intervention method.

[0066] At the time of intervention, the intervention unit can analyze the user's social media activity and suggest a means of intervention. For example, when a user posts on social media about emotions, the intervention unit performs an intervention related to the post. For example, when a user posts on social media that he or she plans to participate in a specific event, the intervention unit performs an intervention related to the event. The intervention unit can also analyze the user's social media activity and suggest an optimal intervention means. This enables more effective intervention by suggesting an intervention means based on the user's social media activity. A part or all of the above-mentioned processing in the intervention unit may be performed, for example, using AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's social media activity and suggest an optimal intervention means.

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

[0068] The collection unit can also collect biometric information of the user. For example, the collection unit can collect biometric information such as the user's heart rate, blood pressure, and body temperature. This makes it possible to generate a painting that takes into account the user's physical condition. For example, 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 according to the user's preferences can be generated. This makes it possible to provide more personalized support.

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

[0070] The generation unit can generate a painting based on the user's hobbies and interests. For example, the generation unit can generate a painting taking into consideration the user's favorite animals, scenery, colors, and the like. This can promote mood improvement by providing a painting according to the user's hobbies and interests. For example, if the user likes cats, a painting with a cat motif can be generated. Also, if the user likes seascapes, a painting depicting a seascape can be generated. This can provide a painting according to the user's hobbies and interests, resulting in higher satisfaction.

[0071] The providing unit can provide paintings to smart home devices in addition to the user's device. For example, the providing unit can display paintings on a smart display or a smart TV. This allows the user to appreciate paintings in various places in the home and achieve a relaxing effect. For example, a painting can be displayed on a smart TV in the living room so that the whole family can appreciate it. In addition, a painting can be displayed on a smart display in the bedroom so that the user can relax before going to sleep. This allows the user to achieve a relaxing effect throughout the entire living space.

[0072] The collection unit can collect dietary information of the user. For example, the collection unit can collect what the user ate and at what time of day the user ate. This makes it possible to generate a painting based on the user's dietary pattern. For example, a painting that has a relaxing effect can be generated after the user has eaten breakfast, and a painting that lifts the mood can be generated after dinner. This makes it possible to provide support according to the user's dietary pattern.

[0073] The analysis unit can analyze the user's sleep data. For example, the analysis unit can analyze how long the user slept and what the quality of the sleep was. This makes it possible to generate a painting based on the user's sleep pattern. For example, if the user slept sufficiently, a painting that energizes the user can be generated, and if the user slept insufficiently, a painting that has a relaxing effect can be generated. This makes it possible to provide support according to the user's sleep pattern.

[0074] The process flow of the first embodiment will be briefly described below.

[0075] Step 1: The collection unit collects information on the weather and the mental state of the previous day. The collection unit can collect weather information from the Internet, for example. The collection unit can also collect data on the mental state of the previous day input by the user. For example, the collection unit obtains weather information using a specific API, and collects data on the mental state of the previous day from the user in the form of a questionnaire. Step 2: The analysis unit analyzes the collected weather information and the data of the previous day's mental state. The analysis unit can analyze the collected data, for example, using statistical analysis or machine learning algorithms. For example, the analysis unit analyzes the relationship between the weather information and the mental state using correlation analysis, and builds a prediction model using regression analysis. Step 3: The generation unit generates a painting that the user likes based on the analysis result. The generation unit can generate a painting based on the analysis result, for example, using a generation AI. For example, the generation unit can generate a painting according to the user's preferences using a text generation AI (e.g., LLM). The generation unit can also generate a painting according to the user's preferences using a multimodal generation AI. For example, the generation unit generates a painting in bright colors on a sunny day and a painting in muted colors on a rainy day. Step 4: The providing unit provides the generated painting to the user's device. The providing unit can provide the generated painting to the user's device, for example, by using an app notification or email transmission. For example, the providing unit can display the painting on the user's smartphone by using an app notification. The providing unit can also send the painting to the user's email address by using email transmission. Step 5: The recording unit records the change in the user's emotions after viewing the painting. The recording unit can record the change in the user's emotions, for example, by using a diary format or input of an emotion score. For example, the recording unit inputs the user's impressions after viewing the painting, such as "I feel better" or "No change." The recording unit can also use the emotion score to quantify and record the change in the user's emotions. Step 6: The intervention unit displays a message encouraging the user to consult with a specialist if the period of time during which the mood does not improve continues. The intervention unit can perform early intervention, for example, by displaying a message encouraging the user to consult with a specialist. For example, the intervention unit displays a message such as "Consult with a specialist" on the user's device.

[0076] (Example 2) The mental state stabilization system according to the embodiment of the present invention is a system that provides new paintings automatically generated by AI every day and collects and records changes in the user's emotions, thereby promoting mental state stabilization and mood improvement. This system generates and provides paintings that the user likes, taking into account the weather and the previous day's mental state. If a period of time continues in which the mood does not improve, early intervention is performed, contributing 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 likes. For example, it is possible to generate paintings with bright colors on sunny days and paintings with calm colors on rainy days. The generated painting is provided to the user's device. The user looks at the provided painting and records changes in emotions. For example, the user inputs impressions such as "I feel better" or "No change" after looking at the painting. This change in emotion is collected and recorded by the AI. If a period of time continues in which the mood does not improve, the AI ​​suggests early intervention. For example, a message encouraging the user to consult with a specialist can be displayed. This is expected to contribute to the prevention of depression and the improvement of dementia. This system allows users to easily and conveniently maintain their daily mental health. In conventional art exchanges, users had to choose their own art, but in this system, AI automatically generates and provides art, saving the user time. In addition, by collecting and recording changes in the user's emotions, more personalized support is possible. As a result, the mental state stabilization system can stabilize the user's mental state and promote improved mood.

[0077] The mental state stabilization system according to the embodiment includes 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 collect weather information from the Internet, for example. The collection unit can also collect data on the mental state of the previous day input by the user. For example, the collection unit obtains weather information using a specific API, and collects 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 a machine learning algorithm. For example, the analysis unit analyzes the relationship between the weather information and the mental state using correlation analysis, and builds a prediction model using regression analysis. The generation unit generates a painting that the user likes based on the analysis result. The generation unit can generate a painting based on the analysis result using, for example, a generation AI. For example, the generation unit generates a painting according to the user's preference using a text generation AI (for example, LLM). The generation unit can also generate a painting according to the user's preference using a multimodal generation AI. For example, the generating unit generates a painting with bright colors on a sunny day and generates a painting with calm colors on a rainy day. The providing unit provides the generated painting to the user's device. The providing unit can provide the generated painting to the user's device, for example, by using an app notification or email transmission. For example, the providing unit displays the painting on the user's smartphone using an app notification. The providing unit can also send the painting to the user's email address using email transmission. The recording unit records the change in the user's emotions after the user has viewed the painting. The recording unit can record the change in the user's emotions, for example, by using a diary format or input of an emotion score. For example, the recording unit inputs the user's impressions such as "I feel better" or "No change" after viewing the painting. The recording unit can also use the emotion score to quantify and record the change in the user's emotions. The intervention unit displays a message encouraging the user to consult with an expert if a period of time continues in which the mood does not improve. The intervention unit can perform early intervention, for example, by displaying a message encouraging the user to consult with an expert.For example, the intervention unit displays a message such as "Consult a specialist" on the user's device. In this way, the mental state stabilization system according to the embodiment can stabilize the user's mental state and promote improvement of the user's mood.

[0078] The collection unit collects information on the weather and the previous day's mental state. The collection unit can collect weather information from the Internet, for example. Specifically, the collection unit acquires the current weather and forecast using an API of a weather data providing service. This allows the collection unit to grasp weather information in the user's place of residence and area of ​​activity in real time. The collection unit can also collect data on the previous day's mental state entered by the user. For example, the collection unit provides the user with a daily questionnaire, and has the user enter the previous day's mood, stress level, sleep quality, and the like. The questionnaire is designed to be easily entered through a smartphone app or web interface, minimizing the burden on the user. Furthermore, the collection unit uses encryption technology to safely store the user's input data and protect privacy. This allows the collection unit to efficiently collect weather information and the user's mental state data and provide them to the analysis unit.

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

[0080] The generation unit generates a painting that the user likes based on the analysis result. The generation unit can generate a painting based on the analysis result, for example, using a generation AI. Specifically, the generation unit generates a painting according to the user's preference using a text generation AI (for example, LLM). For example, if the analysis result shows that the user likes paintings in bright tones, the generation unit generates a painting in bright tones. The generation unit can also generate a painting according to the user's preference using a multimodal generation AI. For example, the generation unit generates a painting in bright tones on a sunny day and generates a painting in calm tones on a rainy day. Furthermore, the generation unit can learn the user's past preference data and generate a more individualized painting. For example, a new painting is generated based on the style or theme of a painting that the user liked in the past. This allows the generation unit to provide an optimal painting for stabilizing the user's mental state. Furthermore, the generation unit can always provide a high-quality painting by evaluating the quality of the generated painting and correcting or regenerating it as necessary. This allows the generation unit to provide an effective means for stabilizing the user's mental state and promoting mood improvement.

[0081] The providing unit provides the generated painting to the user's device. The providing unit can provide the generated painting to the user's device, for example, by using an app notification or email transmission. Specifically, the providing unit displays the painting on the user's smartphone using the app notification. When the user opens the app, the generated painting is displayed, and the user can appreciate the painting. The providing unit can also send the painting to the user's email address using email transmission. This allows the user to appreciate the painting on various devices, such as a smartphone or a personal computer. Furthermore, the providing unit can provide a function for saving the painting on the user's device so that the user can appreciate the painting again later. For example, the providing unit can save the painting in the gallery of the user's smartphone and make it accessible at any time. This allows the providing unit to quickly and effectively provide the generated painting to the user and provide support to stabilize the user's mental state. Furthermore, the providing unit can collect user feedback and use it to improve the providing method. For example, how the user felt about the painting and which providing method was most effective are collected in a questionnaire format, and the collected information is reflected in future improvements to the providing method. This enables the providing unit to realize the optimal providing method according to the user's needs, maximizing the effectiveness of the entire system.

[0082] The recording unit records the change in the user's emotions after viewing the painting. The recording unit can record the change in the user's emotions, for example, by using a diary format or input of an emotion score. Specifically, the recording unit provides an interface for the user to input impressions such as "I feel better" or "No change" after viewing the painting. The user can easily input their impressions through a smartphone app or a web interface. The recording unit can also quantify and record the change in the user's emotions using the emotion score. For example, the user evaluates the mood after viewing the painting on a scale of 1 to 10 and records the score. Furthermore, the recording unit can store the user's emotion data in chronological order and analyze long-term changes. This allows the recording unit to grasp changes in the user's mental state in detail and provide feedback to the analysis unit and generation unit. Furthermore, the recording unit takes security measures to protect the user's privacy and safely stores and manages data. This allows the recording unit to accurately record changes in the user's emotions and provide important data for evaluating the effectiveness of the entire system.

[0083] The intervention unit displays a message encouraging the user to consult with a specialist if the period of time during which the mood does not improve continues. The intervention unit can perform early intervention, for example, by displaying a message encouraging the user to consult with a specialist. Specifically, the intervention unit analyzes the user's emotion data and issues an alert if the user does not see improvement in the mood within a certain period of time. For example, if the user has recorded a low emotion score for more than one week, the intervention unit displays a message such as "Consult a specialist" on the user's device. The intervention unit can also provide links and contact information to facilitate consultation with a specialist. For example, the message can include the contact information of the specialist and a link to online consultation, so that the user can consult immediately. Furthermore, the intervention unit can build a mechanism for providing necessary information to a specialist while protecting the user's privacy. This allows the intervention unit to enable the user to receive assistance from a specialist at an appropriate time and provide support to prevent the deterioration of the user's mental state. Furthermore, the intervention unit can continuously improve the timing and method of intervention based on the user's feedback. This allows the intervention unit to realize effective intervention to stabilize the user's mental state and improve the reliability and effectiveness of the entire system.

[0084] The collection unit can collect weather information from the Internet. The collection unit, for example, uses a specific API to collect weather information from the Internet. For example, the collection unit uses an API of a weather information service to obtain current weather information. The collection unit can also collect weather information using web scraping technology. For example, the collection unit automatically obtains weather information from a weather information site. This makes it possible to generate a painting based on the user's mental state by automatically collecting weather information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause AI to collect weather information.

[0085] The collection unit can collect data on the mental state of the previous day input by the user. The collection unit collects data on the mental state of the previous day input by the user in, for example, a questionnaire format. For example, the collection unit asks the user a question such as "How did you feel yesterday?" and collects data in a format in which the user selects an answer from a selection. The collection unit can also collect data on the mental state of the previous day input by the user in a scale format. For example, the collection unit asks the user a question such as "Please rate your mood yesterday on a scale of 1 to 10" and collects data in a format in which the user inputs a numerical value. This makes it possible to generate a painting based on the mental state of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the input data of the user.

[0086] The analysis unit can analyze the collected weather information and the data of the mental state of the previous day. The analysis unit, for example, analyzes the collected weather information and the data of the mental state of the previous day using correlation analysis. For example, the analysis unit evaluates the relationship between the weather information and the mental state by a correlation coefficient. The analysis unit can also analyze the collected data using regression analysis. For example, the analysis unit constructs a prediction model of the weather information and the mental state and predicts the future mental state. This makes it possible to generate a painting based on the user's mental state. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the collected data.

[0087] The generation unit can generate a painting that the user likes based on the analysis result. The generation unit generates a painting that the user likes based on the analysis result, for example, using a generation AI. For example, the generation unit generates a painting according to the user's preference using a text generation AI (for example, LLM). The generation unit can also generate a painting according to the user's preference using a multimodal generation AI. For example, the generation unit generates a painting in bright colors on a sunny day and generates a painting in calm colors on a rainy day. This promotes mood improvement by generating a painting according to the user's preference. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the analysis result to the AI ​​and cause the AI ​​to generate the painting.

[0088] The providing unit can provide the generated painting to the user's device. The providing unit provides the generated painting to the user's device using, for example, an app notification. For example, the providing unit sends an app notification to the user's smartphone and displays the painting. The providing unit can also provide the generated painting to the user's device using email transmission. For example, the providing unit sends the painting to the user's email address so that the user can view the painting by opening the email. This allows the user to easily receive the painting. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to select a method for providing the painting.

[0089] The recording unit can record the change in the user's emotions after viewing the painting. The recording unit records the change in the user's emotions after viewing the painting in a diary format, for example. For example, the recording unit asks the user a question such as "How did you feel after viewing the painting?" and records the change in emotions in a format in which the user freely describes their impressions. The recording unit can also record the change in the user's emotions after viewing the painting by using the input of an emotion score. For example, the recording unit asks the user a question such as "Please rate how you felt after viewing the painting on a scale of 1 to 10" and records the change in emotions in a format in which the user inputs a numerical value. This enables individualized support by recording the change in the user's emotions. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can cause AI to analyze the change in the user's emotions.

[0090] The intervention unit can display a message encouraging the user to consult with a specialist when a period of time continues in which the user's mood does not improve. The intervention unit, for example, displays a message encouraging the user to consult with a specialist. For example, the intervention unit displays a message such as "Consult a specialist" on the user's device. The intervention unit can also monitor changes in the user's emotions and automatically display a message when a period of time continues in which the user's mood does not improve. For example, the intervention unit displays a message encouraging the user to consult with a specialist when the user's emotion score remains low for a certain period of time. This is expected to contribute to the prevention of depression and the improvement of dementia through early intervention. A part or all of the above-mentioned processing in the intervention unit may be performed, for example, using AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's emotion data and display a message at an appropriate time.

[0091] The collection unit can estimate the user's emotion and adjust the timing of collecting weather information based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting weather information based on the estimated user's emotion. For example, when the user is stressed, the collection unit frequently collects weather information and provides the latest information. In addition, when the user is relaxed, the collection unit can reduce the frequency of collecting weather information and collect it only when necessary. For example, when the user is in a hurry, the collection unit quickly collects weather information and provides it immediately. This enables more appropriate painting generation by collecting weather information at a timing according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can cause an AI to analyze the user's emotion data and adjust the timing of collecting weather information.

[0092] The collection unit can analyze the user's past mental state data and select the optimal collection method. The collection unit, for example, analyzes the user's past mental state data and selects a collection method suited to that time period. For example, the collection unit analyzes data from a time period when the user felt stressed in the past and selects a collection method suited to that time period. The collection unit can also analyze data from a time period when the user was relaxed in the past and select a collection method suited to that time period. For example, the collection unit determines the optimal collection timing based on the user's past mental state data. This allows for more appropriate painting generation by selecting the optimal collection method based on the user's past mental state. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can have AI analyze the user's past mental state data and select the optimal collection method.

[0093] The collection unit can perform filtering based on the user's current activity status and life rhythm when collecting weather information. For example, when the user is out, the collection unit prioritizes collecting weather information at the user's destination. For example, when the user is at home, the collection unit prioritizes collecting weather information around the user's home. The collection unit can also collect weather information at a necessary timing in accordance with the user's life rhythm. For example, the collection unit adjusts the collection of weather information based on the user's activity status and life rhythm. This makes it possible to generate a more appropriate painting by collecting weather information according to the user's activity status and life rhythm. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the user's activity status and life rhythm and adjust the collection of weather information.

[0094] The collection unit can estimate the user's emotion and determine the priority of the weather information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects information on temperature and humidity among the weather information. For example, when the user is relaxed, the collection unit preferentially collects information on sunny and cloudy weather among the weather information. In addition, when the user is in a hurry, the collection unit can also preferentially collect information on the probability of precipitation and wind speed among the weather information. This enables more appropriate painting generation by preferentially collecting weather information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can cause an AI to analyze the user's emotion data and determine the priority of the weather information.

[0095] When collecting weather information, the collection unit can preferentially collect highly relevant information in consideration of the user's geographical location information. For example, when the user is traveling, the collection unit preferentially collects weather information for the travel destination. For example, when the user is commuting, the collection unit preferentially collects weather information for the commuting route. In addition, when the user is at home, the collection unit can also preferentially collect weather information around the home. This makes it possible to generate more appropriate paintings by collecting weather information based on the user's geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to analyze the user's geographical location information and preferentially collect highly relevant weather information.

[0096] The collection unit can analyze the user's social media activities and collect related information when collecting weather information. For example, when a user posts about the weather on social media, the collection unit collects weather information based on the information. For example, when a user posts a plan to participate in a specific event on social media, the collection unit collects weather information for the event. The collection unit can also analyze the user's social media activities and collect related weather information. This makes it possible to generate more appropriate paintings by collecting weather information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can have AI analyze the user's social media activities and collect related weather information.

[0097] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit displays the analysis result in a simple and easy-to-understand format. For example, when the user is relaxed, the analysis unit displays the analysis result in detail so that the user can be interested. In addition, when the user is in a hurry, the analysis unit can quickly display the analysis result and provide only the necessary information. This enables more appropriate painting generation by providing the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can cause an AI to analyze the user's emotion data and adjust the expression method of the analysis.

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

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

[0100] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit summarizes the analysis result in a short form and provides only the necessary information. For example, when the user is relaxed, the analysis unit displays the analysis result in detail so that the user can be interested. In addition, when the user is in a hurry, the analysis unit can quickly display the analysis result and provide only the necessary information. This enables more appropriate painting generation by providing the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can have an AI analyze the user's emotion data and adjust the length of the analysis.

[0101] The analysis unit can determine the priority of the analysis based on the time of data collection during the analysis. For example, the analysis unit prioritizes the analysis of the latest weather information and provides it to the user. For example, the analysis unit prioritizes the analysis of the latest mental state data and provides it to the user. The analysis unit can also prioritize the analysis of the most important information based on the time of data collection. This makes it possible to generate a more appropriate painting by performing an analysis based on the latest information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the time of data collection and determine the priority of the analysis.

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

[0103] The generation unit can estimate the user's emotion and adjust the expression method of the generated painting based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a painting with a relaxed color tone. For example, when the user is stressed, the generation unit generates a painting with a calm color tone. Also, when the user is excited, the generation unit can generate a painting with a vivid color tone. This promotes improving the mood by generating a painting according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can cause an AI to analyze the user's emotion data and adjust the expression method of the painting.

[0104] The generation unit can adjust the level of detail of the generation based on the importance of the analysis result when generating a painting. For example, when the analysis result is important, the generation unit generates a detailed painting. For example, when the analysis result is not so important, the generation unit generates a simple painting. The generation unit can also generate a painting with an optimal level of detail based on the importance of the analysis result. This improves user satisfaction by generating a detailed painting according to the importance of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to analyze the importance of the analysis result and adjust the level of detail of the generation.

[0105] The generation unit can apply different generation algorithms according to the user's past preferences when generating a painting. The generation unit applies, for example, an algorithm that generates a painting with a color tone that the user previously liked. For example, the generation unit applies an algorithm that generates a painting with a theme that the user previously liked. The generation unit can also apply an optimal generation algorithm based on the user's past preferences. This improves the user's satisfaction by generating a painting based on the user's past preferences. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI analyze the user's past preferences and apply an optimal generation algorithm.

[0106] The generation unit can estimate the user's emotion and adjust the length of the painting to be generated based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a longer painting. For example, when the user is stressed, the generation unit generates a shorter painting. In addition, when the user is in a hurry, the generation unit can also generate a short painting that is to the point. This improves the user's satisfaction by adjusting the length of the painting according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can have an AI analyze the user's emotion data and adjust the length of the painting.

[0107] The generation unit can determine the priority of generation based on the time of collection of the analysis results when generating the painting. The generation unit generates the most relevant painting based on, for example, the latest analysis result. For example, the generation unit generates the most important painting preferentially based on the time of collection of the analysis result. The generation unit can also determine the optimal generation order taking into account the time of collection of the analysis result. This improves user satisfaction by generating a painting based on the latest analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have AI analyze the time of collection of the analysis result to determine the priority of generation.

[0108] The generation unit can adjust the order of generation based on the relevance of the analysis results when generating paintings. The generation unit, for example, takes into consideration the relevance of the analysis results and generates the most relevant painting with priority. For example, the generation unit generates the most important painting with priority 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. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to analyze the relevance of the analysis results and adjust the order of generation.

[0109] The providing unit can estimate the user's emotion and adjust the way in which the painting is provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides the painting at a leisurely pace. For example, when the user is stressed, the providing unit quickly provides the painting. Also, when the user is in a hurry, the providing unit can provide the painting immediately. This improves the satisfaction of the user by adjusting the providing method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can cause an AI to analyze the user's emotion data and adjust the way in which the painting is provided.

[0110] The providing unit can select the optimal providing method by referring to the user's past reactions when providing a painting. For example, the providing unit preferentially selects a providing method that the user has preferred in the past. For example, the providing unit analyzes the user's past reactions and determines the optimal providing timing. The providing unit can also select the optimal providing means based on the user's past reactions. This improves the user's satisfaction by selecting a providing method based on the user's past reactions. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the user's past reactions and select the optimal providing method.

[0111] The providing unit can customize the means of providing the painting based on the user's current living situation when providing the painting. For example, when the user is at home, the providing unit provides the painting in an optimal manner for viewing at home. For example, when the user is out, the providing unit provides the painting to a mobile device in an optimal manner. The providing unit can also customize the optimal means of providing according to the user's living situation. This improves the user's satisfaction by customizing the means of providing according to the user's living situation. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI analyze the user's living situation and customize the means of providing.

[0112] The providing unit can estimate the user's emotion and determine the priority order of the paintings to be provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit preferentially provides paintings with a high relaxing effect. For example, when the user is stressed, the providing unit preferentially provides paintings with a high stress reduction effect. In addition, when the user is in a hurry, the providing unit can also preferentially provide paintings that can provide an effect in a short time. This improves the satisfaction of the user by preferentially providing paintings according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can cause an AI to analyze the user's emotion data and determine the priority order of the paintings to be provided.

[0113] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing a painting. For example, when the user is traveling, the providing unit provides a painting related to the culture or scenery of the travel destination. For example, when the user is commuting, the providing unit provides a painting related to the commuting route. The providing unit can also select the optimal providing method based on the geographical location information of the user. This improves the satisfaction of the user by selecting a providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI analyze the geographical location information of the user and select the optimal providing method.

[0114] The providing unit can analyze the user's social media activity and suggest a means of providing the painting when providing the painting. For example, when the user posts on social media about a painting, the providing unit provides a painting related to the post. For example, when the user posts on social media that he / she plans to participate in a specific event, the providing unit provides a painting related to the event. The providing unit can also analyze the user's social media activity and suggest an optimal means of providing. This improves the user's satisfaction by suggesting a means of providing based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI analyze the user's social media activity and suggest the optimal means of providing.

[0115] The recording unit can estimate the user's emotion and adjust the recording method of the emotion change based on the estimated user's emotion. For example, when the user is relaxed, the recording unit records the detailed emotion change. For example, when the user is stressed, the recording unit records the brief emotion change. In addition, when the user is in a hurry, the recording unit can also quickly record the emotion change. This allows the emotion change to be recorded more accurately by adjusting the recording method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can cause an AI to analyze the user's emotion data and adjust the recording method.

[0116] The recording unit can select the optimal recording method by referring to the user's past reactions when recording changes in emotions. For example, the recording unit preferentially selects a recording method that the user has preferred in the past. For example, the recording unit analyzes the user's past reactions and determines the optimal recording timing. The recording unit can also select the optimal recording means based on the user's past reactions. This allows more accurate recording of changes in emotions by selecting a recording method based on the user's past reactions. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can have AI analyze the user's past reactions and select the optimal recording method.

[0117] The recording unit can customize the recording means based on the user's current living situation when recording changes in emotions. For example, when the user is at home, the recording unit records changes in emotions in a manner optimal for recording at home. For example, when the user is out, the recording unit records changes in emotions in a manner optimal for a mobile device. The recording unit can also customize the optimal recording means according to the user's living situation. This allows more accurate recording of changes in emotions by customizing the recording means according to the user's living situation. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can have AI analyze the user's living situation and customize the recording means.

[0118] The recording unit can estimate the user's emotion and determine the priority of emotions to be recorded based on the estimated user's emotion. For example, when the user is relaxed, the recording unit preferentially records emotions with a high relaxation effect. For example, when the user is stressed, the recording unit preferentially records emotions with a high stress reduction effect. In addition, when the user is in a hurry, the recording unit can also preferentially record emotions that can be effective in a short time. In this way, by determining the priority of emotions according to the user's emotion, more accurate changes in emotion can be recorded. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can have an AI analyze the user's emotion data and determine the priority of emotions to be recorded.

[0119] The recording unit can select the optimal recording method in consideration of the geographical location information of the user when recording changes in emotion. For example, when the user is traveling, the recording unit records emotions related to the culture and scenery of the travel destination. For example, when the user is commuting, the recording unit records emotions related to the commuting route. The recording unit can also select the optimal recording method based on the geographical location information of the user. As a result, by selecting a recording method based on the geographical location information of the user, it is possible to record changes in emotion more accurately. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can have AI analyze the geographical location information of the user and select the optimal recording method.

[0120] The recording unit can analyze the user's social media activity and suggest a means of recording when recording changes in emotions. For example, when a user posts on social media about emotions, the recording unit records emotions related to the post. For example, when a user posts on social media that he or she plans to participate in a specific event, the recording unit records emotions related to the event. The recording unit can also analyze the user's social media activity and suggest an optimal recording means. This makes it possible to record changes in emotions more accurately by suggesting a recording means based on the user's social media activity. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can have AI analyze the user's social media activity and suggest an optimal recording means.

[0121] The intervention unit can estimate the user's emotion and adjust the intervention method based on the estimated user's emotion. For example, when the user is relaxed, the intervention unit selects a gentle intervention method. For example, when the user is stressed, the intervention unit selects a quick intervention method. In addition, when the user is in a hurry, the intervention unit can also select a method of immediate intervention. This allows for more effective intervention by adjusting the intervention method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the intervention unit can cause an AI to 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 reactions at the time of intervention. For example, the intervention unit preferentially selects an intervention method that the user has preferred in the past. For example, the intervention unit analyzes the user's past reactions and determines the optimal intervention timing. The intervention unit can also select the optimal intervention means based on the user's past reactions. This enables more effective intervention by selecting an intervention method based on the user's past reactions. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's past reactions and select the optimal intervention method.

[0123] The intervention unit can customize the intervention means based on the user's current living situation at the time of intervention. For example, when the user is at home, the intervention unit selects the optimal method for intervention at home. For example, when the user is out, the intervention unit performs intervention in the optimal method for the mobile device. The intervention unit can also customize the optimal intervention means according to the user's living situation. This enables more effective intervention by customizing the intervention means according to the user's living situation. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's living situation and customize the intervention means.

[0124] The intervention unit can estimate the user's emotion and determine the priority of interventions based on the estimated user's emotion. For example, when the user is relaxed, the intervention unit prioritizes interventions with a high relaxation effect. For example, when the user is stressed, the intervention unit prioritizes interventions with a high stress reduction effect. In addition, when the user is in a hurry, the intervention unit can also prioritize interventions that can be effective in a short time. This enables more effective interventions by determining the priority of interventions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the intervention unit can cause an AI to analyze the user's emotion data and determine the priority of interventions.

[0125] The intervention unit can select the optimal intervention method in consideration of the geographical location information of the user at the time of intervention. For example, when the user is traveling, the intervention unit performs intervention related to the culture and scenery of the travel destination. For example, when the user is commuting, the intervention unit performs intervention related to the commuting route. The intervention unit can also select the optimal intervention method based on the geographical location information of the user. As a result, more effective intervention is possible by selecting an intervention method based on the geographical location information of the user. A part or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the geographical location information of the user and select the optimal intervention method.

[0126] At the time of intervention, the intervention unit can analyze the user's social media activity and suggest a means of intervention. For example, when a user posts on social media about emotions, the intervention unit performs an intervention related to the post. For example, when a user posts on social media that he or she plans to participate in a specific event, the intervention unit performs an intervention related to the event. The intervention unit can also analyze the user's social media activity and suggest an optimal intervention means. This enables more effective intervention by suggesting an intervention means based on the user's social media activity. A part or all of the above-mentioned processing in the intervention unit may be performed, for example, using AI, or may be performed without using AI. For example, the intervention unit can have AI analyze the user's social media activity and suggest an optimal intervention means.

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

[0128] The mental state stabilization system may further include an audio guide unit. The audio guide unit may provide an audio guide with a relaxing effect when the user is viewing a painting. For example, the audio guide unit may provide an explanation regarding the background of the painting or play music with a relaxing effect. The audio guide unit may also provide words of encouragement or advice on how to relax according to the user's emotions. This allows the user to experience a relaxing effect not only visually but also aurally, which may contribute to stabilizing the mental state.

[0129] The collection unit can also collect biometric information of the user. For example, the collection unit can collect biometric information such as the user's heart rate, blood pressure, and body temperature. This makes it possible to generate a painting that takes into account the user's physical condition. For example, 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 according to the user's preferences can be generated. This makes it possible to provide more personalized support.

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

[0131] The generation unit can generate a painting based on the user's hobbies and interests. For example, the generation unit can generate a painting taking into consideration the user's favorite animals, scenery, colors, and the like. This can promote mood improvement by providing a painting according to the user's hobbies and interests. For example, if the user likes cats, a painting with a cat motif can be generated. Also, if the user likes seascapes, a painting depicting a seascape can be generated. This can provide a painting according to the user's hobbies and interests, resulting in higher satisfaction.

[0132] The providing unit can provide paintings to smart home devices in addition to the user's device. For example, the providing unit can display paintings on a smart display or a smart TV. This allows the user to appreciate paintings in various places in the home and achieve a relaxing effect. For example, a painting can be displayed on a smart TV in the living room so that the whole family can appreciate it. In addition, a painting can be displayed on a smart display in the bedroom so that the user can relax before going to sleep. This allows the user to achieve a relaxing effect throughout the entire living space.

[0133] The recording unit can use voice input when recording changes in the user's emotions. For example, the recording unit can record changes in emotions by having the user vocally state his or her impressions after looking at a painting. This allows the user to record changes in emotions without much effort. For example, when the user vocally inputs "looking at this painting made me feel relaxed," the recording unit can record the changes in emotions. Also, when the user vocally inputs "I feel better," the recording unit can record the changes in emotions. This allows the user's changes in emotions to be recorded more accurately.

[0134] The intervention unit can suggest relaxation techniques according to the user's emotions. For example, when the user feels stressed, the intervention unit can suggest relaxation techniques such as deep breathing, meditation, yoga, etc. This allows the user to improve his / her mood more effectively 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." The intervention unit can also display a message to the user such as "Meditate to calm your mind." This can promote improvement in mood by suggesting relaxation techniques according to the user's emotions.

[0135] The collection unit can collect dietary information of the user. For example, the collection unit can collect what the user ate and at what time of day the user ate. This makes it possible to generate a painting based on the user's dietary pattern. For example, a painting that has a relaxing effect can be generated after the user has eaten breakfast, and a painting that lifts the mood can be generated after dinner. This makes it possible to provide support according to the user's dietary pattern.

[0136] The analysis unit can analyze the user's sleep data. For example, the analysis unit can analyze how long the user slept and what the quality of the sleep was. This makes it possible to generate a painting based on the user's sleep pattern. For example, if the user slept sufficiently, a painting that energizes the user can be generated, and if the user slept insufficiently, a painting that has a relaxing effect can be generated. This makes it possible to provide support according to the user's sleep pattern.

[0137] The generation unit can estimate the user's emotion and select a theme of the painting based on the estimated user's emotion. For example, when the user is sad, the generation unit can generate a painting with a theme of comfort. For example, when the user is happy, the generation unit can generate a painting with a theme of celebration. In addition, when the user is anxious, the generation unit can generate a painting with a theme that gives a sense of security. In this way, by providing a painting with a theme according to the user's emotion, it is possible to promote improvement of the user's mood.

[0138] The process flow of the second embodiment will be briefly described below.

[0139] Step 1: The collection unit collects information on the weather and the mental state of the previous day. The collection unit can collect weather information from the Internet, for example. The collection unit can also collect data on the mental state of the previous day input by the user. For example, the collection unit obtains weather information using a specific API, and collects data on the mental state of the previous day from the user in the form of a questionnaire. Step 2: The analysis unit analyzes the collected weather information and the data of the previous day's mental state. The analysis unit can analyze the collected data, for example, using statistical analysis or machine learning algorithms. For example, the analysis unit analyzes the relationship between the weather information and the mental state using correlation analysis, and builds a prediction model using regression analysis. Step 3: The generation unit generates a painting that the user likes based on the analysis result. The generation unit can generate a painting based on the analysis result, for example, using a generation AI. For example, the generation unit can generate a painting according to the user's preferences using a text generation AI (e.g., LLM). The generation unit can also generate a painting according to the user's preferences using a multimodal generation AI. For example, the generation unit generates a painting in bright colors on a sunny day and a painting in muted colors on a rainy day. Step 4: The providing unit provides the generated painting to the user's device. The providing unit can provide the generated painting to the user's device, for example, by using an app notification or email transmission. For example, the providing unit can display the painting on the user's smartphone by using an app notification. The providing unit can also send the painting to the user's email address by using email transmission. Step 5: The recording unit records the change in the user's emotions after viewing the painting. The recording unit can record the change in the user's emotions, for example, by using a diary format or input of an emotion score. For example, the recording unit inputs the user's impressions after viewing the painting, such as "I feel better" or "No change." The recording unit can also use the emotion score to quantify and record the change in the user's emotions. Step 6: The intervention unit displays a message encouraging the user to consult with a specialist if the period of time during which the mood does not improve continues. The intervention unit can perform early intervention, for example, by displaying a message encouraging the user to consult with a specialist. For example, the intervention unit displays a message such as "Consult with a specialist" 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0142] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit is realized, for example, by at least one of the smart device 14 and the data processing device 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 mental state of the previous day input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a painting based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated painting to the user's device. The recording unit is realized, for example, by the control unit 46A of the smart device 14, and records changes in the user's emotions. The intervention unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and displays a message encouraging the user to consult with an expert if a period of time continues in which no improvement in mood is seen. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0151] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 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 mental state of the previous day input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a painting based on the analysis result. The provision unit is realized, 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 realized, for example, by the control unit 46A of the smart glasses 214, and records changes in the user's emotions. The intervention unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and displays a message encouraging the user to consult with a specialist if a period of time continues in which no improvement in mood is observed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

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

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

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

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

[0177] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0183] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0187] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, recording unit, and intervention unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit acquires weather information from the Internet using the communication I / F 44 of the robot 414, and collects data on the mental state of the previous day input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a painting based on the analysis result. The provision unit is realized, 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 realized, for example, by the control unit 46A of the robot 414, and records changes in the user's emotions. The intervention unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and displays a message encouraging the user to consult with an expert if a period of time continues in which no improvement in mood is seen. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0193] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0194] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0197] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0198] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] In addition, 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 upon request from the data processing device 12.

[0203] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0205] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0209] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0211] (Appendix 1) A collection unit for collecting information on the weather or the previous day's mental state; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a painting based on the information analyzed by the analysis unit; a providing unit that provides a user with the painting generated by the generating unit; a recording unit that records a change in a user's emotion regarding the painting provided by the providing unit; and an intervention unit that performs early intervention based on the information recorded by the recording unit. A system characterized by: (Appendix 2) The collecting unit includes: Collecting weather information from the Internet 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect user-entered mental state data from the previous day 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze collected weather information and previous day's mental state data 2. The system of claim 1, (Appendix 5) The generation unit is Generate paintings that the user likes based on the analysis results 2. The system of claim 1, (Appendix 6) The providing unit is The generated painting is delivered to the user's device. 2. The system of claim 1, (Appendix 7) The recording unit is Recording changes in emotions after users view paintings 2. The system of claim 1, (Appendix 8) The intervention unit is If no improvement in mood is seen for a period of time, a message will be displayed encouraging the user to seek professional help. 2. The system of claim 1, (Appendix 9) The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting weather information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Analyze the user's past mental state data and select the optimal collection method 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting weather information, filtering is performed based on the user's current activity status and lifestyle. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Estimate the user's emotions and prioritize the weather information to be collected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting weather information, the system takes into account the user's geographic location information and prioritizes collecting information that is more relevant. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting weather information, analyze users' social media activity to collect related information. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the level of detail based on the importance of weather information and mental state data 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 18) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 21) The generation unit is Estimate the user's emotions and adjust the way the painting is generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The generation unit is When generating paintings, the level of detail is adjusted based on the importance of the analysis results. 2. The system of claim 1, (Appendix 23) The generation unit is When generating paintings, different generation algorithms are applied depending on the user's past preferences. 2. The system of claim 1, (Appendix 24) The generation unit is Estimate the user's emotion and adjust the length of the painting to be generated based on the estimated emotion. 2. The system of claim 1, (Appendix 25) The generation unit is When generating paintings, the priority of generation is determined based on the time when analysis results were collected. 2. The system of claim 1, (Appendix 26) The generation unit is When generating paintings, the order of generation is adjusted based on the relevance of the analysis results. 2. The system of claim 1, (Appendix 27) The providing unit is The method estimates the user's emotions and adjusts the way in which pictures are presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The providing unit is When providing a painting, the optimal method of providing it is selected by referring to the user's past responses. 2. The system of claim 1, (Appendix 29) The providing unit is When providing pictures, customize the means of providing them based on the user's current living situation 2. The system of claim 1, (Appendix 30) The providing unit is Estimate the user's emotions and prioritize the paintings to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 31) The providing unit is When providing paintings, the optimal delivery method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 32) The providing unit is When submitting a painting, the system analyzes the user's social media activity to suggest ways to submit the painting. 2. The system of claim 1, (Appendix 33) The recording unit is To estimate a user's emotion and adjust a method of recording changes in emotion based on the estimated user's emotion. 2. The system of claim 1, (Appendix 34) The recording unit is When recording changes in emotions, the system selects the optimal recording method by referring to the user's past reactions. 2. The system of claim 1, (Appendix 35) The recording unit is When recording changes in emotions, customize the recording method based on the user's current life situation 2. The system of claim 1, (Appendix 36) The recording unit is Estimate the user's emotion and determine the priority of emotions to be recorded based on the estimated user's emotion. 2. The system of claim 1, (Appendix 37) The recording unit is When recording changes in emotions, the optimal recording method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 38) The recording unit is When recording changes in emotions, the system analyzes the user's social media activity and suggests ways to record them. 2. The system of claim 1, (Appendix 39) The intervention unit is Estimating a user's emotions and adjusting an intervention method based on the estimated user's emotions 2. The system of claim 1, (Appendix 40) The intervention unit is When intervening, the optimal intervention method is selected by referring to the user's past responses. 2. The system of claim 1, (Appendix 41) The intervention unit is At the time of intervention, customize the intervention measures based on the user's current life situation 2. The system of claim 1, (Appendix 42) The intervention unit is Estimating user emotions and prioritizing interventions based on the estimated user emotions 2. The system of claim 1, (Appendix 43) The intervention unit is When performing intervention, the optimal intervention method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 44) The intervention unit is During intervention, the system analyzes users' social media activity to suggest intervention measures. 2. The system of claim 1, [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit for collecting information on the weather or the previous day's mental state; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a painting based on the information analyzed by the analysis unit; a providing unit that provides a user with the painting generated by the generating unit; a recording unit that records a change in a user's emotion regarding the painting provided by the providing unit; and an intervention unit that performs early intervention based on the information recorded by the recording unit. A system characterized by:

2. The collecting unit includes: Collecting weather information from the Internet 2. The system of claim 1 .

3. The collecting unit includes: Collect user-entered mental state data from the previous day 2. The system of claim 1 .

4. The analysis unit is Analyze collected weather information and previous day's mental state data 2. The system of claim 1 .

5. The generation unit is Generate paintings that the user likes based on the analysis results 2. The system of claim 1 .

6. The providing unit is The generated painting is delivered to the user's device.

2. The system of claim 1 .

7. The recording unit is Recording changes in emotions after users view paintings 2. The system of claim 1 .

8. The intervention unit is If no improvement in mood is seen for a period of time, a message will be displayed encouraging the user to seek professional help.

2. The system of claim 1 .

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