system

Abstract

An object of a system according to an embodiment is to comprehensively collect, analyze, and visualize data that affects mental health.SOLUTION: A system includes a collection unit, an analysis unit, a visualization unit, and a storage unit. The collection unit collects data of the exercise time, the smartphone usage time, the SNS used, and the quality of sleep. The analysis unit comprehensively analyzes the data collected by the collection unit. The visualization unit visualizes the state of the mental health on the basis of the data analyzed by the analysis unit. The storage unit arranges and stores the data visualized by the visualization unit as a psychological counseling chart.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately collect, analyze, and visualize the wide variety of data that affect mental health in a centralized manner, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively collect, analyze, and visualize data that affect mental health. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, and a storage unit. The collection unit collects data on exercise time, smartphone usage time, SNS usage, and sleep quality. The analysis unit comprehensively analyzes the data collected by the collection unit. The visualization unit visualizes the mental health status based on the data analyzed by the analysis unit. The storage unit organizes and stores the data visualized by the visualization unit as a psychological counseling chart. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively collect, analyze, and visualize data that affect mental health. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A mental healthcare system according to an embodiment of the present invention comprehensively collects, analyzes, visualizes, and stores various data affecting a user's mental health. The mental healthcare system collects data such as exercise time, smartphone usage time, social media usage, and sleep quality, as well as data recorded by the user, such as mood, physical condition, and diary entries. This data is comprehensively analyzed, visualizes the user's mental health status, and organizes and stores it as a psychological counseling chart. For example, the mental healthcare system measures exercise time and sleep quality using device sensors. It also collects data on smartphone usage time and social media usage. Next, it collects data recorded by the user, such as mood, physical condition, and diary entries. This allows it to obtain data on the user's lifestyle habits and subjective mental health. The mental healthcare system then comprehensively analyzes the collected data and evaluates the user's mental health status. For example, it analyzes the impact of exercise time and sleep quality on mental health and provides feedback to the user. The mental healthcare system then displays the analysis results in a graph, allowing the user to grasp their mental health status at a glance. The visualized data is then organized and stored as a psychological counseling chart. This allows the user to comprehensively understand their mental health status and take appropriate measures. It is also possible to receive advice based on detailed data during psychological counseling. For example, if a lack of exercise is having a negative impact on mental health, the user can receive advice to exercise more. This allows the mental healthcare system to comprehensively grasp the user's mental health state and take appropriate measures. It is also possible to receive advice based on detailed data during psychological counseling. For example, if a lack of exercise is having a negative impact on mental health, the user can receive advice to exercise more. This is expected to improve the user's mental health.

[0029] A mental health care system according to an embodiment includes a collection unit, an analysis unit, a visualization unit, and a storage unit. The collection unit collects data on exercise time, smartphone usage time, social media accounts, and sleep quality. For example, the collection unit measures exercise time and sleep quality using a device sensor. The collection unit can also collect data on smartphone usage time and social media accounts. The collection unit can also collect data on mood, physical condition, diary entries, and other data recorded by the user. For example, the collection unit allows the user to record their daily mood and physical condition within an app. The collection unit can also use a diary function to record the events and feelings of the day. The analysis unit comprehensively analyzes the data collected by the collection unit. For example, the analysis unit analyzes the impact of exercise time and sleep quality on mental health. The analysis unit can also analyze changes in mood and physical condition and evaluate the mental health state. The visualization unit visualizes the mental health state based on the data analyzed by the analysis unit. For example, the visualization unit displays the analysis results in a graph, allowing the user to understand their mental health state at a glance. The storage unit organizes and stores the data visualized by the visualization unit as a psychological counseling chart. For example, the storage unit organizes the visualized data and stores it so that it can be used during psychological counseling. This allows the mental health care system according to the embodiment to comprehensively grasp the user's mental health status and take appropriate measures. It also makes it possible to receive advice based on detailed data during psychological counseling. For example, if a lack of exercise is negatively affecting mental health, the user can receive advice to exercise more.

[0030] The collection unit can measure exercise time or sleep quality using a sensor of the device. Examples of the device's sensors include, but are not limited to, an acceleration sensor and a heart rate sensor. For example, the collection unit can measure exercise time using an acceleration sensor. The collection unit can also measure sleep quality using a heart rate sensor. Furthermore, the collection unit can combine multiple sensors to simultaneously measure exercise time and sleep quality. For example, the collection unit combines an acceleration sensor and a heart rate sensor to simultaneously measure exercise time and sleep quality. This allows accurate collection of data on exercise time and sleep quality using the device's sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from the sensor into a generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect data on smartphone usage time or social media used. Smartphone usage time includes, but is not limited to, app usage time and screen-on time. For example, the collection unit collects smartphone app usage time. The collection unit can also collect smartphone screen-on time. Furthermore, the collection unit can collect data on social media used. For example, the collection unit collects data such as the frequency of posts on the social media used by the user, the number of likes, and the number of comments. By collecting data on smartphone usage time and social media used, data on the user's lifestyle habits can be obtained. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input smartphone usage data into a generation AI and have the generation AI analyze the data.

[0032] The collection unit can collect data on mood, physical condition, and diary entries recorded by the user. Mood recording methods include, but are not limited to, mood scores and emotion labels. For example, the collection unit may have the user record their daily mood using a score within the app. The collection unit may also record mood using emotion labels. Physical condition recording methods include, but are not limited to, body temperature, blood pressure, and heart rate. For example, the collection unit may have the user record their daily body temperature. The collection unit may also record blood pressure and heart rate. Diary recording methods include, but are not limited to, daily events and emotional changes. For example, the collection unit may record the user's daily events and feelings as a diary entry. By collecting data on the user's mood, physical condition, diary entries, and other data, data related to subjective mental health can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the mood, physical condition, and diary entries recorded by the user into a generation AI and have the generation AI analyze the data.

[0033] The analysis unit can comprehensively analyze the collected data and evaluate the mental health state. Methods for comprehensive analysis include, but are not limited to, data integration methods and analysis algorithms. The analysis unit can integrate and analyze collected data, such as exercise time, sleep quality, mood, physical condition, and diary entries. The analysis unit can also analyze data using a machine learning model. Criteria for evaluating the mental health state include, but are not limited to, stress level, happiness level, and depressive symptoms. The analysis unit can evaluate, for example, stress level. The analysis unit can also evaluate happiness level and depressive symptoms. This allows for an accurate evaluation of the mental health state by comprehensively analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0034] The visualization unit can display the analysis results in a graph, allowing the user to understand their mental health state at a glance. Types of graphs include, but are not limited to, line graphs, bar graphs, and pie charts. For example, the visualization unit displays the analysis results in a line graph. The visualization unit can also display the analysis results in a bar graph or a pie chart. Displaying the analysis results in a graph makes it easier for the user to visually understand their mental health state. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the analysis results to a generation AI and cause the generation AI to generate a graph.

[0035] The storage unit can organize and store the visualized data as a psychological counseling chart. A psychological counseling chart may include, for example, items to be recorded and a data format, but is not limited to these examples. The storage unit, for example, organizes the visualized data and stores it as a psychological counseling chart. The storage unit can also standardize the data format and store it as a chart. By organizing and storing the visualized data as a psychological counseling chart, detailed advice based on the data can be provided during counseling. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the visualized data into a generation AI and have the generation AI organize and store the chart.

[0036] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit prioritizes collecting data that the user frequently collected in the past. The collection unit can also suggest the optimal collection timing based on the user's past data collection history. Furthermore, the collection unit can customize the collection method based on the user's past data collection history. For example, the collection unit analyzes the type and frequency of data collected by the user in the past and selects the optimal collection method. This enables the optimal collection method to be selected by analyzing the user's past data collection history, enabling efficient data collection. Some 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 input the user's past data collection history into a generation AI and have the generation AI select the optimal collection method.

[0037] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is at work, the collection unit can prioritize collecting work-related data. Also, if the user is on vacation, the collection unit can prioritize collecting data related to that area. Furthermore, if the user has a specific area of ​​interest, the collection unit can prioritize collecting data related to that area. For example, the collection unit filters the data to be collected based on the user's living situation and areas of interest. In this way, by filtering the data based on the user's living situation and areas of interest, highly relevant data can be preferentially collected. Some 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 input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the user prefers image input, the collection unit can also prioritize collecting image data. For example, the collection unit selects the optimal collection means depending on the user's input method. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some 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 input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's trip. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the data.

[0040] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data regarding locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and collects related data. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also suggest the optimal collection timing based on the user's past feedback. Furthermore, the collection unit can adjust the type of data to be collected based on the user's past feedback. For example, the collection unit analyzes feedback provided by the user in the past and customizes the collection method. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described 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 input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and provides detailed results. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the 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 input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm specialized for exercise to exercise data. The analysis unit can also apply an analysis algorithm specialized for sleep to sleep data. The analysis unit can also apply an analysis algorithm specialized for mood to mood data. For example, the analysis unit selects and applies an optimal analysis algorithm depending on the data category. This enables highly accurate data analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to select and apply the optimal analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also understand analysis trends from the user's past analysis results and improve accuracy. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and improves the accuracy of the current analysis result. In this way, the accuracy of the current analysis result can be improved by referring to the user's past analysis results. 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 input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the time when the data was collected. For example, the analysis unit determines the priority of analysis based on the time when the data was collected. This enables analysis that emphasizes the most recent 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 input the time when the data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the 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 input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows the analysis results to be provided that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] The visualization unit can adjust the level of detail of the display based on the importance of the data during visualization. For example, the visualization unit provides a detailed display for data with high importance. The visualization unit can also provide a simplified display for data with low importance. Furthermore, the visualization unit can determine the priority of the display according to the importance of the data. For example, the visualization unit prioritizes display of data with high importance and provides detailed information. This enables efficient data display by adjusting the level of detail of the display based on the importance of the data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0049] The visualization unit can apply different display algorithms depending on the data category during visualization. For example, the visualization unit can apply a display algorithm specialized for exercise to exercise data. The visualization unit can also apply a display algorithm specialized for sleep to sleep data. The visualization unit can also apply a display algorithm specialized for mood to mood data. For example, the visualization unit selects and applies an optimal display algorithm depending on the data category. This enables highly accurate data display by applying different display algorithms depending on the data category. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the data category to the generation AI and cause the generation AI to select and apply the optimal display algorithm.

[0050] The visualization unit can improve the accuracy of the display by referring to the user's past visualization results when visualizing. For example, the visualization unit corrects the current display result based on the user's past visualization results. The visualization unit can also understand display trends and improve accuracy from the user's past visualization results. Furthermore, the visualization unit can adjust the display algorithm by referring to the user's past visualization results. For example, the visualization unit analyzes the user's past visualization results and improves the accuracy of the current display result. In this way, the accuracy of the current display result can be improved by referring to the user's past visualization results. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the user's past visualization results to the generation AI and cause the generation AI to improve the accuracy of the display.

[0051] During visualization, the visualization unit can determine the display priority based on the time when the data was collected. The visualization unit, for example, prioritizes displaying the most recent data. The visualization unit can also prioritize the most recent data while referring to past data. Furthermore, the visualization unit can adjust the display priority according to the time when the data was collected. For example, the visualization unit determines the display priority based on the time when the data was collected. This enables display that prioritizes the most recent data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the time when the data was collected to the generation AI and have the generation AI determine the display priority.

[0052] The visualization unit can adjust the display order based on the relevance of the data during visualization. For example, the visualization unit prioritizes display of highly relevant data. The visualization unit can also postpone display of less relevant data. Furthermore, the visualization unit can adjust the display order according to the relevance of the data. For example, the visualization unit adjusts the display order based on the relevance of the data. This enables efficient data display by adjusting the display order based on the relevance of the data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the display order.

[0053] During visualization, the visualization unit can adjust the use of technical terminology in the display according to the user's level of expertise. For example, the visualization unit can provide display results in simple language to a user with little technical expertise. The visualization unit can also provide display results using detailed technical terminology to a user with technical expertise. Furthermore, the visualization unit can adjust the way in which the display results are expressed according to the user's level of expertise. For example, the visualization unit adjusts the use of technical terminology in the display according to the user's level of expertise. This allows for providing display results that are easy for the user to understand by adjusting the use of technical terminology in the display according to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit stores data with high importance in detail. The storage unit can also store data with low importance in a simplified manner. Furthermore, the storage unit can determine the priority of storage according to the importance of the data. For example, the storage unit prioritizes storing data with high importance and provides detailed information. This enables efficient data storage by adjusting the level of detail of storage based on the importance of the data. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of storage.

[0055] The storage unit can apply different storage algorithms depending on the data category when storing the data. For example, the storage unit can apply a storage algorithm specialized for exercise to exercise data. The storage unit can also apply a storage algorithm specialized for sleep to sleep data. The storage unit can also apply a storage algorithm specialized for mood to mood data. For example, the storage unit selects and applies an optimal storage algorithm depending on the data category. This enables highly accurate data storage by applying different storage algorithms depending on the data category. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input the data category to the generation AI and cause the generation AI to select and apply the optimal storage algorithm.

[0056] The storage unit can improve the accuracy of storage by referring to the user's past storage results when storing data. The storage unit, for example, corrects the current storage result based on the user's past storage results. The storage unit can also understand storage trends from the user's past storage results and improve accuracy. The storage unit can also adjust the storage algorithm by referring to the user's past storage results. For example, the storage unit analyzes the user's past storage results and improves the accuracy of the current storage result. In this way, the accuracy of the current storage result can be improved by referring to the user's past storage results. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past storage results into a generation AI and cause the generation AI to improve the accuracy of storage.

[0057] The storage unit can determine the priority of storage based on the time when the data was collected when storing the data. For example, the storage unit prioritizes storing the most recent data. The storage unit can also prioritize the most recent data while referring to past data. Furthermore, the storage unit can adjust the priority of storage according to the time when the data was collected. For example, the storage unit determines the priority of storage based on the time when the data was collected. This enables storage that prioritizes the most recent data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of storage.

[0058] The storage unit can adjust the order of storage based on the relevance of the data when storing the data. For example, the storage unit prioritizes storing highly relevant data. The storage unit can also postpone storing less relevant data. Furthermore, the storage unit can adjust the order of storage according to the relevance of the data. For example, the storage unit adjusts the order of storage based on the relevance of the data. This enables efficient data storage by adjusting the order of storage based on the relevance of the data. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of storage.

[0059] The storage unit can adjust the use of technical terminology for storage according to the user's level of expertise when storing the results. For example, the storage unit can provide stored results in simple language to a user with little expertise. The storage unit can also provide stored results using detailed technical terminology to a user with expertise. Furthermore, the storage unit can adjust the way in which the stored results are expressed according to the user's level of expertise. For example, the storage unit can adjust the use of technical terminology for storage according to the user's level of expertise. This allows the storage unit to provide stored results that are easy for the user to understand by adjusting the use of technical terminology for storage according to the user's level of expertise. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

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

[0061] The mental healthcare system may further include a nutritional evaluation unit that collects the user's dietary data and evaluates their nutritional status. The nutritional evaluation unit collects the content, calories, and nutrient balance of the user's meals and analyzes their impact on mental health. For example, the nutritional evaluation unit may allow the user to input their daily dietary information through a food recording app. The nutritional evaluation unit may also analyze photos of meals taken by the user and automatically recognize the ingredients and nutrients consumed. Furthermore, the nutritional evaluation unit may integrate the user's dietary data with other collected data to evaluate the relationship between diet and mental health. By utilizing the user's dietary data, the relationship between nutritional status and mental health can be understood and appropriate dietary advice can be provided.

[0062] The mental healthcare system may further include an exercise monitoring unit that monitors the user's exercise data in real time and provides immediate feedback on the effectiveness of the exercise. The exercise monitoring unit collects the type, intensity, and duration of the user's exercise in real time and immediately evaluates the effectiveness of the exercise. For example, the exercise monitoring unit can display the user's heart rate and calories burned in real time while the user is running. The exercise monitoring unit can also evaluate the accuracy of the user's poses and breathing rhythm while the user is doing yoga. Furthermore, the exercise monitoring unit can integrate the user's exercise data with other collected data to comprehensively evaluate the impact of exercise on mental health. In this way, by monitoring the user's exercise data in real time, immediate feedback on the effectiveness of the exercise can be provided and appropriate exercise guidance can be provided.

[0063] The mental healthcare system may further include a sleep analysis unit that analyzes the user's sleep data in detail and provides advice for improving sleep quality. The sleep analysis unit collects data such as the user's sleep patterns, movements during sleep, and heart rate to evaluate the quality of sleep. For example, the sleep analysis unit may analyze how long the user spends in deep sleep and provide advice for increasing the proportion of deep sleep. The sleep analysis unit may also monitor the user's bedroom environment (temperature, humidity, noise, etc.) and make suggestions for creating a comfortable sleeping environment. Furthermore, the sleep analysis unit may integrate the user's sleep data with other collected data and evaluate the relationship between sleep and mental health. This allows for detailed analysis of the user's sleep data to provide specific advice for improving sleep quality and improve mental health.

[0064] The mental healthcare system may further include a hobby evaluation unit that evaluates the user's hobbies and interests and suggests activities for relaxation and stress relief. The hobby evaluation unit collects data on the activities and hobbies in which the user is interested and makes suggestions for relaxation and stress relief. For example, if the user likes listening to music, the hobby evaluation unit may suggest relaxing music. Also, if the user enjoys reading, the hobby evaluation unit may recommend books that are useful for stress relief. Furthermore, the hobby evaluation unit may integrate the user's hobby data with other collected data and evaluate the association between hobbies and mental health. In this way, by evaluating the user's hobbies and interests, specific activities for relaxation and stress relief can be suggested, thereby improving mental health.

[0065] The mental healthcare system may further include a workplace assessment unit that evaluates the user's work environment and suggests measures to reduce workplace stress. The workplace assessment unit collects data on the user's work activities and stress level and evaluates the work environment. For example, if the user is working long hours, the workplace assessment unit may suggest that the user take appropriate breaks. Also, if the user is feeling stressed about interpersonal relationships at work, the workplace assessment unit may recommend that the user improve their communication skills. Furthermore, the workplace assessment unit may integrate the user's workplace data with other collected data and evaluate the relationship between the work environment and mental health. As a result, by evaluating the user's work environment, workplace stress can be reduced and mental health can be improved.

[0066] The mental healthcare system may further include a learning assessment unit that collects the user's learning data and evaluates the user's learning progress and stress level. The learning assessment unit collects data on the content the user is studying, the study time, and the user's stress level during study, and evaluates the user's learning progress and stress level. For example, if the user is studying for an exam, the learning assessment unit may suggest that the user take appropriate breaks. If the user is learning a new skill, the learning assessment unit may visualize the user's learning progress and provide advice to maintain motivation. Furthermore, the learning assessment unit may integrate the user's learning data with other collected data and evaluate the relationship between learning and mental health. In this way, by collecting the user's learning data and evaluating the user's learning progress and stress level, effective study methods may be suggested, leading to improvements in mental health.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects data on exercise time, smartphone usage time, social media usage, and sleep quality. For example, the device's sensors can be used to measure exercise time and sleep quality, and data on smartphone usage time and social media usage can be collected. Data such as user-recorded mood, physical condition, and diary entries can also be collected. For example, within the app, users can record their daily mood and physical condition, and use the diary function to record the events and feelings of the day. Step 2: The analysis unit comprehensively analyzes the data collected by the collection unit. For example, it analyzes the impact of exercise time and sleep quality on mental health, and analyzes changes in mood and physical condition to evaluate the state of mental health. Step 3: The visualization unit visualizes the mental health status based on the data analyzed by the analysis unit. For example, it displays the analysis results in a graph so that the user can understand their mental health status at a glance. Step 4: The storage unit organizes and stores the data visualized by the visualization unit as a psychological counseling chart. For example, the visualized data is organized and stored so that it can be used during psychological counseling.

[0069] (Example 2) A mental healthcare system according to an embodiment of the present invention comprehensively collects, analyzes, visualizes, and stores various data affecting a user's mental health. The mental healthcare system collects data such as exercise time, smartphone usage time, social media usage, and sleep quality, as well as data recorded by the user, such as mood, physical condition, and diary entries. This data is comprehensively analyzed, visualizes the user's mental health status, and organizes and stores it as a psychological counseling chart. For example, the mental healthcare system measures exercise time and sleep quality using device sensors. It also collects data on smartphone usage time and social media usage. Next, it collects data recorded by the user, such as mood, physical condition, and diary entries. This allows it to obtain data on the user's lifestyle habits and subjective mental health. The mental healthcare system then comprehensively analyzes the collected data and evaluates the user's mental health status. For example, it analyzes the impact of exercise time and sleep quality on mental health and provides feedback to the user. The mental healthcare system then displays the analysis results in a graph, allowing the user to grasp their mental health status at a glance. The visualized data is then organized and stored as a psychological counseling chart. This allows the user to comprehensively understand their mental health status and take appropriate measures. It is also possible to receive advice based on detailed data during psychological counseling. For example, if a lack of exercise is having a negative impact on mental health, the user can receive advice to exercise more. This allows the mental healthcare system to comprehensively grasp the user's mental health state and take appropriate measures. It is also possible to receive advice based on detailed data during psychological counseling. For example, if a lack of exercise is having a negative impact on mental health, the user can receive advice to exercise more. This is expected to improve the user's mental health.

[0070] A mental health care system according to an embodiment includes a collection unit, an analysis unit, a visualization unit, and a storage unit. The collection unit collects data on exercise time, smartphone usage time, social media accounts, and sleep quality. For example, the collection unit measures exercise time and sleep quality using a device sensor. The collection unit can also collect data on smartphone usage time and social media accounts. The collection unit can also collect data on mood, physical condition, diary entries, and other data recorded by the user. For example, the collection unit allows the user to record their daily mood and physical condition within an app. The collection unit can also use a diary function to record the events and feelings of the day. The analysis unit comprehensively analyzes the data collected by the collection unit. For example, the analysis unit analyzes the impact of exercise time and sleep quality on mental health. The analysis unit can also analyze changes in mood and physical condition and evaluate the mental health state. The visualization unit visualizes the mental health state based on the data analyzed by the analysis unit. For example, the visualization unit displays the analysis results in a graph, allowing the user to understand their mental health state at a glance. The storage unit organizes and stores the data visualized by the visualization unit as a psychological counseling chart. For example, the storage unit organizes the visualized data and stores it so that it can be used during psychological counseling. This allows the mental health care system according to the embodiment to comprehensively grasp the user's mental health status and take appropriate measures. It also makes it possible to receive advice based on detailed data during psychological counseling. For example, if a lack of exercise is negatively affecting mental health, the user can receive advice to exercise more.

[0071] The collection unit can measure exercise time or sleep quality using a sensor of the device. Examples of the device's sensors include, but are not limited to, an acceleration sensor and a heart rate sensor. For example, the collection unit can measure exercise time using an acceleration sensor. The collection unit can also measure sleep quality using a heart rate sensor. Furthermore, the collection unit can combine multiple sensors to simultaneously measure exercise time and sleep quality. For example, the collection unit combines an acceleration sensor and a heart rate sensor to simultaneously measure exercise time and sleep quality. This allows accurate collection of data on exercise time and sleep quality using the device's sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from the sensor into a generation AI and have the generation AI analyze the data.

[0072] The collection unit can collect data on smartphone usage time or social media used. Smartphone usage time includes, but is not limited to, app usage time and screen-on time. For example, the collection unit collects smartphone app usage time. The collection unit can also collect smartphone screen-on time. Furthermore, the collection unit can collect data on social media used. For example, the collection unit collects data such as the frequency of posts on the social media used by the user, the number of likes, and the number of comments. By collecting data on smartphone usage time and social media used, data on the user's lifestyle habits can be obtained. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input smartphone usage data into a generation AI and have the generation AI analyze the data.

[0073] The collection unit can collect data on mood, physical condition, and diary entries recorded by the user. Mood recording methods include, but are not limited to, mood scores and emotion labels. For example, the collection unit may have the user record their daily mood using a score within the app. The collection unit may also record mood using emotion labels. Physical condition recording methods include, but are not limited to, body temperature, blood pressure, and heart rate. For example, the collection unit may have the user record their daily body temperature. The collection unit may also record blood pressure and heart rate. Diary recording methods include, but are not limited to, daily events and emotional changes. For example, the collection unit may record the user's daily events and feelings as a diary entry. By collecting data on the user's mood, physical condition, diary entries, and other data, data related to subjective mental health can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the mood, physical condition, and diary entries recorded by the user into a generation AI and have the generation AI analyze the data.

[0074] The analysis unit can comprehensively analyze the collected data and evaluate the mental health state. Methods for comprehensive analysis include, but are not limited to, data integration methods and analysis algorithms. The analysis unit can integrate and analyze collected data, such as exercise time, sleep quality, mood, physical condition, and diary entries. The analysis unit can also analyze data using a machine learning model. Criteria for evaluating the mental health state include, but are not limited to, stress level, happiness level, and depressive symptoms. The analysis unit can evaluate, for example, stress level. The analysis unit can also evaluate happiness level and depressive symptoms. This allows for an accurate evaluation of the mental health state by comprehensively analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0075] The visualization unit can display the analysis results in a graph, allowing the user to understand their mental health state at a glance. Types of graphs include, but are not limited to, line graphs, bar graphs, and pie charts. For example, the visualization unit displays the analysis results in a line graph. The visualization unit can also display the analysis results in a bar graph or a pie chart. Displaying the analysis results in a graph makes it easier for the user to visually understand their mental health state. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the analysis results to a generation AI and cause the generation AI to generate a graph.

[0076] The storage unit can organize and store the visualized data as a psychological counseling chart. A psychological counseling chart may include, for example, items to be recorded and a data format, but is not limited to these examples. The storage unit, for example, organizes the visualized data and stores it as a psychological counseling chart. The storage unit can also standardize the data format and store it as a chart. By organizing and storing the visualized data as a psychological counseling chart, detailed advice based on the data can be provided during counseling. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the visualized data into a generation AI and have the generation AI organize and store the chart.

[0077] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to acquire more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This allows the timing of data collection to be adjusted according to the user's emotions, reducing the user's burden and acquiring more detailed data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0078] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit prioritizes collecting data that the user frequently collected in the past. The collection unit can also suggest the optimal collection timing based on the user's past data collection history. Furthermore, the collection unit can customize the collection method based on the user's past data collection history. For example, the collection unit analyzes the type and frequency of data collected by the user in the past and selects the optimal collection method. This enables the optimal collection method to be selected by analyzing the user's past data collection history, enabling efficient data collection. Some 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 input the user's past data collection history into a generation AI and have the generation AI select the optimal collection method.

[0079] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is at work, the collection unit can prioritize collecting work-related data. Also, if the user is on vacation, the collection unit can prioritize collecting data related to that area. Furthermore, if the user has a specific area of ​​interest, the collection unit can prioritize collecting data related to that area. For example, the collection unit filters the data to be collected based on the user's living situation and areas of interest. In this way, by filtering the data based on the user's living situation and areas of interest, highly relevant data can be preferentially collected. Some 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 input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0080] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the user prefers image input, the collection unit can also prioritize collecting image data. For example, the collection unit selects the optimal collection means depending on the user's input method. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some 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 input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0081] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data related to relaxation. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting data related to hurry. By determining the priority of data to be collected according to the user's emotions, highly relevant data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0082] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's trip. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the data.

[0083] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data regarding locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and collects related data. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect data.

[0084] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also suggest the optimal collection timing based on the user's past feedback. Furthermore, the collection unit can adjust the type of data to be collected based on the user's past feedback. For example, the collection unit analyzes feedback provided by the user in the past and customizes the collection method. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described 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 input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and provides detailed results. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the 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 input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm specialized for exercise to exercise data. The analysis unit can also apply an analysis algorithm specialized for sleep to sleep data. The analysis unit can also apply an analysis algorithm specialized for mood to mood data. For example, the analysis unit selects and applies an optimal analysis algorithm depending on the data category. This enables highly accurate data analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to select and apply the optimal analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also understand analysis trends from the user's past analysis results and improve accuracy. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and improves the accuracy of the current analysis result. In this way, the accuracy of the current analysis result can be improved by referring to the user's past analysis results. 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 input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the length of the analysis according to the user's emotions, an analysis result of an appropriate length for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the time when the data was collected. For example, the analysis unit determines the priority of analysis based on the time when the data was collected. This enables analysis that emphasizes the most recent 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 input the time when the data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the 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 input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0092] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows the analysis results to be provided that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0093] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, when the user is stressed, the visualization unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the visualization unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the visualization unit can provide a display method that focuses on the main points. This allows for a highly visible display by adjusting the display method of the visualization according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the visualization unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0094] The visualization unit can adjust the level of detail of the display based on the importance of the data during visualization. For example, the visualization unit provides a detailed display for data with high importance. The visualization unit can also provide a simplified display for data with low importance. Furthermore, the visualization unit can determine the priority of the display according to the importance of the data. For example, the visualization unit prioritizes display of data with high importance and provides detailed information. This enables efficient data display by adjusting the level of detail of the display based on the importance of the data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0095] The visualization unit can apply different display algorithms depending on the data category during visualization. For example, the visualization unit can apply a display algorithm specialized for exercise to exercise data. The visualization unit can also apply a display algorithm specialized for sleep to sleep data. The visualization unit can also apply a display algorithm specialized for mood to mood data. For example, the visualization unit selects and applies an optimal display algorithm depending on the data category. This enables highly accurate data display by applying different display algorithms depending on the data category. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the data category to the generation AI and cause the generation AI to select and apply the optimal display algorithm.

[0096] The visualization unit can improve the accuracy of the display by referring to the user's past visualization results when visualizing. For example, the visualization unit corrects the current display result based on the user's past visualization results. The visualization unit can also understand display trends and improve accuracy from the user's past visualization results. Furthermore, the visualization unit can adjust the display algorithm by referring to the user's past visualization results. For example, the visualization unit analyzes the user's past visualization results and improves the accuracy of the current display result. In this way, the accuracy of the current display result can be improved by referring to the user's past visualization results. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the user's past visualization results to the generation AI and cause the generation AI to improve the accuracy of the display.

[0097] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated user emotions. For example, if the user is stressed, the visualization unit can provide a short, concise display. If the user is relaxed, the visualization unit can also provide a detailed display. If the user is in a hurry, the visualization unit can also provide a concise display. This allows the length of the visualization to be adjusted according to the user's emotions, making it possible to display an appropriate length for the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the display.

[0098] During visualization, the visualization unit can determine the display priority based on the time when the data was collected. The visualization unit, for example, prioritizes displaying the most recent data. The visualization unit can also prioritize the most recent data while referring to past data. Furthermore, the visualization unit can adjust the display priority according to the time when the data was collected. For example, the visualization unit determines the display priority based on the time when the data was collected. This enables display that prioritizes the most recent data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the time when the data was collected to the generation AI and have the generation AI determine the display priority.

[0099] The visualization unit can adjust the display order based on the relevance of the data during visualization. For example, the visualization unit prioritizes display of highly relevant data. The visualization unit can also postpone display of less relevant data. Furthermore, the visualization unit can adjust the display order according to the relevance of the data. For example, the visualization unit adjusts the display order based on the relevance of the data. This enables efficient data display by adjusting the display order based on the relevance of the data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the display order.

[0100] During visualization, the visualization unit can adjust the use of technical terminology in the display according to the user's level of expertise. For example, the visualization unit can provide display results in simple language to a user with little technical expertise. The visualization unit can also provide display results using detailed technical terminology to a user with technical expertise. Furthermore, the visualization unit can adjust the way in which the display results are expressed according to the user's level of expertise. For example, the visualization unit adjusts the use of technical terminology in the display according to the user's level of expertise. This allows for providing display results that are easy for the user to understand by adjusting the use of technical terminology in the display according to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0101] The storage unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can prioritize saving data related to stress. Furthermore, if the user is relaxed, the storage unit can prioritize saving data related to relaxation. Furthermore, if the user is in a hurry, the storage unit can prioritize saving data related to hurry. Thus, by determining the priority of data to be saved according to the user's emotions, highly relevant data can be saved preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, an AI, or without an AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0102] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit stores data with high importance in detail. The storage unit can also store data with low importance in a simplified manner. Furthermore, the storage unit can determine the priority of storage according to the importance of the data. For example, the storage unit prioritizes storing data with high importance and provides detailed information. This enables efficient data storage by adjusting the level of detail of storage based on the importance of the data. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of storage.

[0103] The storage unit can apply different storage algorithms depending on the data category when storing the data. For example, the storage unit can apply a storage algorithm specialized for exercise to exercise data. The storage unit can also apply a storage algorithm specialized for sleep to sleep data. The storage unit can also apply a storage algorithm specialized for mood to mood data. For example, the storage unit selects and applies an optimal storage algorithm depending on the data category. This enables highly accurate data storage by applying different storage algorithms depending on the data category. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input the data category to the generation AI and cause the generation AI to select and apply the optimal storage algorithm.

[0104] The storage unit can improve the accuracy of storage by referring to the user's past storage results when storing data. The storage unit, for example, corrects the current storage result based on the user's past storage results. The storage unit can also understand storage trends from the user's past storage results and improve accuracy. The storage unit can also adjust the storage algorithm by referring to the user's past storage results. For example, the storage unit analyzes the user's past storage results and improves the accuracy of the current storage result. In this way, the accuracy of the current storage result can be improved by referring to the user's past storage results. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past storage results into a generation AI and cause the generation AI to improve the accuracy of storage.

[0105] The storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the storage unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the storage unit can provide a display method that focuses on the main points. This allows the display method of the stored data to be adjusted according to the user's emotions, enabling a highly visible display for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the storage unit can be performed using, for example, an AI, or without an AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0106] The storage unit can determine the priority of storage based on the time when the data was collected when storing the data. For example, the storage unit prioritizes storing the most recent data. The storage unit can also prioritize the most recent data while referring to past data. Furthermore, the storage unit can adjust the priority of storage according to the time when the data was collected. For example, the storage unit determines the priority of storage based on the time when the data was collected. This enables storage that prioritizes the most recent data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of storage.

[0107] The storage unit can adjust the order of storage based on the relevance of the data when storing the data. For example, the storage unit prioritizes storing highly relevant data. The storage unit can also postpone storing less relevant data. Furthermore, the storage unit can adjust the order of storage according to the relevance of the data. For example, the storage unit adjusts the order of storage based on the relevance of the data. This enables efficient data storage by adjusting the order of storage based on the relevance of the data. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of storage.

[0108] The storage unit can adjust the use of technical terminology for storage according to the user's level of expertise when storing the results. For example, the storage unit can provide stored results in simple language to a user with little expertise. The storage unit can also provide stored results using detailed technical terminology to a user with expertise. Furthermore, the storage unit can adjust the way in which the stored results are expressed according to the user's level of expertise. For example, the storage unit can adjust the use of technical terminology for storage according to the user's level of expertise. This allows the storage unit to provide stored results that are easy for the user to understand by adjusting the use of technical terminology for storage according to the user's level of expertise. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, and storage unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can measure exercise time and sleep quality using sensors in the smart device 14 and collect data on smartphone usage time and social media usage. For example, the collection unit can collect data recorded by the user, such as mood, physical condition, and diary entries, through an app on the smart device 14. For example, the analysis unit comprehensively analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates the user's mental health status. For example, the visualization unit displays the analysis results in a graph using the display 40A of the smart device 14, allowing the user to understand their mental health status at a glance. For example, the storage unit organizes and stores the visualized data in the database 24 of the data processing device 12 as a psychological counseling chart. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, and storage unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can measure exercise time and sleep quality using sensors in the smart glasses 214 and collect data on smartphone usage time and social media usage. For example, the collection unit can collect data such as mood, physical condition, and diary entries recorded by the user through an app in the smart glasses 214. For example, the analysis unit comprehensively analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates the state of mental health. For example, the visualization unit displays the analysis results in a graph using the display of the smart glasses 214, allowing the user to understand their mental health state at a glance. For example, the storage unit organizes and stores the visualized data in the database 24 of the data processing device 12 as a psychological counseling chart. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, and storage unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can measure exercise time and sleep quality using sensors in the headset-type terminal 314 and collect data on smartphone usage time and social media usage. For example, the collection unit can collect data recorded by the user, such as mood, physical condition, and diary entries, through an app in the headset-type terminal 314. For example, the analysis unit comprehensively analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates the user's mental health status. For example, the visualization unit displays the analysis results in a graph using the display 343 of the headset-type terminal 314, allowing the user to understand their mental health status at a glance. For example, the storage unit organizes and stores the visualized data in the database 24 of the data processing device 12 as a psychological counseling chart. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, and storage unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can measure exercise time and sleep quality using sensors in the robot 414 and collect data on smartphone usage time and social media usage. For example, the collection unit can collect data recorded by the user, such as mood, physical condition, and diary entries, through an app in the robot 414. For example, the analysis unit comprehensively analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates the user's mental health status. For example, the visualization unit displays the analysis results in a graph using the display of the robot 414, allowing the user to understand their mental health status at a glance. For example, the storage unit organizes and stores the visualized data in the database 24 of the data processing device 12 as a psychological counseling chart.

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

[0110] The mental healthcare system may further include a voice analysis unit that collects the user's voice data and performs voice analysis. The voice analysis unit collects voice data, such as the user's conversations and monologue, and estimates emotions and stress levels. For example, the voice analysis unit may analyze changes in the user's voice tone, speed, and volume to determine whether the user is feeling stressed. The voice analysis unit may also analyze the content of the user's speech and identify positive and negative emotions. Furthermore, the voice analysis unit may integrate the user's voice data with other collected data to perform a more comprehensive mental health assessment. This allows the user's voice data to be utilized to understand the user's mental health status in more detail and take appropriate measures.

[0111] The mental healthcare system may further include a nutritional evaluation unit that collects the user's dietary data and evaluates their nutritional status. The nutritional evaluation unit collects the content, calories, and nutrient balance of the user's meals and analyzes their impact on mental health. For example, the nutritional evaluation unit may allow the user to input their daily dietary information through a food recording app. The nutritional evaluation unit may also analyze photos of meals taken by the user and automatically recognize the ingredients and nutrients consumed. Furthermore, the nutritional evaluation unit may integrate the user's dietary data with other collected data to evaluate the relationship between diet and mental health. By utilizing the user's dietary data, the relationship between nutritional status and mental health can be understood and appropriate dietary advice can be provided.

[0112] The mental healthcare system may further include an exercise monitoring unit that monitors the user's exercise data in real time and provides immediate feedback on the effectiveness of the exercise. The exercise monitoring unit collects the type, intensity, and duration of the user's exercise in real time and immediately evaluates the effectiveness of the exercise. For example, the exercise monitoring unit can display the user's heart rate and calories burned in real time while the user is running. The exercise monitoring unit can also evaluate the accuracy of the user's poses and breathing rhythm while the user is doing yoga. Furthermore, the exercise monitoring unit can integrate the user's exercise data with other collected data to comprehensively evaluate the impact of exercise on mental health. In this way, by monitoring the user's exercise data in real time, immediate feedback on the effectiveness of the exercise can be provided and appropriate exercise guidance can be provided.

[0113] The mental healthcare system may further include a sleep analysis unit that analyzes the user's sleep data in detail and provides advice for improving sleep quality. The sleep analysis unit collects data such as the user's sleep patterns, movements during sleep, and heart rate to evaluate the quality of sleep. For example, the sleep analysis unit may analyze how long the user spends in deep sleep and provide advice for increasing the proportion of deep sleep. The sleep analysis unit may also monitor the user's bedroom environment (temperature, humidity, noise, etc.) and make suggestions for creating a comfortable sleeping environment. Furthermore, the sleep analysis unit may integrate the user's sleep data with other collected data and evaluate the relationship between sleep and mental health. This allows for detailed analysis of the user's sleep data to provide specific advice for improving sleep quality and improve mental health.

[0114] The mental healthcare system may further include a stress monitoring unit that monitors the user's stress level in real time and suggests measures to reduce stress. The stress monitoring unit collects data such as the user's heart rate, electrodermal activity, and breathing pattern in real time to evaluate the user's stress level. For example, if the user feels stressed at work, the stress monitoring unit may suggest breathing techniques to relax or a short break. Furthermore, if the user feels stressed while exercising, the stress monitoring unit may advise the user to adjust the intensity of the exercise. Furthermore, the stress monitoring unit may integrate the user's stress data with other collected data to evaluate the relationship between stress and mental health. Thus, by monitoring the user's stress level in real time, specific measures to reduce stress can be suggested, leading to improved mental health.

[0115] The mental healthcare system may further include an emotion evaluation unit that estimates the user's emotion and evaluates the state of mental health based on the estimated emotion. The emotion evaluation unit analyzes the user's facial expressions, voice, text data, etc. to estimate the emotion. For example, the emotion evaluation unit can analyze the user's facial expression data and identify emotions such as smile, anger, sadness, etc. The emotion evaluation unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the emotion evaluation unit can analyze text data such as diary entries and messages recorded by the user and evaluate changes in emotion. In this way, by utilizing the user's emotion data, the state of mental health can be more accurately evaluated and appropriate measures can be taken.

[0116] The mental healthcare system may further include a social assessment unit that evaluates the user's social connections and suggests measures to reduce loneliness. The social assessment unit collects data such as the user's social networking site usage and frequency of interactions with friends, and evaluates the user's social connections. For example, if the user has few interactions on social networking sites, the social assessment unit may suggest that the user increase communication with friends. If the user feels lonely, the social assessment unit may also recommend that the user join an online community or support group. Furthermore, the social assessment unit may integrate the user's social connection data with other collected data and evaluate the association between social connections and mental health. As a result, by evaluating the user's social connections, it is possible to reduce loneliness and improve mental health.

[0117] The mental healthcare system may further include a hobby evaluation unit that evaluates the user's hobbies and interests and suggests activities for relaxation and stress relief. The hobby evaluation unit collects data on the activities and hobbies in which the user is interested and makes suggestions for relaxation and stress relief. For example, if the user likes listening to music, the hobby evaluation unit may suggest relaxing music. Also, if the user enjoys reading, the hobby evaluation unit may recommend books that are useful for stress relief. Furthermore, the hobby evaluation unit may integrate the user's hobby data with other collected data and evaluate the association between hobbies and mental health. In this way, by evaluating the user's hobbies and interests, specific activities for relaxation and stress relief can be suggested, thereby improving mental health.

[0118] The mental healthcare system may further include a workplace assessment unit that evaluates the user's work environment and suggests measures to reduce workplace stress. The workplace assessment unit collects data on the user's work activities and stress level and evaluates the work environment. For example, if the user is working long hours, the workplace assessment unit may suggest that the user take appropriate breaks. Also, if the user is feeling stressed about interpersonal relationships at work, the workplace assessment unit may recommend that the user improve their communication skills. Furthermore, the workplace assessment unit may integrate the user's workplace data with other collected data and evaluate the relationship between the work environment and mental health. As a result, by evaluating the user's work environment, workplace stress can be reduced and mental health can be improved.

[0119] The mental healthcare system may further include a learning assessment unit that collects the user's learning data and evaluates the user's learning progress and stress level. The learning assessment unit collects data on the content the user is studying, the study time, and the user's stress level during study, and evaluates the user's learning progress and stress level. For example, if the user is studying for an exam, the learning assessment unit may suggest that the user take appropriate breaks. If the user is learning a new skill, the learning assessment unit may visualize the user's learning progress and provide advice to maintain motivation. Furthermore, the learning assessment unit may integrate the user's learning data with other collected data and evaluate the relationship between learning and mental health. In this way, by collecting the user's learning data and evaluating the user's learning progress and stress level, effective study methods may be suggested, leading to improvements in mental health.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects data on exercise time, smartphone usage time, social media usage, and sleep quality. For example, the device's sensors can be used to measure exercise time and sleep quality, and data on smartphone usage time and social media usage can be collected. Data such as user-recorded mood, physical condition, and diary entries can also be collected. For example, within the app, users can record their daily mood and physical condition, and use the diary function to record the events and feelings of the day. Step 2: The analysis unit comprehensively analyzes the data collected by the collection unit. For example, it analyzes the impact of exercise time and sleep quality on mental health, and analyzes changes in mood and physical condition to evaluate the state of mental health. Step 3: The visualization unit visualizes the mental health status based on the data analyzed by the analysis unit. For example, it displays the analysis results in a graph so that the user can understand their mental health status at a glance. Step 4: The storage unit organizes and stores the data visualized by the visualization unit as a psychological counseling chart. For example, the visualized data is organized and stored so that it can be used during psychological counseling.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0127] 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.

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

[0129] 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.

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

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

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

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

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

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0143] 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.

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

[0145] 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.

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

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

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] 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.

[0150] 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.

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

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

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0159] 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.

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

[0161] 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.

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

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

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] 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.

[0167] 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.

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

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

[0182] 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.

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

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] 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.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 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 collects data on exercise time, smartphone usage time, SNS use, and sleep quality. an analysis unit that comprehensively analyzes the data collected by the collection unit; a visualization unit that visualizes a mental health state based on the data analyzed by the analysis unit; a storage unit that organizes and stores the data visualized by the visualization unit as a psychological counseling chart; Equipped with A system characterized by:

2. The collecting unit Measure exercise time or sleep quality using device sensors 2. The system of claim 1.

3. The collecting unit Collect data on smartphone usage time and social media use 2. The system of claim 1.

4. The collecting unit Collecting data on mood, physical condition, and diary entries recorded by users 2. The system of claim 1.

5. The analysis unit Comprehensively analyze the collected data and evaluate the state of mental health 2. The system of claim 1.

6. The visualization unit Displaying analysis results in graphs to allow users to understand their mental health status at a glance 2. The system of claim 1.

7. The storage unit Organize and save the visualized data as a psychological counseling chart 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images