system

Abstract

The system according to this embodiment aims to detect the user's stress and emotional changes and propose appropriate care. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an escalation unit. The collection unit collects the user's smartphone data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes care based on the results of the analysis by the analysis unit. The escalation unit escalates the care proposed by the proposal unit to a specialized institution.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to notice their own stress and the need for mental care and to receive appropriate care.

[0005] The system according to the embodiment aims to detect changes in a user's stress and emotions and propose appropriate care.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an escalation unit. The data collection unit collects data from the user's smartphone. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes care based on the results of the analysis performed by the analysis unit. The escalation unit escalates the case to a specialized institution based on the care proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect the user's stress and emotional changes and suggest appropriate care. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A mental care system according to an embodiment of the present invention is a system that uses a generative AI to analyze a user's smartphone data, detect stress and emotional changes in real time, and automatically proposes necessary care. This mental care system automatically collects the user's smartphone data, and the generative AI analyzes the collected data to detect stress and emotional changes in real time. Based on the stress and emotional changes detected by the generative AI, it automatically proposes necessary care. Furthermore, the generative AI retrieves necessary knowledge from expert databases and guidelines to provide care. This mechanism allows users to receive support unconsciously, reducing psychological barriers. It also enables personalized care tailored to individual conditions, supporting the user's mental health. For example, the mental care system automatically collects the user's smartphone data. For example, it collects data such as SNS usage, message content, and responses to notifications. Next, the generative AI analyzes the collected data to detect stress and emotional changes in real time. The generative AI estimates the user's emotions from, for example, SNS usage and message content. Based on the stress and emotional changes detected by the generative AI, it automatically proposes necessary care. For example, it can offer suggestions for deep breathing and advice to encourage positive thinking. Furthermore, the generating AI retrieves necessary knowledge from expert databases and guidelines to provide care. For instance, if a serious mental health problem is detected, it can prompt referral to a professional organization. In this way, the mental care system can support the user's mental health and reduce psychological barriers. This enables the mental care system to collect, analyze, suggest care, and escalate the issue based on the user's smartphone data.

[0029] The mental care system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an escalation unit. The data collection unit collects data from the user's smartphone. The data collection unit collects data such as SNS usage, message content, and responses to notifications. For example, to collect SNS usage data, the data collection unit can collect login frequency, post content, and the number of likes. The data collection unit can also collect text messages, images, and videos to collect message content. Furthermore, to collect responses to notifications, the data collection unit can collect whether the notification was opened and actions taken in response to the notification. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data to detect changes in the user's stress and emotions in real time. For example, as a specific method for measuring stress, the analysis unit can use changes in heart rate, self-reporting, and changes in behavioral patterns. The analysis unit can also use facial expression analysis, voice analysis, and text analysis as specific methods for measuring changes in emotions. The proposal unit proposes care based on the results analyzed by the analysis unit. The suggestion unit provides suggestions for deep breathing and advice to promote positive thinking, for example, based on detected stress and emotional changes. The suggestion unit can consider specific methods for suggesting deep breathing, such as the timing and content of the suggestion. The suggestion unit can also consider specific content for advice to promote positive thinking, such as examples of specific advice and methods for providing the advice. The escalation unit escalates the care suggested by the suggestion unit to a specialized institution. The escalation unit, for example, encourages referral to a specialized institution when a serious mental health problem is detected. The escalation unit can consider specific criteria for a serious mental health problem, such as self-reporting, changes in behavioral patterns, and expert diagnoses. As a result, the mental care system according to this embodiment can collect, analyze, suggest care, and escalate the user's smartphone data.

[0030] The data collection unit collects user smartphone data. For example, it collects data such as social media usage, message content, and responses to notifications. Specifically, to collect social media usage data, it can collect detailed information such as which social media platforms the user uses, login frequency, post content, number of likes, number of comments, and number of shares. This allows for a comprehensive understanding of the user's social media activity and analysis of what kind of content they are responding to. Furthermore, to collect message content data, the unit can collect data in various formats, including text messages, images, videos, and audio messages. This allows for a detailed understanding of the user's communication patterns and how they express emotions. Additionally, to collect responses to notifications, the unit can record in detail whether a notification was opened and what action was taken in response (e.g., reply, ignore, delete). This allows for analysis of how users respond to different types of notifications and helps understand their interests and stress levels. The data collection unit centrally manages and updates this data in real time, ensuring a constant understanding of the user's current situation. It is also crucial for the data collection unit to implement security measures such as data encryption and access control to ensure data privacy and security. This allows the data collection unit to efficiently collect necessary data while protecting user privacy.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to detect changes in the user's stress and emotions in real time. Specifically, stress can be measured using methods such as changes in heart rate, self-reports, and changes in behavioral patterns. Changes in heart rate are acquired through smartphone sensors or wearable devices and used as an indicator of stress level. Self-reports are analyzed using questionnaires or diary-style data that users regularly input to provide a subjective assessment of stress. Changes in behavioral patterns are inferred from the user's SNS usage, message content, and responses to notifications. The analysis unit integrates this data and uses AI to evaluate the user's stress level in real time. Methods for measuring changes in emotions include facial expression analysis, voice analysis, and text analysis. Facial expression analysis uses the smartphone camera to analyze the user's facial expressions and detect changes in emotions. Voice analysis analyzes the user's voice messages and call content to infer emotions from changes in tone and pitch. Text analysis analyzes the user's message content and SNS posts to evaluate emotions from the words and context used. The analysis unit integrates these analysis results, enabling it to grasp changes in users' emotions in real time. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term stress and emotional trends, comprehensively evaluating the user's mental health status. This allows the analysis unit to quickly and accurately detect changes in users' mental health and provide foundational information for providing appropriate care.

[0032] The suggestion department proposes care based on the results analyzed by the analysis department. For example, based on detected stress and emotional changes, the suggestion department provides suggestions for deep breathing and advice to promote positive thinking. Specifically, it considers the timing and content of deep breathing suggestions to support the user in relaxing. For example, if a user shows a high stress level, the suggestion department can provide guided audio or video for deep breathing and show specific steps to help the user relax. As advice to promote positive thinking, it can suggest feeling grateful to the user or reflecting on positive events. The suggestion department can customize the content and method of providing specific advice according to the user's situation. For example, if a user is feeling stressed in a particular situation, it can provide specific advice on how to deal with that situation. The suggestion department can also collect user feedback and evaluate the effectiveness of the suggestions. This allows the suggestion department to provide optimal care to the user and support the improvement of their mental health. Furthermore, the suggestion department can provide personalized care suggestions tailored to the user's preferences and lifestyle. For example, if a user often relaxes by listening to music, the suggestion department can suggest music with relaxing effects. This allows the proposal department to contribute to improving users' mental health and provide support for users to lead healthier lives.

[0033] The Escalation Department escalates issues to specialized agencies based on the care proposed by the Proposal Department. For example, the Escalation Department facilitates referral to specialized agencies when serious mental health problems are detected. Specifically, it detects serious mental health problems based on user self-reports, changes in behavioral patterns, and expert diagnoses. For instance, if a user exhibits high stress levels over a long period and shows no improvement despite proposed care, the Escalation Department can encourage the user to contact a professional counselor or doctor. Furthermore, if a user exhibits self-harming behavior or suicidal thoughts, the Escalation Department can determine that emergency action is necessary and immediately contact specialized agencies. It is crucial for the Escalation Department to provide necessary information to specialized agencies while protecting user privacy, ensuring appropriate support. Additionally, the Escalation Department can strengthen collaboration with specialized agencies and establish protocols to facilitate smooth support for users. This enables the Escalation Department to respond quickly and appropriately to users' mental health issues and ensure they receive the necessary support. The Escalation Department can also collect user feedback to improve the escalation process. This allows the escalation department to continuously improve its response to users' mental health issues, thereby enhancing the overall reliability and effectiveness of the system.

[0034] The data collection unit can collect data such as SNS usage, message content, and responses to notifications. For example, to collect SNS usage data, the data collection unit can collect login frequency, post content, and the number of likes. The data collection unit can also collect text messages, images, and videos to collect message content. Furthermore, to collect responses to notifications, the data collection unit can collect whether the notification was opened and actions taken in response to the notification. By collecting data such as SNS usage, message content, and responses to notifications, the user's state can be understood.

[0035] The analysis unit can analyze the collected data and detect changes in the user's stress and emotions in real time. For example, the analysis unit can analyze the collected data and detect changes in the user's stress and emotions in real time. For example, the analysis unit can use methods such as changes in heart rate, self-reporting, and changes in behavioral patterns as specific methods for measuring stress. Furthermore, the analysis unit can use methods such as facial expression analysis, voice analysis, and text analysis as specific methods for measuring changes in emotions. This allows for the analysis of collected data and the detection of changes in the user's stress and emotions in real time, enabling the provision of appropriate care.

[0036] The suggestion unit can provide suggestions for deep breathing and advice to promote positive thinking based on detected stress and emotional changes. For example, the suggestion unit can consider specific methods for suggesting deep breathing, such as the timing and content of the suggestion. Furthermore, the suggestion unit can consider specific content for advice to promote positive thinking, such as specific examples of advice and methods of providing it. This allows the system to support the user's mental well-being by providing suggestions for deep breathing and advice to promote positive thinking based on detected stress and emotional changes.

[0037] The escalation department can facilitate referral to specialized agencies when serious mental health problems are detected. For example, the escalation department can facilitate referral to specialized agencies when serious mental health problems are detected. For example, the escalation department can consider self-reports, changes in behavioral patterns, and professional diagnoses as specific criteria for identifying serious mental health problems. This allows for the provision of appropriate support by facilitating referral to specialized agencies when serious mental health problems are detected.

[0038] The system includes a knowledge supplementation unit that extracts necessary knowledge from expert databases and guidelines to provide care. For example, the knowledge supplementation unit can consider specific contents of the expert database, such as expert profiles, past research findings, and guidelines. Furthermore, the knowledge supplementation unit can consider specific contents of the guidelines, such as mental care guidelines and physical care guidelines. This allows for the provision of more appropriate care by extracting necessary knowledge from expert databases and guidelines.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting data from apps and services that the user has frequently used in the past. For example, the data collection unit selects the types of data to collect at specific time periods based on the user's past data collection history. For example, the data collection unit analyzes the user's past data collection history and proposes the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0040] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is working, the data collection unit will prioritize collecting work-related data. If the user is engrossed in a hobby, the data collection unit will collect data related to that hobby. If the user is taking a break, the data collection unit will collect data related to relaxation. By filtering the data based on the user's current activities and areas of interest, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data related to home. By collecting data while considering the user's geographical location information, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts on a particular social media platform, the data collection unit can collect the content of those posts. For example, if a user receives many reactions on a particular social media platform, the data collection unit can collect the content of those reactions. For example, the data collection unit can collect the content of posts from accounts that a user follows on a particular social media platform. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies an image analysis algorithm to image data. For example, the analysis unit applies a speech analysis algorithm to speech data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the data collection period, more effective analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may evaluate the relevance of the data and perform the analysis in the optimal order. By adjusting the order of analysis based on the relevance of the data, more effective analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The proposal unit can adjust the level of detail in its proposals based on the importance of the care provided. For example, the proposal unit will provide detailed proposals for high-importance care, simplified proposals for low-importance care, and proposals with an appropriate level of detail for moderately important care. By adjusting the level of detail in proposals based on the importance of the care, more effective proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of care when making a suggestion. For example, for mental care, the suggestion unit may apply an algorithm that suggests relaxation techniques. For physical care, for example, the suggestion unit may apply an algorithm that suggests exercise and stretching. For social care, for example, the suggestion unit may apply a suggestion algorithm that promotes communication. By applying different suggestion algorithms depending on the category of care, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0049] The proposal department can determine the priority of proposals based on the timing of care delivery. For example, the proposal department may prioritize proposing highly urgent care. For example, the proposal department may postpone proposing less urgent care. For example, the proposal department may evaluate the timing of care delivery and make proposals in the optimal order. This allows for more effective proposals by determining the priority of proposals based on the timing of care delivery. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the care when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant care. For example, the suggestion unit may postpone suggesting less relevant care. For example, the suggestion unit may evaluate the relevance of the care and make suggestions in the optimal order. By adjusting the order of suggestions based on the relevance of the care, more effective suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0051] The escalation unit can analyze the user's past mental health history to select the optimal escalation method during escalation. For example, the escalation unit may prioritize escalation to a specific professional organization based on the user's past mental health history. For example, the escalation unit may analyze the user's past mental health history and propose the most effective escalation method. For example, the escalation unit may adjust the timing of escalation based on the user's past mental health history. In this way, the optimal escalation method can be selected by analyzing the user's past mental health history. Some or all of the above processes in the escalation unit may be performed using AI, for example, or without using AI.

[0052] The escalation unit can customize the means of escalation based on the user's current living situation. For example, if the user is at work, the escalation unit will escalate the issue in a way that does not disrupt their work. For example, if the user is at home, the escalation unit will escalate the issue in a way that takes the home environment into consideration. For example, if the user is traveling, the escalation unit will escalate the issue to a specialized agency at the travel destination. By customizing the means of escalation based on the user's current living situation, more appropriate escalation becomes possible. Some or all of the above-described processes in the escalation unit may be performed using AI, for example, or without using AI.

[0053] The escalation unit can select the most appropriate escalation method when escalating a case, taking into account the user's geographical location. For example, if the user is in a specific location, the escalation unit prioritizes escalating to a specialized agency relevant to that location. For example, if the user is traveling, the escalation unit will escalate to a specialized agency in the user's travel destination. For example, if the user is at home, the escalation unit will escalate to a specialized agency near the user's home. By selecting an escalation method that takes into account the user's geographical location, more appropriate escalation becomes possible. Some or all of the above processing in the escalation unit may be performed using AI, for example, or without using AI.

[0054] The escalation unit can analyze the user's social media activity and propose escalation methods during the escalation process. For example, if the user frequently posts on a particular social media platform, the escalation unit may use the content of those posts as a reference for escalation. For example, if the user receives many reactions on a particular social media platform, the escalation unit may use the content of those reactions as a reference for escalation. For example, the escalation unit may use the content of posts from accounts that the user follows on a particular social media platform as a reference for escalation. In this way, by analyzing the user's social media activity, a more appropriate escalation method can be proposed. Some or all of the above-described processes in the escalation unit may be performed using AI, for example, or without AI.

[0055] The knowledge supplementation unit can select the most suitable knowledge by referring to a database of experts when providing knowledge. For example, the knowledge supplementation unit selects the knowledge most appropriate for the user's situation from the database of experts. For example, the knowledge supplementation unit provides the latest knowledge by referring to a database of experts. For example, the knowledge supplementation unit analyzes a database of experts and provides knowledge that meets the user's needs. In this way, the optimal knowledge can be selected by referring to a database of experts. Some or all of the above processes in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0056] The knowledge supplementation unit can customize the content of the knowledge based on the user's current situation when providing knowledge. For example, if the user is at work, the knowledge supplementation unit will provide knowledge related to work. For example, if the user is at home, the knowledge supplementation unit will provide knowledge related to the home environment. For example, if the user is traveling, the knowledge supplementation unit will provide knowledge related to the travel destination. This makes it possible to provide more appropriate knowledge by customizing the content of the knowledge based on the user's current situation. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0057] The knowledge supplementation unit can provide optimal knowledge by considering the user's geographical location when providing knowledge. For example, if the user is in a specific location, the knowledge supplementation unit will provide knowledge related to that location. For example, if the user is traveling, the knowledge supplementation unit will provide knowledge related to the travel destination. For example, if the user is at home, the knowledge supplementation unit will provide knowledge related to home. By providing knowledge while considering the user's geographical location, it becomes possible to provide more appropriate knowledge. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0058] The knowledge supplementation unit can analyze the user's social media activity and suggest knowledge content when providing knowledge. For example, if the user frequently posts on a particular social media platform, the knowledge supplementation unit will provide knowledge related to the content of those posts. For example, if the user receives many reactions on a particular social media platform, the knowledge supplementation unit will provide knowledge related to the content of those reactions. For example, the knowledge supplementation unit will provide knowledge related to the content of posts from accounts that the user follows on a particular social media platform. This makes it possible to provide more appropriate knowledge by analyzing the user's social media activity. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The mental care system can also analyze the user's sleep patterns and offer suggestions to improve sleep quality. For example, the data collection unit uses the user's smartphone sensors to record sleep duration and depth. The analysis unit analyzes the collected sleep data and evaluates the user's sleep quality. Based on the analysis results, the suggestion unit provides advice to improve sleep quality. For example, it can suggest relaxation methods before bedtime or how to create a suitable sleep environment. This allows the user to get better sleep and is expected to improve overall mental health.

[0061] The mental care system can also analyze a user's eating patterns and suggest improvements to their nutritional balance. For example, the data collection unit collects the type and quantity of meals the user eats through an app that records their meals. The analysis unit analyzes the collected meal data and evaluates the nutritional balance. The suggestion unit provides advice on how to improve nutritional balance based on the analysis results. For example, it can suggest ingredients and recipes to increase the intake of specific nutrients. This allows users to maintain a healthy diet and is expected to improve their mental health.

[0062] The mental care system can also analyze a user's exercise patterns and suggest appropriate exercise plans. For example, the data collection unit records the amount and type of exercise through the user's smartphone or wearable device. The analysis unit analyzes the collected exercise data and evaluates the user's exercise habits. The suggestion unit provides an appropriate exercise plan based on the analysis results. For example, it can suggest exercise menus and timings tailored to the user's physical fitness and goals. This allows users to maintain healthy exercise habits and is expected to improve their mental health.

[0063] A mental care system can also suggest ways to refresh based on the user's hobbies and interests. For example, the data collection unit collects the user's smartphone app usage history and search history. The analysis unit analyzes the collected data to identify the user's hobbies and interests. The suggestion unit provides ways to refresh based on the analysis results. For example, it can provide information on activities and events that the user is interested in, or suggest new hobbies. This can help the user reduce stress and improve their mental health.

[0064] The mental care system can also analyze users' social activities and offer suggestions to enhance social support. For example, the data collection unit collects users' SNS usage and message exchanges. The analysis unit analyzes the collected data and evaluates the frequency and quality of users' social activities. Based on the analysis results, the suggestion unit provides advice to enhance social support. For example, it can suggest ways to promote communication with friends and family or encourage participation in social events. This can help users strengthen their social connections and improve their mental health.

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

[0066] Step 1: The data collection unit collects data from the user's smartphone. For example, it collects data such as SNS usage, message content, and responses to notifications. Specifically, it can collect data such as login frequency, post content, number of likes, text messages, images, videos, whether notifications were opened, and actions taken in response to notifications. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data to detect changes in the user's stress and emotions in real time. Specifically, it can use changes in heart rate, self-reports, changes in behavioral patterns, facial expression analysis, voice analysis, and text analysis. Step 3: The proposal unit proposes care based on the results analyzed by the analysis unit. For example, based on detected stress and emotional changes, it may suggest deep breathing or provide advice to encourage positive thinking. Specifically, it can consider the timing and content of the proposal, examples of specific advice, and how to deliver it. Step 4: The escalation department escalates the case to a specialist based on the care proposed by the proposing department. For example, if a serious mental health problem is detected, they will encourage referral to a specialist. Specifically, they may consider self-reporting, changes in behavioral patterns, and professional diagnoses.

[0067] (Example of form 2) A mental care system according to an embodiment of the present invention is a system that uses a generative AI to analyze a user's smartphone data, detect stress and emotional changes in real time, and automatically proposes necessary care. This mental care system automatically collects the user's smartphone data, and the generative AI analyzes the collected data to detect stress and emotional changes in real time. Based on the stress and emotional changes detected by the generative AI, it automatically proposes necessary care. Furthermore, the generative AI retrieves necessary knowledge from expert databases and guidelines to provide care. This mechanism allows users to receive support unconsciously, reducing psychological barriers. It also enables personalized care tailored to individual conditions, supporting the user's mental health. For example, the mental care system automatically collects the user's smartphone data. For example, it collects data such as SNS usage, message content, and responses to notifications. Next, the generative AI analyzes the collected data to detect stress and emotional changes in real time. The generative AI estimates the user's emotions from, for example, SNS usage and message content. Based on the stress and emotional changes detected by the generative AI, it automatically proposes necessary care. For example, it can offer suggestions for deep breathing and advice to encourage positive thinking. Furthermore, the generating AI retrieves necessary knowledge from expert databases and guidelines to provide care. For instance, if a serious mental health problem is detected, it can prompt referral to a professional organization. In this way, the mental care system can support the user's mental health and reduce psychological barriers. This enables the mental care system to collect, analyze, suggest care, and escalate the issue based on the user's smartphone data.

[0068] The mental care system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an escalation unit. The data collection unit collects data from the user's smartphone. The data collection unit collects data such as SNS usage, message content, and responses to notifications. For example, to collect SNS usage data, the data collection unit can collect login frequency, post content, and the number of likes. The data collection unit can also collect text messages, images, and videos to collect message content. Furthermore, to collect responses to notifications, the data collection unit can collect whether the notification was opened and actions taken in response to the notification. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data to detect changes in the user's stress and emotions in real time. For example, as a specific method for measuring stress, the analysis unit can use changes in heart rate, self-reporting, and changes in behavioral patterns. The analysis unit can also use facial expression analysis, voice analysis, and text analysis as specific methods for measuring changes in emotions. The proposal unit proposes care based on the results analyzed by the analysis unit. The suggestion unit provides suggestions for deep breathing and advice to promote positive thinking, for example, based on detected stress and emotional changes. The suggestion unit can consider specific methods for suggesting deep breathing, such as the timing and content of the suggestion. The suggestion unit can also consider specific content for advice to promote positive thinking, such as examples of specific advice and methods for providing the advice. The escalation unit escalates the care suggested by the suggestion unit to a specialized institution. The escalation unit, for example, encourages referral to a specialized institution when a serious mental health problem is detected. The escalation unit can consider specific criteria for a serious mental health problem, such as self-reporting, changes in behavioral patterns, and expert diagnoses. As a result, the mental care system according to this embodiment can collect, analyze, suggest care, and escalate the user's smartphone data.

[0069] The data collection unit collects user smartphone data. For example, it collects data such as social media usage, message content, and responses to notifications. Specifically, to collect social media usage data, it can collect detailed information such as which social media platforms the user uses, login frequency, post content, number of likes, number of comments, and number of shares. This allows for a comprehensive understanding of the user's social media activity and analysis of what kind of content they are responding to. Furthermore, to collect message content data, the unit can collect data in various formats, including text messages, images, videos, and audio messages. This allows for a detailed understanding of the user's communication patterns and how they express emotions. Additionally, to collect responses to notifications, the unit can record in detail whether a notification was opened and what action was taken in response (e.g., reply, ignore, delete). This allows for analysis of how users respond to different types of notifications and helps understand their interests and stress levels. The data collection unit centrally manages and updates this data in real time, ensuring a constant understanding of the user's current situation. It is also crucial for the data collection unit to implement security measures such as data encryption and access control to ensure data privacy and security. This allows the data collection unit to efficiently collect necessary data while protecting user privacy.

[0070] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to detect changes in the user's stress and emotions in real time. Specifically, stress can be measured using methods such as changes in heart rate, self-reports, and changes in behavioral patterns. Changes in heart rate are acquired through smartphone sensors or wearable devices and used as an indicator of stress level. Self-reports are analyzed using questionnaires or diary-style data that users regularly input to provide a subjective assessment of stress. Changes in behavioral patterns are inferred from the user's SNS usage, message content, and responses to notifications. The analysis unit integrates this data and uses AI to evaluate the user's stress level in real time. Methods for measuring changes in emotions include facial expression analysis, voice analysis, and text analysis. Facial expression analysis uses the smartphone camera to analyze the user's facial expressions and detect changes in emotions. Voice analysis analyzes the user's voice messages and call content to infer emotions from changes in tone and pitch. Text analysis analyzes the user's message content and SNS posts to evaluate emotions from the words and context used. The analysis unit integrates these analysis results, enabling it to grasp changes in users' emotions in real time. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term stress and emotional trends, comprehensively evaluating the user's mental health status. This allows the analysis unit to quickly and accurately detect changes in users' mental health and provide foundational information for providing appropriate care.

[0071] The suggestion department proposes care based on the results analyzed by the analysis department. For example, based on detected stress and emotional changes, the suggestion department provides suggestions for deep breathing and advice to promote positive thinking. Specifically, it considers the timing and content of deep breathing suggestions to support the user in relaxing. For example, if a user shows a high stress level, the suggestion department can provide guided audio or video for deep breathing and show specific steps to help the user relax. As advice to promote positive thinking, it can suggest feeling grateful to the user or reflecting on positive events. The suggestion department can customize the content and method of providing specific advice according to the user's situation. For example, if a user is feeling stressed in a particular situation, it can provide specific advice on how to deal with that situation. The suggestion department can also collect user feedback and evaluate the effectiveness of the suggestions. This allows the suggestion department to provide optimal care to the user and support the improvement of their mental health. Furthermore, the suggestion department can provide personalized care suggestions tailored to the user's preferences and lifestyle. For example, if a user often relaxes by listening to music, the suggestion department can suggest music with relaxing effects. This allows the proposal department to contribute to improving users' mental health and provide support for users to lead healthier lives.

[0072] The Escalation Department escalates issues to specialized agencies based on the care proposed by the Proposal Department. For example, the Escalation Department facilitates referral to specialized agencies when serious mental health problems are detected. Specifically, it detects serious mental health problems based on user self-reports, changes in behavioral patterns, and expert diagnoses. For instance, if a user exhibits high stress levels over a long period and shows no improvement despite proposed care, the Escalation Department can encourage the user to contact a professional counselor or doctor. Furthermore, if a user exhibits self-harming behavior or suicidal thoughts, the Escalation Department can determine that emergency action is necessary and immediately contact specialized agencies. It is crucial for the Escalation Department to provide necessary information to specialized agencies while protecting user privacy, ensuring appropriate support. Additionally, the Escalation Department can strengthen collaboration with specialized agencies and establish protocols to facilitate smooth support for users. This enables the Escalation Department to respond quickly and appropriately to users' mental health issues and ensure they receive the necessary support. The Escalation Department can also collect user feedback to improve the escalation process. This allows the escalation department to continuously improve its response to users' mental health issues, thereby enhancing the overall reliability and effectiveness of the system.

[0073] The data collection unit can collect data such as SNS usage, message content, and responses to notifications. For example, to collect SNS usage data, the data collection unit can collect login frequency, post content, and the number of likes. The data collection unit can also collect text messages, images, and videos to collect message content. Furthermore, to collect responses to notifications, the data collection unit can collect whether the notification was opened and actions taken in response to the notification. By collecting data such as SNS usage, message content, and responses to notifications, the user's state can be understood.

[0074] The analysis unit can analyze the collected data and detect changes in the user's stress and emotions in real time. For example, the analysis unit can analyze the collected data and detect changes in the user's stress and emotions in real time. For example, the analysis unit can use methods such as changes in heart rate, self-reporting, and changes in behavioral patterns as specific methods for measuring stress. Furthermore, the analysis unit can use methods such as facial expression analysis, voice analysis, and text analysis as specific methods for measuring changes in emotions. This allows for the analysis of collected data and the detection of changes in the user's stress and emotions in real time, enabling the provision of appropriate care.

[0075] The suggestion unit can provide suggestions for deep breathing and advice to promote positive thinking based on detected stress and emotional changes. For example, the suggestion unit can consider specific methods for suggesting deep breathing, such as the timing and content of the suggestion. Furthermore, the suggestion unit can consider specific content for advice to promote positive thinking, such as specific examples of advice and methods of providing it. This allows the system to support the user's mental well-being by providing suggestions for deep breathing and advice to promote positive thinking based on detected stress and emotional changes.

[0076] The escalation department can facilitate referral to specialized agencies when serious mental health problems are detected. For example, the escalation department can facilitate referral to specialized agencies when serious mental health problems are detected. For example, the escalation department can consider self-reports, changes in behavioral patterns, and professional diagnoses as specific criteria for identifying serious mental health problems. This allows for the provision of appropriate support by facilitating referral to specialized agencies when serious mental health problems are detected.

[0077] The system includes a knowledge supplementation unit that extracts necessary knowledge from expert databases and guidelines to provide care. For example, the knowledge supplementation unit can consider specific contents of the expert database, such as expert profiles, past research findings, and guidelines. Furthermore, the knowledge supplementation unit can consider specific contents of the guidelines, such as mental care guidelines and physical care guidelines. This allows for the provision of more appropriate care by extracting necessary knowledge from expert databases and guidelines.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the frequency of data collection to collect more detailed data. For example, if the user is relaxed, the data collection unit decreases the frequency of data collection to reduce the user's burden. For example, if the user is in a hurry, the data collection unit collects the necessary data in a short time and quickly sends it to the analysis unit. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0079] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting data from apps and services that the user has frequently used in the past. For example, the data collection unit selects the types of data to collect at specific time periods based on the user's past data collection history. For example, the data collection unit analyzes the user's past data collection history and proposes the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0080] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is working, the data collection unit will prioritize collecting work-related data. If the user is engrossed in a hobby, the data collection unit will collect data related to that hobby. If the user is taking a break, the data collection unit will collect data related to relaxation. By filtering the data based on the user's current activities and areas of interest, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit will prioritize collecting relaxation-related data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority collection of more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data related to home. By collecting data while considering the user's geographical location information, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts on a particular social media platform, the data collection unit can collect the content of those posts. For example, if a user receives many reactions on a particular social media platform, the data collection unit can collect the content of those reactions. For example, the data collection unit can collect the content of posts from accounts that a user follows on a particular social media platform. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides the analysis results in a simple and easy-to-understand format. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies an image analysis algorithm to image data. For example, the analysis unit applies a speech analysis algorithm to speech data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a quick analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the data collection period, more effective analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may evaluate the relevance of the data and perform the analysis in the optimal order. By adjusting the order of analysis based on the relevance of the data, more effective analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0090] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide suggestions that can be implemented quickly. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI.

[0091] The proposal unit can adjust the level of detail in its proposals based on the importance of the care provided. For example, the proposal unit will provide detailed proposals for high-importance care, simplified proposals for low-importance care, and proposals with an appropriate level of detail for moderately important care. By adjusting the level of detail in proposals based on the importance of the care, more effective proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI.

[0092] The suggestion unit can apply different suggestion algorithms depending on the category of care when making a suggestion. For example, for mental care, the suggestion unit may apply an algorithm that suggests relaxation techniques. For physical care, for example, the suggestion unit may apply an algorithm that suggests exercise and stretching. For social care, for example, the suggestion unit may apply a suggestion algorithm that promotes communication. By applying different suggestion algorithms depending on the category of care, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make short, concise suggestions. For example, if the user is relaxed, the suggestion unit will make detailed suggestions. For example, if the user is in a hurry, the suggestion unit will make suggestions that can be acted on quickly. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI.

[0094] The proposal department can determine the priority of proposals based on the timing of care delivery. For example, the proposal department may prioritize proposing highly urgent care. For example, the proposal department may postpone proposing less urgent care. For example, the proposal department may evaluate the timing of care delivery and make proposals in the optimal order. This allows for more effective proposals by determining the priority of proposals based on the timing of care delivery. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of the care when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant care. For example, the suggestion unit may postpone suggesting less relevant care. For example, the suggestion unit may evaluate the relevance of the care and make suggestions in the optimal order. By adjusting the order of suggestions based on the relevance of the care, more effective suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0096] The escalation unit can estimate the user's emotions and adjust the escalation method based on the estimated emotions. For example, if the user is stressed, the escalation unit will quickly escalate the issue to a professional agency. For example, if the user is relaxed, the escalation unit will carefully assess the need for escalation. For example, if the user is in a hurry, the escalation unit will quickly escalate the issue and provide the necessary support. This allows for more appropriate escalation by adjusting the escalation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the escalation unit may be performed using AI or not using AI.

[0097] The escalation unit can analyze the user's past mental health history to select the optimal escalation method during escalation. For example, the escalation unit may prioritize escalation to a specific professional organization based on the user's past mental health history. For example, the escalation unit may analyze the user's past mental health history and propose the most effective escalation method. For example, the escalation unit may adjust the timing of escalation based on the user's past mental health history. In this way, the optimal escalation method can be selected by analyzing the user's past mental health history. Some or all of the above processes in the escalation unit may be performed using AI, for example, or without using AI.

[0098] The escalation unit can customize the means of escalation based on the user's current living situation. For example, if the user is at work, the escalation unit will escalate the issue in a way that does not disrupt their work. For example, if the user is at home, the escalation unit will escalate the issue in a way that takes the home environment into consideration. For example, if the user is traveling, the escalation unit will escalate the issue to a specialized agency at the travel destination. By customizing the means of escalation based on the user's current living situation, more appropriate escalation becomes possible. Some or all of the above-described processes in the escalation unit may be performed using AI, for example, or without using AI.

[0099] The escalation unit can estimate the user's emotions and determine the priority of escalation based on the estimated emotions. For example, if the user is stressed, the escalation unit will set a high priority for escalation. For example, if the user is relaxed, the escalation unit will set a low priority for escalation. For example, if the user is in a hurry, the escalation unit will escalate quickly. This allows for more appropriate escalation by determining the priority of escalation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the escalation unit may be performed using AI, for example, or without AI.

[0100] The escalation unit can select the most appropriate escalation method when escalating a case, taking into account the user's geographical location. For example, if the user is in a specific location, the escalation unit prioritizes escalating to a specialized agency relevant to that location. For example, if the user is traveling, the escalation unit will escalate to a specialized agency in the user's travel destination. For example, if the user is at home, the escalation unit will escalate to a specialized agency near the user's home. By selecting an escalation method that takes into account the user's geographical location, more appropriate escalation becomes possible. Some or all of the above processing in the escalation unit may be performed using AI, for example, or without using AI.

[0101] The escalation unit can analyze the user's social media activity and propose escalation methods during the escalation process. For example, if the user frequently posts on a particular social media platform, the escalation unit may use the content of those posts as a reference for escalation. For example, if the user receives many reactions on a particular social media platform, the escalation unit may use the content of those reactions as a reference for escalation. For example, the escalation unit may use the content of posts from accounts that the user follows on a particular social media platform as a reference for escalation. In this way, by analyzing the user's social media activity, a more appropriate escalation method can be proposed. Some or all of the above-described processes in the escalation unit may be performed using AI, for example, or without AI.

[0102] The knowledge supplementation unit can estimate the user's emotions and adjust the way knowledge is delivered based on the estimated emotions. For example, if the user is stressed, the knowledge supplementation unit will provide simple and easy-to-understand knowledge. For example, if the user is relaxed, the knowledge supplementation unit will provide detailed knowledge. For example, if the user is in a hurry, the knowledge supplementation unit will provide knowledge that can be quickly implemented. This allows for more appropriate knowledge delivery by adjusting the way knowledge is delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without AI.

[0103] The knowledge supplementation unit can select the most suitable knowledge by referring to a database of experts when providing knowledge. For example, the knowledge supplementation unit selects the knowledge most appropriate for the user's situation from the database of experts. For example, the knowledge supplementation unit provides the latest knowledge by referring to a database of experts. For example, the knowledge supplementation unit analyzes a database of experts and provides knowledge that meets the user's needs. In this way, the optimal knowledge can be selected by referring to a database of experts. Some or all of the above processes in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0104] The knowledge supplementation unit can customize the content of the knowledge based on the user's current situation when providing knowledge. For example, if the user is at work, the knowledge supplementation unit will provide knowledge related to work. For example, if the user is at home, the knowledge supplementation unit will provide knowledge related to the home environment. For example, if the user is traveling, the knowledge supplementation unit will provide knowledge related to the travel destination. This makes it possible to provide more appropriate knowledge by customizing the content of the knowledge based on the user's current situation. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0105] The knowledge supplementation unit can estimate the user's emotions and prioritize knowledge based on the estimated emotions. For example, if the user is stressed, the knowledge supplementation unit will prioritize providing knowledge related to stress reduction. For example, if the user is relaxed, the knowledge supplementation unit will prioritize providing knowledge related to relaxation. For example, if the user is in a hurry, the knowledge supplementation unit will prioritize providing knowledge that can be acted on quickly. This makes it possible to provide more appropriate knowledge by prioritizing knowledge based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without AI.

[0106] The knowledge supplementation unit can provide optimal knowledge by considering the user's geographical location when providing knowledge. For example, if the user is in a specific location, the knowledge supplementation unit will provide knowledge related to that location. For example, if the user is traveling, the knowledge supplementation unit will provide knowledge related to the travel destination. For example, if the user is at home, the knowledge supplementation unit will provide knowledge related to home. By providing knowledge while considering the user's geographical location, it becomes possible to provide more appropriate knowledge. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0107] The knowledge supplementation unit can analyze the user's social media activity and suggest knowledge content when providing knowledge. For example, if the user frequently posts on a particular social media platform, the knowledge supplementation unit will provide knowledge related to the content of those posts. For example, if the user receives many reactions on a particular social media platform, the knowledge supplementation unit will provide knowledge related to the content of those reactions. For example, the knowledge supplementation unit will provide knowledge related to the content of posts from accounts that the user follows on a particular social media platform. This makes it possible to provide more appropriate knowledge by analyzing the user's social media activity. Some or all of the above processing in the knowledge supplementation unit may be performed using AI, for example, or without using AI.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The mental care system can also analyze the user's sleep patterns and offer suggestions to improve sleep quality. For example, the data collection unit uses the user's smartphone sensors to record sleep duration and depth. The analysis unit analyzes the collected sleep data and evaluates the user's sleep quality. Based on the analysis results, the suggestion unit provides advice to improve sleep quality. For example, it can suggest relaxation methods before bedtime or how to create a suitable sleep environment. This allows the user to get better sleep and is expected to improve overall mental health.

[0110] The mental care system can also analyze a user's eating patterns and suggest improvements to their nutritional balance. For example, the data collection unit collects the type and quantity of meals the user eats through an app that records their meals. The analysis unit analyzes the collected meal data and evaluates the nutritional balance. The suggestion unit provides advice on how to improve nutritional balance based on the analysis results. For example, it can suggest ingredients and recipes to increase the intake of specific nutrients. This allows users to maintain a healthy diet and is expected to improve their mental health.

[0111] The mental care system can also analyze a user's exercise patterns and suggest appropriate exercise plans. For example, the data collection unit records the amount and type of exercise through the user's smartphone or wearable device. The analysis unit analyzes the collected exercise data and evaluates the user's exercise habits. The suggestion unit provides an appropriate exercise plan based on the analysis results. For example, it can suggest exercise menus and timings tailored to the user's physical fitness and goals. This allows users to maintain healthy exercise habits and is expected to improve their mental health.

[0112] A mental care system can also suggest ways to refresh based on the user's hobbies and interests. For example, the data collection unit collects the user's smartphone app usage history and search history. The analysis unit analyzes the collected data to identify the user's hobbies and interests. The suggestion unit provides ways to refresh based on the analysis results. For example, it can provide information on activities and events that the user is interested in, or suggest new hobbies. This can help the user reduce stress and improve their mental health.

[0113] The mental care system can also analyze users' social activities and offer suggestions to enhance social support. For example, the data collection unit collects users' SNS usage and message exchanges. The analysis unit analyzes the collected data and evaluates the frequency and quality of users' social activities. Based on the analysis results, the suggestion unit provides advice to enhance social support. For example, it can suggest ways to promote communication with friends and family or encourage participation in social events. This can help users strengthen their social connections and improve their mental health.

[0114] A mental care system can estimate a user's emotions and suggest music and video content based on those estimated emotions. For example, the data collection unit collects data on the user's smartphone usage and voice data. The analysis unit analyzes the collected data and estimates the user's emotions. The suggestion unit provides music and video content suitable for relaxation or mood improvement based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing music and videos, and if the user is relaxed, it can suggest mood-enhancing content. This allows users to enjoy content appropriate to their emotions, which is expected to improve their mental health.

[0115] A mental care system can also estimate a user's emotions and suggest relaxation techniques based on those emotions. For example, the data collection unit collects sensor and voice data from the user's smartphone. The analysis unit analyzes the collected data and estimates the user's emotions. The suggestion unit provides relaxation techniques based on the estimated emotions. For example, if the user is feeling stressed, it can suggest deep breathing or meditation techniques, and if the user is relaxed, it can provide advice to maintain that relaxation. This allows the user to practice appropriate relaxation techniques according to their emotions, which is expected to improve their mental health.

[0116] A mental care system can also estimate a user's emotions and adjust the feedback method based on those estimated emotions. For example, the data collection unit collects data on the user's smartphone usage and voice data. The analysis unit analyzes the collected data and estimates the user's emotions. The suggestion unit adjusts the feedback method based on the estimated emotions. For example, if the user is feeling stressed, it can provide gentle and encouraging feedback, and if the user is relaxed, it can provide specific advice. This allows the user to receive appropriate feedback according to their emotions, which is expected to improve their mental health.

[0117] A mental care system can also estimate a user's emotions and suggest exercises based on those emotions. For example, the data collection unit collects sensor and voice data from the user's smartphone. The analysis unit analyzes the collected data and estimates the user's emotions. The suggestion unit then suggests exercises based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching; if the user is relaxed, it can suggest energetic exercises. This allows users to practice exercises appropriate to their emotions, which is expected to improve their mental health.

[0118] A mental care system can also estimate a user's emotions and suggest communication methods based on those estimated emotions. For example, the data collection unit collects data on the user's smartphone usage and voice data. The analysis unit analyzes the collected data and estimates the user's emotions. The suggestion unit then suggests communication methods based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing communication methods; if the user is relaxed, it can suggest proactive communication methods. This allows users to practice appropriate communication methods according to their emotions, which is expected to improve their mental health.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects data from the user's smartphone. For example, it collects data such as SNS usage, message content, and responses to notifications. Specifically, it can collect data such as login frequency, post content, number of likes, text messages, images, videos, whether notifications were opened, and actions taken in response to notifications. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data to detect changes in the user's stress and emotions in real time. Specifically, it can use changes in heart rate, self-reports, changes in behavioral patterns, facial expression analysis, voice analysis, and text analysis. Step 3: The proposal unit proposes care based on the results analyzed by the analysis unit. For example, based on detected stress and emotional changes, it may suggest deep breathing or provide advice to encourage positive thinking. Specifically, it can consider the timing and content of the proposal, examples of specific advice, and how to deliver it. Step 4: The escalation department escalates the case to a specialist based on the care proposed by the proposing department. For example, if a serious mental health problem is detected, they will encourage referral to a specialist. Specifically, they may consider self-reporting, changes in behavioral patterns, and professional diagnoses.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, escalation unit, and knowledge supplementation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects data such as SNS usage, message content, and responses to notifications. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to detect the user's stress and emotional changes in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care based on the detected stress and emotional changes. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12 and prompts referral to a specialized institution when a serious mental health problem is detected. The knowledge supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides care by retrieving necessary knowledge from expert databases and guidelines. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, escalation unit, and knowledge supplementation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data such as SNS usage, message content, and responses to notifications. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to detect the user's stress and emotional changes in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care based on the detected stress and emotional changes. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12 and prompts referral to a specialized institution if a serious mental health problem is detected. The knowledge supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides care by retrieving necessary knowledge from expert databases and guidelines. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, escalation unit, and knowledge supplementation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data such as SNS usage, message content, and responses to notifications. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to detect the user's stress and emotional changes in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care based on the detected stress and emotional changes. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12 and prompts referral to a specialized institution when a serious mental health problem is detected. The knowledge supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides care by retrieving necessary knowledge from expert databases and guidelines. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, escalation unit, and knowledge supplementation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data such as SNS usage, message content, and responses to notifications. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to detect the user's stress and emotional changes in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care based on the detected stress and emotional changes. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12 and prompts referral to a specialized institution when a serious mental health problem is detected. The knowledge supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides care by retrieving necessary knowledge from expert databases and guidelines. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A data collection unit that collects user smartphone data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that proposes care based on the results of the analysis performed by the aforementioned analysis unit, The system includes an escalation unit that escalates the care proposed by the proposal unit to a specialized institution. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as SNS usage, message content, and responses to notifications. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to detect changes in user stress and emotions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on detected stress levels and emotional changes, the system provides suggestions for deep breathing and advice to encourage positive thinking. The system described in Appendix 1, characterized by the features described herein. (Note 5) The escalation unit is, If serious mental health problems are detected, referral to a specialist agency will be encouraged. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating AI is equipped with a knowledge supplementation unit that extracts necessary knowledge from expert databases and guidelines to provide care. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the care being provided. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the care category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on the timing of care delivery. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the care. The system described in Appendix 1, characterized by the features described herein. (Note 25) The escalation unit is, It estimates the user's emotions and adjusts the escalation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The escalation unit is, During escalation, the system analyzes the user's past mental health history to select the most appropriate escalation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The escalation unit is, During escalation, the means of escalation are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The escalation unit is, The system estimates the user's emotions and determines the priority of escalations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The escalation unit is, During escalation, the optimal escalation method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The escalation unit is, During escalation, we analyze the user's social media activity and propose escalation methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned knowledge supplementation unit is It estimates the user's emotions and adjusts how knowledge is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned knowledge supplementation unit is When providing knowledge, we select the most appropriate knowledge by referring to a database of experts. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned knowledge supplementation unit is When providing knowledge, customize the content of the knowledge based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned knowledge supplementation unit is It estimates the user's emotions and prioritizes knowledge based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned knowledge supplementation unit is When providing knowledge, we take the user's geographical location into consideration to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned knowledge supplementation unit is When providing knowledge, we analyze users' social media activity to suggest content for that knowledge. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user smartphone data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that proposes care based on the results of the analysis performed by the aforementioned analysis unit, The system includes an escalation unit that escalates the care proposed by the proposal unit to a specialized institution. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as SNS usage, message content, and responses to notifications. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to detect changes in user stress and emotions in real time. The system according to feature 1.

4. The aforementioned proposal section is, Based on detected stress levels and emotional changes, the system provides suggestions for deep breathing and advice to encourage positive thinking. The system according to feature 1.

5. The escalation unit is, If serious mental health problems are detected, referral to a specialist agency will be encouraged. The system according to feature 1.

6. The generating AI is equipped with a knowledge supplementation unit that extracts necessary knowledge from expert databases and guidelines to provide care. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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