system

Abstract

The system according to this embodiment aims to understand the user's mental health status in detail and provide appropriate countermeasures. [Solution] The system according to the embodiment comprises a dialogue unit, an analysis unit, a suggestion unit, a tracking unit, and a monitoring unit. The dialogue unit collects information through dialogue with the user. The analysis unit analyzes the information collected by the dialogue unit. The suggestion unit proposes countermeasures based on the analysis results obtained by the analysis unit. The tracking unit tracks the user's daily emotions and behavioral patterns. The monitoring unit prompts the user to consult a specialist if a serious condition is detected.

Description

Technical Field

[0006] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 was a problem that it was difficult to grasp the mental health state of individual users in detail and provide appropriate countermeasures.

[0005] The system according to the embodiment aims to grasp the mental health state of the user in detail and provide appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dialogue unit, an analysis unit, a suggestion unit, a tracking unit, and a monitoring unit. The dialogue unit collects information through dialogue with the user. The analysis unit analyzes the information collected by the dialogue unit. The suggestion unit proposes countermeasures based on the analysis results obtained by the analysis unit. The tracking unit tracks the user's daily emotions and behavioral patterns. The monitoring unit prompts the user to consult a specialist if a serious condition is detected. [Effects of the Invention]

[0007] The system according to this embodiment can understand the user's mental health status in detail and provide appropriate countermeasures. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 3 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) The mental health support system according to an embodiment of the present invention is a system that provides individualized mental health support using AI. This system deeply understands the mental health status of each individual through dialogue between the user and the AI, and provides customized support based on that understanding. First, the user initiates a dialogue with the AI. The AI ​​analyzes the information obtained from the dialogue with the user in detail and identifies the individual's stressors and sources of anxiety. Next, based on the analysis results, the AI ​​proposes the most suitable coping methods to the user. For example, it may suggest breathing techniques or meditation techniques for stress reduction, or specific mindfulness activities that can be practiced in daily life, tailored to the user's situation and preferences. Furthermore, this system does not merely provide advice, but continuously tracks the user's daily emotions and behavioral patterns, visualizing changes in their mental health status. This allows the user to objectively understand their own mental health trends and take steps toward long-term improvement. The AI ​​also constantly monitors changes in the user's mental health, and if a serious condition is detected, it provides more comprehensive care, such as prompting consultation with a specialist at an appropriate time. In this way, by effectively combining AI and human specialists, safe and reliable mental healthcare is realized. This allows the mental health support system to efficiently support users' mental health and provide individualized coping strategies.

[0029] The mental health support system according to this embodiment comprises a dialogue unit, an analysis unit, a proposal unit, a tracking unit, and a monitoring unit. The dialogue unit collects information through dialogue with the user. For example, the dialogue unit asks the user questions and collects their answers. The dialogue unit can also analyze the user's voice and text to understand their emotions and intentions. For example, the dialogue unit can analyze the user's tone of voice and word choice to detect signs of stress or anxiety. Furthermore, the dialogue unit can refer to the user's past dialogue history to select the most appropriate dialogue method. For example, the dialogue unit selects a similar dialogue method based on the user's preferred dialogue style in the past. The analysis unit analyzes the information collected by the dialogue unit. For example, the analysis unit uses natural language processing technology to analyze the user's statements. Furthermore, the analysis unit can use machine learning algorithms to predict the user's emotions and behavioral patterns. For example, the analysis unit identifies stressors from the user's statements and proposes coping strategies based on those factors. Furthermore, the analysis unit can refer to the user's past behavioral patterns to optimize the analysis algorithm. The suggestion unit proposes coping strategies based on the analysis results obtained by the analysis unit. For example, the suggestion unit may propose breathing exercises or meditation techniques to reduce stress. The suggestion unit can also propose coping strategies tailored to the user's situation and preferences. For example, the suggestion unit may propose mindfulness activities that help the user relax. The tracking unit tracks the user's daily emotions and behavioral patterns. For example, the tracking unit displays changes in the user's emotions in graphs or charts. The tracking unit can also analyze the user's behavioral patterns and visualize changes in their mental health. The monitoring unit prompts the user to consult a professional if a serious condition is detected. For example, the monitoring unit may suggest consulting a professional if the user's stress level increases. The monitoring unit can also monitor changes in the user's mental health in real time and intervene at the appropriate time. As a result, the mental health support system according to this embodiment can efficiently support the user's mental health and provide individualized coping strategies.

[0030] The dialogue unit collects information through interaction with the user. For example, it asks the user questions and collects their answers. The dialogue unit can also analyze the user's voice and text to understand their emotions and intentions. Specifically, it analyzes the tone of voice, speed, and pauses of the user's voice to infer their emotional state. For example, if the voice tone is low and the speaking speed is slow, it may be determined that the user is feeling depressed. In text-based dialogues, it analyzes the user's word choice and context to understand their emotions and intentions. For example, if the user frequently uses words like "I'm tired" or "I can't do this anymore," it may be determined that they are experiencing accumulated stress and fatigue. Furthermore, the dialogue unit can refer to the user's past dialogue history to select the most appropriate dialogue method. For example, it may select a similar dialogue method based on the user's past relaxed dialogue style and preferred topics. This allows the user to continue the dialogue with a sense of security. The dialogue unit can also provide real-time feedback on the user's responses and adjust questions and topics as the dialogue progresses. For example, if a user shows a strong reaction to a particular topic, the system will delve deeper into that topic to gather more detailed information. This allows the dialogue unit to accurately understand the user's emotions and intentions and collect the necessary foundational information to provide appropriate support.

[0031] The analysis department analyzes the information collected by the dialogue department. For example, the analysis department uses natural language processing technology to analyze the content of user statements. Specifically, it converts user statements into text data and performs keyword extraction and sentiment analysis. For example, it extracts keywords such as "work," "stress," and "tired" from user statements and analyzes the context in which these keywords are used. In sentiment analysis, it classifies the content of user statements into positive, negative, or neutral emotions to understand the user's emotional state. Furthermore, the analysis department can also predict user emotions and behavioral patterns using machine learning algorithms. For example, based on past data, it predicts what emotions a user will feel in a particular situation and proposes coping strategies based on the prediction results. For example, if a user is feeling stressed at work, it suggests coping strategies that were effective in similar situations based on past data. The analysis department can also optimize its analysis algorithms by referring to the user's past behavioral patterns. For example, it analyzes what actions a user has taken in the past and evaluates how those actions are influencing their current emotional state. This allows the analytics department to accurately understand users' emotions and behavioral patterns and provide the foundational data needed to propose appropriate solutions.

[0032] The Proposal Department proposes coping strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department might suggest breathing techniques or meditation methods to reduce stress. Specifically, if a user's stress level is determined to be high, it might suggest breathing techniques such as deep breathing or diaphragmatic breathing and explain how to do so in detail. It might also suggest meditation techniques such as mindfulness meditation or body scan meditation to support the user in relaxing. Furthermore, the Proposal Department can also propose coping strategies tailored to the user's situation and preferences. For example, if a user can relax by listening to music, it might suggest music with relaxing effects. If a user prefers exercise, it might suggest exercise methods effective for stress relief. This allows the Proposal Department to provide coping strategies that meet the user's individual needs and achieve effective mental health support. Additionally, the Proposal Department can track the implementation status of the proposed coping strategies and evaluate their effectiveness. For example, it might monitor the user's emotional state after they have practiced a suggested breathing technique and evaluate its effectiveness. This allows the Proposal Department to continuously improve the accuracy and effectiveness of its suggestions and provide optimal support to users.

[0033] The tracking unit tracks users' daily emotions and behavioral patterns. For example, it displays changes in users' emotions using graphs and charts. Specifically, it collects emotional state and behavioral data that users input daily and displays it visually. For instance, based on the emotional score users input daily, it displays changes in emotions as a line graph, allowing users to understand when their emotions fluctuated significantly. The tracking unit can also analyze users' behavioral patterns and visualize changes in their mental health. For example, it can analyze how a user's emotional state changes after they take a specific action and display the results in a chart. This makes it easier for users to understand the relationship between their actions and emotions. Furthermore, the tracking unit can accumulate user emotional and behavioral data over the long term and perform trend analysis. For example, it can analyze seasonal fluctuations in users' emotional states and their responses to specific events based on data from the past few months. This allows the tracking unit to understand long-term trends in users' mental health and provide foundational data for providing appropriate support.

[0034] The monitoring unit prompts users to consult a professional if a serious condition is detected. For example, if a user's stress level increases, the monitoring unit suggests consulting a professional. Specifically, it monitors the user's emotional state and behavioral patterns in real time and issues an alert if an abnormality is detected. For example, if a user experiences negative emotions for an extended period, it sends a notification prompting them to consult a professional. The monitoring unit can also monitor changes in the user's mental health in real time and intervene at the appropriate time. For example, if a user suddenly starts feeling stressed, it immediately sends a notification suggesting relaxation methods or prompting them to consult a professional. This allows the monitoring unit to detect a deterioration in the user's mental health early and take appropriate measures. Furthermore, with the user's consent, the monitoring unit can share collected data with professionals to provide more effective support. For example, professionals can provide more accurate advice and treatment by conducting counseling based on the user's data. This allows the monitoring unit to efficiently support the user's mental health and intervene appropriately before a serious condition develops.

[0035] The tracking unit can track the user's daily emotions and behavioral patterns and visualize changes in their mental health. For example, the tracking unit can display changes in the user's emotions in graphs or charts. For example, it can display changes in the user's emotions in a line graph. It can also display the user's behavioral patterns in a bar graph. Furthermore, the tracking unit can display changes in the user's emotions in a heatmap. This allows the user to objectively understand their own mental health trends. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's emotional data into a generating AI and have the generating AI create graphs or charts to visualize changes in emotions.

[0036] The suggestion function can propose breathing techniques, meditation techniques, and specific mindfulness activities for stress reduction. For example, the suggestion function can propose breathing techniques for stress reduction. For example, the suggestion function can propose deep breathing techniques. The suggestion function can also propose diaphragmatic breathing techniques. Furthermore, the suggestion function can also propose the 4-7-8 breathing technique. For example, the suggestion function can propose meditation techniques. For example, the suggestion function can propose mindfulness meditation. Furthermore, the suggestion function can also propose focused meditation. Furthermore, the suggestion function can also propose guided meditation. For example, the suggestion function can propose specific mindfulness activities. For example, the suggestion function can propose body scans. Furthermore, the suggestion function can also propose mindfulness walking. Furthermore, the suggestion function can also propose mindfulness journaling. This allows the user to practice appropriate coping mechanisms. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can have a generating AI perform coping mechanism suggestions based on the user's situation and preferences.

[0037] The monitoring unit can prompt consultation with a specialist if a serious condition is detected. For example, the monitoring unit may suggest consultation with a specialist if the user's stress level increases. For example, the monitoring unit may suggest consultation with a specialist if the user's stress level exceeds a certain threshold. The monitoring unit may also suggest consultation with a specialist if an abnormality is detected in the user's behavioral patterns. Furthermore, the monitoring unit may suggest consultation with a specialist if the user's emotional changes are rapid. This ensures that the user receives support from a specialist at the appropriate time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's stress level and behavioral patterns into a generating AI and have the generating AI perform the detection of serious conditions and suggest consultation with a specialist.

[0038] The tracking unit can display the user's mental health trends in graphs and charts. For example, the tracking unit can display changes in the user's emotions in a line graph. The tracking unit can also display the user's behavior patterns in a bar graph. Furthermore, the tracking unit can display changes in the user's emotions in a heatmap. This allows the user to visually understand their mental health status. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's emotional data into a generating AI and have the generating AI create graphs and charts to visualize changes in emotions.

[0039] The suggestion function can propose coping strategies tailored to the user's situation and preferences. For example, the suggestion function can propose breathing techniques for stress reduction based on the user's situation and preferences. For example, the suggestion function can propose deep breathing techniques to help the user relax. The suggestion function can also propose diaphragmatic breathing techniques tailored to the user's preferences. Furthermore, the suggestion function can propose 4-7-8 breathing techniques depending on the user's situation. For example, the suggestion function can propose meditation techniques based on the user's situation and preferences. For example, the suggestion function can propose mindfulness meditation to help the user relax. The suggestion function can also propose focused meditation tailored to the user's preferences. Furthermore, the suggestion function can propose guided meditation tailored to the user's situation. For example, the suggestion function can propose specific mindfulness activities based on the user's situation and preferences. For example, the suggestion function can propose body scans to help the user relax. The suggestion function can also propose mindful walking tailored to the user's preferences. Furthermore, the suggestion function can propose mindfulness journaling tailored to the user's situation. This allows users to receive solutions tailored to their needs. Some or all of the above-described processes in the suggestion section may be performed using AI, for example, or without AI. For example, the suggestion section can have a generating AI suggest solutions based on the user's situation and preferences.

[0040] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can select a similar dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can also adjust the dialogue method to avoid topics the user has avoided in the past. Furthermore, the dialogue unit can select a dialogue method suitable for a specific time period based on the user's past dialogue history. This allows the dialogue unit to provide the user with the most optimal dialogue method. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0041] The dialogue unit can customize the content of the conversation based on the user's current living situation and areas of interest during the conversation. For example, if the user shares their current work situation, the dialogue unit can customize the content based on that information. The dialogue unit can also adjust the content of the conversation based on information if the user talks about their recent hobbies or interests. Furthermore, if the dialogue unit talks about the user's current living environment, the dialogue unit can customize the content based on that information. This allows the dialogue unit to provide the user with highly relevant content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input information about the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the conversation content.

[0042] The dialogue unit can prioritize topics of high relevance based on the user's geographical location during a conversation. For example, the dialogue unit can adjust the conversation based on weather information for the user's current location. Furthermore, the dialogue unit can customize the conversation based on event information in the user's area. In addition, the dialogue unit can bring up topics related to the culture and history of the user's location. This allows for the provision of conversation content tailored to the user's location. Some or all of the above processing in the dialogue unit may be performed using AI, or without AI. For example, the dialogue unit can input the user's geographical location into a generating AI and have the generating AI select topics of high relevance.

[0043] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can adjust the conversation content based on articles the user has recently shared on social media. The dialogue unit can also provide topics related to topics the user follows on social media. Furthermore, the dialogue unit can select topics that the user might be interested in based on their social media activity. This allows the dialogue unit to provide conversation content based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI select relevant topics.

[0044] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can optimize an algorithm to identify stressors based on the user's past behavior patterns. The analysis unit can also optimize an algorithm to identify relaxing activities based on the user's past behavior patterns. Furthermore, the analysis unit can optimize an algorithm to analyze the user's past behavior patterns and propose the optimal coping strategies. This enables optimal analysis based on the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can apply different analysis methods depending on the user's category during the analysis. For example, if the user is a student, the analysis unit can apply a method to analyze stressors related to academics. The analysis unit can also apply a method to analyze workplace stressors if the user is employed. Furthermore, if the user is a housewife, the analysis unit can apply a method to analyze stressors within the home. This enables appropriate analysis according to the user's category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user category information into a generating AI and have the generating AI execute the application of different analysis methods.

[0046] The analysis unit can determine the priority of analyses based on the user's submission timing. For example, if the user is in a hurry, the analysis unit can perform the analysis quickly and provide the results. The analysis unit can also perform analyses with normal priorities if the user is relaxed. Furthermore, if the user sets a specific deadline, the analysis unit can adjust the analysis priority accordingly. This allows for analyses to be performed with priorities that match the user's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user submission timing information into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past health data. The analysis unit can also improve the accuracy of its analysis by referring to the user's social media activity. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's lifestyle data. This enables highly accurate analysis based on the user's relevant data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0048] The proposal unit can adjust the level of detail in its proposals based on the importance of the solutions. For example, if a solution is important, the proposal unit can provide a proposal that includes a detailed explanation. The proposal unit can also provide a proposal that includes a concise explanation if the solution is general. Furthermore, if a solution is urgent, the proposal unit can provide a proposal that can be implemented quickly. This allows for detailed proposals tailored to the importance of the solutions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the importance of the solutions into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0049] The suggestion unit can apply different suggestion algorithms depending on the category of the coping method when making a suggestion. For example, if the coping method is for stress reduction, the suggestion unit can apply a suggestion algorithm that promotes relaxation. The suggestion unit can also apply a suggestion algorithm that provides a sense of security if the coping method is for anxiety relief. Furthermore, if the coping method is for maintaining mental health, the suggestion unit can apply a suggestion algorithm that provides continuous support. This enables optimal suggestions according to the category of the coping method. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input coping method category information into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0050] The proposal department can determine the priority of proposals based on the submission timing of the solutions. For example, the proposal department can prioritize urgent solutions. The proposal department can also prioritize general solutions. Furthermore, if the user sets a specific deadline, the proposal department can adjust the priority of proposals accordingly. This allows proposals to be prioritized according to the submission timing of the solutions. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input information on the submission timing of solutions into a generating AI and have the generating AI determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the solutions when making suggestions. For example, the suggestion unit may suggest the most relevant solution first. The suggestion unit can also postpone less relevant solutions. Furthermore, the suggestion unit can prioritize suggesting highly relevant solutions depending on the user's situation. This allows suggestions to be made in an order that corresponds to the relevance of the solutions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relevance information of the solutions into a generating AI and have the generating AI adjust the order of the suggestions.

[0052] The tracking unit can optimize its tracking algorithm by referring to the user's past emotional patterns during tracking. For example, the tracking unit can predict when stress levels will rise based on the user's past emotional patterns and optimize the tracking algorithm. The tracking unit can also identify times when the user can relax based on the user's past emotional patterns and optimize the tracking algorithm. Furthermore, the tracking unit can analyze the user's past emotional patterns and determine the optimal tracking timing. This enables optimal tracking based on the user's past emotional patterns. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past emotional pattern data into a generating AI and have the generating AI perform the optimization of the tracking algorithm.

[0053] The tracking unit can apply different tracking methods depending on the user's category during tracking. For example, if the user is a student, the tracking unit can apply a method to track stressors related to academics. The tracking unit can also apply a method to track workplace stressors if the user is employed. Furthermore, if the user is a housewife, the tracking unit can apply a method to track stressors within the home. This enables appropriate tracking according to the user's category. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user category information into a generating AI and have the generating AI execute the application of different tracking methods.

[0054] The tracking unit can determine tracking priorities based on the user's submission timing during tracking. For example, if the user is in a hurry, the tracking unit can perform tracking quickly and provide results. The tracking unit can also perform tracking with normal priorities if the user is relaxed. Furthermore, if the user sets a specific deadline, the tracking unit can adjust the tracking priority accordingly. This allows tracking to be performed with priorities according to the user's submission timing. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user submission timing information into a generating AI and have the generating AI determine the tracking priority.

[0055] The tracking unit can improve tracking accuracy by referring to the user's relevant data during tracking. For example, the tracking unit can improve tracking accuracy by referring to the user's past health data. The tracking unit can also improve tracking accuracy by referring to the user's social media activity. Furthermore, the tracking unit can improve tracking accuracy by referring to the user's lifestyle data. This enables highly accurate tracking based on the user's relevant data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's relevant data into a generating AI and have the generating AI perform the tracking accuracy improvement.

[0056] The monitoring unit can optimize the monitoring algorithm by referring to the user's past mental health state during monitoring. For example, the monitoring unit can predict when stress levels will rise based on the user's past mental health state and optimize the monitoring algorithm. The monitoring unit can also identify times when the user can relax based on the user's past mental health state and optimize the monitoring algorithm. Furthermore, the monitoring unit can analyze the user's past mental health state and determine the optimal monitoring timing. This enables optimal monitoring based on the user's past mental health state. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past mental health state data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0057] The monitoring unit can apply different monitoring methods depending on the user's category during monitoring. For example, if the user is a student, the monitoring unit can apply a method to monitor stressors related to academics. The monitoring unit can also apply a method to monitor workplace stressors if the user is employed. Furthermore, if the user is a housewife, the monitoring unit can apply a method to monitor stressors within the home. This enables appropriate monitoring according to the user's category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user category information into a generating AI and have the generating AI execute the application of different monitoring methods.

[0058] The monitoring unit can determine the monitoring priority based on the user's submission timing during monitoring. For example, if the user is in a hurry, the monitoring unit can perform monitoring quickly and provide the results. The monitoring unit can also perform monitoring with normal priority if the user is relaxed. Furthermore, if the user sets a specific deadline, the monitoring unit can adjust the monitoring priority accordingly. This allows monitoring to be performed with priority according to the user's submission timing. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user submission timing information into a generating AI and have the generating AI determine the monitoring priority.

[0059] The monitoring unit can improve the accuracy of monitoring by referring to the user's relevant data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to the user's past health data. The monitoring unit can also improve the accuracy of monitoring by referring to the user's social media activity. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to the user's lifestyle data. This enables highly accurate monitoring based on the user's relevant data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's relevant data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

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

[0061] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can select a similar dialogue method based on the user's preferred dialogue style in the past. It can also adjust the dialogue method to avoid topics the user has avoided in the past. Furthermore, the dialogue unit can select a dialogue method suitable for a specific time period based on the user's past dialogue history. This allows the dialogue unit to provide the user with the most optimal dialogue method. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0062] The dialogue unit can customize the conversation content based on the user's current living situation and areas of interest during a conversation. For example, if the user shares their current work situation, the dialogue unit will customize the conversation content based on that information. The dialogue unit can also adjust the conversation content based on the user's recent hobbies or interests. Furthermore, if the user talks about their current living environment, the dialogue unit can customize the conversation content based on that information. This allows the dialogue unit to provide the user with highly relevant conversation content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input information about the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the conversation content.

[0063] The dialogue unit can prioritize topics of high relevance based on the user's geographical location during a conversation. For example, the dialogue unit can adjust the conversation content based on weather information in the user's current location. It can also customize the conversation content based on event information in the user's area. Furthermore, the dialogue unit can bring up topics related to the culture and history of the user's location. This allows for the provision of conversation content tailored to the user's location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location information into a generating AI and have the generating AI select topics of high relevance.

[0064] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can tailor the conversation based on articles the user has recently shared on social media. It can also provide topics related to topics the user follows on social media. Furthermore, the dialogue unit can select topics that the user might be interested in based on their social media activity. This allows the dialogue unit to provide content based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI select relevant topics.

[0065] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can optimize an algorithm to identify stressors based on the user's past behavior patterns. It can also optimize an algorithm to identify relaxing activities based on the user's past behavior patterns. Furthermore, the analysis unit can optimize an algorithm to analyze the user's past behavior patterns and suggest the optimal coping strategies. This enables optimal analysis based on the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0066] The analysis unit can apply different analysis methods depending on the user's category during the analysis. For example, if the user is a student, the analysis unit can apply a method to analyze stressors related to academics. If the user is employed, the analysis unit can also apply a method to analyze workplace stressors. Furthermore, if the user is a housewife, the analysis unit can apply a method to analyze stressors within the home. This enables appropriate analysis according to the user's category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user category information into a generating AI and have the generating AI execute the application of different analysis methods.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The dialogue unit collects information through interaction with the user. The dialogue unit asks the user questions and collects their answers. It can also analyze the user's voice and text to understand their emotions and intentions. For example, it can analyze the user's tone of voice and word choice to detect signs of stress or anxiety. Furthermore, the dialogue unit can refer to the user's past dialogue history to select the most appropriate dialogue method. Step 2: The analysis unit analyzes the information collected by the dialogue unit. The analysis unit uses natural language processing technology to analyze the user's statements and machine learning algorithms to predict the user's emotions and behavioral patterns. For example, it can identify stressors from the user's statements and suggest coping strategies based on those factors. Furthermore, it can also optimize the analysis algorithm by referring to the user's past behavioral patterns. Step 3: The proposal team proposes solutions based on the analysis results obtained by the analysis team. The proposal team suggests breathing techniques and meditation methods to reduce stress, providing solutions tailored to the user's situation and preferences. For example, they might suggest mindfulness activities that help the user relax. Step 4: The tracking unit tracks the user's daily emotions and behavioral patterns. The tracking unit displays changes in the user's emotions in graphs and charts, analyzes behavioral patterns, and visualizes changes in their mental health. Step 5: The monitoring unit prompts consultation with a specialist if a serious condition is detected. The monitoring unit suggests consultation with a specialist when the user's stress level increases, and monitors changes in mental health in real time to intervene at the appropriate time.

[0069] (Example of form 2) The mental health support system according to an embodiment of the present invention is a system that provides individualized mental health support using AI. This system deeply understands the mental health status of each individual through dialogue between the user and the AI, and provides customized support based on that understanding. First, the user initiates a dialogue with the AI. The AI ​​analyzes the information obtained from the dialogue with the user in detail and identifies the individual's stressors and sources of anxiety. Next, based on the analysis results, the AI ​​proposes the most suitable coping methods to the user. For example, it may suggest breathing techniques or meditation techniques for stress reduction, or specific mindfulness activities that can be practiced in daily life, tailored to the user's situation and preferences. Furthermore, this system does not merely provide advice, but continuously tracks the user's daily emotions and behavioral patterns, visualizing changes in their mental health status. This allows the user to objectively understand their own mental health trends and take steps toward long-term improvement. The AI ​​also constantly monitors changes in the user's mental health, and if a serious condition is detected, it provides more comprehensive care, such as prompting consultation with a specialist at an appropriate time. In this way, by effectively combining AI and human specialists, safe and reliable mental healthcare is realized. This allows the mental health support system to efficiently support users' mental health and provide individualized coping strategies.

[0070] The mental health support system according to this embodiment comprises a dialogue unit, an analysis unit, a proposal unit, a tracking unit, and a monitoring unit. The dialogue unit collects information through dialogue with the user. For example, the dialogue unit asks the user questions and collects their answers. The dialogue unit can also analyze the user's voice and text to understand their emotions and intentions. For example, the dialogue unit can analyze the user's tone of voice and word choice to detect signs of stress or anxiety. Furthermore, the dialogue unit can refer to the user's past dialogue history to select the most appropriate dialogue method. For example, the dialogue unit selects a similar dialogue method based on the user's preferred dialogue style in the past. The analysis unit analyzes the information collected by the dialogue unit. For example, the analysis unit uses natural language processing technology to analyze the user's statements. Furthermore, the analysis unit can use machine learning algorithms to predict the user's emotions and behavioral patterns. For example, the analysis unit identifies stressors from the user's statements and proposes coping strategies based on those factors. Furthermore, the analysis unit can refer to the user's past behavioral patterns to optimize the analysis algorithm. The suggestion unit proposes coping strategies based on the analysis results obtained by the analysis unit. For example, the suggestion unit may propose breathing exercises or meditation techniques to reduce stress. The suggestion unit can also propose coping strategies tailored to the user's situation and preferences. For example, the suggestion unit may propose mindfulness activities that help the user relax. The tracking unit tracks the user's daily emotions and behavioral patterns. For example, the tracking unit displays changes in the user's emotions in graphs or charts. The tracking unit can also analyze the user's behavioral patterns and visualize changes in their mental health. The monitoring unit prompts the user to consult a professional if a serious condition is detected. For example, the monitoring unit may suggest consulting a professional if the user's stress level increases. The monitoring unit can also monitor changes in the user's mental health in real time and intervene at the appropriate time. As a result, the mental health support system according to this embodiment can efficiently support the user's mental health and provide individualized coping strategies.

[0071] The dialogue unit collects information through interaction with the user. For example, it asks the user questions and collects their answers. The dialogue unit can also analyze the user's voice and text to understand their emotions and intentions. Specifically, it analyzes the tone of voice, speed, and pauses of the user's voice to infer their emotional state. For example, if the voice tone is low and the speaking speed is slow, it may be determined that the user is feeling depressed. In text-based dialogues, it analyzes the user's word choice and context to understand their emotions and intentions. For example, if the user frequently uses words like "I'm tired" or "I can't do this anymore," it may be determined that they are experiencing accumulated stress and fatigue. Furthermore, the dialogue unit can refer to the user's past dialogue history to select the most appropriate dialogue method. For example, it may select a similar dialogue method based on the user's past relaxed dialogue style and preferred topics. This allows the user to continue the dialogue with a sense of security. The dialogue unit can also provide real-time feedback on the user's responses and adjust questions and topics as the dialogue progresses. For example, if a user shows a strong reaction to a particular topic, the system will delve deeper into that topic to gather more detailed information. This allows the dialogue unit to accurately understand the user's emotions and intentions and collect the necessary foundational information to provide appropriate support.

[0072] The analysis department analyzes the information collected by the dialogue department. For example, the analysis department uses natural language processing technology to analyze the content of user statements. Specifically, it converts user statements into text data and performs keyword extraction and sentiment analysis. For example, it extracts keywords such as "work," "stress," and "tired" from user statements and analyzes the context in which these keywords are used. In sentiment analysis, it classifies the content of user statements into positive, negative, or neutral emotions to understand the user's emotional state. Furthermore, the analysis department can also predict user emotions and behavioral patterns using machine learning algorithms. For example, based on past data, it predicts what emotions a user will feel in a particular situation and proposes coping strategies based on the prediction results. For example, if a user is feeling stressed at work, it suggests coping strategies that were effective in similar situations based on past data. The analysis department can also optimize its analysis algorithms by referring to the user's past behavioral patterns. For example, it analyzes what actions a user has taken in the past and evaluates how those actions are influencing their current emotional state. This allows the analytics department to accurately understand users' emotions and behavioral patterns and provide the foundational data needed to propose appropriate solutions.

[0073] The Proposal Department proposes coping strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department might suggest breathing techniques or meditation methods to reduce stress. Specifically, if a user's stress level is determined to be high, it might suggest breathing techniques such as deep breathing or diaphragmatic breathing and explain how to do so in detail. It might also suggest meditation techniques such as mindfulness meditation or body scan meditation to support the user in relaxing. Furthermore, the Proposal Department can also propose coping strategies tailored to the user's situation and preferences. For example, if a user can relax by listening to music, it might suggest music with relaxing effects. If a user prefers exercise, it might suggest exercise methods effective for stress relief. This allows the Proposal Department to provide coping strategies that meet the user's individual needs and achieve effective mental health support. Additionally, the Proposal Department can track the implementation status of the proposed coping strategies and evaluate their effectiveness. For example, it might monitor the user's emotional state after they have practiced a suggested breathing technique and evaluate its effectiveness. This allows the Proposal Department to continuously improve the accuracy and effectiveness of its suggestions and provide optimal support to users.

[0074] The tracking unit tracks users' daily emotions and behavioral patterns. For example, it displays changes in users' emotions using graphs and charts. Specifically, it collects emotional state and behavioral data that users input daily and displays it visually. For instance, based on the emotional score users input daily, it displays changes in emotions as a line graph, allowing users to understand when their emotions fluctuated significantly. The tracking unit can also analyze users' behavioral patterns and visualize changes in their mental health. For example, it can analyze how a user's emotional state changes after they take a specific action and display the results in a chart. This makes it easier for users to understand the relationship between their actions and emotions. Furthermore, the tracking unit can accumulate user emotional and behavioral data over the long term and perform trend analysis. For example, it can analyze seasonal fluctuations in users' emotional states and their responses to specific events based on data from the past few months. This allows the tracking unit to understand long-term trends in users' mental health and provide foundational data for providing appropriate support.

[0075] The monitoring unit prompts users to consult a professional if a serious condition is detected. For example, if a user's stress level increases, the monitoring unit suggests consulting a professional. Specifically, it monitors the user's emotional state and behavioral patterns in real time and issues an alert if an abnormality is detected. For example, if a user experiences negative emotions for an extended period, it sends a notification prompting them to consult a professional. The monitoring unit can also monitor changes in the user's mental health in real time and intervene at the appropriate time. For example, if a user suddenly starts feeling stressed, it immediately sends a notification suggesting relaxation methods or prompting them to consult a professional. This allows the monitoring unit to detect a deterioration in the user's mental health early and take appropriate measures. Furthermore, with the user's consent, the monitoring unit can share collected data with professionals to provide more effective support. For example, professionals can provide more accurate advice and treatment by conducting counseling based on the user's data. This allows the monitoring unit to efficiently support the user's mental health and intervene appropriately before a serious condition develops.

[0076] The tracking unit can track the user's daily emotions and behavioral patterns and visualize changes in their mental health. For example, the tracking unit can display changes in the user's emotions in graphs or charts. For example, it can display changes in the user's emotions in a line graph. It can also display the user's behavioral patterns in a bar graph. Furthermore, the tracking unit can display changes in the user's emotions in a heatmap. This allows the user to objectively understand their own mental health trends. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's emotional data into a generating AI and have the generating AI create graphs or charts to visualize changes in emotions.

[0077] The suggestion function can propose breathing techniques, meditation techniques, and specific mindfulness activities for stress reduction. For example, the suggestion function can propose breathing techniques for stress reduction. For example, the suggestion function can propose deep breathing techniques. The suggestion function can also propose diaphragmatic breathing techniques. Furthermore, the suggestion function can also propose the 4-7-8 breathing technique. For example, the suggestion function can propose meditation techniques. For example, the suggestion function can propose mindfulness meditation. Furthermore, the suggestion function can also propose focused meditation. Furthermore, the suggestion function can also propose guided meditation. For example, the suggestion function can propose specific mindfulness activities. For example, the suggestion function can propose body scans. Furthermore, the suggestion function can also propose mindfulness walking. Furthermore, the suggestion function can also propose mindfulness journaling. This allows the user to practice appropriate coping mechanisms. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can have a generating AI perform coping mechanism suggestions based on the user's situation and preferences.

[0078] The monitoring unit can prompt consultation with a specialist if a serious condition is detected. For example, the monitoring unit may suggest consultation with a specialist if the user's stress level increases. For example, the monitoring unit may suggest consultation with a specialist if the user's stress level exceeds a certain threshold. The monitoring unit may also suggest consultation with a specialist if an abnormality is detected in the user's behavioral patterns. Furthermore, the monitoring unit may suggest consultation with a specialist if the user's emotional changes are rapid. This ensures that the user receives support from a specialist at the appropriate time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's stress level and behavioral patterns into a generating AI and have the generating AI perform the detection of serious conditions and suggest consultation with a specialist.

[0079] The tracking unit can display the user's mental health trends in graphs and charts. For example, the tracking unit can display changes in the user's emotions in a line graph. The tracking unit can also display the user's behavior patterns in a bar graph. Furthermore, the tracking unit can display changes in the user's emotions in a heatmap. This allows the user to visually understand their mental health status. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's emotional data into a generating AI and have the generating AI create graphs and charts to visualize changes in emotions.

[0080] The suggestion function can propose coping strategies tailored to the user's situation and preferences. For example, the suggestion function can propose breathing techniques for stress reduction based on the user's situation and preferences. For example, the suggestion function can propose deep breathing techniques to help the user relax. The suggestion function can also propose diaphragmatic breathing techniques tailored to the user's preferences. Furthermore, the suggestion function can propose 4-7-8 breathing techniques depending on the user's situation. For example, the suggestion function can propose meditation techniques based on the user's situation and preferences. For example, the suggestion function can propose mindfulness meditation to help the user relax. The suggestion function can also propose focused meditation tailored to the user's preferences. Furthermore, the suggestion function can propose guided meditation tailored to the user's situation. For example, the suggestion function can propose specific mindfulness activities based on the user's situation and preferences. For example, the suggestion function can propose body scans to help the user relax. The suggestion function can also propose mindful walking tailored to the user's preferences. Furthermore, the suggestion function can propose mindfulness journaling tailored to the user's situation. This allows users to receive solutions tailored to their needs. Some or all of the above-described processes in the suggestion section may be performed using AI, for example, or without AI. For example, the suggestion section can have a generating AI suggest solutions based on the user's situation and preferences.

[0081] The dialogue unit can estimate the user's emotions and adjust the pace of the conversation based on the estimated emotions. For example, if the user is stressed, the dialogue unit can slow down the pace of the conversation to help the user relax. The dialogue unit can also speed up the pace of the conversation to provide information quickly if the user is in a hurry. Furthermore, if the user is relaxed, the dialogue unit can maintain a normal pace of conversation for a natural conversation. This enables conversations that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI adjust the pace of the conversation.

[0082] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can select a similar dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can also adjust the dialogue method to avoid topics the user has avoided in the past. Furthermore, the dialogue unit can select a dialogue method suitable for a specific time period based on the user's past dialogue history. This allows the dialogue unit to provide the user with the most optimal dialogue method. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0083] The dialogue unit can customize the content of the conversation based on the user's current living situation and areas of interest during the conversation. For example, if the user shares their current work situation, the dialogue unit can customize the content based on that information. The dialogue unit can also adjust the content of the conversation based on information if the user talks about their recent hobbies or interests. Furthermore, if the dialogue unit talks about the user's current living environment, the dialogue unit can customize the content based on that information. This allows the dialogue unit to provide the user with highly relevant content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input information about the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the conversation content.

[0084] The dialogue unit can estimate the user's emotions and select conversation topics based on the estimated emotions. For example, if the user is stressed, the dialogue unit can select relaxing topics. The dialogue unit can also select interesting topics if the user is excited. Furthermore, if the user is depressed, the dialogue unit can select encouraging topics. This allows the dialogue unit to provide topics that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI select conversation topics.

[0085] The dialogue unit can prioritize topics of high relevance based on the user's geographical location during a conversation. For example, the dialogue unit can adjust the conversation based on weather information for the user's current location. Furthermore, the dialogue unit can customize the conversation based on event information in the user's area. In addition, the dialogue unit can bring up topics related to the culture and history of the user's location. This allows for the provision of conversation content tailored to the user's location. Some or all of the above processing in the dialogue unit may be performed using AI, or without AI. For example, the dialogue unit can input the user's geographical location into a generating AI and have the generating AI select topics of high relevance.

[0086] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can adjust the conversation content based on articles the user has recently shared on social media. The dialogue unit can also provide topics related to topics the user follows on social media. Furthermore, the dialogue unit can select topics that the user might be interested in based on their social media activity. This allows the dialogue unit to provide conversation content based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI select relevant topics.

[0087] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a detailed analysis to identify the stressors. The analysis unit can also perform a simpler analysis to grasp the overall trend if the user is relaxed. Furthermore, if the user is anxious, the analysis unit can perform an analysis focusing on specific anxiety factors. This enables highly accurate analysis tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of the analysis.

[0088] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can optimize an algorithm to identify stressors based on the user's past behavior patterns. The analysis unit can also optimize an algorithm to identify relaxing activities based on the user's past behavior patterns. Furthermore, the analysis unit can optimize an algorithm to analyze the user's past behavior patterns and propose the optimal coping strategies. This enables optimal analysis based on the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0089] The analysis unit can apply different analysis methods depending on the user's category during the analysis. For example, if the user is a student, the analysis unit can apply a method to analyze stressors related to academics. The analysis unit can also apply a method to analyze workplace stressors if the user is employed. Furthermore, if the user is a housewife, the analysis unit can apply a method to analyze stressors within the home. This enables appropriate analysis according to the user's category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user category information into a generating AI and have the generating AI execute the application of different analysis methods.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is feeling anxious, the analysis unit can provide a display method that provides a sense of security. This allows the analysis results to be provided in a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0091] The analysis unit can determine the priority of analyses based on the user's submission timing. For example, if the user is in a hurry, the analysis unit can perform the analysis quickly and provide the results. The analysis unit can also perform analyses with normal priorities if the user is relaxed. Furthermore, if the user sets a specific deadline, the analysis unit can adjust the analysis priority accordingly. This allows for analyses to be performed with priorities that match the user's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user submission timing information into a generating AI and have the generating AI determine the analysis priority.

[0092] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past health data. The analysis unit can also improve the accuracy of its analysis by referring to the user's social media activity. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's lifestyle data. This enables highly accurate analysis based on the user's relevant data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0093] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit can present suggestions in gentle language. Furthermore, if the user is relaxed, the suggestion unit can present suggestions with detailed explanations. Additionally, if the user is feeling anxious, the suggestion unit can present suggestions in a reassuring manner. This allows the suggestion unit to present suggestions in a way that aligns with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0094] The proposal unit can adjust the level of detail in its proposals based on the importance of the solutions. For example, if a solution is important, the proposal unit can provide a proposal that includes a detailed explanation. The proposal unit can also provide a proposal that includes a concise explanation if the solution is general. Furthermore, if a solution is urgent, the proposal unit can provide a proposal that can be implemented quickly. This allows for detailed proposals tailored to the importance of the solutions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the importance of the solutions into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0095] The suggestion unit can apply different suggestion algorithms depending on the category of the coping method when making a suggestion. For example, if the coping method is for stress reduction, the suggestion unit can apply a suggestion algorithm that promotes relaxation. The suggestion unit can also apply a suggestion algorithm that provides a sense of security if the coping method is for anxiety relief. Furthermore, if the coping method is for maintaining mental health, the suggestion unit can apply a suggestion algorithm that provides continuous support. This enables optimal suggestions according to the category of the coping method. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input coping method category information into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. The suggestion unit can also make a longer suggestion with more detailed explanations if the user is relaxed. Furthermore, if the user is feeling anxious, the suggestion unit can make a reassuring suggestion. This makes it possible to provide suggestions of appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0097] The proposal department can determine the priority of proposals based on the submission timing of the solutions. For example, the proposal department can prioritize urgent solutions. The proposal department can also prioritize general solutions. Furthermore, if the user sets a specific deadline, the proposal department can adjust the priority of proposals accordingly. This allows proposals to be prioritized according to the submission timing of the solutions. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input information on the submission timing of solutions into a generating AI and have the generating AI determine the priority of proposals.

[0098] The suggestion unit can adjust the order of suggestions based on the relevance of the solutions when making suggestions. For example, the suggestion unit may suggest the most relevant solution first. The suggestion unit can also postpone less relevant solutions. Furthermore, the suggestion unit can prioritize suggesting highly relevant solutions depending on the user's situation. This allows suggestions to be made in an order that corresponds to the relevance of the solutions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relevance information of the solutions into a generating AI and have the generating AI adjust the order of the suggestions.

[0099] The tracking unit can estimate the user's emotions and adjust the tracking frequency based on the estimated emotions. For example, if the user is feeling stressed, the tracking unit can track more frequently and provide support. The tracking unit can also track at a normal frequency if the user is relaxed. Furthermore, if the user is feeling anxious, the tracking unit can track at a moderate frequency to provide reassurance. This allows tracking to be performed at an appropriate frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI adjust the tracking frequency.

[0100] The tracking unit can optimize its tracking algorithm by referring to the user's past emotional patterns during tracking. For example, the tracking unit can predict when stress levels will rise based on the user's past emotional patterns and optimize the tracking algorithm. The tracking unit can also identify times when the user can relax based on the user's past emotional patterns and optimize the tracking algorithm. Furthermore, the tracking unit can analyze the user's past emotional patterns and determine the optimal tracking timing. This enables optimal tracking based on the user's past emotional patterns. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past emotional pattern data into a generating AI and have the generating AI perform the optimization of the tracking algorithm.

[0101] The tracking unit can apply different tracking methods depending on the user's category during tracking. For example, if the user is a student, the tracking unit can apply a method to track stressors related to academics. The tracking unit can also apply a method to track workplace stressors if the user is employed. Furthermore, if the user is a housewife, the tracking unit can apply a method to track stressors within the home. This enables appropriate tracking according to the user's category. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user category information into a generating AI and have the generating AI execute the application of different tracking methods.

[0102] The tracking unit can estimate the user's emotions and adjust the display method of the tracking results based on the estimated user emotions. For example, if the user is feeling stressed, the tracking unit can provide a simple and highly visible display method. The tracking unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is feeling anxious, the tracking unit can provide a display method that provides a sense of security. This allows the tracking results to be provided in a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the tracking results.

[0103] The tracking unit can determine tracking priorities based on the user's submission timing during tracking. For example, if the user is in a hurry, the tracking unit can perform tracking quickly and provide results. The tracking unit can also perform tracking with normal priorities if the user is relaxed. Furthermore, if the user sets a specific deadline, the tracking unit can adjust the tracking priority accordingly. This allows tracking to be performed with priorities according to the user's submission timing. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user submission timing information into a generating AI and have the generating AI determine the tracking priority.

[0104] The tracking unit can improve tracking accuracy by referring to the user's relevant data during tracking. For example, the tracking unit can improve tracking accuracy by referring to the user's past health data. The tracking unit can also improve tracking accuracy by referring to the user's social media activity. Furthermore, the tracking unit can improve tracking accuracy by referring to the user's lifestyle data. This enables highly accurate tracking based on the user's relevant data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's relevant data into a generating AI and have the generating AI perform the tracking accuracy improvement.

[0105] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can monitor more frequently and provide support. The monitoring unit can also monitor at a normal frequency if the user is relaxed. Furthermore, if the user is feeling anxious, the monitoring unit can monitor at a moderate frequency to provide reassurance. This allows monitoring to be performed at an appropriate frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the monitoring frequency.

[0106] The monitoring unit can optimize the monitoring algorithm by referring to the user's past mental health state during monitoring. For example, the monitoring unit can predict when stress levels will rise based on the user's past mental health state and optimize the monitoring algorithm. The monitoring unit can also identify times when the user can relax based on the user's past mental health state and optimize the monitoring algorithm. Furthermore, the monitoring unit can analyze the user's past mental health state and determine the optimal monitoring timing. This enables optimal monitoring based on the user's past mental health state. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past mental health state data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0107] The monitoring unit can apply different monitoring methods depending on the user's category during monitoring. For example, if the user is a student, the monitoring unit can apply a method to monitor stressors related to academics. The monitoring unit can also apply a method to monitor workplace stressors if the user is employed. Furthermore, if the user is a housewife, the monitoring unit can apply a method to monitor stressors within the home. This enables appropriate monitoring according to the user's category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user category information into a generating AI and have the generating AI execute the application of different monitoring methods.

[0108] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple and highly visible display method. The monitoring unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is feeling anxious, the monitoring unit can provide a display method that provides a sense of security. This allows the monitoring results to be provided in a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the monitoring results.

[0109] The monitoring unit can determine the monitoring priority based on the user's submission timing during monitoring. For example, if the user is in a hurry, the monitoring unit can perform monitoring quickly and provide the results. The monitoring unit can also perform monitoring with normal priority if the user is relaxed. Furthermore, if the user sets a specific deadline, the monitoring unit can adjust the monitoring priority accordingly. This allows monitoring to be performed with priority according to the user's submission timing. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user submission timing information into a generating AI and have the generating AI determine the monitoring priority.

[0110] The monitoring unit can improve the accuracy of monitoring by referring to the user's relevant data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to the user's past health data. The monitoring unit can also improve the accuracy of monitoring by referring to the user's social media activity. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to the user's lifestyle data. This enables highly accurate monitoring based on the user's relevant data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's relevant data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The dialogue unit can estimate the user's emotions and adjust the pace of the conversation based on the estimated emotions. For example, if the user is stressed, the dialogue unit can slow down the conversation to help the user relax. If the user is in a hurry, the dialogue unit can also speed up the conversation to provide information quickly. Furthermore, if the user is relaxed, the dialogue unit can maintain a normal pace for a natural conversation. This enables conversations that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI adjust the conversation pace.

[0113] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can select a similar dialogue method based on the user's preferred dialogue style in the past. It can also adjust the dialogue method to avoid topics the user has avoided in the past. Furthermore, the dialogue unit can select a dialogue method suitable for a specific time period based on the user's past dialogue history. This allows the dialogue unit to provide the user with the most optimal dialogue method. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0114] The dialogue unit can customize the conversation content based on the user's current living situation and areas of interest during a conversation. For example, if the user shares their current work situation, the dialogue unit will customize the conversation content based on that information. The dialogue unit can also adjust the conversation content based on the user's recent hobbies or interests. Furthermore, if the user talks about their current living environment, the dialogue unit can customize the conversation content based on that information. This allows the dialogue unit to provide the user with highly relevant conversation content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input information about the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the conversation content.

[0115] The dialogue unit can estimate the user's emotions and select conversation topics based on the estimated emotions. For example, if the user is stressed, the dialogue unit can select a relaxing topic. It can also select an interesting topic if the user is excited. Furthermore, if the user is depressed, it can select an encouraging topic. This allows the dialogue unit to provide topics that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI select conversation topics.

[0116] The dialogue unit can prioritize topics of high relevance based on the user's geographical location during a conversation. For example, the dialogue unit can adjust the conversation content based on weather information in the user's current location. It can also customize the conversation content based on event information in the user's area. Furthermore, the dialogue unit can bring up topics related to the culture and history of the user's location. This allows for the provision of conversation content tailored to the user's location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location information into a generating AI and have the generating AI select topics of high relevance.

[0117] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can tailor the conversation based on articles the user has recently shared on social media. It can also provide topics related to topics the user follows on social media. Furthermore, the dialogue unit can select topics that the user might be interested in based on their social media activity. This allows the dialogue unit to provide content based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI select relevant topics.

[0118] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a detailed analysis to identify the stressors. If the user is relaxed, the analysis unit can also perform a simpler analysis to grasp the overall trend. Furthermore, if the user is anxious, the analysis unit can perform an analysis that focuses on specific anxiety factors. This enables highly accurate analysis tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of the analysis.

[0119] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can optimize an algorithm to identify stressors based on the user's past behavior patterns. It can also optimize an algorithm to identify relaxing activities based on the user's past behavior patterns. Furthermore, the analysis unit can optimize an algorithm to analyze the user's past behavior patterns and suggest the optimal coping strategies. This enables optimal analysis based on the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0120] The analysis unit can apply different analysis methods depending on the user's category during the analysis. For example, if the user is a student, the analysis unit can apply a method to analyze stressors related to academics. If the user is employed, the analysis unit can also apply a method to analyze workplace stressors. Furthermore, if the user is a housewife, the analysis unit can apply a method to analyze stressors within the home. This enables appropriate analysis according to the user's category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user category information into a generating AI and have the generating AI execute the application of different analysis methods.

[0121] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is anxious, the analysis unit can provide a display method that provides a sense of security. This allows the analysis results to be provided in a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The dialogue unit collects information through interaction with the user. The dialogue unit asks the user questions and collects their answers. It can also analyze the user's voice and text to understand their emotions and intentions. For example, it can analyze the user's tone of voice and word choice to detect signs of stress or anxiety. Furthermore, the dialogue unit can refer to the user's past dialogue history to select the most appropriate dialogue method. Step 2: The analysis unit analyzes the information collected by the dialogue unit. The analysis unit uses natural language processing technology to analyze the user's statements and machine learning algorithms to predict the user's emotions and behavioral patterns. For example, it can identify stressors from the user's statements and suggest coping strategies based on those factors. Furthermore, it can also optimize the analysis algorithm by referring to the user's past behavioral patterns. Step 3: The proposal team proposes solutions based on the analysis results obtained by the analysis team. The proposal team suggests breathing techniques and meditation methods to reduce stress, providing solutions tailored to the user's situation and preferences. For example, they might suggest mindfulness activities that help the user relax. Step 4: The tracking unit tracks the user's daily emotions and behavioral patterns. The tracking unit displays changes in the user's emotions in graphs and charts, analyzes behavioral patterns, and visualizes changes in their mental health. Step 5: The monitoring unit prompts consultation with a specialist if a serious condition is detected. The monitoring unit suggests consultation with a specialist when the user's stress level increases, and monitors changes in mental health in real time to intervene at the appropriate time.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the dialogue unit, analysis unit, proposal unit, tracking unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the dialogue unit interacts with the user using the microphone 38B and touch panel 38A of the smart device 14 and collects information using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes countermeasures based on the analysis results. The tracking unit is implemented in the specific processing unit 46A of the smart device 14 and tracks the user's daily emotions and behavioral patterns. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12 and prompts the user to consult a specialist if a serious condition is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the dialogue unit, analysis unit, proposal unit, tracking unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the dialogue unit interacts with the user using the microphone 238 of the smart glasses 214 and collects information using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes countermeasures based on the analysis results. The tracking unit is implemented in the specific processing unit 46A of the smart glasses 214 and tracks the user's daily emotions and behavioral patterns. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12 and prompts the user to consult a specialist if a serious condition is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

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

[0147] The 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.

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

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the 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.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 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.

[0159] Each of the multiple elements described above, including the dialogue unit, analysis unit, proposal unit, tracking unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the dialogue unit interacts with the user using the microphone 238 of the headset terminal 314 and collects information using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes countermeasures based on the analysis results. The tracking unit is implemented in the specific processing unit 46A of the headset terminal 314 and tracks the user's daily emotions and behavioral patterns. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12 and prompts the user to consult a specialist if a serious condition is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

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

[0163] The 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.

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

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the dialogue unit, analysis unit, proposal unit, tracking unit, and monitoring unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the dialogue unit interacts with the user using the microphone 238 of the robot 414 and collects information using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes countermeasures based on the analysis results. The tracking unit is implemented, for example, by the control unit 46A of the robot 414 and tracks the user's daily emotions and behavioral patterns. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and prompts the user to consult a specialist if a serious condition is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A dialogue unit that collects information through interaction with the user, An analysis unit that analyzes the information collected by the aforementioned dialogue unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes countermeasures, A tracking unit that tracks the user's daily emotions and behavioral patterns, It includes a monitoring unit that prompts consultation with a specialist if a serious condition is detected. A system characterized by the following features. (Note 2) The aforementioned tracking unit is Track users' daily emotions and behavioral patterns to visualize changes in their mental health. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose breathing techniques, meditation skills, and specific mindfulness activities to reduce stress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, If a serious condition is detected, encourage consultation with a specialist. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned tracking unit is Display user mental health trends in graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose solutions tailored to the user's situation and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the pace of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dialogue unit, Analyze the user's past conversation history and select the optimal conversation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned dialogue unit, During conversations, the content of the dialogue is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned dialogue unit, It estimates the user's emotions and selects conversation topics based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned dialogue unit, During conversations, the system prioritizes topics that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and provides relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the user category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the analysis priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to relevant user 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 in the proposal based on the importance of the solution. 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 category of the solution. 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 submitting proposals, prioritize them based on when the solutions will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of the suggestions based on the relevance of the solutions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is During tracking, the tracking algorithm is optimized by referencing the user's past emotional patterns. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is When tracking, different tracking methods are applied depending on the user category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is It estimates the user's emotions and adjusts how tracking results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tracking unit is During tracking, the tracking priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tracking unit is During tracking, we refer to relevant user data to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, the monitoring algorithm is optimized by referencing the user's past mental health status. The system described in Appendix 1, characterized by the features described herein. (Note 33) The monitoring unit, During monitoring, different monitoring methods are applied depending on the user category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The monitoring unit, During monitoring, the monitoring priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to relevant user data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 dialogue unit that collects information through interaction with the user, An analysis unit that analyzes the information collected by the aforementioned dialogue unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes countermeasures, A tracking unit that tracks the user's daily emotions and behavioral patterns, It includes a monitoring unit that prompts consultation with a specialist if a serious condition is detected. A system characterized by the following features.

2. The aforementioned tracking unit is Track users' daily emotions and behavioral patterns to visualize changes in their mental health. The system according to feature 1.

3. The aforementioned proposal section is, We propose breathing techniques, meditation skills, and specific mindfulness activities to reduce stress. The system according to feature 1.

4. The monitoring unit, If a serious condition is detected, encourage consultation with a specialist. The system according to feature 1.

5. The aforementioned tracking unit is Display user mental health trends in graphs and charts. The system according to feature 1.

6. The aforementioned proposal section is, We propose solutions tailored to the user's situation and preferences. The system according to feature 1.

7. The aforementioned dialogue unit, It estimates the user's emotions and adjusts the pace of the conversation based on those emotions. The system according to feature 1.

8. The aforementioned dialogue unit, Analyze the user's past conversation history and select the optimal conversation method. The system according to feature 1.

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