system

Abstract

The system according to this embodiment aims to improve the user's mental health by analyzing the user's emotions and responding to them individually. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a transmission unit, and an optimization unit. The analysis unit analyzes the user's emotions. The provision unit provides advice based on the emotions analyzed by the analysis unit. The transmission unit sends messages to increase motivation. The optimization unit optimizes the user's schedule.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to appropriately analyze the user's feelings and respond individually.

[0005] The system according to the embodiment aims to improve the user's mental health by analyzing the user's feelings and responding individually.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a provision unit, a transmission unit, and an optimization unit. The analysis unit analyzes the user's feelings. The provision unit provides advice based on the feelings analyzed by the analysis unit. The transmission unit transmits a message for enhancing motivation. The optimization unit optimizes the user's schedule. [Effects of the Invention]

[0007] The system according to this embodiment can improve the user's mental health by analyzing the user's emotions and responding to them individually. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 aims to eliminate depression. This mental health support system comprehensively supports the user's mental health by analyzing the user's emotions, providing advice, sending motivational messages, and optimizing the schedule. For example, the mental health support system collects messages from users and analyzes their emotions. This includes chats between users, emails, and internal communications. The mental health support system regularly checks the user's mood and well-being and identifies stress, negative emotions, and signs of depression. Based on the analysis results, the mental health support system can provide advice to the user and connect them with a professional if necessary. Next, the mental health support system sends motivational messages at appropriate times. These messages are customized for each individual user and are based on their mood and challenges of the day. In response to individual requests, the mental health support system suggests actions and activities that are effective in reducing stress and mood. This includes meditation, walks, relaxation techniques, sleep tips, and suggestions for healthy eating. Finally, the mental health support system helps optimize the user's schedule. It makes suggestions for adjusting the schedule and maintaining a work-rest balance. A mental health support system recognizes how users spend their time and uses that information to improve productivity and manage work-related stress. This allows the system to comprehensively support users' mental health and aim to prevent depression.

[0029] The mental health support system according to this embodiment comprises an analysis unit, a provision unit, a transmission unit, and an optimization unit. The analysis unit analyzes the user's emotions. For example, the analysis unit collects messages from the user and analyzes their emotions. The analysis unit can analyze emotions using technologies such as text analysis, voice analysis, and facial expression analysis. The provision unit provides advice based on the emotions analyzed by the analysis unit. The provision unit provides advice by methods such as text messages, voice messages, and video calls. The provision unit can also connect the user to a specialist if necessary. The transmission unit sends motivational messages. For example, the transmission unit sends messages containing words of encouragement and success stories. The transmission unit can send messages customized to individual users. The optimization unit optimizes the user's schedule. For example, the optimization unit adjusts the schedule based on time management algorithms and prioritization criteria. The optimization unit can make suggestions for maintaining a work-rest balance. Thus, the mental health support system according to this embodiment can aim to prevent depression by analyzing the user's emotions, providing advice, sending motivational messages, and optimizing the schedule.

[0030] The analytics department analyzes user emotions. Specifically, it collects messages from users and analyzes their emotions. The analytics department can analyze emotions using technologies such as text analysis, voice analysis, and facial expression analysis. In text analysis, natural language processing technology is used to analyze user messages and classify emotions as positive, negative, or neutral. For example, keywords such as "tired" or "sad" are detected in messages sent by users, and the emotion is evaluated considering the frequency and context of these keywords. In voice analysis, the tone, pitch, and speed of the user's voice are analyzed to detect changes in emotion. For example, a trembling voice or a slow speaking speed may indicate stress or anxiety. In facial expression analysis, the user's facial expressions are captured with a camera, and emotions are analyzed using facial expression recognition technology. For example, smiles, frown lines, and eye movements are detected, and emotions are inferred from these expressions. By combining these analysis technologies, the analytics department can evaluate user emotions from multiple perspectives and perform more accurate emotion analysis. Furthermore, based on past data and user history, the analytics department can track changes and trends in emotions and evaluate the long-term mental health state. This will enable the analytics department to understand user emotions in real time and build a foundation for providing appropriate support.

[0031] The service provider offers advice based on the emotions analyzed by the analysis department. Specifically, advice is provided through methods such as text messages, voice messages, and video calls. For example, if a user is feeling stressed, the service provider can send a text message introducing relaxation methods and stress management techniques. If a user is feeling anxious, they can send a voice message of encouragement. Furthermore, if a user is experiencing serious mental health problems, the service provider can arrange for them to consult directly with a specialist via video call. The service provider can utilize AI to provide optimal advice and support based on the user's emotional state. For example, AI can analyze the user's emotional data and generate optimal advice based on past data and similar cases. In addition, the service provider can collect user feedback and evaluate the effectiveness of the advice. This allows the service provider to provide more effective support to users and help improve their mental health.

[0032] The messaging system sends messages designed to boost motivation. Specifically, it sends messages containing words of encouragement and examples of success. The messaging system can send customized messages tailored to individual users. For example, if a user achieves a goal, the messaging system will send a message praising that achievement to further increase their motivation. Also, if a user is facing difficulties, the messaging system will send past success stories and words of encouragement to motivate them. The messaging system uses AI to analyze the user's emotional state and behavioral patterns, enabling it to send messages at the optimal time. For example, when a user is feeling stressed, it will send messages introducing relaxation methods and stress management techniques, and when a user is feeling positive, it will send messages encouraging further challenges. In this way, the messaging system can provide effective messages to support the user's mental health and maintain their motivation.

[0033] The optimization unit optimizes the user's schedule. Specifically, it adjusts the schedule based on time management algorithms and prioritization criteria. For example, it considers the user's work and personal schedules and proposes an efficient time allocation. The optimization unit analyzes the user's schedule and makes adjustments to avoid overwork and stress. Furthermore, the optimization unit can make suggestions to maintain a balance between work and rest. For example, if the user is working long hours, it will suggest appropriate break times and recommend activities to refresh. The optimization unit can use AI to analyze the user's schedule data and generate an optimal schedule. For example, it will propose the most efficient time allocation based on the user's past schedule data and behavioral patterns. In addition, the optimization unit can collect user feedback and continuously improve its schedule optimization algorithm. In this way, the optimization unit can improve the user's quality of life and support the maintenance of their mental health.

[0034] The analysis unit can collect messages from users and analyze their sentiment. The analysis unit collects messages by methods such as analyzing chat logs and emails. The analysis unit can analyze the sentiment of the collected messages using text analysis technology. This allows the system to provide appropriate advice by collecting user messages and analyzing their sentiment. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the collected messages into a generating AI and have the generating AI perform the sentiment analysis.

[0035] The service provider can provide advice to the user based on the analysis results and connect them with experts as needed. The service provider can provide advice through methods such as text messages, voice messages, and video calls. Depending on the user's emotional state, the service provider can suggest relaxation techniques and stress management methods. The service provider can also connect the user with experts such as counselors and doctors as needed. In this way, the service provider can support the user's mental health by providing advice based on the analysis results and connecting them with experts as needed. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate advice.

[0036] The sending unit can send customized messages tailored to individual users. For example, the sending unit can customize messages based on the user's past behavioral history and current emotional state. Depending on the user's mood and challenges, the sending unit can send messages containing encouraging words or success stories. This allows for increased motivation by sending personalized messages to individual users. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's behavioral history and emotional state into a generating AI and have the generating AI generate customized messages.

[0037] The sending unit can send messages containing information such as meditation, walking, relaxation techniques, sleep tips, and healthy eating suggestions. For example, it can send messages containing specific suggestions such as meditation guides, walking routes, relaxation methods, sleep tips, and meal recipes. By sending messages containing specific suggestions to support the user's mental health, the sending unit can reduce the user's stress and mood. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's state into a generating AI and have the generating AI generate a message containing appropriate suggestions.

[0038] The optimization unit can make suggestions for adjusting schedules and maintaining a work-rest balance. For example, the optimization unit adjusts schedules by prioritizing tasks or changing timetables. The optimization unit can also make suggestions for setting break times and distributing work to maintain a work-rest balance for the user. This optimizes the user's schedule and helps maintain a work-rest balance. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's schedule information into a generating AI and have the generating AI execute a suggestion for an optimal schedule.

[0039] The optimization unit recognizes how users spend their time and can use that information to improve productivity and manage work stress. For example, the optimization unit can track how users spend their time and suggest efficient work methods and time management techniques. The optimization unit can analyze how users spend their time and suggest stress relief methods and relaxation techniques. This allows the system to recognize how users spend their time and use that information to improve productivity and manage work stress. Some or all of the above processes in the optimization unit may be performed using AI, for example, or not. For example, the optimization unit can input how users spend their time into a generating AI and have the generating AI execute suggestions for improving productivity.

[0040] The analysis unit can analyze a user's past message history and identify patterns of emotional fluctuations. For example, the analysis unit can identify patterns of emotional fluctuations during specific time periods from a user's past message history. The analysis unit can analyze a user's past message history and identify emotional fluctuations in response to specific events or situations. Based on a user's past message history, the analysis unit can identify patterns of emotional fluctuations that persist over the long term. This allows for the identification of emotional fluctuation patterns and the provision of appropriate support by analyzing past message history. 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 past message history into a generating AI and have the generating AI identify patterns of emotional fluctuations.

[0041] The analysis unit can improve the accuracy of sentiment analysis by considering the user's living environment and daily events. For example, the analysis unit can perform sentiment analysis by considering the user's living environment (home, workplace, etc.). The analysis unit can perform sentiment analysis by considering the user's daily events (important events, stressful situations, etc.). The analysis unit can perform sentiment analysis by considering the user's daily rhythm (sleep patterns, eating habits, etc.). In this way, the accuracy of sentiment analysis can be improved by considering the user's living environment and daily events. 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 data on the user's living environment and daily events into a generating AI and have the generating AI perform the task of improving the accuracy of sentiment analysis.

[0042] The analysis unit can analyze a user's social media activity during sentiment analysis to supplement emotional fluctuations. For example, the analysis unit can analyze a user's social media posts to supplement emotional fluctuations. The analysis unit can analyze a user's comments and reactions on social media to supplement emotional fluctuations. The analysis unit can analyze a user's frequency of social media activity to supplement emotional fluctuations. This allows for more accurate sentiment analysis by supplementing emotional fluctuations through the analysis of social media activity. 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 social media data into a generating AI and have the generating AI perform the emotional fluctuation supplementation.

[0043] The analysis unit can analyze emotional fluctuations while considering the user's geographical location information during emotional analysis. For example, the analysis unit can analyze emotional fluctuations when the user is in a specific location. The analysis unit can analyze emotional fluctuations while considering the user's movement patterns. The analysis unit can analyze emotional fluctuations when the user is in a specific region. This allows for a more accurate analysis of emotional fluctuations by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform the emotional fluctuation analysis.

[0044] The service provider can select the most appropriate advice by referring to the user's past advice history when providing advice. For example, the service provider can select the most appropriate advice based on the advice the user has received in the past. The service provider can prioritize providing effective advice from the user's past advice history. The service provider can analyze the user's past advice history and provide the most suitable advice. In this way, the service provider can provide the most appropriate advice by referring to the past advice history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the past advice history into a generating AI and have the generating AI perform the selection of the most appropriate advice.

[0045] The service provider can customize the means of providing advice based on the user's current living situation. For example, if the user is busy, the service provider can provide advice that can be implemented in a short time. If the user is relaxed, the service provider can provide detailed advice. The service provider can customize the means of advice (text, voice, video, etc.) according to the user's living situation. This allows for the provision of more effective advice by customizing the means of advice according to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user living situation data into a generating AI and have the generating AI perform the customization of the means of advice.

[0046] The service provider can provide optimal advice by considering the user's geographical location information when providing advice. For example, if the user is in a specific location, the service provider can provide advice appropriate to that location. The service provider can consider the user's travel patterns and provide advice that can be taken while traveling. If the user is in a specific region, the service provider can provide advice tailored to the characteristics of that region. In this way, by considering geographical location information, the service provider can provide optimal advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0047] The service provider can analyze the user's social media activity to supplement the content of the advice provided. For example, the service provider can analyze the user's social media posts to supplement the content of the advice. The service provider can analyze the user's comments and reactions on social media to supplement the content of the advice. The service provider can analyze the frequency of the user's activity on social media to supplement the content of the advice. In this way, by analyzing social media activity, the service provider can supplement the content of the advice and provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input social media data into a generating AI and have the generating AI perform the supplementation of the advice content.

[0048] The sending unit can select the most suitable message by referring to the user's past message history when sending a message. For example, the sending unit can select the most suitable message based on messages the user has received in the past. The sending unit can prioritize sending messages that were effective based on the user's past message history. The sending unit can analyze the user's past message history and send the most appropriate message. In this way, the optimal message can be sent by referring to the past message history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the past message history into a generating AI and have the generating AI perform the selection of the most suitable message.

[0049] The sending unit can customize the content of a message based on the user's current life situation when sending a message. For example, if the user is busy, the sending unit can send a message that can be completed in a short time. If the user is relaxed, the sending unit can send a detailed message. The sending unit can customize the means of the message (text, voice, video, etc.) according to the user's life situation. This allows for the sending of more effective messages by customizing the content according to the user's life situation. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input user life situation data into a generating AI and have the generating AI perform the customization of the message content.

[0050] The transmission unit can send the most appropriate message when sending a message, taking into account the user's geographical location. For example, if the user is in a specific location, the transmission unit can send a message appropriate for that location. The transmission unit can also send a message that can be executed while the user is moving, taking into account the user's movement patterns. If the user is in a specific region, the transmission unit can send a message that is appropriate for the characteristics of that region. In this way, the transmission unit can send the most appropriate message by taking into account geographical location. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input geographical location information into a generating AI and have the generating AI execute the sending of the most appropriate message.

[0051] The sending unit can analyze the user's social media activity and supplement the message content when sending a message. For example, the sending unit can analyze the user's social media posts and supplement the message content. The sending unit can analyze the user's comments and reactions on social media and supplement the message content. The sending unit can analyze the frequency of the user's activity on social media and supplement the message content. In this way, by analyzing social media activity, the message content can be supplemented and a more appropriate message can be sent. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input social media data into a generating AI and have the generating AI perform message content supplementation.

[0052] The optimization unit can propose an optimal schedule by referring to the user's past schedule history during schedule optimization. For example, the optimization unit can propose an optimal schedule based on schedule patterns that have been effective for the user in the past. The optimization unit can propose a schedule that reduces stress based on the user's past schedule history. The optimization unit can analyze the user's past schedule history and propose the most efficient schedule. In this way, the optimal schedule can be proposed by referring to the past schedule history. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past schedule history into a generating AI and have the generating AI execute the proposal of an optimal schedule.

[0053] The optimization unit can customize the scheduling methods based on the user's current lifestyle when optimizing the schedule. For example, if the user is busy, the optimization unit will prioritize tasks that can be completed in a short time. If the user is relaxed, the optimization unit can provide a detailed schedule. The optimization unit can customize the scheduling methods (text, audio, video, etc.) according to the user's lifestyle. This allows for a more effective schedule by customizing the scheduling methods according to the user's lifestyle. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the scheduling methods.

[0054] The optimization unit can propose an optimal schedule by considering the user's geographical location information during schedule optimization. For example, if the user is in a specific location, the optimization unit can propose a schedule suitable for that location. The optimization unit can also propose tasks that can be performed while traveling by considering the user's travel patterns. If the user is in a specific region, the optimization unit can propose a schedule that is appropriate for the characteristics of that region. In this way, by considering geographical location information, the optimal schedule can be proposed. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical location information into a generating AI and have the generating AI propose an optimal schedule.

[0055] The optimization unit can analyze users' social media activity and supplement the schedule content during schedule optimization. For example, the optimization unit can analyze users' social media posts and supplement the schedule content. The optimization unit can analyze users' comments and reactions on social media and supplement the schedule content. The optimization unit can analyze the frequency of users' social media activity and supplement the schedule content. In this way, by analyzing social media activity, the schedule content can be supplemented and a more appropriate schedule can be provided. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input social media data into a generating AI and have the generating AI perform schedule content supplementation.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The mental health support system can further monitor the user's sleep patterns and provide advice to improve sleep quality. For example, the analysis unit collects the user's sleep data and evaluates sleep quality. The provision unit can suggest improvements to the sleep environment or relaxation techniques if sleep quality is poor. The transmission unit can send messages to improve sleep quality at times when the user is relaxed. The optimization unit can adjust the user's schedule and make suggestions to ensure sufficient sleep.

[0058] The mental health support system can also monitor the user's physical activity and support the development of exercise habits. For example, the analysis unit collects the user's exercise data and evaluates the frequency and intensity of exercise. The provision unit can suggest appropriate exercise programs and exercises if insufficient exercise is detected. The transmission unit can send motivational messages to the user at the time they are exercising. The optimization unit can adjust the user's schedule and make suggestions to ensure they have time for exercise.

[0059] The mental health support system can further monitor the user's eating patterns and support healthy eating habits. For example, the analysis unit collects the user's eating data and evaluates nutritional balance. The provision unit can provide suggestions for healthy meals and recipes if the nutritional balance is unbalanced. The transmission unit can send messages encouraging healthy eating at meal times. The optimization unit can adjust the user's schedule and make suggestions to ensure sufficient time for meals.

[0060] The mental health support system can further monitor users' social activities and provide support to strengthen their social connections. For example, the analysis unit can collect data on users' social activities and evaluate the frequency and quality of their social interactions. The delivery unit can suggest ways to encourage interaction with friends and family if social activity is lacking. The transmission unit can send messages emphasizing the importance of socializing during times when users are likely to be socializing. The optimization unit can adjust users' schedules and suggest ways to ensure they have time for social activities.

[0061] The mental health support system can further monitor users' hobbies and interests and provide support to reduce stress through hobby activities. For example, the analysis unit can collect data on users' hobbies and interests and evaluate the frequency and quality of hobby activities. The provision unit can suggest hobby-related activities and events if there is a lack of hobby activities. The transmission unit can send messages emphasizing the importance of hobbies during the time users are engaged in hobby activities. The optimization unit can adjust users' schedules and make suggestions to ensure they have time for hobby activities.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis department analyzes the user's emotions. For example, the analysis department collects messages from users and analyzes their emotions using technologies such as text analysis, voice analysis, and facial expression analysis. Step 2: The service department provides advice based on the emotions analyzed by the analysis department. The service department can provide advice through methods such as text messages, voice messages, and video calls, and can also connect you with a specialist if necessary. Step 3: The sending unit sends motivational messages. The sending unit can send messages that include, for example, words of encouragement, success stories, etc., and can send customized messages tailored to individual users. Step 4: The optimization unit optimizes the user's schedule. For example, the optimization unit adjusts the schedule based on time management algorithms and prioritization criteria, and makes suggestions to maintain a work-rest balance.

[0064] (Example of form 2) The mental health support system according to an embodiment of the present invention aims to eliminate depression. This mental health support system comprehensively supports the user's mental health by analyzing the user's emotions, providing advice, sending motivational messages, and optimizing the schedule. For example, the mental health support system collects messages from users and analyzes their emotions. This includes chats between users, emails, and internal communications. The mental health support system regularly checks the user's mood and well-being and identifies stress, negative emotions, and signs of depression. Based on the analysis results, the mental health support system can provide advice to the user and connect them with a professional if necessary. Next, the mental health support system sends motivational messages at appropriate times. These messages are customized for each individual user and are based on their mood and challenges of the day. In response to individual requests, the mental health support system suggests actions and activities that are effective in reducing stress and mood. This includes meditation, walks, relaxation techniques, sleep tips, and suggestions for healthy eating. Finally, the mental health support system helps optimize the user's schedule. It makes suggestions for adjusting the schedule and maintaining a work-rest balance. A mental health support system recognizes how users spend their time and uses that information to improve productivity and manage work-related stress. This allows the system to comprehensively support users' mental health and aim to prevent depression.

[0065] The mental health support system according to this embodiment comprises an analysis unit, a provision unit, a transmission unit, and an optimization unit. The analysis unit analyzes the user's emotions. For example, the analysis unit collects messages from the user and analyzes their emotions. The analysis unit can analyze emotions using technologies such as text analysis, voice analysis, and facial expression analysis. The provision unit provides advice based on the emotions analyzed by the analysis unit. The provision unit provides advice by methods such as text messages, voice messages, and video calls. The provision unit can also connect the user to a specialist if necessary. The transmission unit sends motivational messages. For example, the transmission unit sends messages containing words of encouragement and success stories. The transmission unit can send messages customized to individual users. The optimization unit optimizes the user's schedule. For example, the optimization unit adjusts the schedule based on time management algorithms and prioritization criteria. The optimization unit can make suggestions for maintaining a work-rest balance. Thus, the mental health support system according to this embodiment can aim to prevent depression by analyzing the user's emotions, providing advice, sending motivational messages, and optimizing the schedule.

[0066] The analytics department analyzes user emotions. Specifically, it collects messages from users and analyzes their emotions. The analytics department can analyze emotions using technologies such as text analysis, voice analysis, and facial expression analysis. In text analysis, natural language processing technology is used to analyze user messages and classify emotions as positive, negative, or neutral. For example, keywords such as "tired" or "sad" are detected in messages sent by users, and the emotion is evaluated considering the frequency and context of these keywords. In voice analysis, the tone, pitch, and speed of the user's voice are analyzed to detect changes in emotion. For example, a trembling voice or a slow speaking speed may indicate stress or anxiety. In facial expression analysis, the user's facial expressions are captured with a camera, and emotions are analyzed using facial expression recognition technology. For example, smiles, frown lines, and eye movements are detected, and emotions are inferred from these expressions. By combining these analysis technologies, the analytics department can evaluate user emotions from multiple perspectives and perform more accurate emotion analysis. Furthermore, based on past data and user history, the analytics department can track changes and trends in emotions and evaluate the long-term mental health state. This will enable the analytics department to understand user emotions in real time and build a foundation for providing appropriate support.

[0067] The service provider offers advice based on the emotions analyzed by the analysis department. Specifically, advice is provided through methods such as text messages, voice messages, and video calls. For example, if a user is feeling stressed, the service provider can send a text message introducing relaxation methods and stress management techniques. If a user is feeling anxious, they can send a voice message of encouragement. Furthermore, if a user is experiencing serious mental health problems, the service provider can arrange for them to consult directly with a specialist via video call. The service provider can utilize AI to provide optimal advice and support based on the user's emotional state. For example, AI can analyze the user's emotional data and generate optimal advice based on past data and similar cases. In addition, the service provider can collect user feedback and evaluate the effectiveness of the advice. This allows the service provider to provide more effective support to users and help improve their mental health.

[0068] The messaging system sends messages designed to boost motivation. Specifically, it sends messages containing words of encouragement and examples of success. The messaging system can send customized messages tailored to individual users. For example, if a user achieves a goal, the messaging system will send a message praising that achievement to further increase their motivation. Also, if a user is facing difficulties, the messaging system will send past success stories and words of encouragement to motivate them. The messaging system uses AI to analyze the user's emotional state and behavioral patterns, enabling it to send messages at the optimal time. For example, when a user is feeling stressed, it will send messages introducing relaxation methods and stress management techniques, and when a user is feeling positive, it will send messages encouraging further challenges. In this way, the messaging system can provide effective messages to support the user's mental health and maintain their motivation.

[0069] The optimization unit optimizes the user's schedule. Specifically, it adjusts the schedule based on time management algorithms and prioritization criteria. For example, it considers the user's work and personal schedules and proposes an efficient time allocation. The optimization unit analyzes the user's schedule and makes adjustments to avoid overwork and stress. Furthermore, the optimization unit can make suggestions to maintain a balance between work and rest. For example, if the user is working long hours, it will suggest appropriate break times and recommend activities to refresh. The optimization unit can use AI to analyze the user's schedule data and generate an optimal schedule. For example, it will propose the most efficient time allocation based on the user's past schedule data and behavioral patterns. In addition, the optimization unit can collect user feedback and continuously improve its schedule optimization algorithm. In this way, the optimization unit can improve the user's quality of life and support the maintenance of their mental health.

[0070] The analysis unit can collect messages from users and analyze their sentiment. The analysis unit collects messages by methods such as analyzing chat logs and emails. The analysis unit can analyze the sentiment of the collected messages using text analysis technology. This allows the system to provide appropriate advice by collecting user messages and analyzing their sentiment. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the collected messages into a generating AI and have the generating AI perform the sentiment analysis.

[0071] The service provider can provide advice to the user based on the analysis results and connect them with experts as needed. The service provider can provide advice through methods such as text messages, voice messages, and video calls. Depending on the user's emotional state, the service provider can suggest relaxation techniques and stress management methods. The service provider can also connect the user with experts such as counselors and doctors as needed. In this way, the service provider can support the user's mental health by providing advice based on the analysis results and connecting them with experts as needed. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate advice.

[0072] The sending unit can send customized messages tailored to individual users. For example, the sending unit can customize messages based on the user's past behavioral history and current emotional state. Depending on the user's mood and challenges, the sending unit can send messages containing encouraging words or success stories. This allows for increased motivation by sending personalized messages to individual users. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's behavioral history and emotional state into a generating AI and have the generating AI generate customized messages.

[0073] The sending unit can send messages containing information such as meditation, walking, relaxation techniques, sleep tips, and healthy eating suggestions. For example, it can send messages containing specific suggestions such as meditation guides, walking routes, relaxation methods, sleep tips, and meal recipes. By sending messages containing specific suggestions to support the user's mental health, the sending unit can reduce the user's stress and mood. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's state into a generating AI and have the generating AI generate a message containing appropriate suggestions.

[0074] The optimization unit can make suggestions for adjusting schedules and maintaining a work-rest balance. For example, the optimization unit adjusts schedules by prioritizing tasks or changing timetables. The optimization unit can also make suggestions for setting break times and distributing work to maintain a work-rest balance for the user. This optimizes the user's schedule and helps maintain a work-rest balance. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's schedule information into a generating AI and have the generating AI execute a suggestion for an optimal schedule.

[0075] The optimization unit recognizes how users spend their time and can use that information to improve productivity and manage work stress. For example, the optimization unit can track how users spend their time and suggest efficient work methods and time management techniques. The optimization unit can analyze how users spend their time and suggest stress relief methods and relaxation techniques. This allows the system to recognize how users spend their time and use that information to improve productivity and manage work stress. Some or all of the above processes in the optimization unit may be performed using AI, for example, or not. For example, the optimization unit can input how users spend their time into a generating AI and have the generating AI execute suggestions for improving productivity.

[0076] The analysis unit can estimate the user's emotions and adjust the frequency of emotion analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the frequency of emotion analysis to provide real-time support. If the user is relaxed, the analysis unit can decrease the frequency of emotion analysis and provide support only when needed. If the user's emotions fluctuate rapidly, the analysis unit can temporarily increase the frequency of emotion analysis to perform a more detailed analysis. This allows for more appropriate support to be provided by adjusting the frequency of emotion analysis 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 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 perform emotion estimation.

[0077] The analysis unit can analyze a user's past message history and identify patterns of emotional fluctuations. For example, the analysis unit can identify patterns of emotional fluctuations during specific time periods from a user's past message history. The analysis unit can analyze a user's past message history and identify emotional fluctuations in response to specific events or situations. Based on a user's past message history, the analysis unit can identify patterns of emotional fluctuations that persist over the long term. This allows for the identification of emotional fluctuation patterns and the provision of appropriate support by analyzing past message history. 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 past message history into a generating AI and have the generating AI identify patterns of emotional fluctuations.

[0078] The analysis unit can improve the accuracy of sentiment analysis by considering the user's living environment and daily events. For example, the analysis unit can perform sentiment analysis by considering the user's living environment (home, workplace, etc.). The analysis unit can perform sentiment analysis by considering the user's daily events (important events, stressful situations, etc.). The analysis unit can perform sentiment analysis by considering the user's daily rhythm (sleep patterns, eating habits, etc.). In this way, the accuracy of sentiment analysis can be improved by considering the user's living environment and daily events. 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 data on the user's living environment and daily events into a generating AI and have the generating AI perform the task of improving the accuracy of sentiment analysis.

[0079] The analysis unit can estimate the user's emotions and determine the priority of emotion analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the priority of emotion analysis and respond quickly. If the user is relaxed, the analysis unit can lower the priority of emotion analysis and respond only when necessary. If the user's emotions change rapidly, the analysis unit can temporarily increase the priority of emotion analysis to perform a detailed analysis. This allows for a quick response by determining the priority of emotion analysis 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 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 perform emotion estimation.

[0080] The analysis unit can analyze a user's social media activity during sentiment analysis to supplement emotional fluctuations. For example, the analysis unit can analyze a user's social media posts to supplement emotional fluctuations. The analysis unit can analyze a user's comments and reactions on social media to supplement emotional fluctuations. The analysis unit can analyze a user's frequency of social media activity to supplement emotional fluctuations. This allows for more accurate sentiment analysis by supplementing emotional fluctuations through the analysis of social media activity. 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 social media data into a generating AI and have the generating AI perform the emotional fluctuation supplementation.

[0081] The analysis unit can analyze emotional fluctuations while considering the user's geographical location information during emotional analysis. For example, the analysis unit can analyze emotional fluctuations when the user is in a specific location. The analysis unit can analyze emotional fluctuations while considering the user's movement patterns. The analysis unit can analyze emotional fluctuations when the user is in a specific region. This allows for a more accurate analysis of emotional fluctuations by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform the emotional fluctuation analysis.

[0082] The service provider can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling stressed, the service provider can suggest relaxation techniques. If the user is relaxed, the service provider can offer self-improvement advice. If the user is tired, the service provider can offer advice on rest and sleep. By adjusting the content of the advice according to the user's emotions, more appropriate advice can be provided. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the advice content.

[0083] The service provider can select the most appropriate advice by referring to the user's past advice history when providing advice. For example, the service provider can select the most appropriate advice based on the advice the user has received in the past. The service provider can prioritize providing effective advice from the user's past advice history. The service provider can analyze the user's past advice history and provide the most suitable advice. In this way, the service provider can provide the most appropriate advice by referring to the past advice history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the past advice history into a generating AI and have the generating AI perform the selection of the most appropriate advice.

[0084] The service provider can customize the means of providing advice based on the user's current living situation. For example, if the user is busy, the service provider can provide advice that can be implemented in a short time. If the user is relaxed, the service provider can provide detailed advice. The service provider can customize the means of advice (text, voice, video, etc.) according to the user's living situation. This allows for the provision of more effective advice by customizing the means of advice according to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user living situation data into a generating AI and have the generating AI perform the customization of the means of advice.

[0085] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the service provider can prioritize relaxation advice. If the user is relaxed, the service provider can postpone self-improvement advice. If the user's emotions fluctuate rapidly, the service provider can prioritize emotional stabilization advice. This allows for a quick response by prioritizing advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the priority determination of advice.

[0086] The service provider can provide optimal advice by considering the user's geographical location information when providing advice. For example, if the user is in a specific location, the service provider can provide advice appropriate to that location. The service provider can consider the user's travel patterns and provide advice that can be taken while traveling. If the user is in a specific region, the service provider can provide advice tailored to the characteristics of that region. In this way, by considering geographical location information, the service provider can provide optimal advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0087] The service provider can analyze the user's social media activity to supplement the content of the advice provided. For example, the service provider can analyze the user's social media posts to supplement the content of the advice. The service provider can analyze the user's comments and reactions on social media to supplement the content of the advice. The service provider can analyze the frequency of the user's activity on social media to supplement the content of the advice. In this way, by analyzing social media activity, the service provider can supplement the content of the advice and provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input social media data into a generating AI and have the generating AI perform the supplementation of the advice content.

[0088] The sending unit can estimate the user's emotions and adjust the timing of message delivery based on the estimated emotions. For example, if the user is feeling stressed, the sending unit can send a message during a time when the user can relax. If the user is relaxed, the sending unit can send a message at an appropriate time. If the user's emotions change rapidly, the sending unit can send a message immediately. This allows for more effective support by adjusting the timing of message delivery 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 sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of message delivery timing.

[0089] The sending unit can select the most suitable message by referring to the user's past message history when sending a message. For example, the sending unit can select the most suitable message based on messages the user has received in the past. The sending unit can prioritize sending messages that were effective based on the user's past message history. The sending unit can analyze the user's past message history and send the most appropriate message. In this way, the optimal message can be sent by referring to the past message history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the past message history into a generating AI and have the generating AI perform the selection of the most suitable message.

[0090] The sending unit can customize the content of a message based on the user's current life situation when sending a message. For example, if the user is busy, the sending unit can send a message that can be completed in a short time. If the user is relaxed, the sending unit can send a detailed message. The sending unit can customize the means of the message (text, voice, video, etc.) according to the user's life situation. This allows for the sending of more effective messages by customizing the content according to the user's life situation. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input user life situation data into a generating AI and have the generating AI perform the customization of the message content.

[0091] The sending unit can estimate the user's emotions and determine message priorities based on the estimated emotions. For example, if the user is stressed, the sending unit can prioritize sending relaxation messages. If the user is relaxed, the sending unit can postpone sending self-improvement messages. If the user's emotions fluctuate rapidly, the sending unit can prioritize sending emotional stabilization messages. This allows for a quick response by prioritizing messages 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 sending unit may be performed using AI, or not using AI. For example, the sending unit can input user emotion data into a generative AI and have the generative AI perform message prioritization.

[0092] The transmission unit can send the most appropriate message when sending a message, taking into account the user's geographical location. For example, if the user is in a specific location, the transmission unit can send a message appropriate for that location. The transmission unit can also send a message that can be executed while the user is moving, taking into account the user's movement patterns. If the user is in a specific region, the transmission unit can send a message that is appropriate for the characteristics of that region. In this way, the transmission unit can send the most appropriate message by taking into account geographical location. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input geographical location information into a generating AI and have the generating AI execute the sending of the most appropriate message.

[0093] The sending unit can analyze the user's social media activity and supplement the message content when sending a message. For example, the sending unit can analyze the user's social media posts and supplement the message content. The sending unit can analyze the user's comments and reactions on social media and supplement the message content. The sending unit can analyze the frequency of the user's activity on social media and supplement the message content. In this way, by analyzing social media activity, the message content can be supplemented and a more appropriate message can be sent. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input social media data into a generating AI and have the generating AI perform message content supplementation.

[0094] The optimization unit can estimate the user's emotions and adjust the schedule optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit can adjust the schedule to increase rest time. If the user is relaxed, the optimization unit can prioritize tasks that increase concentration. If the user is tired, the optimization unit can prioritize light tasks and postpone heavy tasks. In this way, a more effective schedule can be provided by adjusting the schedule optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform the schedule optimization method adjustment.

[0095] The optimization unit can propose an optimal schedule by referring to the user's past schedule history during schedule optimization. For example, the optimization unit can propose an optimal schedule based on schedule patterns that have been effective for the user in the past. The optimization unit can propose a schedule that reduces stress based on the user's past schedule history. The optimization unit can analyze the user's past schedule history and propose the most efficient schedule. In this way, the optimal schedule can be proposed by referring to the past schedule history. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past schedule history into a generating AI and have the generating AI execute the proposal of an optimal schedule.

[0096] The optimization unit can customize the scheduling methods based on the user's current lifestyle when optimizing the schedule. For example, if the user is busy, the optimization unit will prioritize tasks that can be completed in a short time. If the user is relaxed, the optimization unit can provide a detailed schedule. The optimization unit can customize the scheduling methods (text, audio, video, etc.) according to the user's lifestyle. This allows for a more effective schedule by customizing the scheduling methods according to the user's lifestyle. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the scheduling methods.

[0097] The optimization unit can estimate the user's emotions and determine schedule priorities based on the estimated emotions. For example, if the user is feeling stressed, the optimization unit can prioritize relaxation time. If the user is relaxed, the optimization unit can prioritize important tasks. If the user's emotions fluctuate rapidly, the optimization unit can prioritize time for emotional stabilization. This allows for a quick response by determining schedule priorities 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 optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform schedule prioritization.

[0098] The optimization unit can propose an optimal schedule by considering the user's geographical location information during schedule optimization. For example, if the user is in a specific location, the optimization unit can propose a schedule suitable for that location. The optimization unit can also propose tasks that can be performed while traveling by considering the user's travel patterns. If the user is in a specific region, the optimization unit can propose a schedule that is appropriate for the characteristics of that region. In this way, by considering geographical location information, the optimal schedule can be proposed. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical location information into a generating AI and have the generating AI propose an optimal schedule.

[0099] The optimization unit can analyze users' social media activity and supplement the schedule content during schedule optimization. For example, the optimization unit can analyze users' social media posts and supplement the schedule content. The optimization unit can analyze users' comments and reactions on social media and supplement the schedule content. The optimization unit can analyze the frequency of users' social media activity and supplement the schedule content. In this way, by analyzing social media activity, the schedule content can be supplemented and a more appropriate schedule can be provided. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input social media data into a generating AI and have the generating AI perform schedule content supplementation.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The mental health support system can further monitor the user's sleep patterns and provide advice to improve sleep quality. For example, the analysis unit collects the user's sleep data and evaluates sleep quality. The provision unit can suggest improvements to the sleep environment or relaxation techniques if sleep quality is poor. The transmission unit can send messages to improve sleep quality at times when the user is relaxed. The optimization unit can adjust the user's schedule and make suggestions to ensure sufficient sleep.

[0102] The mental health support system can also monitor the user's physical activity and support the development of exercise habits. For example, the analysis unit collects the user's exercise data and evaluates the frequency and intensity of exercise. The provision unit can suggest appropriate exercise programs and exercises if insufficient exercise is detected. The transmission unit can send motivational messages to the user at the time they are exercising. The optimization unit can adjust the user's schedule and make suggestions to ensure they have time for exercise.

[0103] The mental health support system can further monitor the user's eating patterns and support healthy eating habits. For example, the analysis unit collects the user's eating data and evaluates nutritional balance. The provision unit can provide suggestions for healthy meals and recipes if the nutritional balance is unbalanced. The transmission unit can send messages encouraging healthy eating at meal times. The optimization unit can adjust the user's schedule and make suggestions to ensure sufficient time for meals.

[0104] The mental health support system can further monitor users' social activities and provide support to strengthen their social connections. For example, the analysis unit can collect data on users' social activities and evaluate the frequency and quality of their social interactions. The delivery unit can suggest ways to encourage interaction with friends and family if social activity is lacking. The transmission unit can send messages emphasizing the importance of socializing during times when users are likely to be socializing. The optimization unit can adjust users' schedules and suggest ways to ensure they have time for social activities.

[0105] The mental health support system can further monitor users' hobbies and interests and provide support to reduce stress through hobby activities. For example, the analysis unit can collect data on users' hobbies and interests and evaluate the frequency and quality of hobby activities. The provision unit can suggest hobby-related activities and events if there is a lack of hobby activities. The transmission unit can send messages emphasizing the importance of hobbies during the time users are engaged in hobby activities. The optimization unit can adjust users' schedules and make suggestions to ensure they have time for hobby activities.

[0106] The mental health support system can estimate a user's emotions and, based on those estimates, monitor their stress levels in real time. For example, the analysis unit collects user emotion data and evaluates their stress levels. The provision unit can suggest relaxation techniques and stress management methods if the stress level is high. The transmission unit can send stress-reducing messages to users during times when they are feeling stressed. The optimization unit can adjust the user's schedule and suggest ways to allocate time for stress reduction.

[0107] A mental health support system can estimate a user's emotions and, based on those estimates, provide support to improve the user's motivation. For example, the analysis unit collects user emotion data and evaluates their motivation level. The provision unit can provide advice on goal setting and enhancing a sense of accomplishment if motivation is low. The transmission unit can send motivational messages to users at times when they need it most. The optimization unit can adjust the user's schedule and suggest ways to ensure they have time to maintain their motivation.

[0108] A mental health support system can estimate a user's emotions, predict emotional fluctuations based on those estimates, and provide preventative support. For example, the analysis unit collects user emotional data and identifies patterns of emotional fluctuation. The provision unit can suggest preventative advice and relaxation techniques when emotional fluctuations are predicted. The transmission unit can send preventative messages during the time periods when emotional fluctuations are predicted. The optimization unit can suggest adjusting the user's schedule to allow time for preventing emotional fluctuations.

[0109] A mental health support system can estimate a user's emotions and, based on those estimates, suggest activities to support the user's emotional stability. For example, the analysis unit collects the user's emotional data and evaluates their emotional stability. The provision unit can suggest activities or relaxation techniques to promote emotional stability if the user's emotions are unstable. The transmission unit can send messages to promote emotional stability at times when the user needs it. The optimization unit can adjust the user's schedule and suggest ways to ensure time is available to support emotional stability.

[0110] A mental health support system can estimate a user's emotions and, based on those estimates, provide customized support tailored to the user's emotional fluctuations. For example, the analysis unit collects user emotional data and evaluates emotional fluctuations in real time. The provision unit can suggest appropriate advice and relaxation techniques based on emotional fluctuations. The transmission unit can send customized messages during the time periods when emotional fluctuations are detected. The optimization unit can adjust the user's schedule and suggest ways to ensure they have time to cope with emotional fluctuations.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The analysis department analyzes the user's emotions. For example, the analysis department collects messages from users and analyzes their emotions using technologies such as text analysis, voice analysis, and facial expression analysis. Step 2: The service department provides advice based on the emotions analyzed by the analysis department. The service department can provide advice through methods such as text messages, voice messages, and video calls, and can also connect you with a specialist if necessary. Step 3: The sending unit sends motivational messages. The sending unit can send messages that include, for example, words of encouragement, success stories, etc., and can also send customized messages tailored to individual users. Step 4: The optimization unit optimizes the user's schedule. For example, the optimization unit adjusts the schedule based on time management algorithms and prioritization criteria, and makes suggestions to maintain a work-rest balance.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the analysis unit, provision unit, transmission unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and analyzes their emotions using the control unit 46A. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, generates advice based on the analysis results, and provides it to the user through the output device 40 of the smart device 14. The transmission unit is implemented in the specific processing unit 46A of the smart device 14, and transmits a message to increase motivation. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12, and optimizes the user's schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, provision unit, transmission unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and analyzes their emotions using the control unit 46A. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, which generates advice based on the analysis results and provides it to the user through the speaker 240 of the smart glasses 214. The transmission unit is implemented in the specific processing unit 46A of the smart glasses 214, which transmits motivational messages. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12, which optimizes the user's schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, provision unit, transmission unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314, and analyzes their emotions using the control unit 46A. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates advice based on the analysis results, which is provided to the user through the speaker 240 of the headset terminal 314. The transmission unit is implemented in the specific processing unit 46A of the headset terminal 314, for example, and transmits motivational messages. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and optimizes the user's schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the analysis unit, provision unit, transmission unit, and optimization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and analyzes their emotions using the control unit 46A. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates advice based on the analysis results and provides it to the user through the speaker 240 of the robot 414. The transmission unit is implemented, for example, by the control unit 46A of the robot 414, which transmits motivational messages. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which optimizes the user's schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The analysis department analyzes user emotions, A provision unit that provides advice based on the emotions analyzed by the aforementioned analysis unit, A sending unit that sends messages to boost motivation, It includes an optimization unit that optimizes the user's schedule. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Collect messages from users and analyze their sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the analysis results, we provide advice to the user and connect them with experts as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned transmitting unit Send customized messages to individual users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmitting unit Send messages that include meditation, walking, relaxation techniques, sleep tips, and healthy eating suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, We offer suggestions for adjusting schedules and maintaining a work-life balance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The optimization unit, Recognize how users spend their time and use that information to improve productivity and manage work-related stress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the frequency of emotion analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Analyze the user's past message history to identify patterns in their emotional fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When performing sentiment analysis, we improve the accuracy of the analysis by taking into account the user's living environment and daily events. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and determines the priority of sentiment analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During sentiment analysis, the user's social media activity is analyzed to complement the analysis of emotional fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing emotions, the system considers the user's geographical location to analyze emotional fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing advice, the system selects the most appropriate advice by referring to the user's past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing advice, customize the method of advice based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, analyze the user's social media activity to supplement the advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned transmitting unit It estimates the user's emotions and adjusts the timing of message delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned transmitting unit When sending a message, the system selects the most appropriate message by referring to the user's past message history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned transmitting unit When sending a message, the message content is customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned transmitting unit It estimates the user's emotions and prioritizes messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned transmitting unit When sending a message, the system takes the user's geographical location into consideration to send the most appropriate message. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned transmitting unit When sending a message, the system analyzes the user's social media activity to supplement the message content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, It estimates the user's emotions and adjusts the scheduling optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, When optimizing the schedule, the system refers to the user's past schedule history to suggest the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, When optimizing a schedule, the scheduling method is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, When optimizing the schedule, we propose the optimal schedule considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, When optimizing the schedule, analyze users' social media activity to supplement the schedule content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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. The analysis department analyzes user emotions, A provision unit that provides advice based on the emotions analyzed by the aforementioned analysis unit, A sending unit that sends messages to boost motivation, It includes an optimization unit that optimizes the user's schedule. A system characterized by the following features.

2. The aforementioned analysis unit is Collect messages from users and analyze their sentiment. The system according to feature 1.

3. The aforementioned supply unit is, Based on the analysis results, we provide advice to the user and connect them with experts as needed. The system according to feature 1.

4. The aforementioned transmitting unit Send customized messages to individual users. The system according to feature 1.

5. The aforementioned transmitting unit Send messages that include meditation, walking, relaxation techniques, sleep tips, and healthy eating suggestions. The system according to feature 1.

6. The optimization unit, We offer suggestions for adjusting schedules and maintaining a work-life balance. The system according to feature 1.

7. The optimization unit, Recognize how users spend their time and use that information to improve productivity and manage work-related stress. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and adjusts the frequency of emotion analysis based on the estimated emotions. The system according to feature 1.

Images