system

Abstract

The system according to this embodiment aims to manage caregivers' stress and improve their mental health. [Solution] The system according to this embodiment comprises a reception unit, a suggestion unit, a community unit, an analysis unit, and a coordination unit. The reception unit inputs the user's situation. The suggestion unit suggests relaxation methods and stress management techniques based on the information received by the reception unit. The community unit allows caregivers to interact with each other. The analysis unit analyzes the caregiver's emotions and stress level in real time. The coordination unit arranges counseling sessions with specialists as needed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, individual measures for caregiver stress management and mental health improvement have not been sufficiently taken, and there is room for improvement.

[0005] The system according to the embodiment aims at caregiver stress management and mental health improvement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, a community unit, an analysis unit, and a coordination unit. The reception unit inputs the user's situation. The suggestion unit proposes relaxation methods and stress management techniques based on the information received by the reception unit. The community unit allows caregivers to interact with each other. The analysis unit analyzes the caregiver's emotions and stress levels in real time. The coordination unit arranges counseling sessions with specialists as needed. [Effects of the Invention]

[0007] The system according to this embodiment can manage caregivers' stress and improve their mental health. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The CareWell AI system, according to an embodiment of the present invention, is an innovative program aimed at managing caregivers' stress and improving their mental health. This CareWell AI system provides personalized support using AI to alleviate the mental burden caregivers face from their daily caregiving tasks. For example, users access the app via smartphone or tablet and input their own situation. Based on this information, the AI ​​suggests relaxation methods and stress management techniques. It also includes a community function that allows caregivers to interact with each other, reducing feelings of isolation and providing a supportive environment. Furthermore, the AI ​​can analyze caregivers' emotions and stress levels in real time and, if necessary, arrange counseling sessions with specialists. In this way, the CareWell AI system realizes a new form of mental health support that integrates technology and human support. As a result, the CareWell AI system can facilitate caregiver mental health management and reduce feelings of isolation.

[0029] The CareWell AI system according to this embodiment comprises a reception unit, a suggestion unit, a community unit, an analysis unit, and an adjustment unit. The reception unit inputs the user's situation. The user's situation includes, but is not limited to, health status, living environment, and psychological state. The reception unit can input the user's situation via, for example, a smartphone or tablet. The suggestion unit uses AI to suggest relaxation methods and stress management techniques based on the information received by the reception unit. Relaxation methods include, but are not limited to, deep breathing exercises, meditation, and yoga. Stress management techniques include, but are not limited to, cognitive behavioral therapy, time management, and relaxation methods. The community unit provides functions that allow caregivers to interact with each other. Interaction functions include, but are not limited to, chat functions, forums, and video calls. The analysis unit analyzes emotional states from chat content and input information and determines stress levels. Methods for analyzing emotional states include, but are not limited to, text analysis, voice analysis, and facial expression analysis. Methods for determining stress levels include, but are not limited to, psychological tests, biofeedback, and self-reporting. The adjustment unit arranges counseling sessions with specialists as needed. Methods for arranging counseling sessions include, but are not limited to, reservation systems and specialist selection criteria. As a result, the CareWell AI system according to the embodiment can suggest relaxation methods and stress management techniques tailored to the user's situation, facilitate interaction among caregivers, analyze emotions and stress levels in real time, and arrange counseling sessions with specialists.

[0030] The reception desk inputs the user's information. This information includes, but is not limited to, health status, living environment, and psychological state. The reception desk can input user information via smartphones or tablets, for example. Specifically, users can use a dedicated application to record their daily health status, living environment, and psychological state in detail. For example, health status includes data such as body temperature, blood pressure, heart rate, sleep duration, and diet, while living environment includes housing conditions, family structure, and daily activities. Psychological state includes stress levels, mood swings, and emotional fluctuations. This data can be entered manually by the user, but it can also be automatically collected from wearable devices such as smartwatches and fitness trackers. The reception desk centrally manages this data and creates individual profiles for each user. These profiles include historical data and trends, allowing for long-term tracking of changes in the user's health and psychological state. This enables the reception desk to accurately and comprehensively understand the user's situation and provide the necessary information to subsequent proposal and analysis departments.

[0031] The suggestion department uses AI to propose relaxation methods and stress management techniques based on information received by the reception department. Relaxation methods include, but are not limited to, deep breathing, meditation, and yoga. Stress management techniques include, but are not limited to, cognitive behavioral therapy, time management, and relaxation methods. Specifically, the AI ​​analyzes data on the user's health, psychological state, and living environment to select the most suitable relaxation methods and stress management techniques for each individual user. For example, if a user shows a high stress level, the AI ​​will suggest deep breathing or meditation, and if the user leads a busy life, it will suggest time management techniques. The AI ​​can also customize its suggestions by considering the user's past data and trends. For example, if meditation was effective for a user in the past, it will suggest meditation again, and if it was not effective, it will suggest an alternative method. Furthermore, the suggestion department provides guides and resources for users to practice the suggested relaxation methods and stress management techniques. These include, for example, guided meditation audio and yoga video tutorials. This allows the proposal department to provide support to users in effectively practicing relaxation methods and stress management techniques, thereby improving their health and psychological state.

[0032] The Community Department provides features that allow caregivers to interact with each other. These features include, but are not limited to, chat functions, forums, and video calls. Specifically, the Community Department provides a platform for caregivers to share information and support one another. Using the chat function, caregivers can communicate in real time and share their daily worries and experiences. Forums are used for discussions on specific topics, where knowledge and advice on caregiving are exchanged. Using the video call function, caregivers can talk face-to-face, enabling deeper interaction. Furthermore, the Community Department hosts online seminars and workshops by experts, providing caregivers with opportunities to learn the latest knowledge and skills. This reduces feelings of isolation among caregivers and allows them to support each other and improve the quality of care. In addition, the Community Department can collect caregiver feedback and use it to improve the system and develop new features. In this way, the Community Department can play an important role in promoting interaction among caregivers and improving the quality of care.

[0033] The analysis department analyzes emotional states from chat content and input information to determine stress levels. Methods for analyzing emotional states include, but are not limited to, text analysis, voice analysis, and facial expression analysis. Methods for determining stress levels include, but are not limited to, psychological tests, biofeedback, and self-reporting. Specifically, the AI ​​analyzes chat content using natural language processing technology to identify the user's emotional state. For example, it analyzes the frequency of positive and negative words to understand the user's emotional tendencies. It also uses voice analysis to analyze the user's voice tone, pitch, and speaking speed to evaluate their emotional state. Furthermore, it can read emotions from the user's facial expressions using facial expression analysis. By comprehensively analyzing this data, the analysis department can determine the user's stress level with high accuracy. Based on the determined stress level, the analysis department provides appropriate advice and support to the user. For example, if the stress level is high, it will suggest relaxation methods or arrange counseling sessions with a specialist. The analysis department can also track changes in the user's emotional state and stress level over the long term to understand trends. This allows the analytics department to understand users' emotional states and stress levels in real time and play a crucial role in providing appropriate support.

[0034] The coordination department will arrange counseling sessions with experts as needed. Methods for coordinating counseling sessions include, but are not limited to, a reservation system and expert selection criteria. Specifically, the coordination department will select the most suitable expert based on the user's situation and needs, and schedule the counseling sessions. The reservation system allows users to book counseling sessions at their preferred date and time. The coordination department will also recommend the most suitable expert to the user based on their profile and evaluation. For example, it can select experts with expertise in a specific field or experts who have received high ratings in the past. Furthermore, the coordination department will monitor the progress of the counseling sessions and arrange follow-up sessions as needed. This ensures that users receive continuous support from experts. The coordination department can also collect user feedback and make improvements to enhance the quality of the counseling sessions. In this way, the coordination department plays a crucial role in ensuring users receive appropriate expert support and can help improve their health and mental well-being.

[0035] The reception desk can input the user's status via smartphone or tablet. For example, the reception desk can input the user's status via smartphone or tablet. Smartphones and tablets include, but are not limited to, iOS devices, Android devices, and specific applications. This allows users to input their status via smartphone or tablet.

[0036] The proposal department can use AI to suggest relaxation methods and stress management techniques. For example, the proposal department can use AI to suggest relaxation methods and stress management techniques. AI includes, but is not limited to, machine learning algorithms and natural language processing technologies. This makes it possible to suggest relaxation methods and stress management techniques using AI.

[0037] The community section can provide features that allow caregivers to interact with each other. These features may include, but are not limited to, messaging, video calls, and forums. By providing these features, caregivers can reduce feelings of isolation and create a supportive environment.

[0038] The analysis unit can analyze emotional states from chat content and input information and determine stress levels. For example, the analysis unit can analyze emotional states from chat content and input information and determine stress levels. Methods for analyzing emotional states include, but are not limited to, text analysis, voice analysis, and facial expression analysis. Methods for determining stress levels include, but are not limited to, psychological tests, biofeedback, and self-reporting. This allows for real-time monitoring of caregivers' emotions and stress levels by analyzing emotional states from chat content and input information and determining stress levels.

[0039] The coordination unit can arrange counseling sessions with specialists as needed. For example, the coordination unit can arrange counseling sessions with specialists as required. Methods for arranging counseling sessions include, but are not limited to, reservation systems and specialist selection criteria. This allows caregivers to receive professional support at the appropriate time by arranging counseling sessions with specialists as needed.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can analyze the user's past input history and select the optimal input method. Methods for analyzing past input history include, but are not limited to, data mining techniques and statistical analysis. Optimal input methods include, but are not limited to, voice input, text input, and selection input. For example, it can prioritize suggesting input methods that the user has frequently used in the past (voice, text, etc.). It can predict and suggest input methods to be used during specific time periods based on the user's past input history. It can automatically complete input fields by referring to content the user has entered in the past. In this way, the optimal input method can be selected by analyzing the user's past input history.

[0041] The reception system can filter input content based on the user's current living situation and areas of interest when they enter their situation. For example, the reception system filters input content based on the user's current living situation and areas of interest when they enter their situation. Current living situation includes, but is not limited to, work situation, home environment, and health status. Areas of interest include, but are not limited to, hobbies, topics of interest, and areas of expertise. Methods for filtering input content include, but are not limited to, keyword filtering and content filtering. For example, when a user enters their current living situation, questions related to their areas of interest are displayed preferentially. Input content is automatically filtered based on the user's areas of interest, and unnecessary questions are omitted. Input content is customized according to the user's living situation, and appropriate questions are suggested. This allows for the suggestion of appropriate questions by filtering input content based on the user's current living situation and areas of interest.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting information. For example, the reception desk prioritizes inputting highly relevant information by considering the user's geographical location when inputting information. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, information related to that region will be prioritized for input. Relevant questions will be automatically displayed based on the user's geographical location. If the user is on the move, the input content will be customized based on their current location. In this way, highly relevant information can be prioritized for input by considering the user's geographical location.

[0043] The reception desk can analyze the user's social media activity and input relevant information when the user enters their status. For example, the reception desk can analyze the user's social media activity and input relevant information when the user enters their status. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, it can analyze the user's current interests from their social media activity and display relevant questions. It can automatically complete the input content based on the user's social media activity. It can customize the input content based on the user's social media activity. This allows for the input of relevant information by analyzing the user's social media activity.

[0044] The proposal department can adjust the level of detail in a proposal based on the importance of relaxation methods and stress management techniques. For example, the proposal department adjusts the level of detail in a proposal based on the importance of relaxation methods and stress management techniques. The level of detail in a proposal includes, but is not limited to, the depth, specificity, and conciseness of information. For example, if relaxation methods are highly important, the proposal will include a detailed explanation. If stress management techniques are less important, the proposal will include a concise explanation. The level of detail is adjusted according to the importance of the proposal to provide appropriate information. This allows for the provision of appropriate information by adjusting the level of detail in a proposal based on the importance of relaxation methods and stress management techniques.

[0045] The suggestion function can analyze the user's past responses and apply the optimal suggestion algorithm when making a suggestion. For example, the suggestion function analyzes the user's past responses and applies the optimal suggestion algorithm. Suggestion algorithms include, but are not limited to, recommendation systems and personalized algorithms. For example, it prioritizes providing suggestions that the user has previously accepted favorably. It analyzes the user's past responses and selects the optimal suggestion algorithm. Based on the user's past responses, it customizes the suggested content. This allows the optimal suggestion algorithm to be applied by analyzing the user's past responses.

[0046] The suggestion function can determine the timing of suggestions based on the user's lifestyle. For example, the suggestion function can determine the timing of suggestions based on the user's lifestyle. Lifestyle includes, but is not limited to, sleep patterns, activity times, and meal times. For example, if the user wants to relax in the morning, it will suggest relaxation methods suitable for the morning. If the user feels stressed at night, it will suggest stress management techniques suitable for nighttime. The optimal timing of suggestions is determined based on the user's lifestyle. This allows suggestions to be made at the optimal time by determining the timing based on the user's lifestyle.

[0047] The suggestion department can customize the content of suggestions based on the user's health condition when making a suggestion. For example, the suggestion department customizes the content of suggestions based on the user's health condition when making a suggestion. Health condition includes, but is not limited to, medical data, self-reports, and fitness tracker data. For example, if the user is tired, it suggests relaxation methods that have a high relaxation effect. If the user is in good health, it suggests stress management techniques, including light exercise. The suggestion department customizes the content to be optimal based on the user's health condition. This makes it possible to make appropriate suggestions by customizing the content of suggestions based on the user's health condition.

[0048] The Community Department can analyze a user's past activity history within the community and suggest the most suitable interaction methods. For example, the Community Department analyzes a user's past activity history within the community and suggests the most suitable interaction methods. Activity history includes, but is not limited to, posts, events attended, and comment history. For example, it might prioritize suggesting interaction methods that the user has previously found favorable. It analyzes the user's past activity history and selects the most suitable interaction method. Based on the user's past activity history, it customizes the interaction content. This allows the Community Department to suggest the most suitable interaction methods by analyzing the user's past activity history.

[0049] The community section can suggest optimal interaction partners by considering the user's geographical location within the community. For example, the community section suggests optimal interaction partners by considering the user's geographical location within the community. Optimal interaction partners include, but are not limited to, common interests, geographical proximity, and past interaction history. For example, if a user is in a specific region, it suggests interaction partners related to that region. It automatically selects optimal interaction partners based on the user's geographical location. If a user is on the move, it suggests interaction partners based on their current location. This allows for the suggestion of optimal interaction partners by considering the user's geographical location.

[0050] The analysis unit can optimize its analysis algorithm by referring to the user's past emotional data during analysis. For example, the analysis unit optimizes its analysis algorithm by referring to the user's past emotional data during analysis. Past emotional data includes, but is not limited to, past chat history and self-reported data. For example, it selects the optimal analysis algorithm based on the user's past emotional data. It improves the accuracy of the analysis results by referring to the user's past emotional data. It analyzes the user's past emotional data and customizes the analysis algorithm. This allows for the optimization of the analysis algorithm by referring to the user's past emotional data.

[0051] The analysis unit can perform sentiment analysis while considering the user's geographical location information. For example, the analysis unit can perform sentiment analysis while considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. For example, if the user is in a specific area, sentiment analysis related to that area will be performed. The method of sentiment analysis will be adjusted based on the user's geographical location information. If the user is on the move, sentiment analysis will be performed based on their current location. This makes it possible to perform appropriate sentiment analysis by considering the user's geographical location information.

[0052] The analysis unit can improve the accuracy of sentiment analysis by referring to the user's social media activity during analysis. For example, the analysis unit can improve the accuracy of sentiment analysis by referring to the user's social media activity during analysis. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, it can analyze the user's current emotional state from their social media activity to improve the accuracy of the analysis results. It can adjust the sentiment analysis method based on the user's social media activity. It can customize the sentiment analysis results by referring to the user's social media activity. This allows for improved accuracy of sentiment analysis by referring to the user's social media activity.

[0053] The adjustment unit can select the optimal adjustment method by referring to the user's past counseling history during the adjustment process. For example, the adjustment unit selects the optimal adjustment method by referring to the user's past counseling history during the adjustment process. Past counseling history includes, but is not limited to, past session content and feedback. For example, it selects the optimal adjustment method based on the user's past counseling history. It customizes the adjustment method by referring to the user's past counseling history. It optimizes the adjustment content based on the user's past counseling history. This allows the optimal adjustment method to be selected by referring to the user's past counseling history.

[0054] The adjustment unit can adjust counseling sessions during the adjustment process, taking into account the user's lifestyle and health condition. For example, the adjustment unit can adjust counseling sessions during the adjustment process, taking into account the user's lifestyle and health condition. Lifestyle includes, but is not limited to, sleep patterns, activity times, and meal times. Health condition includes, but is not limited to, medical data, self-reports, and fitness tracker data. For example, it can adjust the timing of counseling sessions based on the user's lifestyle. It can adjust the content of counseling sessions taking into account the user's health condition. It can customize the method of counseling sessions based on the user's lifestyle and health condition. This allows for the provision of appropriate counseling sessions by taking into account the user's lifestyle and health condition.

[0055] The adjustment unit can propose the optimal counseling session during the adjustment process, taking into account the user's geographical location information. For example, the adjustment unit proposes the optimal counseling session during the adjustment process, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, it proposes a counseling session relevant to that region. It automatically selects the optimal counseling session based on the user's geographical location information. If the user is on the move, it proposes a counseling session based on their current location. This allows for the proposal of the optimal counseling session by considering the user's geographical location information.

[0056] The adjustment unit can analyze the user's social media activity during the adjustment process to customize the content of the counseling session. For example, the adjustment unit can analyze the user's social media activity during the adjustment process to customize the content of the counseling session. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, it can analyze the user's current emotional state from their social media activity and customize the content of the counseling session. It can adjust the method of the counseling session based on the user's social media activity. It can optimize the content of the counseling session by referring to the user's social media activity. In this way, the content of the counseling session can be customized by analyzing the user's social media activity.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The CareWell AI system can also collect users' physical health data and suggest stress management techniques based on this data. For example, it can analyze data such as heart rate, sleep patterns, and exercise levels obtained from fitness trackers and smartwatches to understand the user's physical condition. This allows it to suggest relaxation methods if the user is fatigued and recommend light exercise if they are not getting enough exercise. It can also monitor the effectiveness of stress management techniques based on the user's health data and adjust the suggestions as needed. This enables the provision of personalized support tailored to the user's physical health condition.

[0059] The CareWell AI system can also analyze a user's past stress management history and suggest the most suitable stress management techniques. For example, it can record the effectiveness of relaxation methods and stress management techniques the user has tried in the past and use this information to make suggestions for the next time. It prioritizes suggesting methods that the user found effective in the past and avoids suggesting methods that were less effective. It can also identify methods that are effective in specific times of day or situations based on the user's past history and make suggestions based on this. This allows the system to provide the most suitable stress management techniques tailored to the user's individual needs.

[0060] The CareWell AI system can also suggest region-specific stress management techniques and relaxation methods, taking into account the user's geographical location. For example, if a user is in a specific area, it can suggest relaxation facilities and events available in that area. It can also suggest stress management techniques tailored to the local climate and culture. This allows the system to provide more appropriate stress management techniques and relaxation methods based on the user's geographical location.

[0061] The CareWell AI system can also analyze a user's social media activity and suggest stress management techniques based on this analysis. For example, it can analyze a user's social media posts, the number of likes they receive, and the content of their comments to understand their current emotional state. Based on this, it can suggest relaxation methods if the user is feeling stressed, and suggest positive activities if they are relaxed. It can also monitor the effectiveness of stress management techniques based on the user's social media activity and adjust the suggestions as needed. This allows the system to provide more appropriate stress management techniques based on the user's social media activity.

[0062] The CareWell AI system can also suggest optimal stress management techniques and relaxation methods, taking into account the user's lifestyle and health condition. For example, it analyzes data such as the user's sleep patterns, activity times, and meal times, and makes suggestions based on this information. If the user wants to relax in the morning, it will suggest relaxation methods suitable for the morning; if the user feels stressed at night, it will suggest stress management techniques suitable for nighttime. Furthermore, it can monitor the effectiveness of relaxation methods and stress management techniques based on the user's health condition and adjust the suggestions as needed. This allows it to provide more appropriate stress management techniques and relaxation methods based on the user's lifestyle and health condition.

[0063] The CareWell AI system can also suggest the most suitable counseling session by referring to the user's past counseling history. For example, it analyzes the content and feedback from past sessions and adjusts the next session accordingly. It prioritizes suggesting counseling methods that the user has found effective in the past and avoids suggesting methods that were less effective. It can also identify effective counseling methods for specific times of day or situations based on the user's past history and make suggestions based on that. This allows for the provision of optimal counseling sessions tailored to the user's individual needs.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk enters the user's information. This information includes, for example, health status, living environment, and psychological state. The reception desk can enter the user's information via smartphone or tablet. Step 2: The suggestion department uses AI to propose relaxation methods and stress management techniques based on the information received by the reception department. Relaxation methods include deep breathing, meditation, and yoga, while stress management techniques include cognitive behavioral therapy, time management, and relaxation techniques. Step 3: The community section provides features that allow caregivers to interact with each other. These features include chat, forums, and video calls. Step 4: The analysis department analyzes the emotional state from the chat content and input information to determine the stress level. Methods for analyzing the emotional state include text analysis, voice analysis, and facial expression analysis, while methods for determining the stress level include psychological tests, biofeedback, and self-reporting. Step 5: The coordination department will arrange counseling sessions with specialists as needed. Methods for arranging counseling sessions include scheduling systems and criteria for selecting specialists.

[0066] (Example of form 2) The CareWell AI system, according to an embodiment of the present invention, is an innovative program aimed at managing caregivers' stress and improving their mental health. This CareWell AI system provides personalized support using AI to alleviate the mental burden caregivers face from their daily caregiving tasks. For example, users access the app via smartphone or tablet and input their own situation. Based on this information, the AI ​​suggests relaxation methods and stress management techniques. It also includes a community function that allows caregivers to interact with each other, reducing feelings of isolation and providing a supportive environment. Furthermore, the AI ​​can analyze caregivers' emotions and stress levels in real time and, if necessary, arrange counseling sessions with specialists. In this way, the CareWell AI system realizes a new form of mental health support that integrates technology and human support. As a result, the CareWell AI system can facilitate caregiver mental health management and reduce feelings of isolation.

[0067] The CareWell AI system according to this embodiment comprises a reception unit, a suggestion unit, a community unit, an analysis unit, and an adjustment unit. The reception unit inputs the user's situation. The user's situation includes, but is not limited to, health status, living environment, and psychological state. The reception unit can input the user's situation via, for example, a smartphone or tablet. The suggestion unit uses AI to suggest relaxation methods and stress management techniques based on the information received by the reception unit. Relaxation methods include, but are not limited to, deep breathing exercises, meditation, and yoga. Stress management techniques include, but are not limited to, cognitive behavioral therapy, time management, and relaxation methods. The community unit provides functions that allow caregivers to interact with each other. Interaction functions include, but are not limited to, chat functions, forums, and video calls. The analysis unit analyzes emotional states from chat content and input information and determines stress levels. Methods for analyzing emotional states include, but are not limited to, text analysis, voice analysis, and facial expression analysis. Methods for determining stress levels include, but are not limited to, psychological tests, biofeedback, and self-reporting. The adjustment unit arranges counseling sessions with specialists as needed. Methods for arranging counseling sessions include, but are not limited to, reservation systems and specialist selection criteria. As a result, the CareWell AI system according to the embodiment can suggest relaxation methods and stress management techniques tailored to the user's situation, facilitate interaction among caregivers, analyze emotions and stress levels in real time, and arrange counseling sessions with specialists.

[0068] The reception desk inputs the user's information. This information includes, but is not limited to, health status, living environment, and psychological state. The reception desk can input user information via smartphones or tablets, for example. Specifically, users can use a dedicated application to record their daily health status, living environment, and psychological state in detail. For example, health status includes data such as body temperature, blood pressure, heart rate, sleep duration, and diet, while living environment includes housing conditions, family structure, and daily activities. Psychological state includes stress levels, mood swings, and emotional fluctuations. This data can be entered manually by the user, but it can also be automatically collected from wearable devices such as smartwatches and fitness trackers. The reception desk centrally manages this data and creates individual profiles for each user. These profiles include historical data and trends, allowing for long-term tracking of changes in the user's health and psychological state. This enables the reception desk to accurately and comprehensively understand the user's situation and provide the necessary information to subsequent proposal and analysis departments.

[0069] The suggestion department uses AI to propose relaxation methods and stress management techniques based on information received by the reception department. Relaxation methods include, but are not limited to, deep breathing, meditation, and yoga. Stress management techniques include, but are not limited to, cognitive behavioral therapy, time management, and relaxation methods. Specifically, the AI ​​analyzes data on the user's health, psychological state, and living environment to select the most suitable relaxation methods and stress management techniques for each individual user. For example, if a user shows a high stress level, the AI ​​will suggest deep breathing or meditation, and if the user leads a busy life, it will suggest time management techniques. The AI ​​can also customize its suggestions by considering the user's past data and trends. For example, if meditation was effective for a user in the past, it will suggest meditation again, and if it was not effective, it will suggest an alternative method. Furthermore, the suggestion department provides guides and resources for users to practice the suggested relaxation methods and stress management techniques. These include, for example, guided meditation audio and yoga video tutorials. This allows the proposal department to provide support to users in effectively practicing relaxation methods and stress management techniques, thereby improving their health and psychological state.

[0070] The Community Department provides features that allow caregivers to interact with each other. These features include, but are not limited to, chat functions, forums, and video calls. Specifically, the Community Department provides a platform for caregivers to share information and support one another. Using the chat function, caregivers can communicate in real time and share their daily worries and experiences. Forums are used for discussions on specific topics, where knowledge and advice on caregiving are exchanged. Using the video call function, caregivers can talk face-to-face, enabling deeper interaction. Furthermore, the Community Department hosts online seminars and workshops by experts, providing caregivers with opportunities to learn the latest knowledge and skills. This reduces feelings of isolation among caregivers and allows them to support each other and improve the quality of care. In addition, the Community Department can collect caregiver feedback and use it to improve the system and develop new features. In this way, the Community Department can play an important role in promoting interaction among caregivers and improving the quality of care.

[0071] The analysis department analyzes emotional states from chat content and input information to determine stress levels. Methods for analyzing emotional states include, but are not limited to, text analysis, voice analysis, and facial expression analysis. Methods for determining stress levels include, but are not limited to, psychological tests, biofeedback, and self-reporting. Specifically, the AI ​​analyzes chat content using natural language processing technology to identify the user's emotional state. For example, it analyzes the frequency of positive and negative words to understand the user's emotional tendencies. It also uses voice analysis to analyze the user's voice tone, pitch, and speaking speed to evaluate their emotional state. Furthermore, it can read emotions from the user's facial expressions using facial expression analysis. By comprehensively analyzing this data, the analysis department can determine the user's stress level with high accuracy. Based on the determined stress level, the analysis department provides appropriate advice and support to the user. For example, if the stress level is high, it will suggest relaxation methods or arrange counseling sessions with a specialist. The analysis department can also track changes in the user's emotional state and stress level over the long term to understand trends. This allows the analytics department to understand users' emotional states and stress levels in real time and play a crucial role in providing appropriate support.

[0072] The coordination department will arrange counseling sessions with experts as needed. Methods for coordinating counseling sessions include, but are not limited to, a reservation system and expert selection criteria. Specifically, the coordination department will select the most suitable expert based on the user's situation and needs, and schedule the counseling sessions. The reservation system allows users to book counseling sessions at their preferred date and time. The coordination department will also recommend the most suitable expert to the user based on their profile and evaluation. For example, it can select experts with expertise in a specific field or experts who have received high ratings in the past. Furthermore, the coordination department will monitor the progress of the counseling sessions and arrange follow-up sessions as needed. This ensures that users receive continuous support from experts. The coordination department can also collect user feedback and make improvements to enhance the quality of the counseling sessions. In this way, the coordination department plays a crucial role in ensuring users receive appropriate expert support and can help improve their health and mental well-being.

[0073] The reception desk can input the user's status via smartphone or tablet. For example, the reception desk can input the user's status via smartphone or tablet. Smartphones and tablets include, but are not limited to, iOS devices, Android devices, and specific applications. This allows users to input their status via smartphone or tablet.

[0074] The proposal department can use AI to suggest relaxation methods and stress management techniques. For example, the proposal department can use AI to suggest relaxation methods and stress management techniques. AI includes, but is not limited to, machine learning algorithms and natural language processing technologies. This makes it possible to suggest relaxation methods and stress management techniques using AI.

[0075] The community section can provide features that allow caregivers to interact with each other. These features may include, but are not limited to, messaging, video calls, and forums. By providing these features, caregivers can reduce feelings of isolation and create a supportive environment.

[0076] The analysis unit can analyze emotional states from chat content and input information and determine stress levels. For example, the analysis unit can analyze emotional states from chat content and input information and determine stress levels. Methods for analyzing emotional states include, but are not limited to, text analysis, voice analysis, and facial expression analysis. Methods for determining stress levels include, but are not limited to, psychological tests, biofeedback, and self-reporting. This allows for real-time monitoring of caregivers' emotions and stress levels by analyzing emotional states from chat content and input information and determining stress levels.

[0077] The coordination unit can arrange counseling sessions with specialists as needed. For example, the coordination unit can arrange counseling sessions with specialists as required. Methods for arranging counseling sessions include, but are not limited to, reservation systems and specialist selection criteria. This allows caregivers to receive professional support at the appropriate time by arranging counseling sessions with specialists as needed.

[0078] The reception unit can estimate the user's emotions and adjust the timing of situation input based on the estimated emotions. For example, the reception unit can estimate the user's emotions and adjust the timing of situation input based on the estimated emotions. Methods for estimating user emotions include, but are not limited to, facial expression analysis, voice analysis, and text analysis. Methods for adjusting the timing of situation input include, but are not limited to, methods for selecting timing based on the user's emotional state. For example, if the user is stressed, the input timing may be delayed to allow them to input in a relaxed state. If the user is relaxed, input may be prompted immediately to collect detailed information. If the user is in a hurry, input may be completed quickly in the form of simple questions. This allows the user to input in a relaxed state by adjusting the timing of situation input based on their emotions.

[0079] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can analyze the user's past input history and select the optimal input method. Methods for analyzing past input history include, but are not limited to, data mining techniques and statistical analysis. Optimal input methods include, but are not limited to, voice input, text input, and selection input. For example, it can prioritize suggesting input methods that the user has frequently used in the past (voice, text, etc.). It can predict and suggest input methods to be used during specific time periods based on the user's past input history. It can automatically complete input fields by referring to content the user has entered in the past. In this way, the optimal input method can be selected by analyzing the user's past input history.

[0080] The reception system can filter input content based on the user's current living situation and areas of interest when they enter their situation. For example, the reception system filters input content based on the user's current living situation and areas of interest when they enter their situation. Current living situation includes, but is not limited to, work situation, home environment, and health status. Areas of interest include, but are not limited to, hobbies, topics of interest, and areas of expertise. Methods for filtering input content include, but are not limited to, keyword filtering and content filtering. For example, when a user enters their current living situation, questions related to their areas of interest are displayed preferentially. Input content is automatically filtered based on the user's areas of interest, and unnecessary questions are omitted. Input content is customized according to the user's living situation, and appropriate questions are suggested. This allows for the suggestion of appropriate questions by filtering input content based on the user's current living situation and areas of interest.

[0081] The reception desk can estimate the user's emotions and prioritize the information to be entered based on those emotions. For example, the reception desk can estimate the user's emotions and prioritize the information to be entered based on those emotions. Methods for prioritizing the information to be entered include, but are not limited to, importance and urgency assessments. For example, if the user is stressed, important information should be prioritized, and detailed information should be left for later. If the user is relaxed, detailed information should be prioritized. If the user is in a hurry, only the most important information should be entered, allowing for quick completion. This allows for prioritizing important information by determining the information to be entered based on the user's emotions.

[0082] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting information. For example, the reception desk prioritizes inputting highly relevant information by considering the user's geographical location when inputting information. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, information related to that region will be prioritized for input. Relevant questions will be automatically displayed based on the user's geographical location. If the user is on the move, the input content will be customized based on their current location. In this way, highly relevant information can be prioritized for input by considering the user's geographical location.

[0083] The reception desk can analyze the user's social media activity and input relevant information when the user enters their status. For example, the reception desk can analyze the user's social media activity and input relevant information when the user enters their status. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, it can analyze the user's current interests from their social media activity and display relevant questions. It can automatically complete the input content based on the user's social media activity. It can customize the input content based on the user's social media activity. This allows for the input of relevant information by analyzing the user's social media activity.

[0084] The proposal function can estimate the user's emotions and adjust the presentation of the proposal based on those emotions. For example, the proposal function can estimate the user's emotions and adjust the presentation of the proposal based on those emotions. The presentation of the proposal may include, but is not limited to, text, audio, or visual formats. For example, if the user is stressed, a simple and easy-to-understand presentation may be suggested. If the user is relaxed, a presentation with detailed explanations may be suggested. If the user is in a hurry, a concise presentation that gets straight to the point may be suggested. In this way, by adjusting the presentation of the proposal based on the user's emotions, it becomes possible to make proposals that are easy for the user to understand.

[0085] The proposal department can adjust the level of detail in a proposal based on the importance of relaxation methods and stress management techniques. For example, the proposal department adjusts the level of detail in a proposal based on the importance of relaxation methods and stress management techniques. The level of detail in a proposal includes, but is not limited to, the depth, specificity, and conciseness of information. For example, if relaxation methods are highly important, the proposal will include a detailed explanation. If stress management techniques are less important, the proposal will include a concise explanation. The level of detail is adjusted according to the importance of the proposal to provide appropriate information. This allows for the provision of appropriate information by adjusting the level of detail in a proposal based on the importance of relaxation methods and stress management techniques.

[0086] The suggestion function can analyze the user's past responses and apply the optimal suggestion algorithm when making a suggestion. For example, the suggestion function analyzes the user's past responses and applies the optimal suggestion algorithm. Suggestion algorithms include, but are not limited to, recommendation systems and personalized algorithms. For example, it prioritizes providing suggestions that the user has previously accepted favorably. It analyzes the user's past responses and selects the optimal suggestion algorithm. Based on the user's past responses, it customizes the suggested content. This allows the optimal suggestion algorithm to be applied by analyzing the user's past responses.

[0087] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, the suggestion function can estimate the user's emotions and adjust the length of the suggestion based on those emotions. The length of the suggestion includes, but is not limited to, the number of characters, time, and amount of information. For example, if the user is stressed, it will provide a short, to-the-point suggestion. If the user is relaxed, it will provide a longer suggestion with more detailed explanations. If the user is in a hurry, it will provide a concise and quick suggestion. By adjusting the length of the suggestion based on the user's emotions, it becomes possible to provide suggestions that are appropriate for the user.

[0088] The suggestion function can determine the timing of suggestions based on the user's lifestyle. For example, the suggestion function can determine the timing of suggestions based on the user's lifestyle. Lifestyle includes, but is not limited to, sleep patterns, activity times, and meal times. For example, if the user wants to relax in the morning, it will suggest relaxation methods suitable for the morning. If the user feels stressed at night, it will suggest stress management techniques suitable for nighttime. The optimal timing of suggestions is determined based on the user's lifestyle. This allows suggestions to be made at the optimal time by determining the timing based on the user's lifestyle.

[0089] The suggestion department can customize the content of suggestions based on the user's health condition when making a suggestion. For example, the suggestion department customizes the content of suggestions based on the user's health condition when making a suggestion. Health condition includes, but is not limited to, medical data, self-reports, and fitness tracker data. For example, if the user is tired, it suggests relaxation methods that have a high relaxation effect. If the user is in good health, it suggests stress management techniques, including light exercise. The suggestion department customizes the content to be optimal based on the user's health condition. This makes it possible to make appropriate suggestions by customizing the content of suggestions based on the user's health condition.

[0090] The community department can estimate a user's emotions and adjust the method of interaction based on those emotions. For example, the community department can estimate a user's emotions and adjust the method of interaction based on those emotions. These methods of interaction include, but are not limited to, messaging, video calls, and forums. For example, if a user is feeling stressed, it can suggest a relaxing method of interaction. If a user is relaxed, it can suggest a more proactive method of interaction. If a user is feeling lonely, it can suggest a supportive method of interaction. By adjusting the method of interaction based on the user's emotions, the community department can provide users with appropriate ways of interacting.

[0091] The Community Department can analyze a user's past activity history within the community and suggest the most suitable interaction methods. For example, the Community Department analyzes a user's past activity history within the community and suggests the most suitable interaction methods. Activity history includes, but is not limited to, posts, events attended, and comment history. For example, it might prioritize suggesting interaction methods that the user has previously found favorable. It analyzes the user's past activity history and selects the most suitable interaction method. Based on the user's past activity history, it customizes the interaction content. This allows the Community Department to suggest the most suitable interaction methods by analyzing the user's past activity history.

[0092] The community department can estimate user emotions and prioritize interactions based on those emotions. For example, the community department can estimate user emotions and prioritize interactions based on those emotions. Methods for prioritizing interactions include, but are not limited to, importance and urgency assessments. For example, if a user is stressed, important interactions are prioritized. If a user is relaxed, detailed interactions are prioritized. If a user is in a hurry, concise interactions are prioritized. This allows for prioritizing important interactions by determining them based on user emotions.

[0093] The community section can suggest optimal interaction partners by considering the user's geographical location within the community. For example, the community section suggests optimal interaction partners by considering the user's geographical location within the community. Optimal interaction partners include, but are not limited to, common interests, geographical proximity, and past interaction history. For example, if a user is in a specific region, it suggests interaction partners related to that region. It automatically selects optimal interaction partners based on the user's geographical location. If a user is on the move, it suggests interaction partners based on their current location. This allows for the suggestion of optimal interaction partners by considering the user's geographical location.

[0094] The analysis unit can estimate the user's emotions and adjust the emotion analysis method based on the estimated emotions. For example, the analysis unit estimates the user's emotions and adjusts the emotion analysis method based on the estimated emotions. Emotion analysis methods include, but are not limited to, text analysis, voice analysis, and facial expression analysis. For example, if the user is stressed, a detailed emotion analysis is performed to identify the cause of the stress. If the user is relaxed, a concise emotion analysis is performed to identify the factors contributing to the relaxation. If the user is in a hurry, a rapid emotion analysis is performed to provide the necessary information. This allows for appropriate emotion analysis by adjusting the emotion analysis method based on the user's emotions.

[0095] The analysis unit can optimize its analysis algorithm by referring to the user's past emotional data during analysis. For example, the analysis unit optimizes its analysis algorithm by referring to the user's past emotional data during analysis. Past emotional data includes, but is not limited to, past chat history and self-reported data. For example, it selects the optimal analysis algorithm based on the user's past emotional data. It improves the accuracy of the analysis results by referring to the user's past emotional data. It analyzes the user's past emotional data and customizes the analysis algorithm. This allows for the optimization of the analysis algorithm by referring to the user's past emotional data.

[0096] The analysis unit can perform sentiment analysis while considering the user's lifestyle and health status. For example, the analysis unit can perform sentiment analysis while considering the user's lifestyle and health status. Lifestyle includes, but is not limited to, sleep patterns, activity times, and meal times. Health status includes, but is not limited to, medical data, self-reports, and fitness tracker data. For example, it can adjust the timing of sentiment analysis based on the user's lifestyle. It can adjust the method of sentiment analysis considering the user's health status. It can customize the results of sentiment analysis based on the user's lifestyle and health status. This makes it possible to perform appropriate sentiment analysis by considering the user's lifestyle and health status.

[0097] The analysis unit can estimate the user's emotions and adjust the order in which the emotion analysis results are displayed based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the order in which the emotion analysis results are displayed based on the estimated emotions. The order in which the emotion analysis results are displayed includes, but is not limited to, importance ratings and urgency ratings. For example, if the user is stressed, important analysis results will be displayed preferentially. If the user is relaxed, detailed analysis results will be displayed preferentially. If the user is in a hurry, concise analysis results will be displayed preferentially. In this way, by adjusting the order in which the emotion analysis results are displayed based on the user's emotions, important analysis results can be displayed preferentially.

[0098] The analysis unit can perform sentiment analysis while considering the user's geographical location information. For example, the analysis unit can perform sentiment analysis while considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. For example, if the user is in a specific area, sentiment analysis related to that area will be performed. The method of sentiment analysis will be adjusted based on the user's geographical location information. If the user is on the move, sentiment analysis will be performed based on their current location. This makes it possible to perform appropriate sentiment analysis by considering the user's geographical location information.

[0099] The analysis unit can improve the accuracy of sentiment analysis by referring to the user's social media activity during analysis. For example, the analysis unit can improve the accuracy of sentiment analysis by referring to the user's social media activity during analysis. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, it can analyze the user's current emotional state from their social media activity to improve the accuracy of the analysis results. It can adjust the sentiment analysis method based on the user's social media activity. It can customize the sentiment analysis results by referring to the user's social media activity. This allows for improved accuracy of sentiment analysis by referring to the user's social media activity.

[0100] The adjustment unit can estimate the user's emotions and modify the method of adjusting the counseling session based on the estimated emotions. For example, the adjustment unit estimates the user's emotions and modifies the method of adjusting the counseling session based on the estimated emotions. This method of adjusting the counseling session includes, but is not limited to, the session schedule and the criteria for selecting a professional. For example, if the user is stressed, a relaxing counseling session may be suggested. If the user is relaxed, a detailed counseling session may be suggested. If the user is in a hurry, a concise counseling session may be suggested. This allows for the provision of an appropriate counseling session by modifying the method of adjusting the session based on the user's emotions.

[0101] The adjustment unit can select the optimal adjustment method by referring to the user's past counseling history during the adjustment process. For example, the adjustment unit selects the optimal adjustment method by referring to the user's past counseling history during the adjustment process. Past counseling history includes, but is not limited to, past session content and feedback. For example, it selects the optimal adjustment method based on the user's past counseling history. It customizes the adjustment method by referring to the user's past counseling history. It optimizes the adjustment content based on the user's past counseling history. This allows the optimal adjustment method to be selected by referring to the user's past counseling history.

[0102] The adjustment unit can adjust counseling sessions during the adjustment process, taking into account the user's lifestyle and health condition. For example, the adjustment unit can adjust counseling sessions during the adjustment process, taking into account the user's lifestyle and health condition. Lifestyle includes, but is not limited to, sleep patterns, activity times, and meal times. Health condition includes, but is not limited to, medical data, self-reports, and fitness tracker data. For example, it can adjust the timing of counseling sessions based on the user's lifestyle. It can adjust the content of counseling sessions taking into account the user's health condition. It can customize the method of counseling sessions based on the user's lifestyle and health condition. This allows for the provision of appropriate counseling sessions by taking into account the user's lifestyle and health condition.

[0103] The adjustment unit can estimate the user's emotions and determine the priority of counseling sessions based on the estimated emotions. For example, the adjustment unit estimates the user's emotions and determines the priority of counseling sessions based on the estimated emotions. Methods for determining the priority of counseling sessions include, but are not limited to, importance assessment and urgency assessment. For example, if the user is stressed, important counseling sessions will be prioritized. If the user is relaxed, detailed counseling sessions will be prioritized. If the user is in a hurry, concise counseling sessions will be prioritized. In this way, by determining the priority of counseling sessions based on the user's emotions, important counseling sessions can be prioritized.

[0104] The adjustment unit can propose the optimal counseling session during the adjustment process, taking into account the user's geographical location information. For example, the adjustment unit proposes the optimal counseling session during the adjustment process, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, it proposes a counseling session relevant to that region. It automatically selects the optimal counseling session based on the user's geographical location information. If the user is on the move, it proposes a counseling session based on their current location. This allows for the proposal of the optimal counseling session by considering the user's geographical location information.

[0105] The adjustment unit can analyze the user's social media activity during the adjustment process to customize the content of the counseling session. For example, the adjustment unit can analyze the user's social media activity during the adjustment process to customize the content of the counseling session. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, it can analyze the user's current emotional state from their social media activity and customize the content of the counseling session. It can adjust the method of the counseling session based on the user's social media activity. It can optimize the content of the counseling session by referring to the user's social media activity. In this way, the content of the counseling session can be customized by analyzing the user's social media activity.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The CareWell AI system can also collect users' physical health data and suggest stress management techniques based on this data. For example, it can analyze data such as heart rate, sleep patterns, and exercise levels obtained from fitness trackers and smartwatches to understand the user's physical condition. This allows it to suggest relaxation methods if the user is fatigued and recommend light exercise if they are not getting enough exercise. It can also monitor the effectiveness of stress management techniques based on the user's health data and adjust the suggestions as needed. This enables the provision of personalized support tailored to the user's physical health condition.

[0108] The CareWell AI system can also analyze a user's past stress management history and suggest the most suitable stress management techniques. For example, it can record the effectiveness of relaxation methods and stress management techniques the user has tried in the past and use this information to make suggestions for the next time. It prioritizes suggesting methods that the user found effective in the past and avoids suggesting methods that were less effective. It can also identify methods that are effective in specific times of day or situations based on the user's past history and make suggestions based on this. This allows the system to provide the most suitable stress management techniques tailored to the user's individual needs.

[0109] The CareWell AI system can estimate a user's emotions and suggest ways to interact within the community based on those emotions. For example, if a user is feeling stressed, it can suggest relaxing ways to interact; if the user is relaxed, it can suggest more proactive ways to interact. It can also suggest ways to receive support if a user is feeling lonely. This allows the system to provide the most appropriate interaction methods based on the user's emotions, thereby supporting their mental health.

[0110] The CareWell AI system can also suggest region-specific stress management techniques and relaxation methods, taking into account the user's geographical location. For example, if a user is in a specific area, it can suggest relaxation facilities and events available in that area. It can also suggest stress management techniques tailored to the local climate and culture. This allows the system to provide more appropriate stress management techniques and relaxation methods based on the user's geographical location.

[0111] The CareWell AI system can also analyze a user's social media activity and suggest stress management techniques based on this analysis. For example, it can analyze a user's social media posts, the number of likes they receive, and the content of their comments to understand their current emotional state. Based on this, it can suggest relaxation methods if the user is feeling stressed, and suggest positive activities if they are relaxed. It can also monitor the effectiveness of stress management techniques based on the user's social media activity and adjust the suggestions as needed. This allows the system to provide more appropriate stress management techniques based on the user's social media activity.

[0112] The CareWell AI system can estimate a user's emotions and customize the suggested relaxation methods and stress management techniques based on those estimates. For example, if a user is stressed, it will suggest simple and easy-to-understand relaxation methods; if the user is relaxed, it will suggest relaxation methods with detailed explanations. Furthermore, if a user is in a hurry, it can suggest concise and quick stress management techniques. This allows the system to provide more appropriate relaxation methods and stress management techniques based on the user's emotions.

[0113] The CareWell AI system can also suggest optimal stress management techniques and relaxation methods, taking into account the user's lifestyle and health condition. For example, it analyzes data such as the user's sleep patterns, activity times, and meal times, and makes suggestions based on this information. If the user wants to relax in the morning, it will suggest relaxation methods suitable for the morning; if the user feels stressed at night, it will suggest stress management techniques suitable for nighttime. Furthermore, it can monitor the effectiveness of relaxation methods and stress management techniques based on the user's health condition and adjust the suggestions as needed. This allows it to provide more appropriate stress management techniques and relaxation methods based on the user's lifestyle and health condition.

[0114] The CareWell AI system can estimate a user's emotions and customize the content of the counseling session based on those emotions. For example, if a user is feeling stressed, it will suggest a relaxing counseling session; if the user is relaxed, it will suggest a more detailed session. It can also suggest a concise session if the user is in a hurry. This allows for the provision of more appropriate counseling sessions based on the user's emotions.

[0115] The CareWell AI system can also suggest the most suitable counseling session by referring to the user's past counseling history. For example, it analyzes the content and feedback from past sessions and adjusts the next session accordingly. It prioritizes suggesting counseling methods that the user has found effective in the past and avoids suggesting methods that were less effective. It can also identify effective counseling methods for specific times of day or situations based on the user's past history and make suggestions based on that. This allows for the provision of optimal counseling sessions tailored to the user's individual needs.

[0116] The CareWell AI system can estimate the user's emotions and adjust the order in which it displays the emotion analysis results based on those estimates. For example, if the user is stressed, it will prioritize displaying important analysis results; if the user is relaxed, it will prioritize displaying detailed analysis results; and if the user is in a hurry, it can prioritize displaying concise analysis results. This allows the system to provide more appropriate emotion analysis results based on the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk enters the user's information. This information includes, for example, health status, living environment, and psychological state. The reception desk can enter the user's information via smartphone or tablet. Step 2: The suggestion department uses AI to propose relaxation methods and stress management techniques based on the information received by the reception department. Relaxation methods include deep breathing, meditation, and yoga, while stress management techniques include cognitive behavioral therapy, time management, and relaxation techniques. Step 3: The community section provides features that allow caregivers to interact with each other. These features include chat, forums, and video calls. Step 4: The analysis department analyzes the emotional state from the chat content and input information to determine the stress level. Methods for analyzing the emotional state include text analysis, voice analysis, and facial expression analysis, while methods for determining the stress level include psychological tests, biofeedback, and self-reporting. Step 5: The coordination department will arrange counseling sessions with specialists as needed. Methods for arranging counseling sessions include scheduling systems and criteria for selecting specialists.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, proposal unit, community unit, analysis unit, and coordination unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input the user's status through the reception device 38 of the smart device 14. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes relaxation methods and stress management techniques. The community unit is implemented by, for example, the control unit 46A of the smart device 14 and provides a function that allows caregivers to interact with each other. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes emotional states from chat content and input information to determine stress levels. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and coordinates counseling sessions with experts. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, proposal unit, community unit, analysis unit, and coordination unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input the user's situation through the microphone 238 of the smart glasses 214. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes relaxation methods and stress management techniques. The community unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides a function that allows caregivers to interact with each other. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the emotional state from chat content and input information to determine the stress level. The coordination unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and coordinates counseling sessions with experts. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, proposal unit, community unit, analysis unit, and coordination unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input the user's situation through the microphone 238 of the headset terminal 314. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes relaxation methods and stress management techniques. The community unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a function that allows caregivers to interact with each other. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes emotional states from chat content and input information to determine stress levels. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and coordinates counseling sessions with experts. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the reception unit, proposal unit, community unit, analysis unit, and coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input the user's situation through the microphone 238 of the robot 414. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes relaxation methods and stress management techniques. The community unit is implemented by, for example, the control unit 46A of the robot 414 and provides a function that allows caregivers to interact with each other. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes emotional states from chat content and input information to determine stress levels. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and coordinates counseling sessions with experts. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A reception desk where users enter their status, Based on the information received by the aforementioned reception department, there is a proposal department that proposes relaxation methods and stress management techniques, A community club where caregivers can interact with each other, The analysis department analyzes the emotions and stress levels of caregivers in real time, It includes a coordination unit that arranges counseling sessions with specialists as needed. A system characterized by the following features. (Note 2) The aforementioned reception unit is User status is entered via smartphone or tablet. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, AI suggests relaxation methods and stress management techniques. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned community section, Provides a function that allows caregivers to interact with each other. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is The system analyzes emotional states and determines stress levels based on chat content and input information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, If necessary, we will arrange counseling sessions with a specialist. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of situation input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their situation, the system filters the input based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering the status, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of relaxation methods and stress management techniques. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, we analyze the user's past responses and apply the optimal suggestion algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, the timing of the proposal is determined based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, customize the content of the proposal based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned community section, It estimates the user's emotions and adjusts the method of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned community section, We analyze users' past activity history within the community and suggest the most suitable ways for them to interact. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned community section, It estimates the user's emotions and determines the priority of interactions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned community section, We suggest the most suitable partners for interaction, taking into account the user's geographical location within the community. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the emotion analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referencing the user's past sentiment data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During the analysis, sentiment analysis is performed while taking into account the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which the emotion analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, sentiment analysis is performed while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is During analysis, we refer to users' social media activity to improve the accuracy of sentiment analysis. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, The system estimates the user's emotions and modifies the counseling session based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, the system will refer to the user's past counseling history to select the most suitable adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, During the adjustment process, counseling sessions are tailored to take into account the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, The system estimates the user's emotions and prioritizes counseling sessions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, During the scheduling process, we will propose the optimal counseling session considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, During the scheduling process, the content of the counseling session is customized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users enter their status, Based on the information received by the aforementioned reception department, there is a proposal department that proposes relaxation methods and stress management techniques, A community club where caregivers can interact with each other, The analysis department analyzes the emotions and stress levels of caregivers in real time, It includes a coordination unit that arranges counseling sessions with specialists as needed. A system characterized by the following features.

2. The aforementioned reception unit is User status is entered via smartphone or tablet. The system according to feature 1.

3. The aforementioned proposal section is, AI suggests relaxation methods and stress management techniques. The system according to feature 1.

4. The aforementioned community section, Provides a function that allows caregivers to interact with each other. The system according to feature 1.

5. The aforementioned analysis unit is The system analyzes emotional states and determines stress levels based on chat content and input information. The system according to feature 1.

6. The adjustment unit is, If necessary, we will arrange counseling sessions with a specialist. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of situation input based on the estimated user emotions. The system according to feature 1.

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

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