system

Abstract

The system according to this embodiment aims to track the user's emotions and state and provide personalized advice and resources. [Solution] The system according to the embodiment comprises a reception unit, a tracking unit, a provision unit, and a bonding unit. The reception unit receives anonymous conversations from users. The tracking unit analyzes the conversation content received by the reception unit and tracks the user's emotions and state. The provision unit provides custom advice and resources based on the emotions and state tracked by the tracking unit. The bonding unit promotes community bonding based on the advice or resources provided by the provision unit.

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 a directive related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the user's emotions and state have not been adequately tracked and individual advice or resources have not been sufficiently provided, leaving room for improvement.

[0005] The system according to the embodiment aims to track the user's emotions and state and provide individual advice or resources.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a tracking unit, a provision unit, and a coordinating unit. The reception unit receives anonymous conversations from users. The tracking unit analyzes the conversation content received by the reception unit and tracks the user's emotions and state. The provision unit provides custom advice and resources based on the emotions and state tracked by the tracking unit. The coordinating unit promotes community coordinating based on the advice or resources provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can track the user's emotions and state and provide personalized advice and resources. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 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 system according to an embodiment of the present invention is a system that understands the connection between skin and mind and provides safe, anonymous dialogue. This system allows users to initiate dialogue anonymously, tracks the user's emotions and state, and provides customized advice and resources. Furthermore, users can feel a sense of community. For example, when a user initiates a dialogue anonymously, they do not need to provide their name or personal information. If a user is feeling stressed, they can anonymously consult the system. Next, the system tracks the user's emotions and state. The system analyzes the user's dialogue content and input information to understand the user's emotions and state. For example, if a user inputs "tired," the system determines that the user is tired. Based on the user's emotions and state, the system provides customized advice and resources. For example, if a user is stressed, the system provides relaxation methods and stress management advice. Also, if a user needs a specific resource, the system provides that resource. Furthermore, users can feel a sense of community. The system provides a platform where users can interact anonymously with other users. For example, by engaging in dialogue with other users who share similar concerns, users can feel empathy and support. This mechanism allows users to understand the connection between skin and mind and engage in safe, anonymous dialogue. Furthermore, by providing customized advice and resources, and tracking emotions and states, the system can support the user's mental and physical health.

[0029] The system according to this embodiment comprises a reception unit, a tracking unit, a provision unit, and a coordinating unit. The reception unit receives anonymous conversations from users. Users can engage in anonymous conversations in the form of, for example, chat, voice calls, or video calls. The reception unit accepts conversations completely anonymously without requiring the user's name or personal information. For example, if a user is feeling stressed, they can consult the system anonymously. The tracking unit uses natural language processing technology to analyze the content of conversations received by the reception unit and tracks the user's emotions and state. For example, if a user inputs "I'm tired," the tracking unit determines that the user is tired. The tracking unit uses technologies such as morphological analysis, grammatical analysis, and semantic analysis to analyze the content of the user's conversations in detail. The provision unit provides custom advice and resources based on the emotions and state tracked by the tracking unit. For example, if a user is feeling stressed, the provision unit provides advice on relaxation methods and stress management. Also, if a user needs a specific resource, the provision unit provides that resource. The coordinating unit promotes community coordinating based on the advice and resources provided by the provision unit. For example, a platform could be provided that allows users to interact anonymously with other users, enabling them to feel empathy and support. This would allow the system according to the embodiment to support the mental and physical health of users.

[0030] The tracking unit can analyze the user's dialogue using natural language processing techniques and track their emotions and state. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, if the user inputs "tired," the tracking unit uses morphological analysis to extract the word "tired," grammatical analysis to analyze the structure of the sentence, and semantic analysis to determine that the user is tired. The tracking unit can also analyze the user's dialogue in real time and track their emotions and state. For example, if the user inputs "happy" during the conversation, the tracking unit immediately tracks that emotion and understands the user's state. This improves the accuracy of tracking the user's emotions and state by using natural language processing techniques. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can input the user's dialogue into a generating AI, which can then analyze the emotions and state.

[0031] The service provider can offer relaxation methods or stress management advice based on the user's emotions and state. Relaxation methods include, for example, breathing exercises, meditation, and yoga. For example, if a user is feeling stressed, the service provider can suggest breathing exercises. Breathing exercises are a method of achieving relaxation through deep breathing. The service provider can also suggest meditation if the user wants to relax. Meditation is a method of calming the mind and is performed by closing one's eyes and taking deep breaths in a quiet place. Furthermore, if the user wants to move their body, the service provider can suggest yoga. Yoga is a method of achieving relaxation through stretching the body and regulating breathing. Stress management advice includes, for example, time management, exercise, and counseling. For example, if a user is struggling with time management, the service provider can suggest time management methods. Time management methods include prioritizing tasks and creating schedules. The service provider can also suggest exercise if the user feels they are not getting enough exercise. Exercise is important for reducing stress and maintaining physical and mental health. Furthermore, the service provider can also provide counseling resources if the user requires counseling. This allows for the provision of relaxation methods and stress management advice tailored to the user's emotions and state. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's emotions and state into a generating AI, which can then generate relaxation methods and stress management advice.

[0032] The service provider can provide users with specific resources they need. These resources may include, for example, support groups, online tools, and expert contact information. For instance, if a user needs a support group, the service provider can suggest an appropriate one. Support groups are groups where people with similar problems come together to support each other. The service provider can also provide users with appropriate online tools if they need them. These online tools may include stress management apps and relaxation apps. Furthermore, if a user needs expert contact information, the service provider can provide appropriate expert contact information. These expert contacts may include counselors, doctors, and psychologists. This ensures that users receive the resources they need. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's resource needs into a generating AI, which can then suggest appropriate resources.

[0033] The Coordination Unit can provide a platform where users can interact anonymously with other users. Platforms for anonymous interaction include, for example, chat rooms, forums, and video conferencing. For example, the Coordination Unit could suggest that users interact with other users in a chat room. A chat room is a place for exchanging messages in real time and can be joined anonymously. The Coordination Unit could also suggest that users share information with other users in a forum. A forum is a place for discussing specific topics and can be posted anonymously. Furthermore, the Coordination Unit could suggest that users interact face-to-face with other users in a video conference. A video conference is a method for interacting in real time using a camera and can be joined anonymously. This promotes community coordinating by providing a platform where users can interact anonymously with other users. Some or all of the above processing in the Coordination Unit may be performed using, for example, AI, or not. For example, the Coordination Unit could input the user's need for interaction into a generative AI, which could then suggest an appropriate method of interaction.

[0034] The reception desk can analyze the user's past conversation history and select the optimal reception method. For example, the reception desk can prioritize suggesting conversation methods (voice, text, etc.) that the user has frequently used in the past. For instance, if the user has frequently used voice input in the past, the reception desk will prioritize suggesting voice input. The reception desk can also predict and suggest conversation methods that the user will use at specific times of day based on their past conversation history. For example, if the user frequently converses at night, the reception desk will suggest a conversation method suitable for nighttime. Furthermore, the reception desk can automatically apply interface designs that the user has preferred in the past. For example, if the user has preferred a simple design in the past, the reception desk will automatically apply a simple design. This improves user convenience by providing the optimal reception method based on the user's past conversation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past conversation history into a generating AI, which can then select the optimal reception method.

[0035] The reception desk can prioritize receiving conversations that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving conversations related to that region. For instance, when the user is in a specific region, the reception desk will prioritize providing information and support related to that region. The reception desk can also prioritize receiving conversations related to the user's travel destination if the user is traveling. For instance, when the user is traveling, the reception desk will prioritize providing information and support related to the travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving conversations related to information around their home. For instance, when the user is at home, the reception desk will prioritize providing information and support around their home. This improves user convenience by prioritizing the reception of highly relevant conversations based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI, which can then prioritize receiving highly relevant conversations.

[0036] The reception unit can analyze the user's social media activity when receiving a dialogue and accept relevant dialogues. For example, the reception unit can prioritize accepting relevant dialogues based on what the user has shared on social media. For example, if the user has posted about stress on social media, the reception unit will prioritize accepting stress-related dialogues. The reception unit can also prioritize accepting relevant dialogues based on the topics the user follows on social media. For example, if the user follows topics related to health, the reception unit will prioritize accepting health-related dialogues. Furthermore, the reception unit can also prioritize accepting relevant dialogues based on the groups the user participates in on social media. For example, if the user participates in a mental health group, the reception unit will prioritize accepting mental health-related dialogues. This improves user convenience by accepting relevant dialogues based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generating AI, which can then accept relevant dialogues.

[0037] The tracking unit can adjust its tracking algorithm by referring to the user's past emotional data during tracking. For example, the tracking unit can improve accuracy by adjusting the tracking algorithm based on the user's past emotional data. For example, it can extract specific patterns from the user's past emotional data and reflect them in the tracking algorithm. The tracking unit can also analyze the user's past emotional data and optimize the parameters of the tracking algorithm. For example, it can improve tracking accuracy by adjusting the parameters of the tracking algorithm based on the user's past emotional data. Furthermore, the tracking unit can adjust tracking accuracy in real time by referring to the user's past emotional data. For example, it can dynamically adjust tracking accuracy by referring to the user's past emotional data in real time. This improves tracking accuracy by optimizing the tracking algorithm based on the user's past emotional data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past emotional data into a generating AI, which can then adjust the tracking algorithm.

[0038] The tracking unit can apply different tracking methods depending on the category of the user's dialogue content during tracking. For example, if the user is having a dialogue about stress, the tracking unit can apply a stress-specific tracking method. For example, when the user is having a dialogue about stress, the tracking unit will track the causes and effects of stress in detail. The tracking unit can also apply a relaxation-specific tracking method if the user is having a dialogue about relaxation. For example, when the user is having a dialogue about relaxation, the tracking unit will track the effects and methods of relaxation in detail. Furthermore, if the user is having a dialogue about health, the tracking unit can apply a health-specific tracking method. For example, when the user is having a dialogue about health, the tracking unit will track the user's health status and methods for improvement in detail. This enables appropriate data collection by providing tracking methods according to the category of the user's dialogue content. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's dialogue content into a generating AI, and the generating AI can apply a tracking method according to the category.

[0039] The tracking unit can determine tracking priorities based on when the user's dialogue content was submitted. For example, the tracking unit can prioritize tracking recently submitted dialogue content. For instance, it can prioritize analyzing recently submitted dialogue content and track it based on the latest information. The tracking unit can also prioritize tracking dialogue content submitted by the user during specific time periods. For example, it can prioritize analyzing dialogue content submitted by the user at night and track the nighttime status in detail. Furthermore, if the user submits urgent dialogue content, the tracking unit can prioritize tracking that content. For example, when a user submits urgent dialogue content, the tracking unit prioritizes analyzing that content and responds quickly. This allows for prioritizing tracking of highly urgent dialogue content by determining tracking priorities based on when the user's dialogue content was submitted. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the submission timing of the user's dialogue content into a generating AI, which can then determine the tracking priorities.

[0040] The tracking unit can improve tracking accuracy by referring to the user's relevant literature during tracking. For example, the tracking unit can improve accuracy by adjusting the tracking algorithm based on the relevant literature referenced by the user. For example, it can extract specific patterns from the relevant literature referenced by the user and reflect them in the tracking algorithm. The tracking unit can also extract specific patterns from the relevant literature cited by the user and reflect them in the tracking algorithm. For example, it can improve tracking accuracy by adjusting the parameters of the tracking algorithm based on the relevant literature cited by the user. Furthermore, the tracking unit can analyze the relevant literature referenced by the user and optimize the parameters of the tracking algorithm. For example, it can improve tracking accuracy by adjusting the parameters of the tracking algorithm based on the relevant literature referenced by the user. This improves tracking accuracy based on the user's relevant literature, enabling the collection of detailed data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's relevant literature into a generating AI, which can then adjust the tracking algorithm.

[0041] The service provider can apply different advice algorithms depending on the user's emotional and state categories at the time of delivery. For example, if the user is seeking advice about stress, the service provider can apply an advice algorithm specifically for stress management. For example, when the user is seeking advice about stress, the service provider can suggest methods for stress management. The service provider can also apply an advice algorithm specifically for relaxation if the user is seeking advice about relaxation. For example, when the user is seeking advice about relaxation, the service provider can suggest methods for relaxation. Furthermore, if the user is seeking advice about health, the service provider can apply an advice algorithm specifically for health. For example, when the user is seeking advice about health, the service provider can suggest methods for health management. In this way, appropriate advice can be provided by offering advice algorithms that correspond to the user's emotional and state categories. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotional and state into a generating AI, and the generating AI can apply an advice algorithm according to the category.

[0042] The service provider can prioritize advice based on the timing of the user's emotional and state submission. For example, if a user is seeking urgent advice, the service provider can prioritize that advice and respond quickly. The service provider can also provide advice tailored to the time of day if the user is seeking advice at a specific time. For example, if a user is seeking advice at night, the service provider will provide advice appropriate for that time of day. Furthermore, the service provider can prioritize relevant advice based on the user's past consultations. For example, if a user has previously sought advice regarding stress, the service provider will prioritize advice on stress management. This allows for the priority of urgent advice by prioritizing advice based on the timing of the user's emotional and state submission. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the timing of the user's emotional and state submission into a generating AI, which can then determine the priority of advice.

[0043] The service provider can adjust the order of advice based on the relationship between the user's emotions and state at the time of delivery. For example, if the user is feeling stressed, the service provider can first provide advice on stress management. For example, when the user is feeling stressed, the service provider will first suggest methods for stress management. The service provider can also first provide advice on relaxation if the user is seeking relaxation. For example, when the user is seeking relaxation, the service provider will first suggest methods for relaxation. Furthermore, if the service provider is seeking health-related advice, the service provider can first provide health-related advice. For example, when the user is seeking health-related advice, the service provider will first suggest methods for health management. This allows the service provider to provide appropriate advice by adjusting the order of advice based on the relationship between the user's emotions and state. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotions and state into a generating AI, which can then adjust the order of advice.

[0044] The coordinating unit can select the optimal coordinating method by referring to the user's past community activities during coordinating. For example, the coordinating unit can suggest relevant activities based on the community activities the user has participated in in the past. For example, it can suggest relevant activities based on online events and group chats the user has participated in in the past. The coordinating unit can also extract specific patterns from the user's past community activities and suggest the optimal coordinating method. For example, it can extract patterns of activities the user has preferred to participate in in the past and suggest the optimal coordinating method. Furthermore, the coordinating unit can prioritize suggesting activities the user has preferred to participate in in the past. For example, it can prioritize suggesting online events and group chats the user has preferred to participate in in the past. This improves user satisfaction by providing the optimal coordinating method based on the user's past community activities. Some or all of the above processing in the coordinating unit may be performed using AI, for example, or without AI. For example, the coordinating unit can input the user's past community activities into a generating AI, which can then select the optimal coordinating method.

[0045] The bundling unit can select the optimal bundling method when bundling users, taking into account the user's geographical location information. For example, if the user is in a specific region, the bundling unit can suggest community activities related to that region. For instance, when the user is in a specific region, the bundling unit can suggest online events or group chats related to that region. Also, if the user is traveling, the bundling unit can suggest community activities related to their travel destination. For example, when the user is traveling, the bundling unit can suggest online events or group chats related to their travel destination. Furthermore, if the user is at home, the bundling unit can suggest community activities related to information around their home. For example, when the user is at home, the bundling unit can suggest online events or group chats related to information around their home. By providing the optimal bundling method based on the user's geographical location information, user satisfaction is improved. Some or all of the above processing in the bundling unit may be performed using AI, for example, or without AI. For example, the bundling unit can input the user's geographical location information into a generating AI, which can then select the optimal bundling method.

[0046] The bonding unit can analyze a user's social media activity and suggest bonding methods during the bonding process. For example, the bonding unit can suggest relevant community activities based on content shared by the user on social media. For instance, if a user posts about stress on social media, the bonding unit will suggest online events or group chats related to stress. The bonding unit can also suggest relevant community activities based on topics followed by the user on social media. For example, if a user follows topics related to health, the bonding unit will suggest online events or group chats related to health. Furthermore, the bonding unit can suggest relevant community activities based on groups the user participates in on social media. For example, if a user participates in a mental health group, the bonding unit will suggest online events or group chats related to mental health. This improves user satisfaction by providing the most suitable bonding method based on the user's social media activity. Some or all of the above processing in the bonding unit may be performed using AI, for example, or without AI. For example, the bonding unit can input the user's social media activity into a generating AI, which can then suggest the most suitable bonding method.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The reception desk can analyze the user's conversation content and suggest conversation topics based on the user's interests. For example, if the user is talking about their hobbies or special skills, the reception desk can suggest conversation topics related to those hobbies or skills. Furthermore, if the user shows interest in a particular topic, the reception desk can provide information and resources related to that topic. In addition, the reception desk can suggest topics that the user might develop new interests in. This improves the user's conversation experience by providing conversation topics based on their interests.

[0049] The service provider can analyze user interactions and adjust the advice delivery method based on the user's learning style. For example, if a user prefers visual information, the service provider can provide advice using diagrams and charts. If a user prefers auditory information, the service provider can provide advice in the form of audio messages or podcasts. Furthermore, if a user prefers a practical approach, the service provider can provide specific action plans and step-by-step guides. This allows for advice tailored to the user's learning style, thereby promoting understanding and practice.

[0050] The Coordination Unit can analyze user conversations and suggest community activities based on the user's interests. For example, if a user has a specific hobby or interest, the Coordination Unit can suggest online events or group chats related to that hobby or interest. It can also suggest topics that the user might develop new interests in. Furthermore, if a user shares common interests with other users, the Coordination Unit can facilitate interaction based on those shared interests. By providing community activities tailored to the user's interests, it can improve user satisfaction.

[0051] The reception desk can analyze the content of the user's conversation and adjust the way the conversation progresses based on the user's purpose. For example, if the user's purpose is problem solving, the reception desk will ask specific questions and make suggestions aimed at problem solving. If the user's purpose is relaxation, the reception desk can also suggest conversation topics and relaxation methods that promote relaxation. Furthermore, if the user's purpose is information gathering, the reception desk can provide relevant information and resources. In this way, by providing a conversation approach that matches the user's purpose, it is possible to provide a conversation experience that meets the user's needs.

[0052] The service provider can analyze the user's conversation and tailor the advice based on the user's purpose. For example, if the user's goal is problem solving, the service provider can provide specific solutions and step-by-step guides. If the user's goal is relaxation, the service provider can also provide relaxation methods and resources. Furthermore, if the user's goal is information gathering, the service provider can provide relevant information and resources. By providing advice tailored to the user's purpose, the service provider can offer support that meets the user's needs.

[0053] The Coordination Unit can analyze user conversations and tailor community activity suggestions based on the user's purpose. For example, if a user's goal is problem-solving, the Coordination Unit can suggest support groups or forums that can help solve the problem. If a user's goal is relaxation, the Coordination Unit can also suggest online events or group chats that can help with relaxation. Furthermore, if a user's goal is information gathering, the Coordination Unit can provide relevant information and resources. By providing community activities that match the user's purpose, user satisfaction can be improved.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk accepts anonymous conversations from users. Users can engage in anonymous conversations via chat, voice calls, video calls, etc. The reception desk accepts conversations completely anonymously, without requiring the user's name or personal information. Step 2: The tracking unit uses natural language processing technology to analyze the dialogue content received by the reception unit and track the user's emotions and state. For example, it uses technologies such as morphological analysis, grammatical analysis, and semantic analysis to analyze the user's dialogue content in detail. Step 3: The service provider provides customized advice and resources based on the emotions and states tracked by the tracking service provider. For example, if the user is feeling stressed, it provides advice on relaxation methods and stress management. It also provides resources if the user needs specific resources. Step 4: The Cohesion team fosters community cohesion based on the advice and resources provided by the Provider team. For example, they provide a platform where users can interact anonymously with other users and feel empathy and support.

[0056] (Example of form 2) The system according to an embodiment of the present invention is a system that understands the connection between skin and mind and provides safe, anonymous dialogue. This system allows users to initiate dialogue anonymously, tracks the user's emotions and state, and provides customized advice and resources. Furthermore, users can feel a sense of community. For example, when a user initiates a dialogue anonymously, they do not need to provide their name or personal information. If a user is feeling stressed, they can anonymously consult the system. Next, the system tracks the user's emotions and state. The system analyzes the user's dialogue content and input information to understand the user's emotions and state. For example, if a user inputs "tired," the system determines that the user is tired. Based on the user's emotions and state, the system provides customized advice and resources. For example, if a user is stressed, the system provides relaxation methods and stress management advice. Also, if a user needs a specific resource, the system provides that resource. Furthermore, users can feel a sense of community. The system provides a platform where users can interact anonymously with other users. For example, by engaging in dialogue with other users who share similar concerns, users can feel empathy and support. This mechanism allows users to understand the connection between skin and mind and engage in safe, anonymous dialogue. Furthermore, by providing customized advice and resources, and tracking emotions and states, the system can support the user's mental and physical health.

[0057] The system according to this embodiment comprises a reception unit, a tracking unit, a provision unit, and a coordinating unit. The reception unit receives anonymous conversations from users. Users can engage in anonymous conversations in the form of, for example, chat, voice calls, or video calls. The reception unit accepts conversations completely anonymously without requiring the user's name or personal information. For example, if a user is feeling stressed, they can consult the system anonymously. The tracking unit uses natural language processing technology to analyze the content of conversations received by the reception unit and tracks the user's emotions and state. For example, if a user inputs "I'm tired," the tracking unit determines that the user is tired. The tracking unit uses technologies such as morphological analysis, grammatical analysis, and semantic analysis to analyze the content of the user's conversations in detail. The provision unit provides custom advice and resources based on the emotions and state tracked by the tracking unit. For example, if a user is feeling stressed, the provision unit provides advice on relaxation methods and stress management. Also, if a user needs a specific resource, the provision unit provides that resource. The coordinating unit promotes community coordinating based on the advice and resources provided by the provision unit. For example, a platform could be provided that allows users to interact anonymously with other users, enabling them to feel empathy and support. This would allow the system according to the embodiment to support the mental and physical health of users.

[0058] The tracking unit can analyze the user's dialogue using natural language processing techniques and track their emotions and state. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, if the user inputs "tired," the tracking unit uses morphological analysis to extract the word "tired," grammatical analysis to analyze the structure of the sentence, and semantic analysis to determine that the user is tired. The tracking unit can also analyze the user's dialogue in real time and track their emotions and state. For example, if the user inputs "happy" during the conversation, the tracking unit immediately tracks that emotion and understands the user's state. This improves the accuracy of tracking the user's emotions and state by using natural language processing techniques. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not. For example, the tracking unit can input the user's dialogue into a generating AI, which can then analyze the emotions and state.

[0059] The service provider can offer relaxation methods or stress management advice based on the user's emotions and state. Relaxation methods include, for example, breathing exercises, meditation, and yoga. For example, if a user is feeling stressed, the service provider can suggest breathing exercises. Breathing exercises are a method of achieving relaxation through deep breathing. The service provider can also suggest meditation if the user wants to relax. Meditation is a method of calming the mind and is performed by closing one's eyes and taking deep breaths in a quiet place. Furthermore, if the user wants to move their body, the service provider can suggest yoga. Yoga is a method of achieving relaxation through stretching the body and regulating breathing. Stress management advice includes, for example, time management, exercise, and counseling. For example, if a user is struggling with time management, the service provider can suggest time management methods. Time management methods include prioritizing tasks and creating schedules. The service provider can also suggest exercise if the user feels they are not getting enough exercise. Exercise is important for reducing stress and maintaining physical and mental health. Furthermore, the service provider can also provide counseling resources if the user requires counseling. This allows for the provision of relaxation methods and stress management advice tailored to the user's emotions and state. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's emotions and state into a generating AI, which can then generate relaxation methods and stress management advice.

[0060] The service provider can provide users with specific resources they need. These resources may include, for example, support groups, online tools, and expert contact information. For instance, if a user needs a support group, the service provider can suggest an appropriate one. Support groups are groups where people with similar problems come together to support each other. The service provider can also provide users with appropriate online tools if they need them. These online tools may include stress management apps and relaxation apps. Furthermore, if a user needs expert contact information, the service provider can provide appropriate expert contact information. These expert contacts may include counselors, doctors, and psychologists. This ensures that users receive the resources they need. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's resource needs into a generating AI, which can then suggest appropriate resources.

[0061] The Coordination Unit can provide a platform where users can interact anonymously with other users. Platforms for anonymous interaction include, for example, chat rooms, forums, and video conferencing. For example, the Coordination Unit could suggest that users interact with other users in a chat room. A chat room is a place for exchanging messages in real time and can be joined anonymously. The Coordination Unit could also suggest that users share information with other users in a forum. A forum is a place for discussing specific topics and can be posted anonymously. Furthermore, the Coordination Unit could suggest that users interact face-to-face with other users in a video conference. A video conference is a method for interacting in real time using a camera and can be joined anonymously. This promotes community coordinating by providing a platform where users can interact anonymously with other users. Some or all of the above processing in the Coordination Unit may be performed using, for example, AI, or not. For example, the Coordination Unit could input the user's need for interaction into a generative AI, which could then suggest an appropriate method of interaction.

[0062] The reception unit can estimate the user's emotions and adjust the way it handles the conversation based on those emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. A simple interface is easy to use when the user is stressed, allowing them to quickly start a conversation. The reception unit can also provide detailed input options and suggest customizable input methods when the user is relaxed. Detailed input options allow the user to provide more information when they are relaxed. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow them to quickly start a conversation. Voice input is a way for users to start a conversation without hassle when they are in a hurry. This improves user convenience by providing a way to handle conversations that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the way the interaction is handled.

[0063] The reception desk can analyze the user's past conversation history and select the optimal reception method. For example, the reception desk can prioritize suggesting conversation methods (voice, text, etc.) that the user has frequently used in the past. For instance, if the user has frequently used voice input in the past, the reception desk will prioritize suggesting voice input. The reception desk can also predict and suggest conversation methods that the user will use at specific times of day based on their past conversation history. For example, if the user frequently converses at night, the reception desk will suggest a conversation method suitable for nighttime. Furthermore, the reception desk can automatically apply interface designs that the user has preferred in the past. For example, if the user has preferred a simple design in the past, the reception desk will automatically apply a simple design. This improves user convenience by providing the optimal reception method based on the user's past conversation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past conversation history into a generating AI, which can then select the optimal reception method.

[0064] The reception desk can filter conversations based on the user's current psychological state when receiving them. For example, if the user is feeling anxious, the reception desk can prioritize displaying conversations that provide reassurance. For instance, when the user is feeling anxious, the reception desk will prioritize displaying relaxing content and encouraging messages. The reception desk can also prioritize displaying conversations that promote calmness if the user is agitated. For example, when the user is agitated, the reception desk will prioritize displaying calming advice and relaxation methods. Furthermore, if the user is tired, the reception desk can prioritize displaying concise and easy-to-understand conversations. For example, when the user is tired, the reception desk will prioritize displaying concise and to-the-point conversations. By filtering according to the user's psychological state, appropriate conversation content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's psychological state into a generating AI, which can then perform filtering.

[0065] The reception desk can estimate the user's emotions and determine the priority of the conversations to be received based on the estimated emotions. For example, if the user is showing an urgent emotion, the reception desk can prioritize the conversation. For example, when the user is showing an urgent emotion, the reception desk will prioritize that conversation and respond quickly. The reception desk can also prioritize other conversations if the user is relaxed. For example, when the user is relaxed, the reception desk will prioritize other conversations that are more urgent. Furthermore, if the user is feeling stressed, the reception desk can quickly receive the conversation and provide support. For example, when the user is feeling stressed, the reception desk will quickly receive the conversation and provide support to reduce stress. In this way, by determining the priority of conversations according to the user's emotions, it is possible to prioritize conversations that are more urgent. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's emotions into a generating AI, which can then determine the priority of the conversation.

[0066] The reception desk can prioritize receiving conversations that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving conversations related to that region. For instance, when the user is in a specific region, the reception desk will prioritize providing information and support related to that region. The reception desk can also prioritize receiving conversations related to the user's travel destination if the user is traveling. For instance, when the user is traveling, the reception desk will prioritize providing information and support related to the travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving conversations related to information around their home. For instance, when the user is at home, the reception desk will prioritize providing information and support around their home. This improves user convenience by prioritizing the reception of highly relevant conversations based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI, which can then prioritize receiving highly relevant conversations.

[0067] The reception unit can analyze the user's social media activity when receiving a dialogue and accept relevant dialogues. For example, the reception unit can prioritize accepting relevant dialogues based on what the user has shared on social media. For example, if the user has posted about stress on social media, the reception unit will prioritize accepting stress-related dialogues. The reception unit can also prioritize accepting relevant dialogues based on the topics the user follows on social media. For example, if the user follows topics related to health, the reception unit will prioritize accepting health-related dialogues. Furthermore, the reception unit can also prioritize accepting relevant dialogues based on the groups the user participates in on social media. For example, if the user participates in a mental health group, the reception unit will prioritize accepting mental health-related dialogues. This improves user convenience by accepting relevant dialogues based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generating AI, which can then accept relevant dialogues.

[0068] The tracking unit can estimate the user's emotions and adjust the tracking accuracy based on the estimated emotions. For example, if the user is stressed, the tracking unit can increase the tracking accuracy to collect more detailed data. For instance, when a user is stressed, the tracking unit tracks emotional changes in detail to identify the cause of the stress. The tracking unit can also loosen the tracking accuracy to collect more general data if the user is relaxed. For example, when a user is relaxed, the tracking unit roughly tracks emotional changes to evaluate the effect of relaxation. Furthermore, if the user is in a hurry, the tracking unit can adjust the tracking accuracy to quickly collect data. For example, when a user is in a hurry, the tracking unit quickly tracks emotional changes to collect the necessary data. This allows for detailed data collection by providing tracking accuracy that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's emotions into a generating AI, which can then adjust the tracking accuracy.

[0069] The tracking unit can adjust its tracking algorithm by referring to the user's past emotional data during tracking. For example, the tracking unit can improve accuracy by adjusting the tracking algorithm based on the user's past emotional data. For example, it can extract specific patterns from the user's past emotional data and reflect them in the tracking algorithm. The tracking unit can also analyze the user's past emotional data and optimize the parameters of the tracking algorithm. For example, it can improve tracking accuracy by adjusting the parameters of the tracking algorithm based on the user's past emotional data. Furthermore, the tracking unit can adjust tracking accuracy in real time by referring to the user's past emotional data. For example, it can dynamically adjust tracking accuracy by referring to the user's past emotional data in real time. This improves tracking accuracy by optimizing the tracking algorithm based on the user's past emotional data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past emotional data into a generating AI, which can then adjust the tracking algorithm.

[0070] The tracking unit can apply different tracking methods depending on the category of the user's dialogue content during tracking. For example, if the user is having a dialogue about stress, the tracking unit can apply a stress-specific tracking method. For example, when the user is having a dialogue about stress, the tracking unit will track the causes and effects of stress in detail. The tracking unit can also apply a relaxation-specific tracking method if the user is having a dialogue about relaxation. For example, when the user is having a dialogue about relaxation, the tracking unit will track the effects and methods of relaxation in detail. Furthermore, if the user is having a dialogue about health, the tracking unit can apply a health-specific tracking method. For example, when the user is having a dialogue about health, the tracking unit will track the user's health status and methods for improvement in detail. This enables appropriate data collection by providing tracking methods according to the category of the user's dialogue content. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's dialogue content into a generating AI, and the generating AI can apply a tracking method according to the category.

[0071] The tracking unit can estimate the user's emotions and adjust the display method of the tracking results based on the estimated emotions. For example, if the user is nervous, the tracking unit can provide a simple and highly visible display method. For example, when the user is nervous, the tracking unit displays the tracking results using simple graphs or charts. The tracking unit can also provide a display method that includes detailed information if the user is relaxed. For example, when the user is relaxed, the tracking unit displays the tracking results using detailed text or diagrams. Furthermore, if the user is in a hurry, the tracking unit can provide a concise display method. For example, when the user is in a hurry, the tracking unit provides a concise display method that summarizes the key points. By providing a display method that matches the user's emotions, highly visible tracking results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's emotions into a generating AI, which can then adjust how the tracking results are displayed.

[0072] The tracking unit can determine tracking priorities based on when the user's dialogue content was submitted. For example, the tracking unit can prioritize tracking recently submitted dialogue content. For instance, it can prioritize analyzing recently submitted dialogue content and track it based on the latest information. The tracking unit can also prioritize tracking dialogue content submitted by the user during specific time periods. For example, it can prioritize analyzing dialogue content submitted by the user at night and track the nighttime status in detail. Furthermore, if the user submits urgent dialogue content, the tracking unit can prioritize tracking that content. For example, when a user submits urgent dialogue content, the tracking unit prioritizes analyzing that content and responds quickly. This allows for prioritizing tracking of highly urgent dialogue content by determining tracking priorities based on when the user's dialogue content was submitted. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the submission timing of the user's dialogue content into a generating AI, which can then determine the tracking priorities.

[0073] The tracking unit can improve tracking accuracy by referring to the user's relevant literature during tracking. For example, the tracking unit can improve accuracy by adjusting the tracking algorithm based on the relevant literature referenced by the user. For example, it can extract specific patterns from the relevant literature referenced by the user and reflect them in the tracking algorithm. The tracking unit can also extract specific patterns from the relevant literature cited by the user and reflect them in the tracking algorithm. For example, it can improve tracking accuracy by adjusting the parameters of the tracking algorithm based on the relevant literature cited by the user. Furthermore, the tracking unit can analyze the relevant literature referenced by the user and optimize the parameters of the tracking algorithm. For example, it can improve tracking accuracy by adjusting the parameters of the tracking algorithm based on the relevant literature referenced by the user. This improves tracking accuracy based on the user's relevant literature, enabling the collection of detailed data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's relevant literature into a generating AI, which can then adjust the tracking algorithm.

[0074] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider can offer advice in gentle language. For instance, when the user is stressed, the service provider can suggest relaxation methods in gentle language. The service provider can also offer advice with detailed explanations if the user is relaxed. For example, when the user is relaxed, the service provider can offer stress management advice with detailed explanations. Furthermore, if the user is in a hurry, the service provider can offer concise and to-the-point advice. For example, when the user is in a hurry, the service provider can suggest concise and to-the-point relaxation methods. This deepens the user's understanding by providing advice in a way that is appropriate to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotions into a generating AI, which can then adjust how the advice is expressed.

[0075] The service provider can adjust the level of detail of the advice based on the user's emotions and state at the time of delivery. For example, if the user is stressed, the service provider can provide concise and easy-to-understand advice. For example, when the user is stressed, the service provider can suggest concise and to-the-point relaxation methods. The service provider can also provide advice with detailed explanations if the user is relaxed. For example, when the user is relaxed, the service provider can provide stress management advice with detailed explanations. Furthermore, if the user is in a hurry, the service provider can provide to-the-point advice. For example, when the user is in a hurry, the service provider can suggest concise and to-the-point relaxation methods. This allows for the provision of appropriate advice by adjusting the level of detail according to the user's emotions and state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotions and state into the generating AI, which can then adjust the level of detail in the advice.

[0076] The service provider can apply different advice algorithms depending on the user's emotional and state categories at the time of delivery. For example, if the user is seeking advice about stress, the service provider can apply an advice algorithm specifically for stress management. For example, when the user is seeking advice about stress, the service provider can suggest methods for stress management. The service provider can also apply an advice algorithm specifically for relaxation if the user is seeking advice about relaxation. For example, when the user is seeking advice about relaxation, the service provider can suggest methods for relaxation. Furthermore, if the user is seeking advice about health, the service provider can apply an advice algorithm specifically for health. For example, when the user is seeking advice about health, the service provider can suggest methods for health management. In this way, appropriate advice can be provided by offering advice algorithms that correspond to the user's emotional and state categories. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotional and state into a generating AI, and the generating AI can apply an advice algorithm according to the category.

[0077] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. For instance, when the user is in a hurry, the service provider can suggest a simple, concise relaxation method. The service provider can also provide longer advice with more detailed explanations if the user is relaxed. For instance, when the user is relaxed, the service provider can provide stress management advice with detailed explanations. Furthermore, if the user is agitated, the service provider can provide advice with visually stimulating effects. For instance, when the user is agitated, the service provider can suggest a relaxation method with visually stimulating effects. This enhances user understanding by providing advice of appropriate length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's emotions into a generating AI, which can then adjust the length of the advice.

[0078] The service provider can prioritize advice based on the timing of the user's emotional and state submission. For example, if a user is seeking urgent advice, the service provider can prioritize that advice and respond quickly. The service provider can also provide advice tailored to the time of day if the user is seeking advice at a specific time. For example, if a user is seeking advice at night, the service provider will provide advice appropriate for that time of day. Furthermore, the service provider can prioritize relevant advice based on the user's past consultations. For example, if a user has previously sought advice regarding stress, the service provider will prioritize advice on stress management. This allows for the priority of urgent advice by prioritizing advice based on the timing of the user's emotional and state submission. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the timing of the user's emotional and state submission into a generating AI, which can then determine the priority of advice.

[0079] The service provider can adjust the order of advice based on the relationship between the user's emotions and state at the time of delivery. For example, if the user is feeling stressed, the service provider can first provide advice on stress management. For example, when the user is feeling stressed, the service provider will first suggest methods for stress management. The service provider can also first provide advice on relaxation if the user is seeking relaxation. For example, when the user is seeking relaxation, the service provider will first suggest methods for relaxation. Furthermore, if the service provider is seeking health-related advice, the service provider can first provide health-related advice. For example, when the user is seeking health-related advice, the service provider will first suggest methods for health management. This allows the service provider to provide appropriate advice by adjusting the order of advice based on the relationship between the user's emotions and state. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotions and state into a generating AI, which can then adjust the order of advice.

[0080] The cohesion unit can estimate a user's emotions and adjust the community cohesion method based on the estimated emotions. For example, if a user is feeling lonely, the cohesion unit can prioritize providing conversations that facilitate interaction with other users. For instance, when a user is feeling lonely, the cohesion unit can suggest chat rooms or forums to facilitate interaction with other users. Furthermore, if a user is feeling stressed, the cohesion unit can suggest support groups to alleviate that stress. For example, when a user is feeling stressed, the cohesion unit can suggest support groups to alleviate that stress. Additionally, if a user is relaxed, the cohesion unit can suggest community activities with a relaxed atmosphere. For example, when a user is relaxed, the cohesion unit can suggest online events or group chats with a relaxed atmosphere. This improves user satisfaction by providing community cohesion methods tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the coordinating unit may be performed using AI, for example, or without AI. For example, the coordinating unit can input user emotions into a generating AI, which can then adjust how the community is cohesive.

[0081] The coordinating unit can select the optimal coordinating method by referring to the user's past community activities during coordinating. For example, the coordinating unit can suggest relevant activities based on the community activities the user has participated in in the past. For example, it can suggest relevant activities based on online events and group chats the user has participated in in the past. The coordinating unit can also extract specific patterns from the user's past community activities and suggest the optimal coordinating method. For example, it can extract patterns of activities the user has preferred to participate in in the past and suggest the optimal coordinating method. Furthermore, the coordinating unit can prioritize suggesting activities the user has preferred to participate in in the past. For example, it can prioritize suggesting online events and group chats the user has preferred to participate in in the past. This improves user satisfaction by providing the optimal coordinating method based on the user's past community activities. Some or all of the above processing in the coordinating unit may be performed using AI, for example, or without AI. For example, the coordinating unit can input the user's past community activities into a generating AI, which can then select the optimal coordinating method.

[0082] The bonding unit can customize bonding methods based on the user's current psychological state during bonding. For example, if the user is feeling anxious, the bonding unit can suggest bonding methods that provide a sense of security. For instance, when the user is feeling anxious, the bonding unit can suggest online events or group chats that provide a sense of security. The bonding unit can also suggest bonding methods that promote calmness if the user is feeling agitated. For example, when the user is agitated, the bonding unit can suggest forums or support groups that promote calmness. Furthermore, if the user is feeling tired, the bonding unit can suggest bonding methods that promote relaxation. For example, when the user is tired, the bonding unit can suggest online events or group chats that promote relaxation. By providing bonding methods that are tailored to the user's psychological state, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the bonding unit may be performed using AI, for example, or without AI. For example, the binding unit can input the user's psychological state into a generating AI, which can then customize the binding method.

[0083] The cohesion unit can estimate a user's emotions and determine the priority of community cohesion based on those emotions. For example, if a user is feeling lonely, the cohesion unit can prioritize promoting interaction with other users. For instance, when a user is feeling lonely, the cohesion unit can suggest chat rooms or forums to facilitate interaction with other users. Similarly, if a user is feeling stressed, the cohesion unit can prioritize suggesting support groups to alleviate stress. For example, when a user is stressed, the cohesion unit can suggest support groups to reduce stress. Furthermore, if a user is relaxed, the cohesion unit can prioritize suggesting community activities with a relaxed atmosphere. For example, when a user is relaxed, the cohesion unit can suggest online events or group chats with a relaxed atmosphere. This improves user satisfaction by providing community cohesion priorities that align with the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the coordinating unit may be performed using AI, for example, or without AI. For example, the coordinating unit can input user emotions into a generating AI, which can then determine the priority of community coordinating.

[0084] The bundling unit can select the optimal bundling method when bundling users, taking into account the user's geographical location information. For example, if the user is in a specific region, the bundling unit can suggest community activities related to that region. For instance, when the user is in a specific region, the bundling unit can suggest online events or group chats related to that region. Also, if the user is traveling, the bundling unit can suggest community activities related to their travel destination. For example, when the user is traveling, the bundling unit can suggest online events or group chats related to their travel destination. Furthermore, if the user is at home, the bundling unit can suggest community activities related to information around their home. For example, when the user is at home, the bundling unit can suggest online events or group chats related to information around their home. By providing the optimal bundling method based on the user's geographical location information, user satisfaction is improved. Some or all of the above processing in the bundling unit may be performed using AI, for example, or without AI. For example, the bundling unit can input the user's geographical location information into a generating AI, which can then select the optimal bundling method.

[0085] The bonding unit can analyze a user's social media activity and suggest bonding methods during the bonding process. For example, the bonding unit can suggest relevant community activities based on content shared by the user on social media. For instance, if a user posts about stress on social media, the bonding unit will suggest online events or group chats related to stress. The bonding unit can also suggest relevant community activities based on topics followed by the user on social media. For example, if a user follows topics related to health, the bonding unit will suggest online events or group chats related to health. Furthermore, the bonding unit can suggest relevant community activities based on groups the user participates in on social media. For example, if a user participates in a mental health group, the bonding unit will suggest online events or group chats related to mental health. This improves user satisfaction by providing the most suitable bonding method based on the user's social media activity. Some or all of the above processing in the bonding unit may be performed using AI, for example, or without AI. For example, the bonding unit can input the user's social media activity into a generating AI, which can then suggest the most suitable bonding method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, tracking unit, provision unit, and bundling unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives anonymous conversations from the user. The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the user's emotions and state using natural language processing technology. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides custom advice and resources. The bundling unit is implemented by the control unit 46A of the smart device 14 and provides a platform in which the user can interact anonymously with other users. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, tracking unit, provision unit, and bundling unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives anonymous conversations from the user. The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the user's emotions and state using natural language processing technology. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides custom advice and resources. The bundling unit is implemented by the control unit 46A of the smart glasses 214 and provides a platform on which the user can interact anonymously with other users. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, tracking unit, provision unit, and bundling unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives anonymous conversations from the user. The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the user's emotions and state using natural language processing technology. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides custom advice and resources. The bundling unit is implemented by the control unit 46A of the headset terminal 314 and provides a platform in which the user can interact anonymously with other users. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, tracking unit, provision unit, and bundling unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives anonymous conversations from users. The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the user's emotions and state using natural language processing technology. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides custom advice and resources. The bundling unit is implemented by the control unit 46A of the robot 414 and provides a platform where users can interact anonymously with other users.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can analyze the user's conversation content and suggest conversation topics based on the user's interests. For example, if the user is talking about their hobbies or special skills, the reception desk can suggest conversation topics related to those hobbies or skills. Furthermore, if the user shows interest in a particular topic, the reception desk can provide information and resources related to that topic. In addition, the reception desk can suggest topics that the user might develop new interests in. This improves the user's conversation experience by providing conversation topics based on their interests.

[0088] The tracking unit can analyze the user's conversations and estimate their health status. For example, if a user frequently states that they feel tired or stressed, the tracking unit can estimate that the user's health is deteriorating. Conversely, if a user frequently expresses positive emotions, the tracking unit can also estimate that the user's health is good. Furthermore, the tracking unit can provide health-related advice based on the user's conversations. This allows the system to understand the user's health status and provide appropriate advice, thereby supporting the user's health management.

[0089] The service provider can analyze user interactions and adjust the advice delivery method based on the user's learning style. For example, if a user prefers visual information, the service provider can provide advice using diagrams and charts. If a user prefers auditory information, the service provider can provide advice in the form of audio messages or podcasts. Furthermore, if a user prefers a practical approach, the service provider can provide specific action plans and step-by-step guides. This allows for advice tailored to the user's learning style, thereby promoting understanding and practice.

[0090] The service provider can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is feeling stressed, the service provider can immediately offer advice on relaxation techniques. If the user is relaxed, the service provider can also offer advice on new challenges or goal setting. Furthermore, if the user is in a hurry, the service provider can offer actionable advice that can be implemented in a short amount of time. This allows for support tailored to the user's needs by providing advice at a time that matches their emotions.

[0091] The Coordination Unit can analyze user conversations and suggest community activities based on the user's interests. For example, if a user has a specific hobby or interest, the Coordination Unit can suggest online events or group chats related to that hobby or interest. It can also suggest topics that the user might develop new interests in. Furthermore, if a user shares common interests with other users, the Coordination Unit can facilitate interaction based on those shared interests. By providing community activities tailored to the user's interests, it can improve user satisfaction.

[0092] The reception desk can analyze the content of the user's conversation and adjust the way the conversation progresses based on the user's purpose. For example, if the user's purpose is problem solving, the reception desk will ask specific questions and make suggestions aimed at problem solving. If the user's purpose is relaxation, the reception desk can also suggest conversation topics and relaxation methods that promote relaxation. Furthermore, if the user's purpose is information gathering, the reception desk can provide relevant information and resources. In this way, by providing a conversation approach that matches the user's purpose, it is possible to provide a conversation experience that meets the user's needs.

[0093] The tracking unit analyzes the user's dialogue and can track changes in the user's emotions in real time. For example, if the user uses words that indicate a change in emotion during the dialogue, the tracking unit can immediately detect that change. Furthermore, if the user uses facial expressions or tone of voice that indicate a change in emotion during the dialogue, the tracking unit can also track those changes in real time. In addition, the tracking unit can adjust the way the dialogue progresses based on the user's emotional changes. This allows for real-time tracking of the user's emotional changes and appropriate responses, thereby improving the user's dialogue experience.

[0094] The service provider can analyze the user's conversation and tailor the advice based on the user's purpose. For example, if the user's goal is problem solving, the service provider can provide specific solutions and step-by-step guides. If the user's goal is relaxation, the service provider can also provide relaxation methods and resources. Furthermore, if the user's goal is information gathering, the service provider can provide relevant information and resources. By providing advice tailored to the user's purpose, the service provider can offer support that meets the user's needs.

[0095] The Coordination Unit can analyze user conversations and tailor community activity suggestions based on the user's purpose. For example, if a user's goal is problem-solving, the Coordination Unit can suggest support groups or forums that can help solve the problem. If a user's goal is relaxation, the Coordination Unit can also suggest online events or group chats that can help with relaxation. Furthermore, if a user's goal is information gathering, the Coordination Unit can provide relevant information and resources. By providing community activities that match the user's purpose, user satisfaction can be improved.

[0096] The Coordination Unit can estimate a user's emotions and adjust its community activity suggestions based on those emotions. For example, if a user is feeling lonely, the Coordination Unit can suggest online events or group chats to facilitate interaction with other users. If a user is feeling stressed, the Coordination Unit can also suggest support groups or relaxation events to alleviate stress. Furthermore, if a user is relaxed, the Coordination Unit can suggest community activities with a relaxed atmosphere. By providing community activities that match the user's emotions, user satisfaction can be improved.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk accepts anonymous conversations from users. Users can engage in anonymous conversations via chat, voice calls, video calls, etc. The reception desk accepts conversations completely anonymously, without requiring the user's name or personal information. Step 2: The tracking unit uses natural language processing technology to analyze the dialogue content received by the reception unit and track the user's emotions and state. For example, it uses technologies such as morphological analysis, grammatical analysis, and semantic analysis to analyze the user's dialogue content in detail. Step 3: The service provider provides customized advice and resources based on the emotions and states tracked by the tracking service provider. For example, if the user is feeling stressed, it provides advice on relaxation methods and stress management. It also provides resources if the user needs specific resources. Step 4: The Cohesion team fosters community cohesion based on the advice and resources provided by the Provider team. For example, they provide a platform where users can interact anonymously with other users and feel empathy and support.

[0099] 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.

[0100] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0101] 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.

[0102] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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. 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.

[0117] 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.

[0118] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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. 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.

[0133] 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.

[0134] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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. 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.

[0150] 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.

[0151] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] [Explanation of symbols]

[0171] 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 that accepts anonymous messages from users, A tracking unit analyzes the content of the conversation received by the reception unit and tracks the user's emotions and state. A provisioning unit that provides custom advice and resources based on the emotions and states tracked by the aforementioned tracking unit, The system comprises a cohesion unit that promotes community cohesion based on advice or resources provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned tracking unit is Using natural language processing technology, we analyze user conversations and track their emotions and states. The system according to feature 1.

3. The aforementioned supply unit is, Provides relaxation methods or stress management advice based on the user's emotions and state. The system according to feature 1.

4. The aforementioned supply unit is, If a user needs a specific resource, provide that resource. The system according to feature 1.

5. The aforementioned binding portion is We provide a platform that allows users to interact with other users anonymously. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the way it handles interactions based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past interaction history and select the appropriate reception method. The system according to feature 1.

8. The aforementioned reception unit is When accepting a conversation request, filtering is performed based on the user's current psychological state. The system according to feature 1.

Images