system

Abstract

The system according to this embodiment aims to provide appropriate advice and learning content to people who care for individuals with mental health issues. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of the user's situation and challenges. The analysis unit analyzes the information received by the reception unit. The generation unit generates advice and learning content based on the information analyzed by the analysis unit. The provision unit provides the advice and learning content generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for people caring for the mentally ill to obtain appropriate advice and learning content, and they are likely to be stressed.

[0005] The system according to the embodiment aims to provide appropriate advice and learning content to people caring for the mentally ill.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of the user's situation and challenges. The analysis unit analyzes the information received by the reception unit. The generation unit generates advice and learning content based on the information analyzed by the analysis unit. The provision unit provides the advice and learning content generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate advice and learning content to people who care for individuals with mental health issues. [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 mental care platform according to an embodiment of the present invention is a platform for providing appropriate advice and learning to people who care for individuals with mental health problems (counselors, family members, colleagues) and caregivers, taking into consideration the high likelihood that they themselves may experience mental health problems or high levels of stress. This platform utilizes a multimodal large-scale language model (LLM) to learn from information such as text, audio, images, and videos, and provides advice and learning content tailored to the user's needs. For example, it provides customized content such as stress management, relaxation techniques, and self-improvement content for mindset change. First, the user inputs their situation and challenges. For example, they input a specific situation such as, "I've been feeling stressed at work lately." This information is input into the multimodal LLM. Next, the multimodal LLM analyzes the input information and generates optimal advice and learning content for the user. For example, relaxation techniques for stress management and self-improvement content for mindset change are generated. The generated advice and learning content are provided to the user. For example, videos on relaxation techniques and text content for self-improvement are provided. This allows the user to effectively manage their own mental health. This system allows those who care for individuals with mental health issues and caregivers to reduce their own mental health problems and stress, and to receive appropriate mental care. For example, counselors can learn and practice relaxation techniques to manage their own stress, enabling them to provide more effective counseling. Similarly, caregivers can improve their awareness and provide better care services by learning self-improvement content. In this way, by utilizing multimodal LLM, those who care for individuals with mental health issues and caregivers can effectively manage their own mental health and maintain their physical and mental well-being. This supports them in leading fulfilling lives. As a result, the mental care platform can effectively provide mental care by offering advice and learning content tailored to the user's situation and challenges.

[0029] The mental care platform according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of the user's situation and challenges. The user's situation and challenges include, but are not limited to, work stress, learning difficulties, and health problems. For example, the reception unit accepts input from the user of a specific situation, such as "I've been feeling stressed at work lately." The reception unit can also accept input from the user of the situation and challenges using voice input or image input. For example, it can accept the user saying "I've been feeling stressed at work lately" aloud. It can also accept the user explaining the situation using images or videos. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, but is not limited to, methods such as text analysis, sentiment analysis, and data mining. For example, the analysis unit analyzes the user's input using text analysis. The analysis unit can also analyze the user's emotions using sentiment analysis. The analysis unit can also analyze the user's past data using data mining. The generation unit generates advice and learning content based on the information analyzed by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit uses a generation AI to generate relaxation techniques for stress management. The generation unit can also use a generation AI to generate self-improvement content for mindset change. Furthermore, the generation unit can use a generation AI to generate customized advice tailored to the user's situation. The provision unit provides the advice and learning content generated by the generation unit. Provision is performed in, for example, text, audio, images, videos, etc., but is not limited to such examples. For example, the provision unit provides a video of the generated relaxation techniques. Furthermore, the provision unit can also provide generated text content for self-improvement. Furthermore, the provision unit can provide the generated advice in audio format. In this way, the mental care platform according to the embodiment can effectively provide mental care by providing advice and learning content tailored to the user's situation and challenges.Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without AI. For example, the reception unit may input user input into the AI, the analysis unit may analyze the input using the AI, the generation unit may generate advice and learning content using the AI, and the provision unit may provide the content generated using the AI.

[0030] The reception desk receives input from users regarding their situation and challenges. These challenges may include, but are not limited to, work stress, learning difficulties, or health problems. For example, the reception desk accepts specific information such as, "I've been feeling stressed at work lately." It can also accept input from users via voice or image. For instance, it can accept a voice message stating, "I've been feeling stressed at work lately." It can also accept explanations using images or videos. The reception desk utilizes natural language processing (NLP) technology to process user input quickly and accurately. NLP technology analyzes user input and categorizes it appropriately. For example, if a user inputs "I've been feeling stressed at work lately," NLP technology categorizes it as "work stress" and sends it to the analysis department. In the case of voice input, speech recognition technology converts the voice data into text data and similarly sends it to the analysis department. For image and video input, image recognition technology analyzes the user's facial expressions and environment to understand their situation. This allows the reception desk to accommodate diverse user input methods and collect accurate information. Furthermore, the reception desk has a function to encrypt and securely store entered information to protect user privacy. This allows users to confidently enter their situation and concerns.

[0031] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as text analysis, sentiment analysis, and data mining, but is not limited to these examples. For instance, the analysis unit uses text analysis to analyze user input. It can also analyze user emotions using sentiment analysis. Furthermore, it can analyze user past data using data mining. The analysis unit combines these analysis methods to comprehensively understand the user's situation and challenges. For example, text analysis extracts keywords from user input to identify the type and severity of the problems the user is facing. Sentiment analysis analyzes emotional tone from user input and audio data to evaluate the user's emotional state. Data mining analyzes the user's past input data and behavioral history to identify user tendencies and patterns. This allows the analysis unit to analyze the user's situation and challenges from multiple angles and provide more accurate information to the generation unit. Furthermore, the analysis unit can continuously improve analysis accuracy using machine learning algorithms. For example, it can train algorithms based on past analysis results and user feedback to improve analysis accuracy. This allows the analysis unit to more accurately understand the user's situation and challenges, and to provide a foundation for generating appropriate advice and learning content.

[0032] The generation unit generates advice and learning content based on the information analyzed by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit can use a generation AI to generate relaxation techniques for stress management. The generation unit can also use a generation AI to generate self-improvement content for mindset change. Furthermore, the generation unit can use a generation AI to generate customized advice tailored to the user's situation. The generation unit takes the information provided by the analysis unit as prompts for the generation AI and generates advice and content best suited to the user's situation and challenges. For example, if the user inputs "I've been feeling stressed at work lately," the generation AI will suggest relaxation techniques for stress management and activities to relieve stress. Also, if the user is having difficulty learning, the generation AI will generate effective learning methods and self-improvement content to boost motivation. The generation unit scrutinizes the output of the generation AI and performs appropriate filtering before providing it to the user. This allows the generation unit to provide users with high-quality advice and content. In addition, the generation unit can adjust the prompts and algorithms of the generation AI based on user feedback to improve generation accuracy. This allows the generation unit to continuously provide customized advice and content tailored to the user's situation and challenges.

[0033] The delivery unit provides advice and learning content generated by the generation unit. This delivery can be in various formats, including, but is not limited to, text, audio, images, and videos. For example, the delivery unit can provide generated relaxation videos. It can also provide generated self-improvement text content. Furthermore, it can provide generated advice in audio format. The delivery unit provides content in the most suitable format according to the user's preferences and circumstances. For example, if the user prefers visual information, it provides content in video or image format; if the user prefers auditory information, it provides content in audio format. The delivery unit delivers content in a format suitable for the user's device, ensuring easy access. In addition, the delivery unit collects user feedback to continuously improve the quality of the content it provides. For example, it provides a feature that allows users to leave ratings and comments on the provided content, and uses this feedback to improve the content. The delivery unit can also analyze the user's usage history to provide personalized content tailored to their preferences and needs. This allows the delivery unit to provide advice and learning content in the most suitable format and content for the user, effectively supporting mental care.

[0034] The generation unit can generate relaxation techniques for stress management. For example, it can generate breathing exercises. For example, it can generate content explaining how to take deep breaths. The generation unit can also generate meditation. For example, it can generate content explaining the steps of meditation. The generation unit can also generate yoga. For example, it can generate content explaining yoga poses. By generating relaxation techniques for stress management, the user's stress can be reduced. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user input into a generation AI and use the generation AI to generate relaxation techniques.

[0035] The generation unit can generate self-improvement content for mindset change. For example, the generation unit can generate videos to improve motivation. For example, the generation unit can generate videos introducing success stories. The generation unit can also generate content explaining methods of self-assessment. For example, the generation unit can generate content explaining the steps of self-assessment. The generation unit can also generate content explaining methods of goal setting. For example, the generation unit can generate content explaining the steps of goal setting. In this way, by generating self-improvement content for mindset change, the user's awareness can be improved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input user input into a generation AI and use the generation AI to generate self-improvement content.

[0036] The service provider can provide generated relaxation technique videos. For example, the service provider can provide yoga instruction videos. For example, the service provider can provide videos explaining yoga poses. The service provider can also provide meditation guide videos. For example, the service provider can provide videos explaining the steps of meditation. The service provider can also provide breathing technique videos. For example, the service provider can provide videos explaining how to take deep breaths. By providing relaxation technique videos, users can learn relaxation techniques visually. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide generated relaxation technique videos using AI.

[0037] The service provider can provide generated text content for self-improvement. For example, the service provider can provide essays for self-improvement. For example, the service provider can provide essays introducing success stories. The service provider can also provide text content explaining methods of self-assessment. For example, the service provider can provide text content explaining the steps of self-assessment. The service provider can also provide text content explaining methods of goal setting. For example, the service provider can provide text content explaining the steps of goal setting. In this way, by providing text content for self-improvement, users can engage in self-improvement. 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 provide generated text content for self-improvement using AI.

[0038] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest similar input methods by referring to content the user has entered in the past. In this way, the optimal input method can be suggested by analyzing the user's past input 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 input history into AI and select the optimal input method.

[0039] The reception desk can filter input content based on the user's current situation and challenges. For example, if the user is feeling stressed, the reception desk will prioritize input content related to stress management. Similarly, if the user is relaxed, the reception desk can prioritize input content related to relaxation techniques. Furthermore, if the user is seeking a change in mindset, the reception desk can prioritize input content related to self-improvement. This allows for the collection of more relevant information by filtering input content based on the user's current situation and challenges. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and challenges into an AI and have the AI ​​perform the filtering of the input content.

[0040] The reception desk can prioritize receiving input content that is 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 stress management methods related to that region. Similarly, if the user is in a specific location, the reception desk can prioritize receiving relaxation methods related to that location. Furthermore, if the user is in a specific environment, the reception desk can prioritize receiving self-improvement content related to that environment. This allows for the collection of more relevant information by considering 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 instance, the reception desk can input the user's geographical location into AI and prioritize receiving input content that is highly relevant.

[0041] The reception desk can analyze a user's social media activity and accept relevant input. For example, if a user posts on social media expressing stress, the reception desk will prioritize accepting input related to stress management. Similarly, if a user shows interest in relaxation techniques on social media, the reception desk can prioritize accepting input related to relaxation techniques. Furthermore, if a user shows interest in self-improvement on social media, the reception desk can prioritize accepting input related to self-improvement. This allows for the collection of more relevant information by analyzing a user's social media activity. Some or all of the processing described above in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity into AI and accept relevant input.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's past data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past stress management data. It can also improve the accuracy of its analysis by referring to the user's past relaxation methods data. Furthermore, it can improve the accuracy of its analysis by referring to the user's past self-improvement data. In this way, the accuracy of the analysis can be improved by referring to the user's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI to improve the accuracy of its analysis.

[0043] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can analyze appropriate stress management methods while considering the user's age. It can also analyze appropriate relaxation methods while considering the user's gender. Furthermore, the analysis unit can analyze appropriate self-improvement content while considering the user's occupation. By considering the user's attribute information, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's attribute information into AI and perform the analysis.

[0044] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user is in a specific region, the analysis unit can analyze stress management methods related to that region. It can also analyze relaxation methods related to a specific location if the user is in that location. Furthermore, if a user is in a specific environment, the analysis unit can analyze self-improvement content related to that environment. This allows for more appropriate analysis results by considering the geographical distribution of users. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of users into AI and perform the analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research literature on stress management. It can also improve the accuracy of its analysis by referring to the latest research literature on relaxation techniques. It can also improve the accuracy of its analysis by referring to the latest research literature on self-improvement. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature into AI to improve the accuracy of its analysis.

[0046] The generation unit can adjust the level of detail of the generated content based on the importance of the user's problem during generation. For example, if the user's problem is important, the generation unit will generate detailed content. If the user's problem is minor, the generation unit can also generate concise content. If the user's problem is moderate, the generation unit can also generate content with a moderate level of detail. By adjusting the level of detail of the content based on the importance of the user's problem, more appropriate content can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the importance of the user's problem into the generation AI and adjust the level of detail of the generated content.

[0047] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is seeking stress management, the generation unit can apply a generation algorithm specialized in stress management. Similarly, if the user is seeking relaxation techniques, the generation unit can apply a generation algorithm specialized in relaxation techniques. Furthermore, if the user is seeking self-improvement, the generation unit can apply a generation algorithm specialized in self-improvement. This allows for the provision of more appropriate content by applying different generation algorithms according to the user's category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's category into a generation AI and apply a different generation algorithm.

[0048] The generation unit can determine the priority of content to generate based on the user's submission timing during the generation process. For example, if the user is in a hurry, the generation unit will prioritize content that can be generated quickly. Alternatively, if the user has ample time, the generation unit can prioritize detailed content. Furthermore, if the user has set a specific deadline, the generation unit can generate content to meet that deadline. This allows for the delivery of content at a more appropriate time by prioritizing content based on the user's submission timing. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's submission timing into the generation AI to determine the priority of content to generate.

[0049] The generation unit can adjust the order of content generated based on user relevance during the generation process. For example, the generation unit may prioritize generating content related to stress management. It can also prioritize generating content related to relaxation techniques. Furthermore, it can prioritize generating content related to self-improvement. By adjusting the order of content based on user relevance, more relevant information can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user relevance into the generation AI and adjust the order of content to be generated.

[0050] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also predict and suggest a specific display method based on the user's past operation history. Furthermore, the service provider can provide the optimal display method based on the display methods that the user has preferred to use in the past. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past operation history into AI and select the optimal display method.

[0051] The service provider can adjust the display method based on the user's current situation at the time of delivery. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. By adjusting the display method based on the user's current situation, a more appropriate display becomes possible. 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 current situation into AI and adjust the display method.

[0052] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Also, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. 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 device information into AI and select the optimal display method.

[0053] The service provider can provide multilingual content at the time of delivery, according to the user's language settings. For example, the service provider can automatically set the language of the content based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide content in a specific language if the user selects that language. This allows the service provider to accommodate a larger number of users by providing multilingual content according to the user's language settings. 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 language settings into AI and provide multilingual content.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze the user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest input methods that the user will use at specific times based on their past input history. Furthermore, it can suggest similar input methods by referring to content the user has entered in the past. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into AI and select the optimal input method.

[0056] The reception desk can filter input content based on the user's current situation and challenges. For example, if the user is feeling stressed, it can prioritize input related to stress management. Similarly, if the user is relaxed, it can prioritize input related to relaxation techniques. Furthermore, if the user is seeking a change in mindset, it can prioritize input related to self-improvement. This allows for the collection of more relevant information by filtering input content based on the user's current situation and challenges. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation and challenges into the AI ​​and have the AI ​​perform the filtering of the input content.

[0057] The reception desk can prioritize receiving input content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving stress management methods related to that region. Similarly, if the user is in a specific location, it can prioritize receiving relaxation methods related to that location. Furthermore, if the user is in a specific environment, it can prioritize receiving self-improvement content related to that environment. This allows for the collection of more relevant information by considering 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 AI and prioritize receiving input content that is highly relevant.

[0058] The analysis unit can improve the accuracy of its analysis by referring to the user's past data during the analysis process. For example, it can improve the accuracy of the analysis by referring to the user's past stress management data. It can also improve the accuracy of the analysis by referring to the user's past relaxation methods. Furthermore, it can improve the accuracy of the analysis by referring to the user's past self-improvement data. In this way, the accuracy of the analysis can be improved by referring to the user's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI to improve the accuracy of the analysis.

[0059] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. 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 device information into AI and select the optimal display method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives input from the user about their situation and challenges. These challenges may include, for example, work stress, learning difficulties, or health problems. The reception desk accepts the user to input specific details of their situation. It can also accept input using voice or image input. For example, the reception desk can accept a user saying, "I've been feeling stressed at work lately," or explain their situation using images or videos. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and data mining. The analysis unit can analyze the user's input using text analysis, analyze the user's emotions using sentiment analysis, and analyze the user's past data using data mining. Step 3: The generation unit generates advice and learning content based on the information analyzed by the analysis unit. Generation is performed, for example, using a generation AI. The generation unit can use the generation AI to generate relaxation methods for stress management and self-improvement content for mindset change. It can also generate customized advice tailored to the user's situation. Step 4: The provider unit provides the advice and learning content generated by the generator unit. The provision can be in the form of text, audio, images, or videos. The provider unit can provide generated relaxation videos, self-improvement text content, or advice in audio format.

[0062] (Example of form 2) The mental care platform according to an embodiment of the present invention is a platform for providing appropriate advice and learning to people who care for individuals with mental health problems (counselors, family members, colleagues) and caregivers, taking into consideration the high likelihood that they themselves may experience mental health problems or high levels of stress. This platform utilizes a multimodal large-scale language model (LLM) to learn from information such as text, audio, images, and videos, and provides advice and learning content tailored to the user's needs. For example, it provides customized content such as stress management, relaxation techniques, and self-improvement content for mindset change. First, the user inputs their situation and challenges. For example, they input a specific situation such as, "I've been feeling stressed at work lately." This information is input into the multimodal LLM. Next, the multimodal LLM analyzes the input information and generates optimal advice and learning content for the user. For example, relaxation techniques for stress management and self-improvement content for mindset change are generated. The generated advice and learning content are provided to the user. For example, videos on relaxation techniques and text content for self-improvement are provided. This allows the user to effectively manage their own mental health. This system allows those who care for individuals with mental health issues and caregivers to reduce their own mental health problems and stress, and to receive appropriate mental care. For example, counselors can learn and practice relaxation techniques to manage their own stress, enabling them to provide more effective counseling. Similarly, caregivers can improve their awareness and provide better care services by learning self-improvement content. In this way, by utilizing multimodal LLM, those who care for individuals with mental health issues and caregivers can effectively manage their own mental health and maintain their physical and mental well-being. This supports them in leading fulfilling lives. As a result, the mental care platform can effectively provide mental care by offering advice and learning content tailored to the user's situation and challenges.

[0063] The mental care platform according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of the user's situation and challenges. The user's situation and challenges include, but are not limited to, work stress, learning difficulties, and health problems. For example, the reception unit accepts input from the user of a specific situation, such as "I've been feeling stressed at work lately." The reception unit can also accept input from the user of the situation and challenges using voice input or image input. For example, it can accept the user saying "I've been feeling stressed at work lately" aloud. It can also accept the user explaining the situation using images or videos. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, but is not limited to, methods such as text analysis, sentiment analysis, and data mining. For example, the analysis unit analyzes the user's input using text analysis. The analysis unit can also analyze the user's emotions using sentiment analysis. The analysis unit can also analyze the user's past data using data mining. The generation unit generates advice and learning content based on the information analyzed by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit uses a generation AI to generate relaxation techniques for stress management. The generation unit can also use a generation AI to generate self-improvement content for mindset change. Furthermore, the generation unit can use a generation AI to generate customized advice tailored to the user's situation. The provision unit provides the advice and learning content generated by the generation unit. Provision is performed in, for example, text, audio, images, videos, etc., but is not limited to such examples. For example, the provision unit provides a video of the generated relaxation techniques. Furthermore, the provision unit can also provide generated text content for self-improvement. Furthermore, the provision unit can provide the generated advice in audio format. In this way, the mental care platform according to the embodiment can effectively provide mental care by providing advice and learning content tailored to the user's situation and challenges.Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without AI. For example, the reception unit may input user input into the AI, the analysis unit may analyze the input using the AI, the generation unit may generate advice and learning content using the AI, and the provision unit may provide the content generated using the AI.

[0064] The reception desk receives input from users regarding their situation and challenges. These challenges may include, but are not limited to, work stress, learning difficulties, or health problems. For example, the reception desk accepts specific information such as, "I've been feeling stressed at work lately." It can also accept input from users via voice or image. For instance, it can accept a voice message stating, "I've been feeling stressed at work lately." It can also accept explanations using images or videos. The reception desk utilizes natural language processing (NLP) technology to process user input quickly and accurately. NLP technology analyzes user input and categorizes it appropriately. For example, if a user inputs "I've been feeling stressed at work lately," NLP technology categorizes it as "work stress" and sends it to the analysis department. In the case of voice input, speech recognition technology converts the voice data into text data and similarly sends it to the analysis department. For image and video input, image recognition technology analyzes the user's facial expressions and environment to understand their situation. This allows the reception desk to accommodate diverse user input methods and collect accurate information. Furthermore, the reception desk has a function to encrypt and securely store entered information to protect user privacy. This allows users to confidently enter their situation and concerns.

[0065] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as text analysis, sentiment analysis, and data mining, but is not limited to these examples. For instance, the analysis unit uses text analysis to analyze user input. It can also analyze user emotions using sentiment analysis. Furthermore, it can analyze user past data using data mining. The analysis unit combines these analysis methods to comprehensively understand the user's situation and challenges. For example, text analysis extracts keywords from user input to identify the type and severity of the problems the user is facing. Sentiment analysis analyzes emotional tone from user input and audio data to evaluate the user's emotional state. Data mining analyzes the user's past input data and behavioral history to identify user tendencies and patterns. This allows the analysis unit to analyze the user's situation and challenges from multiple angles and provide more accurate information to the generation unit. Furthermore, the analysis unit can continuously improve analysis accuracy using machine learning algorithms. For example, it can train algorithms based on past analysis results and user feedback to improve analysis accuracy. This allows the analysis unit to more accurately understand the user's situation and challenges, and to provide a foundation for generating appropriate advice and learning content.

[0066] The generation unit generates advice and learning content based on the information analyzed by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit can use a generation AI to generate relaxation techniques for stress management. The generation unit can also use a generation AI to generate self-improvement content for mindset change. Furthermore, the generation unit can use a generation AI to generate customized advice tailored to the user's situation. The generation unit takes the information provided by the analysis unit as prompts for the generation AI and generates advice and content best suited to the user's situation and challenges. For example, if the user inputs "I've been feeling stressed at work lately," the generation AI will suggest relaxation techniques for stress management and activities to relieve stress. Also, if the user is having difficulty learning, the generation AI will generate effective learning methods and self-improvement content to boost motivation. The generation unit scrutinizes the output of the generation AI and performs appropriate filtering before providing it to the user. This allows the generation unit to provide users with high-quality advice and content. In addition, the generation unit can adjust the prompts and algorithms of the generation AI based on user feedback to improve generation accuracy. This allows the generation unit to continuously provide customized advice and content tailored to the user's situation and challenges.

[0067] The delivery unit provides advice and learning content generated by the generation unit. This delivery can be in various formats, including, but is not limited to, text, audio, images, and videos. For example, the delivery unit can provide generated relaxation videos. It can also provide generated self-improvement text content. Furthermore, it can provide generated advice in audio format. The delivery unit provides content in the most suitable format according to the user's preferences and circumstances. For example, if the user prefers visual information, it provides content in video or image format; if the user prefers auditory information, it provides content in audio format. The delivery unit delivers content in a format suitable for the user's device, ensuring easy access. In addition, the delivery unit collects user feedback to continuously improve the quality of the content it provides. For example, it provides a feature that allows users to leave ratings and comments on the provided content, and uses this feedback to improve the content. The delivery unit can also analyze the user's usage history to provide personalized content tailored to their preferences and needs. This allows the delivery unit to provide advice and learning content in the most suitable format and content for the user, effectively supporting mental care.

[0068] The generation unit can generate relaxation techniques for stress management. For example, it can generate breathing exercises. For example, it can generate content explaining how to take deep breaths. The generation unit can also generate meditation. For example, it can generate content explaining the steps of meditation. The generation unit can also generate yoga. For example, it can generate content explaining yoga poses. By generating relaxation techniques for stress management, the user's stress can be reduced. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user input into a generation AI and use the generation AI to generate relaxation techniques.

[0069] The generation unit can generate self-improvement content for mindset change. For example, the generation unit can generate videos to improve motivation. For example, the generation unit can generate videos introducing success stories. The generation unit can also generate content explaining methods of self-assessment. For example, the generation unit can generate content explaining the steps of self-assessment. The generation unit can also generate content explaining methods of goal setting. For example, the generation unit can generate content explaining the steps of goal setting. In this way, by generating self-improvement content for mindset change, the user's awareness can be improved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input user input into a generation AI and use the generation AI to generate self-improvement content.

[0070] The service provider can provide generated relaxation technique videos. For example, the service provider can provide yoga instruction videos. For example, the service provider can provide videos explaining yoga poses. The service provider can also provide meditation guide videos. For example, the service provider can provide videos explaining the steps of meditation. The service provider can also provide breathing technique videos. For example, the service provider can provide videos explaining how to take deep breaths. By providing relaxation technique videos, users can learn relaxation techniques visually. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide generated relaxation technique videos using AI.

[0071] The service provider can provide generated text content for self-improvement. For example, the service provider can provide essays for self-improvement. For example, the service provider can provide essays introducing success stories. The service provider can also provide text content explaining methods of self-assessment. For example, the service provider can provide text content explaining the steps of self-assessment. The service provider can also provide text content explaining methods of goal setting. For example, the service provider can provide text content explaining the steps of goal setting. In this way, by providing text content for self-improvement, users can engage in self-improvement. 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 provide generated text content for self-improvement using AI.

[0072] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the input acceptance to provide a relaxing environment. If the user is relaxed, the reception unit can speed up the input acceptance to collect information quickly. If the user is in a hurry, the reception unit can make the input acceptance immediate to respond quickly. By adjusting the timing of input acceptance according to the user's emotions, information can be collected at a more appropriate time. 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 reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0073] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest similar input methods by referring to content the user has entered in the past. In this way, the optimal input method can be suggested by analyzing the user's past input 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 input history into AI and select the optimal input method.

[0074] The reception desk can filter input content based on the user's current situation and challenges. For example, if the user is feeling stressed, the reception desk will prioritize input content related to stress management. Similarly, if the user is relaxed, the reception desk can prioritize input content related to relaxation techniques. Furthermore, if the user is seeking a change in mindset, the reception desk can prioritize input content related to self-improvement. This allows for the collection of more relevant information by filtering input content based on the user's current situation and challenges. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and challenges into an AI and have the AI ​​perform the filtering of the input content.

[0075] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize input content related to stress management. It can also prioritize input content related to relaxation techniques if the user is relaxed. Furthermore, if the user is seeking a change in mindset, it can prioritize input content related to self-improvement. This allows for the collection of more important information by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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, or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0076] The reception desk can prioritize receiving input content that is 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 stress management methods related to that region. Similarly, if the user is in a specific location, the reception desk can prioritize receiving relaxation methods related to that location. Furthermore, if the user is in a specific environment, the reception desk can prioritize receiving self-improvement content related to that environment. This allows for the collection of more relevant information by considering 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 instance, the reception desk can input the user's geographical location into AI and prioritize receiving input content that is highly relevant.

[0077] The reception desk can analyze a user's social media activity and accept relevant input. For example, if a user posts on social media expressing stress, the reception desk will prioritize accepting input related to stress management. Similarly, if a user shows interest in relaxation techniques on social media, the reception desk can prioritize accepting input related to relaxation techniques. Furthermore, if a user shows interest in self-improvement on social media, the reception desk can prioritize accepting input related to self-improvement. This allows for the collection of more relevant information by analyzing a user's social media activity. Some or all of the processing described above in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity into AI and accept relevant input.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit may prioritize analyzing information related to stress management. It may also prioritize analyzing information related to relaxation techniques if the user is relaxed. Furthermore, if the user is seeking a change in mindset, it may prioritize analyzing information related to self-improvement. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the user's past data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past stress management data. It can also improve the accuracy of its analysis by referring to the user's past relaxation methods data. Furthermore, it can improve the accuracy of its analysis by referring to the user's past self-improvement data. In this way, the accuracy of the analysis can be improved by referring to the user's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI to improve the accuracy of its analysis.

[0080] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can analyze appropriate stress management methods while considering the user's age. It can also analyze appropriate relaxation methods while considering the user's gender. Furthermore, the analysis unit can analyze appropriate self-improvement content while considering the user's occupation. By considering the user's attribute information, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's attribute information into AI and perform the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying analysis results related to stress management. It can also prioritize displaying analysis results related to relaxation techniques if the user is relaxed. Furthermore, if the user is seeking a change in mindset, it can prioritize displaying analysis results related to self-improvement. This allows for prioritizing the display of more important information by adjusting the display order of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0082] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user is in a specific region, the analysis unit can analyze stress management methods related to that region. It can also analyze relaxation methods related to a specific location if the user is in that location. Furthermore, if a user is in a specific environment, the analysis unit can analyze self-improvement content related to that environment. This allows for more appropriate analysis results by considering the geographical distribution of users. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of users into AI and perform the analysis.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research literature on stress management. It can also improve the accuracy of its analysis by referring to the latest research literature on relaxation techniques. It can also improve the accuracy of its analysis by referring to the latest research literature on self-improvement. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature into AI to improve the accuracy of its analysis.

[0084] The generation unit can estimate the user's emotions and adjust the way the generated content is presented based on the estimated emotions. For example, if the user is stressed, the generation unit can generate content using a relaxing presentation. If the user is relaxed, the generation unit can also generate content using a more detailed presentation. Furthermore, if the user is seeking a change of perspective, the generation unit can generate content using a more stimulating presentation. This allows for the provision of more effective content by adjusting the presentation style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and adjust the presentation style of the content based on the emotions.

[0085] The generation unit can adjust the level of detail of the generated content based on the importance of the user's problem during generation. For example, if the user's problem is important, the generation unit will generate detailed content. If the user's problem is minor, the generation unit can also generate concise content. If the user's problem is moderate, the generation unit can also generate content with a moderate level of detail. By adjusting the level of detail of the content based on the importance of the user's problem, more appropriate content can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the importance of the user's problem into the generation AI and adjust the level of detail of the generated content.

[0086] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is seeking stress management, the generation unit can apply a generation algorithm specialized in stress management. Similarly, if the user is seeking relaxation techniques, the generation unit can apply a generation algorithm specialized in relaxation techniques. Furthermore, if the user is seeking self-improvement, the generation unit can apply a generation algorithm specialized in self-improvement. This allows for the provision of more appropriate content by applying different generation algorithms according to the user's category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's category into a generation AI and apply a different generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the user is stressed, the generation unit can generate short, concise content. If the user is relaxed, the generation unit can also generate longer content with detailed explanations. Furthermore, if the user is seeking a change of perspective, the generation unit can generate content with visually stimulating effects. By adjusting the length of the content according to the user's emotions, more effective content can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and adjust the length of the content based on the emotions.

[0088] The generation unit can determine the priority of content to generate based on the user's submission timing during the generation process. For example, if the user is in a hurry, the generation unit will prioritize content that can be generated quickly. Alternatively, if the user has ample time, the generation unit can prioritize detailed content. Furthermore, if the user has set a specific deadline, the generation unit can generate content to meet that deadline. This allows for the delivery of content at a more appropriate time by prioritizing content based on the user's submission timing. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's submission timing into the generation AI to determine the priority of content to generate.

[0089] The generation unit can adjust the order of content generated based on user relevance during the generation process. For example, the generation unit may prioritize generating content related to stress management. It can also prioritize generating content related to relaxation techniques. Furthermore, it can prioritize generating content related to self-improvement. By adjusting the order of content based on user relevance, more relevant information can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user relevance into the generation AI and adjust the order of content to be generated.

[0090] The service provider can estimate the user's emotions and adjust how the content is displayed based on those emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. If the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting how the content is displayed according to the user's emotions, a more effective display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and adjust the display method based on the emotions.

[0091] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also predict and suggest a specific display method based on the user's past operation history. Furthermore, the service provider can provide the optimal display method based on the display methods that the user has preferred to use in the past. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past operation history into AI and select the optimal display method.

[0092] The service provider can adjust the display method based on the user's current situation at the time of delivery. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. By adjusting the display method based on the user's current situation, a more appropriate display becomes possible. 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 current situation into AI and adjust the display method.

[0093] The service provider can estimate the user's emotions and adjust the operation procedures for the content it provides based on the estimated emotions. For example, if the user is stressed, the service provider can simplify the operation procedures to make them more intuitive. If the user is relaxed, the service provider can also provide detailed operation procedures and suggest customizable methods. If the user is in a hurry, the service provider can minimize the operation procedures to allow for quick operation. This allows for more intuitive operation by adjusting the operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and adjust the operation procedures based on the emotions.

[0094] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Also, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. 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 device information into AI and select the optimal display method.

[0095] The service provider can provide multilingual content at the time of delivery, according to the user's language settings. For example, the service provider can automatically set the language of the content based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide content in a specific language if the user selects that language. This allows the service provider to accommodate a larger number of users by providing multilingual content according to the user's language settings. 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 language settings into AI and provide multilingual content.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, it can prioritize analyzing information related to stress management. If the user is relaxed, it can prioritize analyzing information related to relaxation techniques. Furthermore, if the user is seeking a change in mindset, it can prioritize analyzing information related to self-improvement. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0098] The service provider can estimate the user's emotions and adjust how the content is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the content display method according to the user's emotions, a more effective display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and adjust the display method based on the emotions.

[0099] The generation unit can estimate the user's emotions and adjust the way the generated content is expressed based on the estimated user emotions. For example, if the user is stressed, the content can be generated using a relaxing expression. If the user is relaxed, the content can be generated using a more detailed expression. Furthermore, if the user is seeking a change of perspective, the content can be generated using a more stimulating expression. By adjusting the way the content is expressed according to the user's emotions, more effective content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and adjust the way the content is expressed based on the emotions.

[0100] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the timing of input acceptance can be delayed to provide a relaxing environment. If the user is relaxed, the timing of input acceptance can be accelerated to collect information quickly. Furthermore, if the user is in a hurry, the timing of input acceptance can be made immediate to respond quickly. In this way, by adjusting the timing of input acceptance according to the user's emotions, information can be collected at a more appropriate time. 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 reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0101] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, analysis results related to stress management can be displayed with the highest priority. If the user is relaxed, analysis results related to relaxation techniques can be displayed with priority. Furthermore, if the user is seeking a change in mindset, analysis results related to self-improvement can be displayed with priority. In this way, by adjusting the display order of analysis results according to the user's emotions, more important information can be displayed with priority. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0102] The reception desk can analyze the user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also predict and suggest input methods that the user will use at specific times based on their past input history. Furthermore, it can suggest similar input methods by referring to content the user has entered in the past. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into AI and select the optimal input method.

[0103] The reception desk can filter input content based on the user's current situation and challenges. For example, if the user is feeling stressed, it can prioritize input related to stress management. Similarly, if the user is relaxed, it can prioritize input related to relaxation techniques. Furthermore, if the user is seeking a change in mindset, it can prioritize input related to self-improvement. This allows for the collection of more relevant information by filtering input content based on the user's current situation and challenges. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation and challenges into the AI ​​and have the AI ​​perform the filtering of the input content.

[0104] The reception desk can prioritize receiving input content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving stress management methods related to that region. Similarly, if the user is in a specific location, it can prioritize receiving relaxation methods related to that location. Furthermore, if the user is in a specific environment, it can prioritize receiving self-improvement content related to that environment. This allows for the collection of more relevant information by considering 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 AI and prioritize receiving input content that is highly relevant.

[0105] The analysis unit can improve the accuracy of its analysis by referring to the user's past data during the analysis process. For example, it can improve the accuracy of the analysis by referring to the user's past stress management data. It can also improve the accuracy of the analysis by referring to the user's past relaxation methods. Furthermore, it can improve the accuracy of the analysis by referring to the user's past self-improvement data. In this way, the accuracy of the analysis can be improved by referring to the user's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI to improve the accuracy of the analysis.

[0106] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. 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 device information into AI and select the optimal display method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives input from the user about their situation and challenges. These challenges may include, for example, work stress, learning difficulties, or health problems. The reception desk accepts the user to input specific details of their situation. It can also accept input using voice or image input. For example, the reception desk can accept a user saying, "I've been feeling stressed at work lately," or explain their situation using images or videos. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and data mining. The analysis unit can analyze the user's input using text analysis, analyze the user's emotions using sentiment analysis, and analyze the user's past data using data mining. Step 3: The generation unit generates advice and learning content based on the information analyzed by the analysis unit. Generation is performed, for example, using a generation AI. The generation unit can use the generation AI to generate relaxation methods for stress management and self-improvement content for mindset change. It can also generate customized advice tailored to the user's situation. Step 4: The provider unit provides the advice and learning content generated by the generator unit. The provision can be in the form of text, audio, images, or videos. The provider unit can provide generated relaxation videos, self-improvement text content, or advice in audio format.

[0109] 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input of the user's situation and challenges. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and learning content based on the analyzed information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated advice and learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 microphone 238 of the smart glasses 214 and receives input of the user's situation and challenges. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and learning content based on the analyzed information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated advice and learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 microphone 238 of the headset terminal 314 and receives input of the user's situation and challenges. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and learning content based on the analyzed information. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated advice and learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 microphone 238 of the robot 414 and receives input of the user's situation and tasks. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and learning content based on the analyzed information. The provision unit is implemented by the speaker 240 of the robot 414 and provides the generated advice and learning content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk that accepts input about the user's situation and issues, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates advice and learning content based on the information analyzed by the analysis unit, The system includes a providing unit that provides advice and learning content generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate relaxation techniques for stress management The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate self-improvement content for mindset change. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides videos of generated relaxation techniques. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides generated text content for self-improvement. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Filter input based on the user's current situation and challenges. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system prioritizes accepting input that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyzes users' social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system improves the accuracy of the analysis by referencing the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical distribution of users will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, adjust the level of detail of the generated content based on the importance of the user's problem. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the priority of the content to be generated is determined based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the order of generated content is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the display method will be adjusted based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interaction steps for the content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, multilingual support will be provided according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 input about the user's situation and issues, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates advice and learning content based on the information analyzed by the analysis unit, The system includes a providing unit that provides advice and learning content generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate relaxation techniques for stress management The system according to feature 1.

3. The generating unit is Generate self-improvement content for mindset change. The system according to feature 1.

4. The aforementioned supply unit is, Provides videos of generated relaxation techniques. The system according to feature 1.

5. The aforementioned supply unit is, Provides generated text content for self-improvement. The system according to feature 1.

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

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

8. The aforementioned reception unit is Filter input based on the user's current situation and challenges. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is The system prioritizes accepting input that is highly relevant to the user's geographical location. The system according to feature 1.

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