system

Abstract

The system according to this embodiment aims to provide empathetic and appropriate advice to users regarding their worries and problems. [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 from the user regarding their worries or problems. The analysis unit analyzes the information received by the reception unit. The generation unit generates advice based on the information analyzed by the analysis unit. The provision unit provides the advice 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to obtain a person who can understand and empathize with daily stress and life troubles, or to obtain professional advice.

[0005] The system according to the embodiment aims to provide empathic and appropriate advice for the troubles and problems of users.

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 from the user regarding their concerns or problems. The analysis unit analyzes the information received by the reception unit. The generation unit generates advice based on the information analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide empathetic and appropriate advice to users regarding their worries and problems. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 AI ​​Life Consultant System according to an embodiment of the present invention is an AI-based service that provides empathetic and appropriate advice to users regarding their daily stress and life worries. The AI ​​Life Consultant System allows users to consult about their worries and problems and obtain solutions and advice through dialogue with the AI ​​Life Consultant. The AI ​​utilizes past consultation data and psychological insights to provide optimal advice for individual consultations. For example, a user inputs their worries and problems into the AI ​​Life Consultant. For instance, they might input specific worries such as "I'm stressed out at work" or "I'm having trouble with relationships." This information is then input into the AI ​​Life Consultant. Next, the AI ​​Life Consultant analyzes the input information and generates optimal advice based on past consultation data and psychological insights. For example, it might refer to advice provided to users with similar worries in the past, or solutions using psychological methods. The generated advice is then provided to the user. For example, specific advice such as "To reduce work stress, it's important to take regular breaks" or "To resolve relationship problems, it's important to strive for open communication" might be provided. This mechanism allows users to easily consult at any time and gain emotional stability and a step towards resolution by having their feelings understood. The AI ​​Life Consultant possesses psychological techniques and counseling knowledge, and provides concrete solutions and advice through dialogue with the user. For example, if a user consults about "work-related stress," the AI ​​Life Consultant will suggest specific methods for stress reduction based on past consultation data. Similarly, if a user consults about "problems with interpersonal relationships," the AI ​​Life Consultant will use psychological techniques to explain the importance of open communication and suggest specific communication methods. In this way, the AI ​​Life Consultant supports users in achieving emotional stability and taking steps toward resolving their daily stresses and life problems by providing empathetic and appropriate advice.This allows the AI ​​life consultant system to provide empathetic and appropriate advice to users regarding their worries and problems.

[0029] The AI ​​life consultant system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input from the user regarding their worries and problems. These worries and problems include, but are not limited to, personal problems, workplace problems, and health-related problems. The reception unit can receive the user's worries and problems by methods such as text input, voice input, and image input. The analysis unit analyzes the information received by the reception unit. The analysis is performed by methods such as text analysis, sentiment analysis, and data mining, but is not limited to these. The analysis unit analyzes the user's worries and problems using, for example, natural language processing technology. The analysis unit can also analyze the user's emotions using sentiment analysis technology. The analysis unit can also analyze past consultation data using data mining technology. The generation unit generates advice based on the information analyzed by the analysis unit. The advice is provided in, for example, text format, audio format, video format, etc., but is not limited to these. The generation unit generates advice using, for example, text generation AI (e.g., LLM). Furthermore, the generation unit can generate advice in voice format using voice generation AI. The generation unit can also generate advice in video format using video generation AI. The delivery unit provides the advice generated by the generation unit. The delivery unit can provide advice, for example, through web applications or mobile applications. The delivery unit can also provide advice via email or messaging apps. Furthermore, the delivery unit can provide advice via video calls or voice calls. As a result, the AI ​​life consultant system according to this embodiment can provide empathetic and appropriate advice to users regarding their worries and problems.

[0030] The reception desk receives input from users regarding their concerns and problems. These concerns and problems include, but are not limited to, personal issues, workplace issues, and health-related issues. The reception desk can receive user concerns and problems through methods such as text input, voice input, and image input. Specifically, for text input, users can enter their concerns through a dedicated web form or chatbot. For voice input, users can communicate their concerns using a microphone, which is then converted to text using speech recognition technology. For image input, users can upload photos or screenshots to provide visual information. This allows the reception desk to support diverse input methods, enabling users to communicate their concerns in the most convenient way. The reception desk also temporarily stores the entered information and performs necessary preprocessing before sending it to the analysis department. This includes, for example, noise reduction of audio data and resolution adjustment of image data. This allows the reception desk to accurately and efficiently receive user concerns and problems.

[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 natural language processing technology to analyze users' concerns and problems. Specifically, it analyzes text data using natural language processing technology to extract the content of the user's concerns and important keywords. It can also analyze users' emotions using sentiment analysis technology. Sentiment analysis evaluates the tone and intensity of emotions from the user's text and audio data to determine the level of stress and anxiety the user is experiencing. Furthermore, the analysis unit can analyze past consultation data using data mining technology. This allows it to identify similar past cases and patterns of successful advice, collecting foundational data to provide optimal advice to the current user. By combining these technologies, the analysis unit can analyze users' concerns and problems from multiple perspectives and provide more accurate information to the generation unit.

[0032] The generation unit generates advice based on the information analyzed by the analysis unit. The advice may be provided in various formats, such as text, audio, or video, but is not limited to these examples. For instance, the generation unit can use text generation AI (e.g., LLM) to generate advice. Specifically, LLM generates appropriate advice for the user based on keywords and sentiment information provided by the analysis unit. For example, if a user is concerned about workplace stress, LLM utilizes past data and expertise to suggest stress management methods and specific countermeasures. The generation unit can also generate audio advice using speech generation AI. Speech generation AI provides advice in a natural voice, allowing users to receive information in a more easily understandable format. Furthermore, the generation unit can generate video advice using video generation AI. Video advice includes visual information, allowing users to form more concrete images. This enables the generation unit to provide advice in diverse formats tailored to user needs.

[0033] The service provider delivers advice generated by the generator. The service provider can deliver advice through, for example, web applications or mobile applications. Specifically, users can receive advice through a dedicated application, view past advice, and ask additional questions within the application. The service provider can also deliver advice via email or messaging apps, allowing users to receive advice at their convenience. Furthermore, the service provider can deliver advice via video or voice calls. Advice delivered via video or voice calls allows for real-time interaction, enabling users to ask questions and seek clarification on the spot. This allows the service provider to deliver advice flexibly and quickly to users, providing appropriate support for their concerns and problems. Additionally, the service provider can collect user feedback and continuously improve the quality of advice. For example, it can provide a function to evaluate how users felt about the advice provided and how helpful it was, and use this feedback to improve the entire system. This ensures that the service provider always provides users with the best possible advice.

[0034] The analysis unit includes a search unit that searches past consultation data. The search unit can, for example, search consultation data for the past year. The search unit can also, for example, search consultation data for a specific category. The search unit can also, for example, search past consultation data using keyword search. This allows for the provision of more appropriate advice based on past consultation data. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past consultation data into AI, and the AI ​​can search for the most appropriate advice.

[0035] The analysis unit includes a knowledge utilization unit that leverages psychological insights. The knowledge utilization unit can, for example, leverage insights from cognitive behavioral therapy. The knowledge utilization unit can also, for example, leverage insights from positive psychology. The knowledge utilization unit can, for example, analyze the user's worries and problems using psychological methods. This allows for the provision of more effective advice by leveraging psychological insights. Some or all of the above-described processes in the knowledge utilization unit may be performed using AI, for example, or without AI. For example, the knowledge utilization unit can input psychological insights into AI, which can then generate optimal advice.

[0036] The service unit includes a feedback receiving unit that receives user feedback. The feedback receiving unit can, for example, receive text feedback. The feedback receiving unit can also, for example, receive evaluation scores. The feedback receiving unit can also, for example, receive voice feedback. This allows for improvement in the quality of advice by receiving user feedback. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user feedback into AI, which can then analyze the feedback.

[0037] The feedback receiving unit collects user responses and provides them to the analysis unit. The feedback receiving unit can, for example, perform a user sentiment analysis and provide the results to the analysis unit. The feedback receiving unit can also, for example, collect user behavior logs and provide that data to the analysis unit. The feedback receiving unit can also, for example, collect user text feedback and provide its content to the analysis unit. This allows the collected user responses to be reflected in future advice. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user response data into AI, which can then analyze the responses.

[0038] The analysis unit analyzes user responses provided by the feedback reception unit and incorporates them into future advice. The analysis unit can, for example, analyze user feedback using text analysis technology. The analysis unit can also, for example, analyze user emotions using sentiment analysis technology. The analysis unit can also, for example, analyze user behavior logs using data mining technology. By analyzing user responses, the quality of future advice can be improved. 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 response data into AI, which can then analyze the responses.

[0039] The reception desk analyzes the user's past consultation history and proposes the optimal input format. For example, the reception desk can automatically display as suggestions any concerns or problems that the user has frequently entered in the past. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest concerns or problems that the user will use at a specific time of day based on their past consultation history. This improves input efficiency by suggesting the optimal input format based on past consultation 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 consultation history data into AI, and the AI ​​can suggest the optimal input format.

[0040] The reception unit filters the input content based on the user's current living situation and areas of interest when they input their worries or problems. For example, when a user inputs their current living situation, the reception unit can automatically display relevant worries or problems as suggestions. The reception unit can also prioritize receiving relevant worries or problems based on the user's areas of interest. The reception unit can also analyze the user's living situation and areas of interest and suggest the most appropriate input content. This allows for more relevant input by filtering the input content based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's living situation and areas of interest data into the AI, which can then filter the input content to the most appropriate level.

[0041] The reception desk prioritizes accepting input that is highly relevant to the user's geographical location when they input their concerns or problems. For example, if the user is in a specific region, the reception desk can prioritize accepting concerns or problems related to that region. The reception desk can also automatically display region-specific problems as candidates based on the user's geographical location. The reception desk can also prioritize accepting relevant concerns or problems based on the user's current location. This allows for appropriate responses to region-specific problems by considering the user's geographical location. 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 geographical location data into the AI, which can then filter the input to the most relevant content.

[0042] The reception desk analyzes the user's social media activity when they input their concerns or problems, and accepts relevant input. For example, the reception desk can analyze the user's social media activity and automatically display relevant concerns or problems as suggestions. For example, the reception desk can also prioritize receiving relevant concerns or problems based on topics the user frequently mentions on social media. For example, the reception desk can suggest the most relevant input based on the user's social media activity. This allows for more relevant input by analyzing the user's social media activity. 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 social media activity data into AI, which can then filter the most relevant input.

[0043] The analysis unit optimizes the analysis algorithm by referring to past consultation data during the analysis. The analysis unit can, for example, select the optimal analysis algorithm based on past consultation data. The analysis unit can also, for example, analyze past consultation data to improve the accuracy of the analysis algorithm. The analysis unit can also, for example, optimize the analysis algorithm by referring to past consultation data. This improves the accuracy of the analysis algorithm by referring to past consultation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past consultation data into AI, and the AI ​​can select the optimal analysis algorithm.

[0044] The analysis unit performs analysis while considering the user's attribute information. For example, the analysis unit can select the optimal analysis method by considering the user's age and gender. For example, the analysis unit can also perform analysis by considering the user's occupation and living situation. For example, the analysis unit can customize the analysis results based on the user's attribute information. This makes it possible to perform more customized analysis by considering the user's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information data into AI, and the AI ​​can select the optimal analysis method.

[0045] The analysis unit performs analysis while considering the geographical distribution of users. For example, the analysis unit can analyze region-specific problems by considering the user's place of residence. For example, the analysis unit can also customize the analysis results based on the geographical distribution of users. For example, the analysis unit can select the optimal analysis method by considering the geographical distribution of users. This makes it possible to respond appropriately to region-specific problems by considering the geographical distribution of users. 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 geographical distribution data into AI, and the AI ​​can select the optimal analysis method.

[0046] The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit can optimize the analysis algorithm based on relevant literature. For example, the analysis unit can also improve the accuracy of the analysis results by referring to relevant literature. For example, the analysis unit can improve the analysis method based on relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into the AI, and the AI ​​can select the optimal analysis algorithm.

[0047] The generation unit optimizes the generation algorithm by referring to past consultation data when generating advice. The generation unit can, for example, select the optimal generation algorithm based on past consultation data. The generation unit can also, for example, analyze past consultation data to improve the accuracy of the generation algorithm. The generation unit can also, for example, optimize the generation algorithm by referring to past consultation data. This improves the accuracy of the generation algorithm by referring to past consultation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input past consultation data into AI, and the AI ​​can select the optimal generation algorithm.

[0048] The generation unit generates advice while considering the user's attribute information. For example, the generation unit can generate optimal advice by considering the user's age and gender. For example, the generation unit can generate advice by considering the user's occupation and living situation. For example, the generation unit can customize the advice based on the user's attribute information. This allows for more customized advice to be provided by considering the user's attribute information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user attribute information data into AI, and the AI ​​can generate optimal advice.

[0049] The generation unit considers the geographical distribution of users when generating advice. For example, the generation unit can provide region-specific advice by considering the user's place of residence. For example, the generation unit can also customize advice based on the geographical distribution of users. For example, the generation unit can generate optimal advice by considering the geographical distribution of users. This allows for the provision of region-specific advice by considering the geographical distribution of users. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical distribution data into AI, and the AI ​​can generate optimal advice.

[0050] The generation unit improves the accuracy of its generation by referring to relevant literature when generating advice. For example, the generation unit can optimize its generation algorithm based on relevant literature. The generation unit can also improve the accuracy of its generation results by referring to relevant literature. The generation unit can also improve its generation method based on relevant literature. As a result, the accuracy of the generation is improved by referring to relevant literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into AI, and the AI ​​can select the optimal generation algorithm.

[0051] The service provider selects the optimal service method when providing advice, by referring to the user's past feedback. For example, the service provider can select the optimal service method based on the user's past feedback. For example, the service provider can also analyze the user's past feedback and customize the service method. For example, the service provider can optimize the service method by referring to the user's past feedback. This improves the accuracy of the service method by referring to past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past feedback data into AI, and the AI ​​can select the optimal service method.

[0052] The service provider takes user attribute information into consideration when providing advice. For example, the service provider can provide optimal advice by considering the user's age and gender. For example, the service provider can also provide advice by considering the user's occupation and living situation. For example, the service provider can customize the advice based on the user's attribute information. This allows for more customized advice to be provided by considering the user's attribute 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 user attribute information data into AI, and the AI ​​can provide optimal advice.

[0053] The service provider selects the optimal delivery method when providing advice, taking into account the user's geographical location information. For example, the service provider can provide region-specific advice by considering the user's current location. For example, the service provider can also select the optimal delivery method based on the user's geographical location information. For example, the service provider can provide relevant advice based on the user's current location. This allows for the provision of region-specific advice by considering the user's geographical location 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 geographical location data into AI, which can then select the optimal delivery method.

[0054] The service provider analyzes the user's social media activity when providing advice and proposes a means of providing it. For example, the service provider can analyze the user's social media activity and propose the most suitable method of providing advice. For example, the service provider can provide relevant advice based on topics that the user frequently mentions on social media. For example, the service provider can customize the method of providing advice based on the user's social media activity. This allows for the proposal of a more appropriate means of providing advice by analyzing the user's social media activity. 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 social media activity data into AI, which can then propose the most suitable means of providing advice.

[0055] The search unit optimizes the search algorithm by referring to past consultation data during a search. For example, the search unit can select the optimal search algorithm based on past consultation data. The search unit can also improve the accuracy of the search algorithm by analyzing past consultation data. The search unit can also optimize the search algorithm by referring to past consultation data. This improves the accuracy of the search algorithm by referring to past consultation data. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past consultation data into AI, and the AI ​​can select the optimal search algorithm.

[0056] The search unit performs searches while considering the geographical distribution of users. For example, the search unit can search for region-specific problems by considering the user's place of residence. For example, the search unit can also customize search results based on the geographical distribution of users. For example, the search unit can select the optimal search method by considering the geographical distribution of users. This makes it possible to respond appropriately to region-specific problems by considering the geographical distribution of users. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input user geographical distribution data into AI, and the AI ​​can select the optimal search method.

[0057] The knowledge utilization unit optimizes the scope of knowledge application by referring to past consultation data when utilizing knowledge. For example, the knowledge utilization unit can select the optimal knowledge based on past consultation data. For example, the knowledge utilization unit can also optimize the scope of knowledge application by analyzing past consultation data. For example, the knowledge utilization unit can optimize the scope of knowledge application by referring to past consultation data. As a result, the scope of knowledge application is optimized by referring to past consultation data. Some or all of the above processing in the knowledge utilization unit may be performed using AI, for example, or without using AI. For example, the knowledge utilization unit can input past consultation data into AI, and the AI ​​can select the optimal knowledge.

[0058] The knowledge utilization unit applies knowledge while considering the geographical distribution of users. For example, the knowledge utilization unit can provide region-specific knowledge by considering the user's place of residence. For example, the knowledge utilization unit can also customize knowledge based on the geographical distribution of users. For example, the knowledge utilization unit can provide optimal knowledge by considering the geographical distribution of users. In this way, region-specific knowledge can be provided by considering the geographical distribution of users. Some or all of the above processing in the knowledge utilization unit may be performed using AI, for example, or without AI. For example, the knowledge utilization unit can input the user's geographical distribution data into AI, and the AI ​​can provide optimal knowledge.

[0059] The feedback receiving unit selects the optimal receiving method by referring to the user's past feedback history when receiving feedback. For example, the feedback receiving unit can select the optimal receiving method based on the user's past feedback history. For example, the feedback receiving unit can also analyze the user's past feedback history and customize the receiving method. For example, the feedback receiving unit can optimize the receiving method by referring to the user's past feedback history. This improves the accuracy of the receiving method by referring to past feedback history. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without using AI. For example, the feedback receiving unit can input past feedback history data into AI, and the AI ​​can select the optimal receiving method.

[0060] The feedback receiving unit selects the optimal receiving method when receiving feedback, taking into account the user's geographical location information. The feedback receiving unit can, for example, prioritize receiving region-specific feedback by considering the user's current location. The feedback receiving unit can also, for example, select the optimal receiving method based on the user's geographical location information. The feedback receiving unit can also, for example, prioritize receiving relevant feedback based on the user's current location. This ensures that region-specific feedback is prioritized by considering the user's geographical location information. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input the user's geographical location data into AI, which can then select the optimal receiving method.

[0061] The feedback reception unit analyzes the user's social media activity when receiving feedback and proposes the optimal method of receiving it. For example, the feedback reception unit can analyze the user's social media activity and propose the optimal method of receiving feedback. For example, the feedback reception unit can also prioritize receiving relevant feedback based on topics that the user frequently mentions on social media. For example, the feedback reception unit can customize the feedback reception method based on the user's social media activity. This allows for the proposal of a more appropriate feedback reception method by analyzing the user's social media activity. Some or all of the above processing in the feedback reception unit may be performed using AI, for example, or without AI. For example, the feedback reception unit can input the user's social media activity data into AI, which can then propose the optimal method of receiving feedback.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk analyzes the user's past consultation history and proposes the optimal input format. For example, the reception desk can automatically display as suggestions any concerns or problems that the user has frequently entered in the past. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest concerns or problems that the user will use at a specific time of day based on their past consultation history. This improves input efficiency by suggesting the optimal input format based on past consultation 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 consultation history data into AI, and the AI ​​can suggest the optimal input format.

[0064] The reception unit filters the input content based on the user's current living situation and areas of interest when they input their worries or problems. For example, when a user inputs their current living situation, the reception unit can automatically display relevant worries or problems as suggestions. The reception unit can also prioritize receiving relevant worries or problems based on the user's areas of interest. The reception unit can also analyze the user's living situation and areas of interest and suggest the most appropriate input content. This allows for more relevant input by filtering the input content based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's living situation and areas of interest data into the AI, which can then filter the input content to the most appropriate level.

[0065] The reception desk prioritizes accepting input that is highly relevant to the user's geographical location when they input their concerns or problems. For example, if the user is in a specific region, the reception desk can prioritize accepting concerns or problems related to that region. The reception desk can also automatically display region-specific problems as candidates based on the user's geographical location. The reception desk can also prioritize accepting relevant concerns or problems based on the user's current location. This allows for appropriate responses to region-specific problems by considering the user's geographical location. 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 geographical location data into the AI, which can then filter the input to the most relevant content.

[0066] The analysis unit optimizes the analysis algorithm by referring to past consultation data during the analysis. The analysis unit can, for example, select the optimal analysis algorithm based on past consultation data. The analysis unit can also, for example, analyze past consultation data to improve the accuracy of the analysis algorithm. The analysis unit can also, for example, optimize the analysis algorithm by referring to past consultation data. This improves the accuracy of the analysis algorithm by referring to past consultation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past consultation data into AI, and the AI ​​can select the optimal analysis algorithm.

[0067] The analysis unit performs analysis while considering the user's attribute information. For example, the analysis unit can select the optimal analysis method by considering the user's age and gender. For example, the analysis unit can also perform analysis by considering the user's occupation and living situation. For example, the analysis unit can customize the analysis results based on the user's attribute information. This makes it possible to perform more customized analysis by considering the user's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information data into AI, and the AI ​​can select the optimal analysis method.

[0068] The analysis unit performs analysis while considering the geographical distribution of users. For example, the analysis unit can analyze region-specific problems by considering the user's place of residence. For example, the analysis unit can also customize the analysis results based on the geographical distribution of users. For example, the analysis unit can select the optimal analysis method by considering the geographical distribution of users. This makes it possible to respond appropriately to region-specific problems by considering the geographical distribution of users. 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 geographical distribution data into AI, and the AI ​​can select the optimal analysis method.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives input from users regarding their concerns and problems. These concerns and problems may include personal issues, work-related issues, health-related issues, etc. The reception desk can receive users' concerns and problems through methods such as text input, voice input, and image input. 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 use natural language processing technology to analyze the user's worries and problems, and sentiment analysis technology to analyze the user's emotions. It can also use data mining technology to analyze past consultation data. Step 3: The generation unit generates advice based on the information analyzed by the analysis unit. The advice is provided in various formats, such as text, audio, or video. The generation unit can generate advice using text generation AI (e.g., LLM), audio generation AI, or video generation AI. Step 4: The provider delivers the advice generated by the generator. The provider can deliver the advice through web applications or mobile applications. It can also deliver the advice via email or messaging apps. Furthermore, it can deliver the advice via video calls or voice calls.

[0071] (Example of form 2) The AI ​​Life Consultant System according to an embodiment of the present invention is an AI-based service that provides empathetic and appropriate advice to users regarding their daily stress and life worries. The AI ​​Life Consultant System allows users to consult about their worries and problems and obtain solutions and advice through dialogue with the AI ​​Life Consultant. The AI ​​utilizes past consultation data and psychological insights to provide optimal advice for individual consultations. For example, a user inputs their worries and problems into the AI ​​Life Consultant. For instance, they might input specific worries such as "I'm stressed out at work" or "I'm having trouble with relationships." This information is then input into the AI ​​Life Consultant. Next, the AI ​​Life Consultant analyzes the input information and generates optimal advice based on past consultation data and psychological insights. For example, it might refer to advice provided to users with similar worries in the past, or solutions using psychological methods. The generated advice is then provided to the user. For example, specific advice such as "To reduce work stress, it's important to take regular breaks" or "To resolve relationship problems, it's important to strive for open communication" might be provided. This mechanism allows users to easily consult at any time and gain emotional stability and a step towards resolution by having their feelings understood. The AI ​​Life Consultant possesses psychological techniques and counseling knowledge, and provides concrete solutions and advice through dialogue with the user. For example, if a user consults about "work-related stress," the AI ​​Life Consultant will suggest specific methods for stress reduction based on past consultation data. Similarly, if a user consults about "problems with interpersonal relationships," the AI ​​Life Consultant will use psychological techniques to explain the importance of open communication and suggest specific communication methods. In this way, the AI ​​Life Consultant supports users in achieving emotional stability and taking steps toward resolving their daily stresses and life problems by providing empathetic and appropriate advice.This allows the AI ​​life consultant system to provide empathetic and appropriate advice to users regarding their worries and problems.

[0072] The AI ​​life consultant system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input from the user regarding their worries and problems. These worries and problems include, but are not limited to, personal problems, workplace problems, and health-related problems. The reception unit can receive the user's worries and problems by methods such as text input, voice input, and image input. The analysis unit analyzes the information received by the reception unit. The analysis is performed by methods such as text analysis, sentiment analysis, and data mining, but is not limited to these. The analysis unit analyzes the user's worries and problems using, for example, natural language processing technology. The analysis unit can also analyze the user's emotions using sentiment analysis technology. The analysis unit can also analyze past consultation data using data mining technology. The generation unit generates advice based on the information analyzed by the analysis unit. The advice is provided in, for example, text format, audio format, video format, etc., but is not limited to these. The generation unit generates advice using, for example, text generation AI (e.g., LLM). Furthermore, the generation unit can generate advice in voice format using voice generation AI. The generation unit can also generate advice in video format using video generation AI. The delivery unit provides the advice generated by the generation unit. The delivery unit can provide advice, for example, through web applications or mobile applications. The delivery unit can also provide advice via email or messaging apps. Furthermore, the delivery unit can provide advice via video calls or voice calls. As a result, the AI ​​life consultant system according to this embodiment can provide empathetic and appropriate advice to users regarding their worries and problems.

[0073] The reception desk receives input from users regarding their concerns and problems. These concerns and problems include, but are not limited to, personal issues, workplace issues, and health-related issues. The reception desk can receive user concerns and problems through methods such as text input, voice input, and image input. Specifically, for text input, users can enter their concerns through a dedicated web form or chatbot. For voice input, users can communicate their concerns using a microphone, which is then converted to text using speech recognition technology. For image input, users can upload photos or screenshots to provide visual information. This allows the reception desk to support diverse input methods, enabling users to communicate their concerns in the most convenient way. The reception desk also temporarily stores the entered information and performs necessary preprocessing before sending it to the analysis department. This includes, for example, noise reduction of audio data and resolution adjustment of image data. This allows the reception desk to accurately and efficiently receive user concerns and problems.

[0074] 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 natural language processing technology to analyze users' concerns and problems. Specifically, it analyzes text data using natural language processing technology to extract the content of the user's concerns and important keywords. It can also analyze users' emotions using sentiment analysis technology. Sentiment analysis evaluates the tone and intensity of emotions from the user's text and audio data to determine the level of stress and anxiety the user is experiencing. Furthermore, the analysis unit can analyze past consultation data using data mining technology. This allows it to identify similar past cases and patterns of successful advice, collecting foundational data to provide optimal advice to the current user. By combining these technologies, the analysis unit can analyze users' concerns and problems from multiple perspectives and provide more accurate information to the generation unit.

[0075] The generation unit generates advice based on the information analyzed by the analysis unit. The advice may be provided in various formats, such as text, audio, or video, but is not limited to these examples. For instance, the generation unit can use text generation AI (e.g., LLM) to generate advice. Specifically, LLM generates appropriate advice for the user based on keywords and sentiment information provided by the analysis unit. For example, if a user is concerned about workplace stress, LLM utilizes past data and expertise to suggest stress management methods and specific countermeasures. The generation unit can also generate audio advice using speech generation AI. Speech generation AI provides advice in a natural voice, allowing users to receive information in a more easily understandable format. Furthermore, the generation unit can generate video advice using video generation AI. Video advice includes visual information, allowing users to form more concrete images. This enables the generation unit to provide advice in diverse formats tailored to user needs.

[0076] The service provider delivers advice generated by the generator. The service provider can deliver advice through, for example, web applications or mobile applications. Specifically, users can receive advice through a dedicated application, view past advice, and ask additional questions within the application. The service provider can also deliver advice via email or messaging apps, allowing users to receive advice at their convenience. Furthermore, the service provider can deliver advice via video or voice calls. Advice delivered via video or voice calls allows for real-time interaction, enabling users to ask questions and seek clarification on the spot. This allows the service provider to deliver advice flexibly and quickly to users, providing appropriate support for their concerns and problems. Additionally, the service provider can collect user feedback and continuously improve the quality of advice. For example, it can provide a function to evaluate how users felt about the advice provided and how helpful it was, and use this feedback to improve the entire system. This ensures that the service provider always provides users with the best possible advice.

[0077] The analysis unit includes a search unit that searches past consultation data. The search unit can, for example, search consultation data for the past year. The search unit can also, for example, search consultation data for a specific category. The search unit can also, for example, search past consultation data using keyword search. This allows for the provision of more appropriate advice based on past consultation data. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past consultation data into AI, and the AI ​​can search for the most appropriate advice.

[0078] The analysis unit includes a knowledge utilization unit that leverages psychological insights. The knowledge utilization unit can, for example, leverage insights from cognitive behavioral therapy. The knowledge utilization unit can also, for example, leverage insights from positive psychology. The knowledge utilization unit can, for example, analyze the user's worries and problems using psychological methods. This allows for the provision of more effective advice by leveraging psychological insights. Some or all of the above-described processes in the knowledge utilization unit may be performed using AI, for example, or without AI. For example, the knowledge utilization unit can input psychological insights into AI, which can then generate optimal advice.

[0079] The service unit includes a feedback receiving unit that receives user feedback. The feedback receiving unit can, for example, receive text feedback. The feedback receiving unit can also, for example, receive evaluation scores. The feedback receiving unit can also, for example, receive voice feedback. This allows for improvement in the quality of advice by receiving user feedback. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user feedback into AI, which can then analyze the feedback.

[0080] The feedback receiving unit collects user responses and provides them to the analysis unit. The feedback receiving unit can, for example, perform a user sentiment analysis and provide the results to the analysis unit. The feedback receiving unit can also, for example, collect user behavior logs and provide that data to the analysis unit. The feedback receiving unit can also, for example, collect user text feedback and provide its content to the analysis unit. This allows the collected user responses to be reflected in future advice. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user response data into AI, which can then analyze the responses.

[0081] The analysis unit analyzes user responses provided by the feedback reception unit and incorporates them into future advice. The analysis unit can, for example, analyze user feedback using text analysis technology. The analysis unit can also, for example, analyze user emotions using sentiment analysis technology. The analysis unit can also, for example, analyze user behavior logs using data mining technology. By analyzing user responses, the quality of future advice can be improved. 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 response data into AI, which can then analyze the responses.

[0082] The reception desk estimates the user's emotions and adjusts the input method for worries and problems based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of worries and problems. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotion.

[0083] The reception desk analyzes the user's past consultation history and proposes the optimal input format. For example, the reception desk can automatically display as suggestions any concerns or problems that the user has frequently entered in the past. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest concerns or problems that the user will use at a specific time of day based on their past consultation history. This improves input efficiency by suggesting the optimal input format based on past consultation 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 consultation history data into AI, and the AI ​​can suggest the optimal input format.

[0084] The reception unit filters the input content based on the user's current living situation and areas of interest when they input their worries or problems. For example, when a user inputs their current living situation, the reception unit can automatically display relevant worries or problems as suggestions. The reception unit can also prioritize receiving relevant worries or problems based on the user's areas of interest. The reception unit can also analyze the user's living situation and areas of interest and suggest the most appropriate input content. This allows for more relevant input by filtering the input content based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's living situation and areas of interest data into the AI, which can then filter the input content to the most appropriate level.

[0085] The reception desk estimates the user's emotions and prioritizes input based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize receiving urgent worries or problems. For example, if the user is relaxed, the reception desk can prioritize receiving detailed input. For example, if the user is in a hurry, the reception desk can prioritize receiving concise input. This allows for prioritizing more important content by determining the priority of input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate emotions.

[0086] The reception desk prioritizes accepting input that is highly relevant to the user's geographical location when they input their concerns or problems. For example, if the user is in a specific region, the reception desk can prioritize accepting concerns or problems related to that region. The reception desk can also automatically display region-specific problems as candidates based on the user's geographical location. The reception desk can also prioritize accepting relevant concerns or problems based on the user's current location. This allows for appropriate responses to region-specific problems by considering the user's geographical location. 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 geographical location data into the AI, which can then filter the input to the most relevant content.

[0087] The reception desk analyzes the user's social media activity when they input their concerns or problems, and accepts relevant input. For example, the reception desk can analyze the user's social media activity and automatically display relevant concerns or problems as suggestions. For example, the reception desk can also prioritize receiving relevant concerns or problems based on topics the user frequently mentions on social media. For example, the reception desk can suggest the most relevant input based on the user's social media activity. This allows for more relevant input by analyzing the user's social media activity. 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 social media activity data into AI, which can then filter the most relevant input.

[0088] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis method and provide results quickly. For example, if the user is relaxed, the analysis unit can use a detailed analysis method and provide deeper insights. For example, if the user is in a hurry, the analysis unit can use a rapid analysis method and provide results quickly. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0089] The analysis unit optimizes the analysis algorithm by referring to past consultation data during the analysis. The analysis unit can, for example, select the optimal analysis algorithm based on past consultation data. The analysis unit can also, for example, analyze past consultation data to improve the accuracy of the analysis algorithm. The analysis unit can also, for example, optimize the analysis algorithm by referring to past consultation data. This improves the accuracy of the analysis algorithm by referring to past consultation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past consultation data into AI, and the AI ​​can select the optimal analysis algorithm.

[0090] The analysis unit performs analysis while considering the user's attribute information. For example, the analysis unit can select the optimal analysis method by considering the user's age and gender. For example, the analysis unit can also perform analysis by considering the user's occupation and living situation. For example, the analysis unit can customize the analysis results based on the user's attribute information. This makes it possible to perform more customized analysis by considering the user's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information data into AI, and the AI ​​can select the optimal analysis method.

[0091] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can estimate emotions.

[0092] The analysis unit performs analysis while considering the geographical distribution of users. For example, the analysis unit can analyze region-specific problems by considering the user's place of residence. For example, the analysis unit can also customize the analysis results based on the geographical distribution of users. For example, the analysis unit can select the optimal analysis method by considering the geographical distribution of users. This makes it possible to respond appropriately to region-specific problems by considering the geographical distribution of users. 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 geographical distribution data into AI, and the AI ​​can select the optimal analysis method.

[0093] The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit can optimize the analysis algorithm based on relevant literature. For example, the analysis unit can also improve the accuracy of the analysis results by referring to relevant literature. For example, the analysis unit can improve the analysis method based on relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into the AI, and the AI ​​can select the optimal analysis algorithm.

[0094] The generation unit estimates the user's emotions and adjusts the advice generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can provide detailed advice. For example, if the user is in a hurry, the generation unit can provide concise and to-the-point advice. For example, if the user is stressed, the generation unit can provide empathetic and gentle advice. By adjusting the generation method according to the user's emotions, more appropriate advice 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 AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can estimate emotions.

[0095] The generation unit optimizes the generation algorithm by referring to past consultation data when generating advice. The generation unit can, for example, select the optimal generation algorithm based on past consultation data. The generation unit can also, for example, analyze past consultation data to improve the accuracy of the generation algorithm. The generation unit can also, for example, optimize the generation algorithm by referring to past consultation data. This improves the accuracy of the generation algorithm by referring to past consultation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input past consultation data into AI, and the AI ​​can select the optimal generation algorithm.

[0096] The generation unit generates advice while considering the user's attribute information. For example, the generation unit can generate optimal advice by considering the user's age and gender. For example, the generation unit can generate advice by considering the user's occupation and living situation. For example, the generation unit can customize the advice based on the user's attribute information. This allows for more customized advice to be provided by considering the user's attribute information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user attribute information data into AI, and the AI ​​can generate optimal advice.

[0097] The generation unit estimates the user's emotions and adjusts the way advice is displayed based on the estimated emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the generation unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the generation unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can estimate the emotions.

[0098] The generation unit considers the geographical distribution of users when generating advice. For example, the generation unit can provide region-specific advice by considering the user's place of residence. For example, the generation unit can also customize advice based on the geographical distribution of users. For example, the generation unit can generate optimal advice by considering the geographical distribution of users. This allows for the provision of region-specific advice by considering the geographical distribution of users. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical distribution data into AI, and the AI ​​can generate optimal advice.

[0099] The generation unit improves the accuracy of its generation by referring to relevant literature when generating advice. For example, the generation unit can optimize its generation algorithm based on relevant literature. The generation unit can also improve the accuracy of its generation results by referring to relevant literature. The generation unit can also improve its generation method based on relevant literature. As a result, the accuracy of the generation is improved by referring to relevant literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into AI, and the AI ​​can select the optimal generation algorithm.

[0100] The service provider estimates the user's emotions and adjusts the method of providing advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide advice in gentle language. For example, if the user is relaxed, the service provider can provide detailed advice. For example, if the user is in a hurry, the service provider can provide concise and to-the-point advice. By adjusting the method of delivery according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate the emotions.

[0101] The service provider selects the optimal service method when providing advice, by referring to the user's past feedback. For example, the service provider can select the optimal service method based on the user's past feedback. For example, the service provider can also analyze the user's past feedback and customize the service method. For example, the service provider can optimize the service method by referring to the user's past feedback. This improves the accuracy of the service method by referring to past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past feedback data into AI, and the AI ​​can select the optimal service method.

[0102] The service provider takes user attribute information into consideration when providing advice. For example, the service provider can provide optimal advice by considering the user's age and gender. For example, the service provider can also provide advice by considering the user's occupation and living situation. For example, the service provider can customize the advice based on the user's attribute information. This allows for more customized advice to be provided by considering the user's attribute 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 user attribute information data into AI, and the AI ​​can provide optimal advice.

[0103] The service provider estimates the user's emotions and prioritizes advice based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing urgent advice. For example, if the user is relaxed, the service provider can prioritize providing detailed advice. For example, if the user is in a hurry, the service provider can prioritize providing concise advice. This allows for the prioritization of more important advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can then estimate the emotions.

[0104] The service provider selects the optimal delivery method when providing advice, taking into account the user's geographical location information. For example, the service provider can provide region-specific advice by considering the user's current location. For example, the service provider can also select the optimal delivery method based on the user's geographical location information. For example, the service provider can provide relevant advice based on the user's current location. This allows for the provision of region-specific advice by considering the user's geographical location 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 geographical location data into AI, which can then select the optimal delivery method.

[0105] The service provider analyzes the user's social media activity when providing advice and proposes a means of providing it. For example, the service provider can analyze the user's social media activity and propose the most suitable method of providing advice. For example, the service provider can provide relevant advice based on topics that the user frequently mentions on social media. For example, the service provider can customize the method of providing advice based on the user's social media activity. This allows for the proposal of a more appropriate means of providing advice by analyzing the user's social media activity. 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 social media activity data into AI, which can then propose the most suitable means of providing advice.

[0106] The search unit estimates the user's emotions and adjusts the search method based on the estimated emotions. For example, if the user is stressed, the search unit can use a simple search method to provide results quickly. If the user is relaxed, the search unit can use a detailed search method to provide deeper insights. If the user is in a hurry, the search unit can use a rapid search method to provide results quickly. This allows for more appropriate searches by adjusting the search method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0107] The search unit optimizes the search algorithm by referring to past consultation data during a search. For example, the search unit can select the optimal search algorithm based on past consultation data. The search unit can also improve the accuracy of the search algorithm by analyzing past consultation data. The search unit can also optimize the search algorithm by referring to past consultation data. This improves the accuracy of the search algorithm by referring to past consultation data. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past consultation data into AI, and the AI ​​can select the optimal search algorithm.

[0108] The search unit estimates the user's emotions and adjusts the display method of search results based on the estimated emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible display method. For example, if the user is relaxed, the search unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the search unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI, and the generative AI can estimate emotions.

[0109] The search unit performs searches while considering the geographical distribution of users. For example, the search unit can search for region-specific problems by considering the user's place of residence. For example, the search unit can also customize search results based on the geographical distribution of users. For example, the search unit can select the optimal search method by considering the geographical distribution of users. This makes it possible to respond appropriately to region-specific problems by considering the geographical distribution of users. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input user geographical distribution data into AI, and the AI ​​can select the optimal search method.

[0110] The insight utilization unit estimates the user's emotions and adjusts how insights are utilized based on the estimated emotions. For example, if the user is stressed, the insight utilization unit can provide simple insights and deliver results quickly. For example, if the user is relaxed, the insight utilization unit can provide detailed insights and deliver deeper insights. For example, if the user is in a hurry, the insight utilization unit can provide rapid insights and deliver results quickly. This allows for the provision of more appropriate insights by adjusting how insights are utilized 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 insight utilization unit may be performed using AI or not using AI. For example, the insight utilization unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0111] The knowledge utilization unit optimizes the scope of knowledge application by referring to past consultation data when utilizing knowledge. For example, the knowledge utilization unit can select the optimal knowledge based on past consultation data. For example, the knowledge utilization unit can also optimize the scope of knowledge application by analyzing past consultation data. For example, the knowledge utilization unit can optimize the scope of knowledge application by referring to past consultation data. As a result, the scope of knowledge application is optimized by referring to past consultation data. Some or all of the above processing in the knowledge utilization unit may be performed using AI, for example, or without using AI. For example, the knowledge utilization unit can input past consultation data into AI, and the AI ​​can select the optimal knowledge.

[0112] The insight utilization unit estimates the user's emotions and adjusts the way insights are displayed based on the estimated emotions. For example, if the user is nervous, the insight utilization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the insight utilization unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the insight utilization unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, more appropriate insights can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the insight utilization unit may be performed using AI, for example, or without AI. For example, the insight utilization unit can input user emotion data into a generative AI, and the generative AI can estimate emotions.

[0113] The knowledge utilization unit applies knowledge while considering the geographical distribution of users. For example, the knowledge utilization unit can provide region-specific knowledge by considering the user's place of residence. For example, the knowledge utilization unit can also customize knowledge based on the geographical distribution of users. For example, the knowledge utilization unit can provide optimal knowledge by considering the geographical distribution of users. In this way, region-specific knowledge can be provided by considering the geographical distribution of users. Some or all of the above processing in the knowledge utilization unit may be performed using AI, for example, or without AI. For example, the knowledge utilization unit can input the user's geographical distribution data into AI, and the AI ​​can provide optimal knowledge.

[0114] The feedback reception unit estimates the user's emotions and adjusts the feedback reception method based on the estimated emotions. For example, if the user is stressed, the feedback reception unit can provide a simple feedback form and minimize the input steps. For example, if the user is relaxed, the feedback reception unit can provide detailed feedback options and suggest a customizable feedback method. For example, if the user is in a hurry, the feedback reception unit can prioritize voice feedback to allow for quick feedback input. This allows for more appropriate feedback by adjusting the reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback reception unit may be performed using AI or not using AI. For example, the feedback reception unit can input user emotion data into a generative AI, which can estimate emotions.

[0115] The feedback receiving unit selects the optimal receiving method by referring to the user's past feedback history when receiving feedback. For example, the feedback receiving unit can select the optimal receiving method based on the user's past feedback history. For example, the feedback receiving unit can also analyze the user's past feedback history and customize the receiving method. For example, the feedback receiving unit can optimize the receiving method by referring to the user's past feedback history. This improves the accuracy of the receiving method by referring to past feedback history. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without using AI. For example, the feedback receiving unit can input past feedback history data into AI, and the AI ​​can select the optimal receiving method.

[0116] The feedback receiving unit estimates the user's emotions and determines the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback receiving unit can prioritize receiving urgent feedback. For example, if the user is relaxed, the feedback receiving unit can prioritize receiving detailed feedback. For example, if the user is in a hurry, the feedback receiving unit can prioritize receiving concise feedback. This allows for prioritizing more important feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback receiving unit may be performed using AI or not using AI. For example, the feedback receiving unit can input user emotion data into a generative AI, which can estimate emotions.

[0117] The feedback receiving unit selects the optimal receiving method when receiving feedback, taking into account the user's geographical location information. The feedback receiving unit can, for example, prioritize receiving region-specific feedback by considering the user's current location. The feedback receiving unit can also, for example, select the optimal receiving method based on the user's geographical location information. The feedback receiving unit can also, for example, prioritize receiving relevant feedback based on the user's current location. This ensures that region-specific feedback is prioritized by considering the user's geographical location information. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input the user's geographical location data into AI, which can then select the optimal receiving method.

[0118] The feedback reception unit analyzes the user's social media activity when receiving feedback and proposes the optimal method of receiving it. For example, the feedback reception unit can analyze the user's social media activity and propose the optimal method of receiving feedback. For example, the feedback reception unit can also prioritize receiving relevant feedback based on topics that the user frequently mentions on social media. For example, the feedback reception unit can customize the feedback reception method based on the user's social media activity. This allows for the proposal of a more appropriate feedback reception method by analyzing the user's social media activity. Some or all of the above processing in the feedback reception unit may be performed using AI, for example, or without AI. For example, the feedback reception unit can input the user's social media activity data into AI, which can then propose the optimal method of receiving feedback.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The reception desk estimates the user's emotions and adjusts the input method for worries and problems based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of worries and problems. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotion.

[0121] The reception desk analyzes the user's past consultation history and proposes the optimal input format. For example, the reception desk can automatically display as suggestions any concerns or problems that the user has frequently entered in the past. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest concerns or problems that the user will use at a specific time of day based on their past consultation history. This improves input efficiency by suggesting the optimal input format based on past consultation 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 consultation history data into AI, and the AI ​​can suggest the optimal input format.

[0122] The reception unit filters the input content based on the user's current living situation and areas of interest when they input their worries or problems. For example, when a user inputs their current living situation, the reception unit can automatically display relevant worries or problems as suggestions. The reception unit can also prioritize receiving relevant worries or problems based on the user's areas of interest. The reception unit can also analyze the user's living situation and areas of interest and suggest the most appropriate input content. This allows for more relevant input by filtering the input content based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's living situation and areas of interest data into the AI, which can then filter the input content to the most appropriate level.

[0123] The reception desk estimates the user's emotions and prioritizes input based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize receiving urgent worries or problems. For example, if the user is relaxed, the reception desk can prioritize receiving detailed input. For example, if the user is in a hurry, the reception desk can prioritize receiving concise input. This allows for prioritizing more important content by determining the priority of input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate emotions.

[0124] The reception desk prioritizes accepting input that is highly relevant to the user's geographical location when they input their concerns or problems. For example, if the user is in a specific region, the reception desk can prioritize accepting concerns or problems related to that region. The reception desk can also automatically display region-specific problems as candidates based on the user's geographical location. The reception desk can also prioritize accepting relevant concerns or problems based on the user's current location. This allows for appropriate responses to region-specific problems by considering the user's geographical location. 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 geographical location data into the AI, which can then filter the input to the most relevant content.

[0125] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis method and provide results quickly. For example, if the user is relaxed, the analysis unit can use a detailed analysis method and provide deeper insights. For example, if the user is in a hurry, the analysis unit can use a rapid analysis method and provide results quickly. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0126] The analysis unit optimizes the analysis algorithm by referring to past consultation data during the analysis. The analysis unit can, for example, select the optimal analysis algorithm based on past consultation data. The analysis unit can also, for example, analyze past consultation data to improve the accuracy of the analysis algorithm. The analysis unit can also, for example, optimize the analysis algorithm by referring to past consultation data. This improves the accuracy of the analysis algorithm by referring to past consultation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past consultation data into AI, and the AI ​​can select the optimal analysis algorithm.

[0127] The analysis unit performs analysis while considering the user's attribute information. For example, the analysis unit can select the optimal analysis method by considering the user's age and gender. For example, the analysis unit can also perform analysis by considering the user's occupation and living situation. For example, the analysis unit can customize the analysis results based on the user's attribute information. This makes it possible to perform more customized analysis by considering the user's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information data into AI, and the AI ​​can select the optimal analysis method.

[0128] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can estimate emotions.

[0129] The analysis unit performs analysis while considering the geographical distribution of users. For example, the analysis unit can analyze region-specific problems by considering the user's place of residence. For example, the analysis unit can also customize the analysis results based on the geographical distribution of users. For example, the analysis unit can select the optimal analysis method by considering the geographical distribution of users. This makes it possible to respond appropriately to region-specific problems by considering the geographical distribution of users. 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 geographical distribution data into AI, and the AI ​​can select the optimal analysis method.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The reception desk receives input from users regarding their concerns and problems. These concerns and problems may include personal issues, work-related issues, health-related issues, etc. The reception desk can receive users' concerns and problems through methods such as text input, voice input, and image input. 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 use natural language processing technology to analyze the user's worries and problems, and sentiment analysis technology to analyze the user's emotions. It can also use data mining technology to analyze past consultation data. Step 3: The generation unit generates advice based on the information analyzed by the analysis unit. The advice is provided in various formats, such as text, audio, or video. The generation unit can generate advice using text generation AI (e.g., LLM), audio generation AI, or video generation AI. Step 4: The provider delivers the advice generated by the generator. The provider can deliver the advice through web applications or mobile applications. It can also deliver the advice via email or messaging apps. Furthermore, it can deliver the advice via video calls or voice calls.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 control unit 46A of the smart device 14 and receives the user's concerns and problems via text input or voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated advice to the user. 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.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In 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.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0150] 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.

[0151] 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 control unit 46A of the smart glasses 214 and receives the user's concerns and problems via text input or voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated advice to the user. 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.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 control unit 46A of the headset terminal 314 and receives the user's concerns and problems via text input or voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated advice to the user. 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.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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 control unit 46A of the robot 414 and receives the user's concerns and problems via text input or voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated advice to the user. 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) A reception desk that accepts user inquiries and problems, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates advice based on the information analyzed by the analysis unit, The system includes a providing unit that provides advice generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a search function to search past consultation data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes a knowledge utilization department that leverages psychological insights. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It includes a feedback reception section to receive user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback receiving unit is Collect user responses and provide them to the analysis unit. The system described in Appendix 4, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The user's response, provided by the aforementioned feedback reception unit, will be analyzed and reflected in future advice. The system described in Appendix 4, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how users input their worries and problems based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past consultation history and suggest the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input their worries or problems, the system filters the input based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their worries or problems, the system prioritizes accepting input that is highly relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their worries or problems, the system analyzes their social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past consultation data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) 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 19) The generating unit is It estimates the user's emotions and adjusts how advice is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating advice, the generation algorithm is optimized by referring to past consultation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating advice, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how advice is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating advice, the system takes into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating advice, we refer to relevant literature to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how advice is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, we refer to the user's past feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, we will take into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and propose methods for providing it. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned search unit, It estimates the user's sentiment and adjusts the search method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned search unit, When performing a search, the search algorithm is optimized by referring to past consultation data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned search unit, When performing a search, the system takes into account the geographical distribution of users. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned knowledge utilization unit is, We estimate user emotions and adjust how we use those insights based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned knowledge utilization unit is, When utilizing knowledge, refer to past consultation data to optimize the scope of application of that knowledge. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned knowledge utilization unit is, It estimates the user's emotions and adjusts how insights are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned knowledge utilization unit is, When utilizing insights, apply them while considering the geographical distribution of users. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned feedback receiving unit is It estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned feedback receiving unit is When receiving feedback, the system will refer to the user's past feedback history to select the most suitable method for receiving it. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned feedback receiving unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned feedback receiving unit is When receiving feedback, the system will select the most appropriate method of receiving it, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned feedback receiving unit is When receiving feedback, we analyze the user's social media activity and suggest the most suitable method of receiving it. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0204] 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 user inquiries and problems, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates advice based on the information analyzed by the analysis unit, The system comprises a providing unit that provides advice generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a search function to search past consultation data. The system according to feature 1.

3. The aforementioned analysis unit, It includes a knowledge utilization department that leverages psychological insights. The system according to feature 1.

4. The aforementioned supply unit is, It includes a feedback reception section to receive user feedback. The system according to feature 1.

5. The aforementioned feedback receiving unit is Collect user responses and provide them to the analysis unit. The system according to feature 4.

6. The aforementioned analysis unit, The user's response, provided by the aforementioned feedback reception unit, will be analyzed and reflected in future advice. The system according to feature 4.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how users input their worries and problems based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past consultation history and suggest the optimal input format. The system according to feature 1.

9. The aforementioned reception unit is When users input their worries or problems, the system filters the input based on their current living situation and areas of interest. The system according to feature 1.

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

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