system

Abstract

The system according to this embodiment aims to provide personalized conversations in which adolescents can seek advice anonymously and safely. [Solution] The system according to the embodiment comprises a reception unit, a dialogue generation unit, an emotion analysis unit, a goal setting support unit, a progress monitoring unit, and a data provision unit for parents. The reception unit receives user input. The dialogue generation unit generates personalized dialogues based on the information received by the reception unit. The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. The goal setting support unit provides support for goal setting and habit formation based on the emotional state understood by the emotion analysis unit. The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. The data provision unit for parents provides anonymized data to parents based on the progress monitored by the progress monitoring 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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a personalized dialogue service that allows adolescents to consult anonymously and comfortably is not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized dialogue that allows adolescents to consult anonymously and comfortably.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a dialogue generation unit, an emotion analysis unit, a goal setting support unit, a progress monitoring unit, and a data provision unit for parents. The reception unit receives user input. The dialogue generation unit generates personalized dialogues based on the information received by the reception unit. The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. The goal setting support unit provides support for goal setting and habit formation based on the emotional state understood by the emotion analysis unit. The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. The data provision unit for parents provides anonymized data to parents based on the progress monitored by the progress monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized conversations in which adolescents can seek advice anonymously and safely. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 ​​mentor system according to an embodiment of the present invention is a generative AI-powered service that provides support for the concerns of adolescents. This AI mentor system allows for anonymous and secure consultation 24 hours a day, 365 days a year, and provides personalized dialogue tailored to the user's age, personality, and interests. Specifically, it addresses issues such as school life, friendships, family problems, career counseling, and physical changes, and provides guidance on goal setting, habit formation, learning support, and stress management techniques. It also provides anonymized data and advice on how to interact with parents. For example, the user inputs their consultation content in text or voice. This input is analyzed by the generative AI, and a personalized dialogue is generated based on the user's age, personality, and interests. For example, a child with concerns about school life will be provided with advice on interpersonal relationships at school and study methods. Next, the generative AI performs emotional analysis to understand the user's emotional state. This allows it to appropriately understand and respond to the stress and anxiety the user is experiencing. For example, a child struggling with friendships will be provided with appropriate dialogue based on emotional analysis, and will be taught stress management techniques. Furthermore, the generative AI provides support for goal setting and habit formation. The AI ​​mentor proposes concrete action plans for the goals set by the user and monitors their progress. For example, as learning support, it sets daily study time and checks the degree of achievement, thereby helping users form study habits. It also provides anonymized data to parents and offers advice on how to interact with their children. This allows parents to understand their child's situation and provide appropriate support. For example, if a child is struggling with career choices, the AI ​​mentor provides parents with advice on how to counsel and support their child. In this way, the AI ​​mentor addresses the diverse concerns of adolescents and supports their mental health through personalized dialogue. It also offers comprehensive support for parents, making it a beneficial service for both children and parents. In this way, the AI ​​mentor system can empathize with the concerns of adolescents and support their mental health.

[0029] The AI ​​mentor system according to this embodiment comprises a reception unit, a dialogue generation unit, an emotion analysis unit, a goal setting support unit, a progress monitoring unit, and a data provision unit for parents. The reception unit receives user input. User input includes, but is not limited to, text input or voice input. For example, the reception unit accepts the user inputting the content of their consultation in text. The reception unit can also accept the user inputting the content of their consultation in voice. Furthermore, the reception unit can analyze the user's input and pass it on to an appropriate dialogue generation unit. The dialogue generation unit uses a generation AI to generate a personalized dialogue based on the information received by the reception unit. For example, the dialogue generation unit generates a dialogue based on the user's age, personality, and interests. For example, for a child who has concerns about school life, the dialogue generation unit generates a dialogue that provides advice on interpersonal relationships at school and study methods. The dialogue generation unit can also generate an appropriate dialogue based on emotion analysis for a child who has concerns about friendships. The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. The emotion analysis unit analyzes the user's emotional state using, for example, generative AI. For instance, it estimates emotions from the user's text and voice to understand stress and anxiety. The emotion analysis unit can also provide appropriate dialogue based on the user's emotional state. The goal setting support unit provides support for goal setting and habit formation based on the emotional state identified by the emotion analysis unit. For example, the goal setting support unit proposes specific action plans for the goals set by the user. For example, as learning support, the goal setting support unit sets daily study time and checks the degree of achievement to help the user form study habits. The goal setting support unit can also collaborate with the progress monitoring unit to support the user's goal achievement. The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. For example, the progress monitoring unit periodically checks the user's goal achievement and provides feedback. For example, the progress monitoring unit records the user's study time and achievement level to monitor progress.Furthermore, the progress monitoring unit can also provide appropriate advice according to the user's progress. The parent data provision unit provides anonymized data to parents based on the progress monitored by the progress monitoring unit. For example, the parent data provision unit provides parents with anonymized data on the user's progress and emotional state. For example, the parent data provision unit displays the user's progress as a graph or chart and provides it to parents. The parent data provision unit can also provide parents with advice on how to interact with their children. For example, if a child is struggling with career choices, the parent data provision unit can provide parents with advice on how to counsel and support their child regarding their future. Thus, the AI ​​mentor system according to the embodiment can receive user input, generate personalized dialogue, understand emotional states, support goal setting and habit formation, monitor progress, and provide anonymized data to parents.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input and voice input. For example, the reception unit can accept user input of their consultation details in text. It can also accept user input of their consultation details in voice. Furthermore, the reception unit can analyze the user's input and pass it on to the appropriate dialogue generation unit. Specifically, the reception unit uses natural language processing technology to analyze the user's input and extract keywords and context. For example, if a user inputs "I had a fight with a friend at school," the reception unit extracts keywords such as "school," "friend," and "fight" and passes them on to the dialogue generation unit. In the case of voice input, speech recognition technology is used to convert the voice to text and analyze it similarly. Furthermore, the reception unit can classify the user's input and assign it to the appropriate category. For example, classifying it into different categories such as consultations about academics, consultations about friendships, and consultations about family environment allows the dialogue generation unit to generate more appropriate dialogues. The reception unit can also temporarily store the user's input for use in subsequent processing. This makes it possible to track what kind of inquiries a user has made in the past and provide ongoing support.

[0031] The dialogue generation unit uses a generation AI to generate personalized dialogues based on information received by the reception unit. For example, the dialogue generation unit generates dialogues based on the user's age, personality, and interests. Specifically, the generation AI uses a large-scale language model (LLM) to analyze the user's input and generate appropriate responses. For example, if a user inputs "I had a fight with a friend at school," the dialogue generation unit will generate a response such as "That must have been terrible. What was the fight about?" The dialogue generation unit can also refer to the user's past input and dialogue history to provide consistent dialogues. For example, for a user who previously consulted about "not doing well in their studies," it can ask follow-up questions such as "Have you tried the study methods we discussed last time?" Furthermore, the dialogue generation unit can generate dialogues that take into account the user's emotional state. Based on emotional information provided by the emotion analysis unit, if the user is feeling stressed, it will take appropriate action, such as offering words of encouragement. In this way, the dialogue generation unit can provide users with personalized and emotionally resonant dialogues.

[0032] The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. For example, the emotion analysis unit analyzes the user's emotional state using generative AI. Specifically, the emotion analysis unit estimates emotions from the user's text or voice using natural language processing technology. For example, if the user inputs "very sad," the emotion analysis unit detects the emotion "sadness" and provides feedback to the dialogue generation unit. In the case of voice input, it can also estimate the user's emotional state by analyzing features such as tone, pitch, and speed. Furthermore, the emotion analysis unit can track the user's emotional state over time to understand long-term emotional changes. This allows for analysis of the user's tendency to experience certain emotions in different situations, enabling the provision of more appropriate support. For example, if a user is prone to stress during specific times or situations, it can provide advice on how to relax during those times. The emotion analysis unit can also adjust the content of the dialogue generated by the dialogue generation unit based on the user's emotional state. This allows for the provision of more appropriate and effective dialogue to the user.

[0033] The Goal Setting Support Department provides support for goal setting and habit formation based on the emotional state identified by the Emotion Analysis Department. For example, the Goal Setting Support Department proposes specific action plans for goals set by the user. Specifically, the Goal Setting Support Department breaks down the user's goals into achievable steps. For example, if a user sets the goal of "studying for one hour every day," the Goal Setting Support Department will propose a specific action plan such as "studying for 30 minutes twice a day." The Goal Setting Support Department can also monitor the user's progress and adjust goals as needed. For example, if a user fails to achieve their goal, the department will analyze the cause and revise the goal to a more realistic range. Furthermore, the Goal Setting Support Department also provides support to maintain the user's motivation. For example, by acknowledging the user's efforts, such as sending a message of praise when a goal is achieved, the department encourages continued effort. In this way, the Goal Setting Support Department can provide specific support to make it easier for users to achieve their goals and support habit formation.

[0034] The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. For example, the progress monitoring unit periodically checks the user's goal achievement and provides feedback. Specifically, the progress monitoring unit collects user behavior data and evaluates progress toward the goals. For example, if a user is using an app to record their daily study time, the unit collects that data and evaluates their goal achievement. The progress monitoring unit can also provide appropriate advice according to the user's progress. For example, if a user has not achieved their goal, the unit analyzes the cause and proposes solutions. Furthermore, the progress monitoring unit visualizes the user's progress data so that the user can grasp their progress at a glance. For example, by visually displaying the user's progress using graphs and charts, the unit makes it easier for the user to feel the results of their efforts. In this way, the progress monitoring unit can provide support for users to work effectively toward their goals.

[0035] The Parent Data Provision Department provides anonymized data to parents based on progress monitored by the Progress Monitoring Department. For example, the Parent Data Provision Department provides parents with anonymized data on the user's progress and emotional state. Specifically, the Parent Data Provision Department visualizes the user's progress data as graphs and charts, allowing parents to grasp their child's progress at a glance. The Parent Data Provision Department can also provide parents with advice on how to interact with their children. For example, if a child is struggling with career choices, the department can advise parents on how to counsel and support their child. Furthermore, the Parent Data Provision Department can send reports periodically so that parents can regularly check their child's progress. This allows parents to always know their child's progress and provide appropriate support. The Parent Data Provision Department plays an important role in facilitating communication between parents and children and supporting the child's growth.

[0036] The emotion analysis unit includes a notification unit that understands the user's emotional state and notifies a specialist if an anomaly is detected. The emotion analysis unit analyzes the user's emotional state using, for example, generative AI. For example, the emotion analysis unit estimates emotions from the user's text or voice and understands stress and anxiety. The emotion analysis unit includes a notification unit that notifies a specialist if an anomaly is detected. For example, the emotion analysis unit notifies a specialist if the user's emotional score exceeds a certain threshold. The emotion analysis unit can also notify a specialist if it detects a pattern of abnormal behavior. For example, the emotion analysis unit detects abnormal behavior from the user's text or voice and notifies a specialist. In this way, the emotion analysis unit can understand the user's emotional state and notify a specialist if an anomaly is detected. Some or all of the above processing in the emotion analysis unit may be performed using, for example, AI, or without AI. For example, the emotion analysis unit can input the user's text or voice data into a generative AI and have the generative AI perform the analysis of the emotional state.

[0037] The dialogue generation unit includes an information provision unit that provides information based on the user's interests. The dialogue generation unit generates dialogues based on the user's interests, for example, using a generation AI. For example, the dialogue generation unit generates dialogues that provide information related to the user's interests based on the user's past dialogue history and profile information. For example, if the user is interested in sports, the dialogue generation unit generates dialogues that provide information about sports. The dialogue generation unit can also generate dialogues that provide information about music if the user is interested in music. In this way, the dialogue generation unit can provide information based on the user's interests. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input data about the user's interests into the generation AI and have the generation AI execute dialogues to provide information.

[0038] The goal-setting support unit proposes specific action plans for the goals set by the user. For example, the goal-setting support unit can help the user develop study habits by setting daily study time and checking the degree of achievement as part of learning support. The goal-setting support unit can also collaborate with the progress monitoring unit to support the user in achieving their goals. This allows the goal-setting support unit to propose specific action plans for the goals set by the user. Some or all of the above processes in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's goal data into a generating AI and have the generating AI execute the action plan proposal.

[0039] The progress monitoring unit monitors the user's progress and confirms their level of achievement. For example, the progress monitoring unit periodically checks the user's goal achievement and provides feedback. For example, the progress monitoring unit records the user's study time and achievement level and monitors their progress. The progress monitoring unit can also provide appropriate advice according to the user's progress. In this way, the progress monitoring unit can monitor the user's progress and confirm their level of achievement. Some or all of the above processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's progress data into a generating AI and have the generating AI perform progress monitoring.

[0040] The Parent Data Provision Department provides anonymized data and offers advice to parents on how to interact with their children. For example, the Parent Data Provision Department provides parents with anonymized data on the user's progress and emotional state. For example, the Parent Data Provision Department displays the user's progress as graphs or charts and provides them to parents. The Parent Data Provision Department can also offer advice to parents on how to interact with their children. For example, if a child is struggling with career choices, the Parent Data Provision Department can offer advice to parents on how to counsel them and how to support them. In this way, the Parent Data Provision Department can offer advice to parents on how to interact with their children. Some or all of the above processing in the Parent Data Provision Department may be performed using AI or not. For example, the Parent Data Provision Department can input user progress data into a generating AI and have the generating AI perform data anonymization and advice generation.

[0041] The reception desk analyzes the user's past consultation history and selects the most suitable reception method. For example, the reception desk may prioritize suggesting consultation methods (text, voice, etc.) that the user has frequently used in the past. The reception desk can also analyze the content of the user's past consultations and automatically suggest relevant topics. Furthermore, the reception desk can suggest the most suitable reception method for a specific time of day based on the user's past consultation history. This allows the reception desk to analyze the user's past consultation history and select the most suitable reception method. 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 past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0042] The reception desk filters the user's current living situation and areas of interest upon receiving the request. For example, the reception desk prioritizes receiving inquiries based on the user's current living situation (school, home, etc.). The reception desk can also filter inquiries based on the user's areas of interest (sports, music, etc.). Furthermore, the reception desk can suggest the most suitable consultation method based on the user's living situation and areas of interest. This allows the reception desk to filter inquiries based on the user's current living situation and areas of interest. 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 data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The reception desk prioritizes receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inquiries related to that region. The reception desk can also prioritize inquiries concerning region-specific issues based on the user's geographical location. Furthermore, the reception desk can prioritize inquiries that offer local resources and support, taking into account the user's location. This allows the reception desk to prioritize receiving inquiries that are highly relevant, taking into account 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 a generating AI and have the generating AI prioritize receiving inquiries that are highly relevant.

[0044] The reception desk analyzes the user's social media activity upon receiving a request and accepts relevant inquiries. For example, the reception desk can identify the user's current interests and problems from their social media activity and accept relevant inquiries. The reception desk can also analyze the user's statements and posts on social media and suggest relevant consultation topics. Furthermore, the reception desk can suggest the most suitable consultation method based on the user's social media activity. This allows the reception desk to analyze the user's social media activity and accept relevant inquiries. 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 a generating AI and have the generating AI perform the task of accepting relevant inquiries.

[0045] The dialogue generation unit adjusts the level of detail in the dialogue based on the importance of the consultation content during dialogue generation. For example, the dialogue generation unit uses a generation AI to evaluate the importance of the consultation content and adjust the level of detail in the dialogue. For example, if the consultation content is important, the dialogue generation unit generates a dialogue that provides detailed explanations and specific advice. The dialogue generation unit can also generate a dialogue that provides concise advice if the consultation content is general. Furthermore, if the consultation content is urgent, the dialogue generation unit can generate a dialogue to enable a quick response. In this way, the dialogue generation unit can adjust the level of detail in the dialogue based on the importance of the consultation content. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input consultation content data into the generation AI and have the generation AI perform the adjustment of the level of detail in the dialogue.

[0046] The dialogue generation unit applies different dialogue algorithms depending on the category of the consultation content when generating dialogue. For example, the dialogue generation unit uses a generation AI to classify the category of the consultation content and apply an appropriate dialogue algorithm. For example, if the consultation concerns school life, the dialogue generation unit applies an education-related dialogue algorithm. The dialogue generation unit can also apply a psychological dialogue algorithm if the consultation concerns friendships. Furthermore, if the consultation concerns family problems, the dialogue generation unit can apply a dialogue algorithm specifically for family problems. In this way, the dialogue generation unit can apply different dialogue algorithms depending on the category of the consultation content. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input consultation content data into the generation AI and have the generation AI execute the application of the dialogue algorithm.

[0047] The dialogue generation unit determines the priority of dialogues based on when the consultation content was submitted. For example, the dialogue generation unit uses a generation AI to evaluate the submission timing of the consultation content and determine the priority of dialogues. For example, the dialogue generation unit prioritizes generating dialogues for consultations that have been submitted recently. The dialogue generation unit can also prioritize generating dialogues for consultations that have been left unattended for a long time. Furthermore, the dialogue generation unit can also prioritize generating dialogues for consultations that are of high urgency. In this way, the dialogue generation unit can determine the priority of dialogues based on when the consultation content was submitted. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input consultation content submission timing data into the generation AI and have the generation AI perform the determination of dialogue priority.

[0048] The dialogue generation unit adjusts the order of dialogues based on the relevance of the consultation content during dialogue generation. For example, the dialogue generation unit uses a generation AI to evaluate the relevance of the consultation content and adjust the order of dialogues. For example, the dialogue generation unit prioritizes generating dialogues for consultation content that is highly relevant. The dialogue generation unit can also postpone the generation of dialogues for consultation content that is less relevant. Furthermore, the dialogue generation unit can optimize the order of dialogues based on the relevance of the consultation content. In this way, the dialogue generation unit can adjust the order of dialogues based on the relevance of the consultation content. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input relevance data of the consultation content into the generation AI and have the generation AI perform the adjustment of the order of dialogues.

[0049] The emotion analysis unit improves the accuracy of emotion analysis by considering the interrelationships of the consultation content during the analysis. For example, the emotion analysis unit uses generative AI to evaluate the interrelationships of the consultation content and improve the accuracy of emotion analysis. For example, the emotion analysis unit analyzes the interrelationships of the consultation content and tracks changes in emotions. The emotion analysis unit can also improve the accuracy of emotion analysis by considering the interrelationships of the consultation content. Furthermore, the emotion analysis unit can identify patterns of emotions based on the interrelationships of the consultation content. This allows the emotion analysis unit to improve the accuracy of emotion analysis by considering the interrelationships of the consultation content. Some or all of the above processing in the emotion analysis unit is performed using generative AI. For example, the emotion analysis unit can input interrelationship data of the consultation content into the generative AI and have the generative AI perform the improvement of the accuracy of emotion analysis.

[0050] The emotion analysis unit performs emotion analysis while considering the client's attribute information. For example, the emotion analysis unit uses a generative AI to evaluate the client's attribute information and perform emotion analysis. For example, the emotion analysis unit considers the client's age and gender when performing emotion analysis. The emotion analysis unit can also perform emotion analysis while considering the client's interests and concerns. Furthermore, the emotion analysis unit can also perform emotion analysis while considering the client's past consultation history. In this way, the emotion analysis unit can perform emotion analysis while considering the client's attribute information. Some or all of the above processing in the emotion analysis unit is performed using a generative AI. For example, the emotion analysis unit can input the client's attribute information data into the generative AI and have the generative AI perform emotion analysis.

[0051] The emotion analysis unit performs emotion analysis while considering the geographical distribution of the consultation content. For example, the emotion analysis unit uses generative AI to evaluate the geographical distribution of the consultation content and perform emotion analysis. For example, the emotion analysis unit analyzes the geographical distribution of the consultation content and identifies emotion patterns for each region. The emotion analysis unit can also perform region-specific emotion analysis while considering the geographical distribution. Furthermore, the emotion analysis unit can track changes in emotion based on the geographical distribution. This allows the emotion analysis unit to perform emotion analysis while considering the geographical distribution of the consultation content. Some or all of the above processing in the emotion analysis unit is performed using generative AI. For example, the emotion analysis unit can input geographical distribution data of the consultation content into the generative AI and have the generative AI perform the emotion analysis.

[0052] The emotion analysis unit improves the accuracy of its emotion analysis by referring to relevant literature related to the consultation content during the analysis. For example, the emotion analysis unit uses generative AI to evaluate relevant literature related to the consultation content and improve the accuracy of its emotion analysis. For example, the emotion analysis unit refers to literature related to the consultation content to improve the accuracy of its emotion analysis. The emotion analysis unit can also identify emotion patterns based on the relevant literature. Furthermore, the emotion analysis unit can supplement the results of its emotion analysis by referring to relevant literature. In this way, the emotion analysis unit can improve the accuracy of its emotion analysis by referring to relevant literature related to the consultation content. Some or all of the above processes in the emotion analysis unit are performed using generative AI. For example, the emotion analysis unit can input data on relevant literature related to the consultation content into the generative AI and have the generative AI perform the task of improving the accuracy of its emotion analysis.

[0053] The goal-setting support unit analyzes the user's past behavioral history to select the optimal goal-setting method when setting goals. For example, the goal-setting support unit analyzes the user's past behavioral history and selects the optimal goal-setting method. For example, the goal-setting support unit analyzes the user's past behavioral history and sets achievable goals. The goal-setting support unit can also set goals that increase motivation based on the user's past successes. Furthermore, the goal-setting support unit can consider the user's past failures and set realistic goals. In this way, the goal-setting support unit can analyze the user's past behavioral history and select the optimal goal-setting method. Some or all of the above processes in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal goal-setting method.

[0054] The goal-setting support unit customizes the means of goal setting based on the user's current living situation when setting goals. For example, the goal-setting support unit sets realistic goals based on the user's current living situation (school, home, etc.). The goal-setting support unit can also set achievable goals in accordance with the user's daily rhythm. Furthermore, the goal-setting support unit can set flexible goals according to the user's living situation. This allows the goal-setting support unit to customize the means of goal setting based on the user's current living situation. Some or all of the above processing in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of goal setting.

[0055] The goal-setting support unit selects the optimal goal-setting method when setting goals, taking into account the user's geographical location information. For example, the goal-setting support unit sets region-specific goals based on the user's geographical location information. The goal-setting support unit can also set realistic goals considering the user's location information. Furthermore, the goal-setting support unit can set flexible goals according to the user's geographical conditions. This allows the goal-setting support unit to select the optimal goal-setting method considering the user's geographical location information. Some or all of the above processing in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal goal-setting method.

[0056] The goal-setting support unit analyzes the user's social media activity and proposes methods for setting goals. For example, the goal-setting support unit can analyze the user's social media activity and propose methods for setting goals. For example, the goal-setting support unit can identify the user's interests from their social media activity and set goals based on those interests. The goal-setting support unit can also analyze the user's statements and posts on social media and propose relevant goals. Furthermore, the goal-setting support unit can set goals that increase motivation based on the user's social media activity. In this way, the goal-setting support unit can analyze the user's social media activity and propose methods for setting goals. Some or all of the above processing in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's social media activity data into a generating AI and have the generating AI propose methods for setting goals.

[0057] The progress monitoring unit analyzes the user's past progress history to select the optimal monitoring method during progress monitoring. For example, the progress monitoring unit analyzes the user's past progress history and selects the optimal monitoring method. For example, the progress monitoring unit analyzes the user's past progress history and selects an effective monitoring method. The progress monitoring unit can also select a monitoring method that enhances motivation based on the user's past successes. Furthermore, the progress monitoring unit can consider the user's past failures and select a realistic monitoring method. In this way, the progress monitoring unit can analyze the user's past progress history and select the optimal monitoring method. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's past progress history data into a generating AI and have the generating AI select the optimal monitoring method.

[0058] The progress monitoring unit customizes the monitoring methods based on the user's current living situation when monitoring progress. For example, the progress monitoring unit selects a realistic monitoring method based on the user's current living situation (school, home, etc.). The progress monitoring unit can also select an effective monitoring method that matches the user's daily rhythm. Furthermore, the progress monitoring unit can select a flexible monitoring method according to the user's living situation. This allows the progress monitoring unit to customize the monitoring methods based on the user's current living situation. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input user living situation data into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0059] The progress monitoring unit selects the optimal monitoring method when monitoring progress, taking into account the user's geographical location information. For example, the progress monitoring unit selects the optimal monitoring method by considering the user's geographical location information. For example, the progress monitoring unit selects a region-specific monitoring method based on the user's geographical location information. The progress monitoring unit can also select a realistic monitoring method by considering the user's location information. Furthermore, the progress monitoring unit can select a flexible monitoring method according to the user's geographical conditions. In this way, the progress monitoring unit can select the optimal monitoring method by considering the user's geographical location information. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal monitoring method.

[0060] The progress monitoring unit analyzes the user's social media activity and proposes monitoring methods during progress monitoring. For example, the progress monitoring unit analyzes the user's social media activity and proposes monitoring methods. For example, the progress monitoring unit identifies the progress status from the user's social media activity and proposes a monitoring method based on that. The progress monitoring unit can also analyze the user's statements and posts on social media and propose relevant monitoring methods. Furthermore, the progress monitoring unit can propose monitoring methods that enhance motivation based on the user's social media activity. In this way, the progress monitoring unit can analyze the user's social media activity and propose monitoring methods. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of monitoring methods.

[0061] The Parent Data Provision Department analyzes the user's past data provision history to select the optimal provision method when providing data to parents. For example, the Parent Data Provision Department analyzes the user's past data provision history to select the optimal provision method. For example, the Parent Data Provision Department analyzes the user's past data provision history to select an effective provision method. The Parent Data Provision Department can also select a provision method that increases motivation based on the user's past successes. Furthermore, the Parent Data Provision Department can consider the user's past failures to select a realistic provision method. In this way, the Parent Data Provision Department can analyze the user's past data provision history to select the optimal provision method. Some or all of the above processing in the Parent Data Provision Department may be performed using AI or not. For example, the Parent Data Provision Department can input the user's past data provision history data into a generating AI and have the generating AI select the optimal provision method.

[0062] The parent data provision unit customizes the means of data provision based on the user's current living situation when providing data to parents. For example, the parent data provision unit can customize the means of data provision based on the user's current living situation. For example, the parent data provision unit can select a realistic data provision method based on the user's current living situation (school, home, etc.). The parent data provision unit can also select an effective data provision method that matches the user's daily rhythm. Furthermore, the parent data provision unit can select a flexible data provision method according to the user's living situation. This allows the parent data provision unit to customize the means of data provision based on the user's current living situation. Some or all of the above processing in the parent data provision unit may be performed using AI or not. For example, the parent data provision unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the data provision method.

[0063] The parent data provision unit selects the optimal data provision method when providing data to parents, taking into account the user's geographical location information. For example, the parent data provision unit selects the optimal data provision method by considering the user's geographical location information. For example, the parent data provision unit selects a region-specific data provision method based on the user's geographical location information. The parent data provision unit can also select a practical data provision method by considering the user's location information. Furthermore, the parent data provision unit can select a flexible data provision method according to the user's geographical conditions. In this way, the parent data provision unit can select the optimal data provision method by considering the user's geographical location information. Some or all of the above processing in the parent data provision unit may be performed using AI or not. For example, the parent data provision unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal data provision method.

[0064] The Parent Data Provision Department analyzes users' social media activity and proposes methods for providing data to parents. For example, the Parent Data Provision Department can analyze users' social media activity and propose methods for providing data. For example, the Parent Data Provision Department can identify parent data from users' social media activity and propose methods for providing it based on that data. The Parent Data Provision Department can also analyze users' statements and posts on social media and propose relevant data provision methods. Furthermore, the Parent Data Provision Department can propose data provision methods that enhance motivation based on users' social media activity. In this way, the Parent Data Provision Department can analyze users' social media activity and propose methods for providing data. Some or all of the above processing in the Parent Data Provision Department may be performed using AI or not. For example, the Parent Data Provision Department can input users' social media activity data into a generating AI and have the generating AI propose methods for providing data.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The AI ​​mentor system can also be equipped with a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and sleep patterns to understand their health condition. If, for instance, the health management unit detects an abnormally high heart rate, it may indicate stress or anxiety and suggest relaxation techniques. Furthermore, it can analyze the user's sleep patterns and suggest appropriate sleep habits if sleep deprivation persists. In addition, the health management unit can monitor the user's diet and exercise habits to support healthy lifestyles. This allows the AI ​​mentor system to comprehensively support the user's health and maintain their physical and mental well-being.

[0067] The AI ​​mentor system can also include a learning style analysis unit that analyzes the user's learning style. This unit, for example, analyzes the user's learning history and methods to suggest the optimal learning style. For instance, if the user prefers visual learning, it might suggest a learning plan that heavily utilizes visual content. Similarly, if the user prefers auditory learning, it could suggest a learning plan that utilizes audio content. Furthermore, the learning style analysis unit can consider the user's learning pace and concentration level to suggest an effective learning schedule. This allows the AI ​​mentor system to provide effective learning support tailored to the user's learning style.

[0068] The AI ​​mentor system can also be equipped with an activity suggestion unit that leverages the user's hobbies and skills. This unit suggests appropriate activities based on the user's interests and skills. For example, if the user enjoys sports, it will suggest sports-related activities. If the user enjoys music, it can suggest music-related activities. Furthermore, if the user is interested in art, it can suggest art-related activities. This allows the AI ​​mentor system to suggest activities that utilize the user's hobbies and skills, thereby stimulating their interest.

[0069] The AI ​​mentor system can also be equipped with a visual feedback section that visualizes the user's learning progress. This visual feedback section can, for example, display the user's learning progress as graphs or charts, providing visual feedback. For instance, it could display the user's learning time and achievement level as graphs, allowing for a quick overview of their progress. It could also display the user's goal achievement level as charts, allowing them to visually experience a sense of accomplishment. Furthermore, the visual feedback section can analyze the user's learning patterns and suggest effective learning methods. This allows the AI ​​mentor system to visually provide feedback on the user's learning progress and enhance their motivation to learn.

[0070] The AI ​​mentoring system can also include a regional support unit that provides region-specific support by considering the user's geographical location. For example, the regional support unit provides support for region-specific issues based on the user's geographical location. If the user is in a specific region, the regional support unit provides information and resources relevant to that region. It can also provide information on local events and activities to help users participate in their local communities. Furthermore, the regional support unit can collaborate with local experts and support groups to provide users with appropriate support. This allows the AI ​​mentoring system to provide region-specific support by considering the user's geographical location.

[0071] The AI ​​mentoring system can also include a social media analysis unit that analyzes the user's social media activity and provides relevant advice. For example, the social media analysis unit can analyze the user's social media activity to identify current interests and problems. It can also analyze the user's social media posts and provide relevant advice. Furthermore, the social media analysis unit can identify interests from the user's social media activity and suggest activities based on those interests. In addition, the social media analysis unit can provide motivational advice based on the user's social media activity. This allows the AI ​​mentoring system to analyze the user's social media activity and provide relevant advice.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception unit receives user input. User input includes text input and voice input. For example, it accepts the user to enter their consultation details in text or voice. The reception unit also analyzes the user's input and passes it to the appropriate dialogue generation unit. Step 2: The dialogue generation unit generates a personalized dialogue based on the information received by the reception unit. The dialogue generation unit generates a dialogue based on the user's age, personality, and interests. For example, for a child who has concerns about school life, it generates a dialogue that provides advice on relationships at school and study methods. Step 3: The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. The emotion analysis unit uses generation AI to analyze the user's emotional state, estimates emotions from the user's text and voice, and understands stress and anxiety. Step 4: The Goal Setting Support Department provides support for goal setting and habit formation based on the emotional state identified by the Emotion Analysis Department. For example, it proposes a concrete action plan for the goals set by the user, sets daily study time as learning support, and checks the degree of achievement to help the user form a learning habit. Step 5: The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. For example, it periodically checks the user's goal achievement and provides feedback. It also records the user's study time and achievement level to monitor progress. Step 6: The Parent Data Provision Unit provides anonymized data to parents based on the progress monitored by the Progress Monitoring Unit. For example, it provides parents with anonymized user progress and emotional status, and displays the progress as graphs or charts.

[0074] (Example of form 2) The AI ​​mentor system according to an embodiment of the present invention is a generative AI-powered service that provides support for the concerns of adolescents. This AI mentor system allows for anonymous and secure consultation 24 hours a day, 365 days a year, and provides personalized dialogue tailored to the user's age, personality, and interests. Specifically, it addresses issues such as school life, friendships, family problems, career counseling, and physical changes, and provides guidance on goal setting, habit formation, learning support, and stress management techniques. It also provides anonymized data and advice on how to interact with parents. For example, the user inputs their consultation content in text or voice. This input is analyzed by the generative AI, and a personalized dialogue is generated based on the user's age, personality, and interests. For example, a child with concerns about school life will be provided with advice on interpersonal relationships at school and study methods. Next, the generative AI performs emotional analysis to understand the user's emotional state. This allows it to appropriately understand and respond to the stress and anxiety the user is experiencing. For example, a child struggling with friendships will be provided with appropriate dialogue based on emotional analysis, and will be taught stress management techniques. Furthermore, the generative AI provides support for goal setting and habit formation. The AI ​​mentor proposes concrete action plans for the goals set by the user and monitors their progress. For example, as learning support, it sets daily study time and checks the degree of achievement, thereby helping users form study habits. It also provides anonymized data to parents and offers advice on how to interact with their children. This allows parents to understand their child's situation and provide appropriate support. For example, if a child is struggling with career choices, the AI ​​mentor provides parents with advice on how to counsel and support their child. In this way, the AI ​​mentor addresses the diverse concerns of adolescents and supports their mental health through personalized dialogue. It also offers comprehensive support for parents, making it a beneficial service for both children and parents. In this way, the AI ​​mentor system can empathize with the concerns of adolescents and support their mental health.

[0075] The AI ​​mentor system according to this embodiment comprises a reception unit, a dialogue generation unit, an emotion analysis unit, a goal setting support unit, a progress monitoring unit, and a data provision unit for parents. The reception unit receives user input. User input includes, but is not limited to, text input or voice input. For example, the reception unit accepts the user inputting the content of their consultation in text. The reception unit can also accept the user inputting the content of their consultation in voice. Furthermore, the reception unit can analyze the user's input and pass it on to an appropriate dialogue generation unit. The dialogue generation unit uses a generation AI to generate a personalized dialogue based on the information received by the reception unit. For example, the dialogue generation unit generates a dialogue based on the user's age, personality, and interests. For example, for a child who has concerns about school life, the dialogue generation unit generates a dialogue that provides advice on interpersonal relationships at school and study methods. The dialogue generation unit can also generate an appropriate dialogue based on emotion analysis for a child who has concerns about friendships. The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. The emotion analysis unit analyzes the user's emotional state using, for example, generative AI. For instance, it estimates emotions from the user's text and voice to understand stress and anxiety. The emotion analysis unit can also provide appropriate dialogue based on the user's emotional state. The goal setting support unit provides support for goal setting and habit formation based on the emotional state identified by the emotion analysis unit. For example, the goal setting support unit proposes specific action plans for the goals set by the user. For example, as learning support, the goal setting support unit sets daily study time and checks the degree of achievement to help the user form study habits. The goal setting support unit can also collaborate with the progress monitoring unit to support the user's goal achievement. The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. For example, the progress monitoring unit periodically checks the user's goal achievement and provides feedback. For example, the progress monitoring unit records the user's study time and achievement level to monitor progress.Furthermore, the progress monitoring unit can also provide appropriate advice according to the user's progress. The parent data provision unit provides anonymized data to parents based on the progress monitored by the progress monitoring unit. For example, the parent data provision unit provides parents with anonymized data on the user's progress and emotional state. For example, the parent data provision unit displays the user's progress as a graph or chart and provides it to parents. The parent data provision unit can also provide parents with advice on how to interact with their children. For example, if a child is struggling with career choices, the parent data provision unit can provide parents with advice on how to counsel and support their child regarding their future. Thus, the AI ​​mentor system according to the embodiment can receive user input, generate personalized dialogue, understand emotional states, support goal setting and habit formation, monitor progress, and provide anonymized data to parents.

[0076] The reception unit receives user input. User input includes, but is not limited to, text input and voice input. For example, the reception unit can accept user input of their consultation details in text. It can also accept user input of their consultation details in voice. Furthermore, the reception unit can analyze the user's input and pass it on to the appropriate dialogue generation unit. Specifically, the reception unit uses natural language processing technology to analyze the user's input and extract keywords and context. For example, if a user inputs "I had a fight with a friend at school," the reception unit extracts keywords such as "school," "friend," and "fight" and passes them on to the dialogue generation unit. In the case of voice input, speech recognition technology is used to convert the voice to text and analyze it similarly. Furthermore, the reception unit can classify the user's input and assign it to the appropriate category. For example, classifying it into different categories such as consultations about academics, consultations about friendships, and consultations about family environment allows the dialogue generation unit to generate more appropriate dialogues. The reception unit can also temporarily store the user's input for use in subsequent processing. This makes it possible to track what kind of inquiries a user has made in the past and provide ongoing support.

[0077] The dialogue generation unit uses a generation AI to generate personalized dialogues based on information received by the reception unit. For example, the dialogue generation unit generates dialogues based on the user's age, personality, and interests. Specifically, the generation AI uses a large-scale language model (LLM) to analyze the user's input and generate appropriate responses. For example, if a user inputs "I had a fight with a friend at school," the dialogue generation unit will generate a response such as "That must have been terrible. What was the fight about?" The dialogue generation unit can also refer to the user's past input and dialogue history to provide consistent dialogues. For example, for a user who previously consulted about "not doing well in their studies," it can ask follow-up questions such as "Have you tried the study methods we discussed last time?" Furthermore, the dialogue generation unit can generate dialogues that take into account the user's emotional state. Based on emotional information provided by the emotion analysis unit, if the user is feeling stressed, it will take appropriate action, such as offering words of encouragement. In this way, the dialogue generation unit can provide users with personalized and emotionally resonant dialogues.

[0078] The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. For example, the emotion analysis unit analyzes the user's emotional state using generative AI. Specifically, the emotion analysis unit estimates emotions from the user's text or voice using natural language processing technology. For example, if the user inputs "very sad," the emotion analysis unit detects the emotion "sadness" and provides feedback to the dialogue generation unit. In the case of voice input, it can also estimate the user's emotional state by analyzing features such as tone, pitch, and speed. Furthermore, the emotion analysis unit can track the user's emotional state over time to understand long-term emotional changes. This allows for analysis of the user's tendency to experience certain emotions in different situations, enabling the provision of more appropriate support. For example, if a user is prone to stress during specific times or situations, it can provide advice on how to relax during those times. The emotion analysis unit can also adjust the content of the dialogue generated by the dialogue generation unit based on the user's emotional state. This allows for the provision of more appropriate and effective dialogue to the user.

[0079] The Goal Setting Support Department provides support for goal setting and habit formation based on the emotional state identified by the Emotion Analysis Department. For example, the Goal Setting Support Department proposes specific action plans for goals set by the user. Specifically, the Goal Setting Support Department breaks down the user's goals into achievable steps. For example, if a user sets the goal of "studying for one hour every day," the Goal Setting Support Department will propose a specific action plan such as "studying for 30 minutes twice a day." The Goal Setting Support Department can also monitor the user's progress and adjust goals as needed. For example, if a user fails to achieve their goal, the department will analyze the cause and revise the goal to a more realistic range. Furthermore, the Goal Setting Support Department also provides support to maintain the user's motivation. For example, by acknowledging the user's efforts, such as sending a message of praise when a goal is achieved, the department encourages continued effort. In this way, the Goal Setting Support Department can provide specific support to make it easier for users to achieve their goals and support habit formation.

[0080] The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. For example, the progress monitoring unit periodically checks the user's goal achievement and provides feedback. Specifically, the progress monitoring unit collects user behavior data and evaluates progress toward the goals. For example, if a user is using an app to record their daily study time, the unit collects that data and evaluates their goal achievement. The progress monitoring unit can also provide appropriate advice according to the user's progress. For example, if a user has not achieved their goal, the unit analyzes the cause and proposes solutions. Furthermore, the progress monitoring unit visualizes the user's progress data so that the user can grasp their progress at a glance. For example, by visually displaying the user's progress using graphs and charts, the unit makes it easier for the user to feel the results of their efforts. In this way, the progress monitoring unit can provide support for users to work effectively toward their goals.

[0081] The Parent Data Provision Department provides anonymized data to parents based on progress monitored by the Progress Monitoring Department. For example, the Parent Data Provision Department provides parents with anonymized data on the user's progress and emotional state. Specifically, the Parent Data Provision Department visualizes the user's progress data as graphs and charts, allowing parents to grasp their child's progress at a glance. The Parent Data Provision Department can also provide parents with advice on how to interact with their children. For example, if a child is struggling with career choices, the department can advise parents on how to counsel and support their child. Furthermore, the Parent Data Provision Department can send reports periodically so that parents can regularly check their child's progress. This allows parents to always know their child's progress and provide appropriate support. The Parent Data Provision Department plays an important role in facilitating communication between parents and children and supporting the child's growth.

[0082] The emotion analysis unit includes a notification unit that understands the user's emotional state and notifies a specialist if an anomaly is detected. The emotion analysis unit analyzes the user's emotional state using, for example, generative AI. For example, the emotion analysis unit estimates emotions from the user's text or voice and understands stress and anxiety. The emotion analysis unit includes a notification unit that notifies a specialist if an anomaly is detected. For example, the emotion analysis unit notifies a specialist if the user's emotional score exceeds a certain threshold. The emotion analysis unit can also notify a specialist if it detects a pattern of abnormal behavior. For example, the emotion analysis unit detects abnormal behavior from the user's text or voice and notifies a specialist. In this way, the emotion analysis unit can understand the user's emotional state and notify a specialist if an anomaly is detected. Some or all of the above processing in the emotion analysis unit may be performed using, for example, AI, or without AI. For example, the emotion analysis unit can input the user's text or voice data into a generative AI and have the generative AI perform the analysis of the emotional state.

[0083] The dialogue generation unit includes an information provision unit that provides information based on the user's interests. The dialogue generation unit generates dialogues based on the user's interests, for example, using a generation AI. For example, the dialogue generation unit generates dialogues that provide information related to the user's interests based on the user's past dialogue history and profile information. For example, if the user is interested in sports, the dialogue generation unit generates dialogues that provide information about sports. The dialogue generation unit can also generate dialogues that provide information about music if the user is interested in music. In this way, the dialogue generation unit can provide information based on the user's interests. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input data about the user's interests into the generation AI and have the generation AI execute dialogues to provide information.

[0084] The goal-setting support unit proposes specific action plans for the goals set by the user. For example, the goal-setting support unit can help the user develop study habits by setting daily study time and checking the degree of achievement as part of learning support. The goal-setting support unit can also collaborate with the progress monitoring unit to support the user in achieving their goals. This allows the goal-setting support unit to propose specific action plans for the goals set by the user. Some or all of the above processes in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's goal data into a generating AI and have the generating AI execute the action plan proposal.

[0085] The progress monitoring unit monitors the user's progress and confirms their level of achievement. For example, the progress monitoring unit periodically checks the user's goal achievement and provides feedback. For example, the progress monitoring unit records the user's study time and achievement level and monitors their progress. The progress monitoring unit can also provide appropriate advice according to the user's progress. In this way, the progress monitoring unit can monitor the user's progress and confirm their level of achievement. Some or all of the above processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's progress data into a generating AI and have the generating AI perform progress monitoring.

[0086] The Parent Data Provision Department provides anonymized data and offers advice to parents on how to interact with their children. For example, the Parent Data Provision Department provides parents with anonymized data on the user's progress and emotional state. For example, the Parent Data Provision Department displays the user's progress as graphs or charts and provides them to parents. The Parent Data Provision Department can also offer advice to parents on how to interact with their children. For example, if a child is struggling with career choices, the Parent Data Provision Department can offer advice to parents on how to counsel them and how to support them. In this way, the Parent Data Provision Department can offer advice to parents on how to interact with their children. Some or all of the above processing in the Parent Data Provision Department may be performed using AI or not. For example, the Parent Data Provision Department can input user progress data into a generating AI and have the generating AI perform data anonymization and advice generation.

[0087] The reception desk estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The reception desk estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception desk estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The reception desk adjusts the timing of input acceptance based on the user's emotions. For example, if the reception desk is stressed, it may delay the timing of input acceptance to provide a relaxing environment. Also, if the user is relaxed, the reception desk may accept input immediately to facilitate a smooth conversation. Furthermore, if the user is in a hurry, the reception desk may accept input quickly and start a conversation immediately. In this way, the reception desk can adjust the timing of input acceptance 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 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. For example, the reception desk can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the timing of input reception.

[0088] The reception desk analyzes the user's past consultation history and selects the most suitable reception method. For example, the reception desk may prioritize suggesting consultation methods (text, voice, etc.) that the user has frequently used in the past. The reception desk can also analyze the content of the user's past consultations and automatically suggest relevant topics. Furthermore, the reception desk can suggest the most suitable reception method for a specific time of day based on the user's past consultation history. This allows the reception desk to analyze the user's past consultation history and select the most suitable reception method. 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 past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0089] The reception desk filters the user's current living situation and areas of interest upon receiving the request. For example, the reception desk prioritizes receiving inquiries based on the user's current living situation (school, home, etc.). The reception desk can also filter inquiries based on the user's areas of interest (sports, music, etc.). Furthermore, the reception desk can suggest the most suitable consultation method based on the user's living situation and areas of interest. This allows the reception desk to filter inquiries based on the user's current living situation and areas of interest. 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 data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0090] The reception desk estimates the user's emotions and determines the priority of the consultation content to be received based on the estimated emotions. The reception desk estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception desk estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The reception desk determines the priority of the consultation content to be received based on the user's emotions. For example, if the reception desk is feeling strong anxiety, it will receive that consultation content with the highest priority. The reception desk can also receive it with the normal priority if the user is relaxed. Furthermore, if the reception desk is in a hurry, it can also prioritize consultations that require a quick response. In this way, the reception desk can determine the priority of the consultation content to be received 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 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 generating AI, which can then perform emotion estimation and determine the priority of consultation topics.

[0091] The reception desk prioritizes receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inquiries related to that region. The reception desk can also prioritize inquiries concerning region-specific issues based on the user's geographical location. Furthermore, the reception desk can prioritize inquiries that offer local resources and support, taking into account the user's location. This allows the reception desk to prioritize receiving inquiries that are highly relevant, taking into account 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 a generating AI and have the generating AI prioritize receiving inquiries that are highly relevant.

[0092] The reception desk analyzes the user's social media activity upon receiving a request and accepts relevant inquiries. For example, the reception desk can identify the user's current interests and problems from their social media activity and accept relevant inquiries. The reception desk can also analyze the user's statements and posts on social media and suggest relevant consultation topics. Furthermore, the reception desk can suggest the most suitable consultation method based on the user's social media activity. This allows the reception desk to analyze the user's social media activity and accept relevant inquiries. 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 a generating AI and have the generating AI perform the task of accepting relevant inquiries.

[0093] The dialogue generation unit estimates the user's emotions and adjusts the way the dialogue is expressed based on the estimated emotions. The dialogue generation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the dialogue generation unit estimates emotions from the user's facial expressions and voice, and grasps stress and anxiety. The dialogue generation unit adjusts the way the dialogue is expressed based on the user's emotions. For example, if the user is sad, the dialogue generation unit generates dialogue using gentle language. Also, if the user is excited, the dialogue generation unit can generate dialogue in a calm tone. Furthermore, if the user is relaxed, the dialogue generation unit can generate dialogue in a friendly tone. In this way, the dialogue generation unit can adjust the way the dialogue is expressed 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 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 dialogue generation unit is performed using generative AI. For example, the dialogue generation unit can input user emotion data into the generating AI and have the generating AI adjust the way the dialogue is expressed.

[0094] The dialogue generation unit adjusts the level of detail in the dialogue based on the importance of the consultation content during dialogue generation. For example, the dialogue generation unit uses a generation AI to evaluate the importance of the consultation content and adjust the level of detail in the dialogue. For example, if the consultation content is important, the dialogue generation unit generates a dialogue that provides detailed explanations and specific advice. The dialogue generation unit can also generate a dialogue that provides concise advice if the consultation content is general. Furthermore, if the consultation content is urgent, the dialogue generation unit can generate a dialogue to enable a quick response. In this way, the dialogue generation unit can adjust the level of detail in the dialogue based on the importance of the consultation content. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input consultation content data into the generation AI and have the generation AI perform the adjustment of the level of detail in the dialogue.

[0095] The dialogue generation unit applies different dialogue algorithms depending on the category of the consultation content when generating dialogue. For example, the dialogue generation unit uses a generation AI to classify the category of the consultation content and apply an appropriate dialogue algorithm. For example, if the consultation concerns school life, the dialogue generation unit applies an education-related dialogue algorithm. The dialogue generation unit can also apply a psychological dialogue algorithm if the consultation concerns friendships. Furthermore, if the consultation concerns family problems, the dialogue generation unit can apply a dialogue algorithm specifically for family problems. In this way, the dialogue generation unit can apply different dialogue algorithms depending on the category of the consultation content. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input consultation content data into the generation AI and have the generation AI execute the application of the dialogue algorithm.

[0096] The dialogue generation unit estimates the user's emotions and adjusts the length of the dialogue based on the estimated emotions. The dialogue generation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the dialogue generation unit estimates emotions from the user's facial expressions and voice to grasp stress and anxiety. The dialogue generation unit adjusts the length of the dialogue based on the user's emotions. For example, if the user is in a hurry, the dialogue generation unit generates a short, to-the-point dialogue. Also, if the user is relaxed, the dialogue generation unit can generate a longer dialogue with detailed explanations. Furthermore, if the user is excited, the dialogue generation unit can generate a dialogue with visually stimulating effects. In this way, the dialogue generation unit can adjust the length of the dialogue 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 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 dialogue generation unit is performed using generative AI. For example, the dialogue generation unit can input user emotion data into the generating AI and have the generating AI adjust the length of the dialogue.

[0097] The dialogue generation unit determines the priority of dialogues based on when the consultation content was submitted. For example, the dialogue generation unit uses a generation AI to evaluate the submission timing of the consultation content and determine the priority of dialogues. For example, the dialogue generation unit prioritizes generating dialogues for consultations that have been submitted recently. The dialogue generation unit can also prioritize generating dialogues for consultations that have been left unattended for a long time. Furthermore, the dialogue generation unit can also prioritize generating dialogues for consultations that are of high urgency. In this way, the dialogue generation unit can determine the priority of dialogues based on when the consultation content was submitted. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input consultation content submission timing data into the generation AI and have the generation AI perform the determination of dialogue priority.

[0098] The dialogue generation unit adjusts the order of dialogues based on the relevance of the consultation content during dialogue generation. For example, the dialogue generation unit uses a generation AI to evaluate the relevance of the consultation content and adjust the order of dialogues. For example, the dialogue generation unit prioritizes generating dialogues for consultation content that is highly relevant. The dialogue generation unit can also postpone the generation of dialogues for consultation content that is less relevant. Furthermore, the dialogue generation unit can optimize the order of dialogues based on the relevance of the consultation content. In this way, the dialogue generation unit can adjust the order of dialogues based on the relevance of the consultation content. Some or all of the above processing in the dialogue generation unit is performed using a generation AI. For example, the dialogue generation unit can input relevance data of the consultation content into the generation AI and have the generation AI perform the adjustment of the order of dialogues.

[0099] The sentiment analysis unit estimates the user's emotions and adjusts the criteria for sentiment analysis based on the estimated emotions. The sentiment analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the sentiment analysis unit estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The sentiment analysis unit adjusts the criteria for sentiment analysis based on the user's emotions. For example, if the user is sad, the sentiment analysis unit relaxes the criteria for sentiment analysis and performs a more detailed analysis. Conversely, if the user is excited, the sentiment analysis unit can tighten the criteria for sentiment analysis and perform a calmer analysis. Furthermore, if the user is relaxed, the sentiment analysis unit can apply the normal criteria for sentiment analysis. This allows the sentiment analysis unit to adjust the criteria for sentiment analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sentiment analysis unit are performed using generative AI. For example, the emotion analysis unit can input user emotion data into a generating AI and have the generating AI adjust the criteria for emotion analysis.

[0100] The emotion analysis unit improves the accuracy of emotion analysis by considering the interrelationships of the consultation content during the analysis. For example, the emotion analysis unit uses generative AI to evaluate the interrelationships of the consultation content and improve the accuracy of emotion analysis. For example, the emotion analysis unit analyzes the interrelationships of the consultation content and tracks changes in emotions. The emotion analysis unit can also improve the accuracy of emotion analysis by considering the interrelationships of the consultation content. Furthermore, the emotion analysis unit can identify patterns of emotions based on the interrelationships of the consultation content. This allows the emotion analysis unit to improve the accuracy of emotion analysis by considering the interrelationships of the consultation content. Some or all of the above processing in the emotion analysis unit is performed using generative AI. For example, the emotion analysis unit can input interrelationship data of the consultation content into the generative AI and have the generative AI perform the improvement of the accuracy of emotion analysis.

[0101] The emotion analysis unit performs emotion analysis while considering the client's attribute information. For example, the emotion analysis unit uses a generative AI to evaluate the client's attribute information and perform emotion analysis. For example, the emotion analysis unit considers the client's age and gender when performing emotion analysis. The emotion analysis unit can also perform emotion analysis while considering the client's interests and concerns. Furthermore, the emotion analysis unit can also perform emotion analysis while considering the client's past consultation history. In this way, the emotion analysis unit can perform emotion analysis while considering the client's attribute information. Some or all of the above processing in the emotion analysis unit is performed using a generative AI. For example, the emotion analysis unit can input the client's attribute information data into the generative AI and have the generative AI perform emotion analysis.

[0102] The sentiment analysis unit estimates the user's emotions and adjusts the order in which the sentiment analysis results are displayed based on the estimated emotions. The sentiment analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the sentiment analysis unit estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The sentiment analysis unit adjusts the order in which the sentiment analysis results are displayed based on the user's emotions. For example, if the user is showing strong emotions, the sentiment analysis unit will display those results with the highest priority. It can also display the results in the normal order if the user is relaxed. Furthermore, if the user is in a hurry, the sentiment analysis unit can prioritize displaying important sentiment analysis results. This allows the sentiment analysis unit to adjust the order in which the sentiment analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sentiment analysis unit are performed using generative AI. For example, the emotion analysis unit can input user emotion data into a generating AI and have the generating AI adjust the display order of the emotion analysis results.

[0103] The emotion analysis unit performs emotion analysis while considering the geographical distribution of the consultation content. For example, the emotion analysis unit uses generative AI to evaluate the geographical distribution of the consultation content and perform emotion analysis. For example, the emotion analysis unit analyzes the geographical distribution of the consultation content and identifies emotion patterns for each region. The emotion analysis unit can also perform region-specific emotion analysis while considering the geographical distribution. Furthermore, the emotion analysis unit can track changes in emotion based on the geographical distribution. This allows the emotion analysis unit to perform emotion analysis while considering the geographical distribution of the consultation content. Some or all of the above processing in the emotion analysis unit is performed using generative AI. For example, the emotion analysis unit can input geographical distribution data of the consultation content into the generative AI and have the generative AI perform the emotion analysis.

[0104] The emotion analysis unit improves the accuracy of its emotion analysis by referring to relevant literature related to the consultation content during the analysis. For example, the emotion analysis unit uses generative AI to evaluate relevant literature related to the consultation content and improve the accuracy of its emotion analysis. For example, the emotion analysis unit refers to literature related to the consultation content to improve the accuracy of its emotion analysis. The emotion analysis unit can also identify emotion patterns based on the relevant literature. Furthermore, the emotion analysis unit can supplement the results of its emotion analysis by referring to relevant literature. In this way, the emotion analysis unit can improve the accuracy of its emotion analysis by referring to relevant literature related to the consultation content. Some or all of the above processes in the emotion analysis unit are performed using generative AI. For example, the emotion analysis unit can input data on relevant literature related to the consultation content into the generative AI and have the generative AI perform the task of improving the accuracy of its emotion analysis.

[0105] The goal-setting support unit estimates the user's emotions and adjusts the goal-setting method based on the estimated emotions. The goal-setting support unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the goal-setting support unit estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The goal-setting support unit adjusts the goal-setting method based on the user's emotions. For example, if the user is stressed, the goal-setting support unit sets easy goals to help them achieve a sense of accomplishment. Conversely, if the user is relaxed, the goal-setting support unit can set challenging goals to increase motivation. Furthermore, if the user is in a hurry, the goal-setting support unit can set short-term goals to help them achieve them quickly. This allows the goal-setting support unit to adjust the goal-setting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the goal-setting support unit are performed using generative AI. For example, the goal-setting support unit can input user emotion data into a generating AI and have the generating AI adjust the goal-setting method.

[0106] The goal-setting support unit analyzes the user's past behavioral history to select the optimal goal-setting method when setting goals. For example, the goal-setting support unit analyzes the user's past behavioral history and selects the optimal goal-setting method. For example, the goal-setting support unit analyzes the user's past behavioral history and sets achievable goals. The goal-setting support unit can also set goals that increase motivation based on the user's past successes. Furthermore, the goal-setting support unit can consider the user's past failures and set realistic goals. In this way, the goal-setting support unit can analyze the user's past behavioral history and select the optimal goal-setting method. Some or all of the above processes in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal goal-setting method.

[0107] The goal-setting support unit customizes the means of goal setting based on the user's current living situation when setting goals. For example, the goal-setting support unit sets realistic goals based on the user's current living situation (school, home, etc.). The goal-setting support unit can also set achievable goals in accordance with the user's daily rhythm. Furthermore, the goal-setting support unit can set flexible goals according to the user's living situation. This allows the goal-setting support unit to customize the means of goal setting based on the user's current living situation. Some or all of the above processing in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of goal setting.

[0108] The goal-setting support unit estimates the user's emotions and determines the priority of goal setting based on the estimated user emotions. The goal-setting support unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the goal-setting support unit estimates emotions from the user's facial expressions and voice to grasp stress and anxiety. The goal-setting support unit determines the priority of goal setting based on the user's emotions. For example, if the user shows strong motivation, the goal-setting support unit will prioritize setting challenging goals. Also, if the user is feeling anxious, the goal-setting support unit can prioritize setting easier goals. Furthermore, if the user is relaxed, the goal-setting support unit can set goals with normal priority. In this way, the goal-setting support unit can determine the priority of goal setting 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 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 goal-setting support unit is performed using generative AI. For example, the goal-setting support unit can input user emotion data into a generating AI and have the generating AI determine the priority of goal setting.

[0109] The goal-setting support unit selects the optimal goal-setting method when setting goals, taking into account the user's geographical location information. For example, the goal-setting support unit sets region-specific goals based on the user's geographical location information. The goal-setting support unit can also set realistic goals considering the user's location information. Furthermore, the goal-setting support unit can set flexible goals according to the user's geographical conditions. This allows the goal-setting support unit to select the optimal goal-setting method considering the user's geographical location information. Some or all of the above processing in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal goal-setting method.

[0110] The goal-setting support unit analyzes the user's social media activity and proposes methods for setting goals. For example, the goal-setting support unit can analyze the user's social media activity and propose methods for setting goals. For example, the goal-setting support unit can identify the user's interests from their social media activity and set goals based on those interests. The goal-setting support unit can also analyze the user's statements and posts on social media and propose relevant goals. Furthermore, the goal-setting support unit can set goals that increase motivation based on the user's social media activity. In this way, the goal-setting support unit can analyze the user's social media activity and propose methods for setting goals. Some or all of the above processing in the goal-setting support unit may be performed using AI or not. For example, the goal-setting support unit can input the user's social media activity data into a generating AI and have the generating AI propose methods for setting goals.

[0111] The progress monitoring unit estimates the user's emotions and adjusts the progress monitoring method based on the estimated user emotions. The progress monitoring unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the progress monitoring unit estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The progress monitoring unit adjusts the progress monitoring method based on the user's emotions. For example, if the progress monitoring unit is stressed, it reduces the frequency of progress monitoring and provides a relaxing environment. Conversely, if the user is relaxed, the progress monitoring unit can perform progress monitoring at the normal frequency. Furthermore, if the user is in a hurry, the progress monitoring unit can quickly monitor progress and provide immediate feedback. In this way, the progress monitoring unit can adjust the progress monitoring method 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 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 progress monitoring unit is performed using generative AI. For example, the progress monitoring unit can input user emotion data into a generating AI and have the generating AI adjust the progress monitoring method.

[0112] The progress monitoring unit analyzes the user's past progress history to select the optimal monitoring method during progress monitoring. For example, the progress monitoring unit analyzes the user's past progress history and selects the optimal monitoring method. For example, the progress monitoring unit analyzes the user's past progress history and selects an effective monitoring method. The progress monitoring unit can also select a monitoring method that enhances motivation based on the user's past successes. Furthermore, the progress monitoring unit can consider the user's past failures and select a realistic monitoring method. In this way, the progress monitoring unit can analyze the user's past progress history and select the optimal monitoring method. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's past progress history data into a generating AI and have the generating AI select the optimal monitoring method.

[0113] The progress monitoring unit customizes the monitoring methods based on the user's current living situation when monitoring progress. For example, the progress monitoring unit selects a realistic monitoring method based on the user's current living situation (school, home, etc.). The progress monitoring unit can also select an effective monitoring method that matches the user's daily rhythm. Furthermore, the progress monitoring unit can select a flexible monitoring method according to the user's living situation. This allows the progress monitoring unit to customize the monitoring methods based on the user's current living situation. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input user living situation data into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0114] The progress monitoring unit estimates the user's emotions and determines the priority of progress monitoring based on the estimated user emotions. The progress monitoring unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the progress monitoring unit estimates emotions from the user's facial expressions and voice to grasp stress and anxiety. The progress monitoring unit determines the priority of progress monitoring based on the user's emotions. For example, the progress monitoring unit prioritizes monitoring progress when the user shows strong motivation. The progress monitoring unit can also prioritize simple progress monitoring when the user is feeling anxious. Furthermore, the progress monitoring unit can monitor progress with normal priority when the user is relaxed. In this way, the progress monitoring unit can determine the priority of progress monitoring 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 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 progress monitoring unit is performed using generative AI. For example, the progress monitoring unit can input user emotion data into a generating AI and have the generating AI determine the priority of progress monitoring.

[0115] The progress monitoring unit selects the optimal monitoring method when monitoring progress, taking into account the user's geographical location information. For example, the progress monitoring unit selects the optimal monitoring method by considering the user's geographical location information. For example, the progress monitoring unit selects a region-specific monitoring method based on the user's geographical location information. The progress monitoring unit can also select a realistic monitoring method by considering the user's location information. Furthermore, the progress monitoring unit can select a flexible monitoring method according to the user's geographical conditions. In this way, the progress monitoring unit can select the optimal monitoring method by considering the user's geographical location information. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal monitoring method.

[0116] The progress monitoring unit analyzes the user's social media activity and proposes monitoring methods during progress monitoring. For example, the progress monitoring unit analyzes the user's social media activity and proposes monitoring methods. For example, the progress monitoring unit identifies the progress status from the user's social media activity and proposes a monitoring method based on that. The progress monitoring unit can also analyze the user's statements and posts on social media and propose relevant monitoring methods. Furthermore, the progress monitoring unit can propose monitoring methods that enhance motivation based on the user's social media activity. In this way, the progress monitoring unit can analyze the user's social media activity and propose monitoring methods. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of monitoring methods.

[0117] The parent data delivery unit estimates the user's emotions and adjusts the delivery method of parent data based on the estimated user emotions. The parent data delivery unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the parent data delivery unit estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The parent data delivery unit adjusts the delivery method of parent data based on the user's emotions. For example, if the user is stressed, the parent data delivery unit reduces the frequency of parent data delivery to provide a relaxing environment. Conversely, if the user is relaxed, the parent data delivery unit can deliver parent data at the normal frequency. Furthermore, if the user is in a hurry, the parent data delivery unit can provide parent data quickly and provide immediate feedback. This allows the parent data delivery unit to adjust the delivery method of parent data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the parent data provision department is performed using a generative AI. For example, the parent data provision department can input user emotion data into the generative AI and have the generative AI adjust the method of providing parent data.

[0118] The Parent Data Provision Department analyzes the user's past data provision history to select the optimal provision method when providing data to parents. For example, the Parent Data Provision Department analyzes the user's past data provision history to select the optimal provision method. For example, the Parent Data Provision Department analyzes the user's past data provision history to select an effective provision method. The Parent Data Provision Department can also select a provision method that increases motivation based on the user's past successes. Furthermore, the Parent Data Provision Department can consider the user's past failures to select a realistic provision method. In this way, the Parent Data Provision Department can analyze the user's past data provision history to select the optimal provision method. Some or all of the above processing in the Parent Data Provision Department may be performed using AI or not. For example, the Parent Data Provision Department can input the user's past data provision history data into a generating AI and have the generating AI select the optimal provision method.

[0119] The parent data provision unit customizes the means of data provision based on the user's current living situation when providing data to parents. For example, the parent data provision unit can customize the means of data provision based on the user's current living situation. For example, the parent data provision unit can select a realistic data provision method based on the user's current living situation (school, home, etc.). The parent data provision unit can also select an effective data provision method that matches the user's daily rhythm. Furthermore, the parent data provision unit can select a flexible data provision method according to the user's living situation. This allows the parent data provision unit to customize the means of data provision based on the user's current living situation. Some or all of the above processing in the parent data provision unit may be performed using AI or not. For example, the parent data provision unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the data provision method.

[0120] The parental data provider unit estimates the user's emotions and prioritizes parental data based on the estimated emotions. The parental data provider unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the parental data provider unit estimates emotions from the user's facial expressions and voice to understand stress and anxiety. The parental data provider unit prioritizes parental data based on the user's emotions. For example, if the user shows strong motivation, the parental data provider unit will prioritize providing parental data. The parental data provider unit can also prioritize providing simpler parental data if the user is feeling anxious. Furthermore, if the user is relaxed, the parental data provider unit can provide parental data with normal priority. This allows the parental data provider unit to prioritize parental data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the parent data provision department is performed using a generative AI. For example, the parent data provision department can input user emotion data into the generative AI and have the generative AI determine the priority of parent data.

[0121] The parent data provision unit selects the optimal data provision method when providing data to parents, taking into account the user's geographical location information. For example, the parent data provision unit selects the optimal data provision method by considering the user's geographical location information. For example, the parent data provision unit selects a region-specific data provision method based on the user's geographical location information. The parent data provision unit can also select a practical data provision method by considering the user's location information. Furthermore, the parent data provision unit can select a flexible data provision method according to the user's geographical conditions. In this way, the parent data provision unit can select the optimal data provision method by considering the user's geographical location information. Some or all of the above processing in the parent data provision unit may be performed using AI or not. For example, the parent data provision unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal data provision method.

[0122] The Parent Data Provision Department analyzes users' social media activity and proposes methods for providing data to parents. For example, the Parent Data Provision Department can analyze users' social media activity and propose methods for providing data. For example, the Parent Data Provision Department can identify parent data from users' social media activity and propose methods for providing it based on that data. The Parent Data Provision Department can also analyze users' statements and posts on social media and propose relevant data provision methods. Furthermore, the Parent Data Provision Department can propose data provision methods that enhance motivation based on users' social media activity. In this way, the Parent Data Provision Department can analyze users' social media activity and propose methods for providing data. Some or all of the above processing in the Parent Data Provision Department may be performed using AI or not. For example, the Parent Data Provision Department can input users' social media activity data into a generating AI and have the generating AI propose methods for providing data.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The AI ​​mentor system can also be equipped with a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and sleep patterns to understand their health condition. If, for instance, the health management unit detects an abnormally high heart rate, it may indicate stress or anxiety and suggest relaxation techniques. Furthermore, it can analyze the user's sleep patterns and suggest appropriate sleep habits if sleep deprivation persists. In addition, the health management unit can monitor the user's diet and exercise habits to support healthy lifestyles. This allows the AI ​​mentor system to comprehensively support the user's health and maintain their physical and mental well-being.

[0125] The AI ​​mentor system can also include a learning style analysis unit that analyzes the user's learning style. This unit, for example, analyzes the user's learning history and methods to suggest the optimal learning style. For instance, if the user prefers visual learning, it might suggest a learning plan that heavily utilizes visual content. Similarly, if the user prefers auditory learning, it could suggest a learning plan that utilizes audio content. Furthermore, the learning style analysis unit can consider the user's learning pace and concentration level to suggest an effective learning schedule. This allows the AI ​​mentor system to provide effective learning support tailored to the user's learning style.

[0126] The AI ​​mentor system may further include a dialogue tone adjustment unit that estimates the user's emotions and adjusts the tone of the dialogue based on the estimated emotions. The dialogue tone adjustment unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is sad, the dialogue tone adjustment unit will generate a dialogue in a gentle tone. It can also generate a dialogue in a calm tone if the user is excited. Furthermore, it can generate a dialogue in a friendly tone if the user is relaxed. This allows the AI ​​mentor system to provide a dialogue in an appropriate tone that matches the user's emotions.

[0127] The AI ​​mentor system may further include a goal difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of goal setting based on the estimated emotions. The goal difficulty adjustment unit estimates the user's emotions, for example, using an emotion engine or generative AI. For instance, if the user is feeling stressed, the goal difficulty adjustment unit might set easy goals to help them achieve a sense of accomplishment. Conversely, if the user is relaxed, the goal difficulty adjustment unit might set challenging goals to increase their motivation. Furthermore, if the user is in a hurry, the goal difficulty adjustment unit might set short-term goals to help them achieve them quickly. This allows the AI ​​mentor system to adjust the difficulty of goal setting based on the user's emotions.

[0128] The AI ​​mentor system may further include a progress frequency adjustment unit that estimates the user's emotions and adjusts the frequency of progress monitoring based on the estimated emotions. The progress frequency adjustment unit estimates the user's emotions using, for example, an emotion engine or generative AI. For instance, if the user is stressed, the progress frequency adjustment unit reduces the frequency of progress monitoring to provide a more relaxed environment. Conversely, if the user is relaxed, the progress frequency adjustment unit can monitor progress at a normal frequency. Furthermore, if the user is in a hurry, the progress frequency adjustment unit can quickly monitor progress and provide immediate feedback. This allows the AI ​​mentor system to adjust the frequency of progress monitoring based on the user's emotions.

[0129] The AI ​​mentor system can also be equipped with an activity suggestion unit that leverages the user's hobbies and skills. This unit suggests appropriate activities based on the user's interests and skills. For example, if the user enjoys sports, it will suggest sports-related activities. If the user enjoys music, it can suggest music-related activities. Furthermore, if the user is interested in art, it can suggest art-related activities. This allows the AI ​​mentor system to suggest activities that utilize the user's hobbies and skills, thereby stimulating their interest.

[0130] The AI ​​mentor system can also be equipped with a visual feedback section that visualizes the user's learning progress. This visual feedback section can, for example, display the user's learning progress as graphs or charts, providing visual feedback. For instance, it could display the user's learning time and achievement level as graphs, allowing for a quick overview of their progress. It could also display the user's goal achievement level as charts, allowing them to visually experience a sense of accomplishment. Furthermore, the visual feedback section can analyze the user's learning patterns and suggest effective learning methods. This allows the AI ​​mentor system to visually provide feedback on the user's learning progress and enhance their motivation to learn.

[0131] The AI ​​mentor system may further include a parent data adjustment unit that estimates the user's emotions and adjusts how parent data is provided based on the estimated emotions. The parent data adjustment unit estimates the user's emotions using, for example, an emotion engine or generative AI. For instance, if the user is stressed, the parent data adjustment unit may reduce the frequency of parent data provision to provide a more relaxed environment. Conversely, if the user is relaxed, the parent data adjustment unit may provide parent data at a normal frequency. Furthermore, if the user is in a hurry, the parent data adjustment unit may provide parent data quickly and offer immediate feedback. This allows the AI ​​mentor system to adjust how parent data is provided based on the user's emotions.

[0132] The AI ​​mentoring system can also include a regional support unit that provides region-specific support by considering the user's geographical location. For example, the regional support unit provides support for region-specific issues based on the user's geographical location. If the user is in a specific region, the regional support unit provides information and resources relevant to that region. It can also provide information on local events and activities to help users participate in their local communities. Furthermore, the regional support unit can collaborate with local experts and support groups to provide users with appropriate support. This allows the AI ​​mentoring system to provide region-specific support by considering the user's geographical location.

[0133] The AI ​​mentoring system can also include a social media analysis unit that analyzes the user's social media activity and provides relevant advice. For example, the social media analysis unit can analyze the user's social media activity to identify current interests and problems. It can also analyze the user's social media posts and provide relevant advice. Furthermore, the social media analysis unit can identify interests from the user's social media activity and suggest activities based on those interests. In addition, the social media analysis unit can provide motivational advice based on the user's social media activity. This allows the AI ​​mentoring system to analyze the user's social media activity and provide relevant advice.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The reception unit receives user input. User input includes text input and voice input. For example, it accepts the user to enter their consultation details in text or voice. The reception unit also analyzes the user's input and passes it to the appropriate dialogue generation unit. Step 2: The dialogue generation unit generates a personalized dialogue based on the information received by the reception unit. The dialogue generation unit generates a dialogue based on the user's age, personality, and interests. For example, for a child who has concerns about school life, it generates a dialogue that provides advice on relationships at school and study methods. Step 3: The emotion analysis unit understands the user's emotional state based on the dialogue generated by the dialogue generation unit. The emotion analysis unit uses generation AI to analyze the user's emotional state, estimates emotions from the user's text and voice, and understands stress and anxiety. Step 4: The Goal Setting Support Department provides support for goal setting and habit formation based on the emotional state identified by the Emotion Analysis Department. For example, it proposes a concrete action plan for the goals set by the user, sets daily study time as learning support, and checks the degree of achievement to help the user form a learning habit. Step 5: The progress monitoring unit monitors progress based on the goals set by the goal setting support unit. For example, it periodically checks the user's goal achievement and provides feedback. It also records the user's study time and achievement level to monitor progress. Step 6: The Parent Data Provision Unit provides anonymized data to parents based on the progress monitored by the Progress Monitoring Unit. For example, it provides parents with anonymized user progress and emotional status, and displays the progress as graphs or charts.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, dialogue generation unit, emotion analysis unit, goal setting support unit, progress monitoring unit, and parent data provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text or voice input from the user. The dialogue generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized dialogues using generation AI. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotional state. The goal setting support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the user in setting goals. The progress monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and monitors the user's progress. The parent data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides anonymized data to the parent. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, dialogue generation unit, emotion analysis unit, goal setting support unit, progress monitoring unit, and parent data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The dialogue generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized dialogue using generation AI. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotional state. The goal setting support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the user in setting goals. The progress monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and monitors the user's progress. The parent data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides anonymized data to the parent. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In 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.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 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.

[0171] Each of the multiple elements described above, including the reception unit, dialogue generation unit, emotion analysis unit, goal setting support unit, progress monitoring unit, and parent data provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The dialogue generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized dialogue using a generation AI. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotional state. The goal setting support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports the user in setting goals. The progress monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and monitors the user's progress. The parent data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides anonymized data to the parent. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the reception unit, dialogue generation unit, emotion analysis unit, goal setting support unit, progress monitoring unit, and parent data provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The dialogue generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates personalized dialogue using generation AI. The emotion analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotional state. The goal setting support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports the user in setting goals. The progress monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the user's progress. The parent data provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides anonymized data to the parent. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) A reception area that receives user input, A dialogue generation unit generates a personalized dialogue based on the information received by the reception unit, An emotion analysis unit that grasps the user's emotional state based on the dialogue generated by the dialogue generation unit, Based on the emotional state identified by the aforementioned emotion analysis unit, the goal-setting support unit provides support for goal setting and habit formation. A progress monitoring unit monitors progress based on the goals set by the aforementioned goal setting support unit, The system includes a parent data provision unit that provides anonymized data to parents based on the progress monitored by the progress monitoring unit. A system characterized by the following features. (Note 2) The aforementioned emotion analysis unit, It includes a notification unit that understands the user's emotional state and notifies a specialist if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 3) The dialogue generation unit, It includes an information provision unit that provides information based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned goal-setting support unit, Propose a concrete action plan for the goals set by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned progress monitoring unit, Monitor user progress and verify achievement levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data provision unit for parents, We provide anonymized data and offer advice to parents on how to interact with their children. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept 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 a user submits a request, the system prioritizes accepting inquiries that are highly relevant to their situation, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and accepts related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 13) The dialogue generation unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The dialogue generation unit, When generating dialogue, adjust the level of detail in the dialogue based on the importance of the consultation topic. The system described in Appendix 1, characterized by the features described herein. (Note 15) The dialogue generation unit, When generating dialogues, different dialogue algorithms are applied depending on the category of the consultation topic. The system described in Appendix 1, characterized by the features described herein. (Note 16) The dialogue generation unit, It estimates the user's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The dialogue generation unit, When generating dialogues, the priority of the dialogues is determined based on when the consultation content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The dialogue generation unit, When generating dialogues, the order of the dialogues is adjusted based on the relevance of the topics discussed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the criteria for sentiment analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned emotion analysis unit, When performing emotional analysis, consider the interrelationships between the topics discussed to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned emotion analysis unit, When performing emotional analysis, the client's attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the order in which the sentiment analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned emotion analysis unit, When conducting emotional analysis, the geographical distribution of the consultation topics should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned emotion analysis unit, When performing emotional analysis, refer to relevant literature on the subject matter of the consultation to improve the accuracy of the emotional analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned goal-setting support unit, We estimate the user's emotions and adjust the goal-setting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned goal-setting support unit, When setting goals, the system analyzes the user's past behavioral history to select the optimal goal-setting method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned goal-setting support unit, When setting goals, the goal-setting method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned goal-setting support unit, The system estimates user emotions and prioritizes goal setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned goal-setting support unit, When setting goals, the optimal goal-setting method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned goal-setting support unit, When setting goals, we analyze users' social media activity and propose methods for setting those goals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned progress monitoring unit, We estimate user sentiment and adjust progress monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned progress monitoring unit, During progress monitoring, the system analyzes the user's past progress history to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned progress monitoring unit, During progress monitoring, the monitoring method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned progress monitoring unit, The system estimates user sentiment and prioritizes progress monitoring based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned progress monitoring unit, When monitoring progress, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned progress monitoring unit, During progress monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned data provision unit for parents, We estimate the user's emotions and adjust how parental data is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned data provision unit for parents, When providing data to parents, we analyze the user's past data provision history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned data provision unit for parents, When providing data to parents, customize the data provision method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned data provision unit for parents, It estimates user sentiment and prioritizes parental data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned data provision unit for parents, When providing data to parents, the optimal data delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned data provision unit for parents, When providing data to parents, we analyze users' social media activity and propose methods for providing that data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0208] 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 area that receives user input, A dialogue generation unit generates a personalized dialogue based on the information received by the reception unit, An emotion analysis unit that grasps the user's emotional state based on the dialogue generated by the dialogue generation unit, Based on the emotional state identified by the aforementioned emotion analysis unit, the goal-setting support unit provides support for goal setting and habit formation. A progress monitoring unit monitors progress based on the goals set by the aforementioned goal setting support unit, The system includes a parent data provision unit that provides anonymized data to parents based on the progress monitored by the progress monitoring unit. A system characterized by the following features.

2. The aforementioned emotion analysis unit, It includes a notification unit that understands the user's emotional state and notifies a specialist if an anomaly is detected. The system according to feature 1.

3. The dialogue generation unit, It includes an information provision unit that provides information based on the user's interests. The system according to feature 1.

4. The aforementioned goal-setting support unit, Propose a concrete action plan for the goals set by the user. The system according to feature 1.

5. The aforementioned progress monitoring unit, Monitor user progress and verify achievement levels. The system according to feature 1.

6. The aforementioned data provision unit for parents, We provide anonymized data and offer advice to parents on how to interact with their children. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system according to feature 1.

9. The aforementioned reception unit is During registration, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system according to feature 1.

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