system

Abstract

The system according to this embodiment aims to provide personalized lifestyle suggestions based on the user's personality assessment results. [Solution] The system according to the embodiment comprises a diagnostic unit, a proposal unit, and a support unit. The diagnostic unit receives the user's MBTI personality assessment. The proposal unit makes lifestyle suggestions based on the diagnostic results obtained by the diagnostic unit. The support unit provides the user with the content proposed by the proposal unit and provides ongoing support in response to the user's questions.

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, personalized lifestyle proposals based on the results of user personality diagnosis have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to make personalized lifestyle proposals based on the results of user personality diagnosis.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a diagnostic unit, a proposal unit, and a support unit. The diagnostic unit receives the user's MBTI personality assessment. The proposal unit makes lifestyle suggestions based on the diagnostic results obtained by the diagnostic unit. The support unit provides the user with the content proposed by the proposal unit and provides ongoing support in response to the user's questions. [Effects of the Invention]

[0007] The system according to this embodiment can suggest a personalized lifestyle based on the user's personality assessment results. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Life Adviser system according to an embodiment of the present invention is a system in which a generating AI makes personalized lifestyle suggestions based on the user's MBTI personality test results. The AI ​​Life Adviser system works by having the user take an MBTI personality test on an app, and then the generating AI makes lifestyle suggestions tailored to the user's personality based on the results. The suggestions cover a wide range of areas, including work, hobbies, relationships, health, and personal growth. Users can receive continuous support by reviewing the suggestions on the app and asking questions to the generating AI. For example, the user takes an MBTI personality test on the app. The test results form the basis for the personalized lifestyle suggestions made by the generating AI. For example, if the user is an "INTJ" personality type, the generating AI provides appropriate advice on work, hobbies, and relationships based on that personality type. Next, the generating AI makes lifestyle suggestions based on the test results. For example, an INTJ type user might be offered advice on jobs that utilize their analytical skills, hobbies they can pursue independently, and ways to build deep relationships. In terms of health, suggestions are made regarding stress management and appropriate exercise methods. Furthermore, the user can review the suggestions on the app and ask questions to the generating AI. For example, if a user asks, "I want to find a new hobby," the generating AI will suggest hobbies that suit the user's personality type. Similarly, if a user asks, "I want to reduce work-related stress," the generating AI will suggest stress management methods. In this way, the AI ​​Life Adviser system provides personalized lifestyle suggestions and ongoing support based on the user's MBTI personality assessment results, helping users maximize their potential and live fulfilling lives. This enables the AI ​​Life Adviser system to provide personalized lifestyle suggestions and ongoing support based on the user's MBTI personality assessment results.

[0029] The AI ​​Life Adviser system according to this embodiment comprises a diagnostic unit, a suggestion unit, and a support unit. The diagnostic unit receives the user's MBTI personality assessment. The diagnostic unit enables the user to take the MBTI personality assessment on an app, for example. The diagnostic unit displays the questions for the user to answer and collects the answers. For example, the diagnostic unit provides an interface for the user to answer the personality assessment questions. The diagnostic unit also analyzes the user's answers and generates MBTI personality assessment results. For example, the diagnostic unit uses an algorithm to identify the MBTI personality type based on the user's answers. The suggestion unit makes lifestyle suggestions based on the diagnostic results obtained by the diagnostic unit. The suggestion unit generates lifestyle suggestions based on the user's personality type, for example, using a generative AI. The suggestion unit makes suggestions regarding work, hobbies, relationships, health, and self-growth. For example, the suggestion unit makes suggestions for work and hobbies that suit the user's personality type. The suggestion unit also provides advice on relationships and health management based on the user's personality type. For example, the suggestion unit makes suggestions about stress management and appropriate exercise methods based on the user's personality type. The support unit provides the user with the content suggested by the suggestion unit and provides ongoing support in response to the user's questions. The support unit allows the user to check the suggested content on the app, for example. The support unit also allows the user to ask questions to the generating AI. For example, if the user asks the support unit, "I want to find a new hobby," the generating AI will suggest a hobby that suits the user's personality type. Also, if the user asks the support unit, "I want to reduce work-related stress," the generating AI will suggest methods for stress management. In this way, the AI ​​Life Adviser system according to the embodiment can provide personalized lifestyle suggestions and ongoing support based on the user's MBTI personality assessment results.

[0030] The diagnostic unit accepts user MBTI personality assessments. For example, it enables users to take the MBTI personality assessment within an app. Specifically, the diagnostic unit provides an interface for users to answer personality assessment questions. This interface is designed for intuitive user interaction, and questions are provided in various formats, including multiple-choice and open-ended formats. Once a user enters their answers, the diagnostic unit collects them in real time and stores them in a database. Furthermore, the diagnostic unit analyzes the user's responses and generates MBTI personality assessment results. The analysis utilizes natural language processing techniques and machine learning algorithms to analyze user response patterns in detail. For example, based on the user's responses, the diagnostic unit evaluates various indicators such as extraversion, introversion, sensing, intuition, thinking, feeling, judging, and perceiving, ultimately identifying the MBTI personality type. This process is carried out while anonymizing the user's response data and protecting privacy. The assessment results are provided to the user in a visually easy-to-understand format, for example, using graphs and charts to explain the characteristics of the personality type. Furthermore, the diagnostic unit also has a function that allows users to save their past diagnostic results and perform re-diagnosis and comparison. This enables the diagnostic unit to efficiently and accurately assess the user's personality and provide them with useful information.

[0031] The Proposal Department provides lifestyle suggestions based on the diagnostic results obtained by the Diagnostic Department. For example, the Proposal Department uses generative AI to generate lifestyle suggestions based on the user's personality type. Specifically, the Proposal Department takes the user's MBTI personality type as input, and the generative AI generates optimal lifestyle suggestions. The generative AI extracts information from a vast database that matches the user's personality type and provides specific suggestions regarding work, hobbies, relationships, health, and personal growth. For example, if the user's personality type is "INTJ," the Proposal Department suggests jobs that utilize strategic thinking and hobbies that offer a unique perspective. The Proposal Department also provides relationship advice and health management suggestions based on the user's personality type. For example, if the user is "ENFP," the Proposal Department suggests ways to build relationships that leverage their sociable personality and methods for stress management. The generative AI learns from the user's past behavioral data and feedback to continuously improve the accuracy of its suggestions. Furthermore, the Proposal Department provides specific steps and resources to make it easier for the user to implement the suggestions. For example, the Proposal Department provides information on online courses and communities to help the user start a new hobby. Furthermore, the proposal department collects feedback from users after they implement the proposed solutions and incorporates it into future proposals. This allows the proposal department to provide users with personalized lifestyle suggestions and improve their quality of life.

[0032] The support department provides users with the content suggested by the suggestion department and offers ongoing support in response to user questions. Specifically, the support department enables users to view the suggested content within the app. The suggested content is customized based on the user's MBTI personality type and displayed in a visually easy-to-understand format. For example, if a user asks, "I want to find a new hobby," the support department's generating AI will suggest hobbies that suit the user's personality type. Based on the user's personality type and past behavioral data, the generating AI selects the most suitable hobby and provides detailed information and instructions on how to get started. Also, if a user asks, "I want to reduce work-related stress," the support department's generating AI will suggest stress management methods. Based on the user's personality type, the generating AI provides specific stress management techniques and resources. For example, it may suggest methods such as deep breathing, meditation, and exercise, and provides detailed explanations of the effects and methods of practice for each. Furthermore, the support department provides real-time support for any questions or problems that arise when users implement the suggested content. For example, if a user asks about the equipment and resources needed to start a suggested hobby, the support department will provide that information quickly. Furthermore, the support department collects user feedback and continuously improves its suggestions and the quality of its support. This allows the support department to provide users with continuous and personalized support, thereby improving their quality of life.

[0033] The proposal department can make suggestions regarding work, hobbies, relationships, health, and personal growth. For example, the proposal department can suggest appropriate jobs based on the user's personality type. For instance, it can suggest career paths and ways to improve the work environment that suit the user's personality type. The proposal department can also suggest new hobbies based on the user's personality type. For example, it can suggest ways to deepen hobbies that suit the user's personality type. The proposal department can also suggest ways to improve relationships based on the user's personality type. For example, it can suggest methods to improve communication and relationship building that suit the user's personality type. The proposal department can also make suggestions for health management based on the user's personality type. For example, it can suggest ways to improve exercise habits and diet that suit the user's personality type. The proposal department can also make suggestions for personal growth based on the user's personality type. For example, it can suggest methods to improve skills and self-development that suit the user's personality type. In this way, the proposal department can make suggestions across a wide range of fields based on the user's personality.

[0034] The support unit can provide specific advice in response to user questions. For example, if a user asks, "I want to find a new hobby," the generating AI can suggest hobbies that suit the user's personality type. Similarly, if a user asks, "I want to reduce work-related stress," the generating AI can suggest stress management methods. For example, the support unit can suggest stress management methods based on the user's personality type. Furthermore, if a user asks, "I want to improve my relationships," the generating AI can suggest ways to improve communication. For example, the support unit can suggest relationship building methods based on the user's personality type. This allows the support unit to provide specific advice in response to user questions. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input a user's question into a generating AI, which can then output specific advice.

[0035] The diagnostic unit can analyze the user's past diagnostic results and provide feedback to improve the accuracy of the diagnosis. For example, the diagnostic unit can analyze trends in questions the user has answered in the past and add new questions to improve the accuracy of the diagnosis. For example, the diagnostic unit can generate new questions based on the user's past answer data. The diagnostic unit can also compare the user's past and current diagnostic results and provide feedback on the changes. For example, the diagnostic unit can compare the user's past and current diagnostic results and analyze the changes. The diagnostic unit can also provide advice to improve the accuracy of the diagnosis based on the user's past diagnostic results. For example, the diagnostic unit can generate advice to improve the accuracy of the diagnosis based on the user's past diagnostic results. In this way, the diagnostic unit can improve the accuracy of the diagnosis by analyzing the user's past diagnostic results. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input the user's past diagnostic results into a generating AI, and the generating AI can generate feedback to improve the accuracy of the diagnosis.

[0036] The diagnostic unit can customize the order of questions during the diagnostic process based on the user's lifestyle and areas of interest. For example, if the user is interested in work, the diagnostic unit will prioritize work-related questions. For example, the diagnostic unit will select work-related questions for the user. The diagnostic unit can also prioritize health-related questions if the user is interested in health. For example, the diagnostic unit will select health-related questions for the user. The diagnostic unit can also prioritize hobby-related questions if the user is interested in hobbies. For example, the diagnostic unit will select hobby-related questions for the user. In this way, the diagnostic unit can customize the order of questions based on the user's lifestyle and areas of interest. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then customize the order of questions.

[0037] The diagnostic unit can prioritize questions that are highly relevant to the user's geographical location during the diagnostic process. For example, if the user lives in an urban area, the diagnostic unit will prioritize questions related to urban life. For example, the diagnostic unit will select questions related to urban life if the user lives in a rural area. For example, the diagnostic unit will select questions related to rural life if the user lives in a rural area. Furthermore, if the diagnostic unit lives abroad, it will prioritize questions related to the culture and lifestyle of that country. For example, the diagnostic unit will select questions related to living abroad if the user lives abroad. This allows the diagnostic unit to prioritize questions that are highly relevant to the user's geographical location. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's geographical location information into a generating AI, which can then select highly relevant questions.

[0038] The diagnostic unit can analyze the user's social media activity during the diagnostic process and add relevant questions. For example, the diagnostic unit can add relevant questions based on the content the user frequently posts on social media. For example, the diagnostic unit can analyze the content the user frequently posts on social media and generate relevant questions. The diagnostic unit can also analyze the user's social media friendships and add relevant questions. For example, the diagnostic unit can analyze the user's social media interests and add relevant questions. For example, the diagnostic unit can analyze the user's interests and generate relevant questions. This allows the diagnostic unit to analyze the user's social media activity and add relevant questions. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's social media activity data into a generating AI, which can then generate relevant questions.

[0039] The suggestion unit can provide optimal suggestions by analyzing the user's past behavior history when making suggestions. For example, the suggestion unit can provide similar suggestions based on suggestions the user has previously selected. For example, the suggestion unit can analyze the user's past selection data and generate similar suggestions. The suggestion unit can also select the most effective suggestion from the user's past behavior history. For example, the suggestion unit can analyze the user's past behavior data and select the most effective suggestion. The suggestion unit can also analyze the user's past behavior history and provide new suggestions. For example, the suggestion unit can generate new suggestions based on the user's past behavior data. In this way, the suggestion unit can analyze the user's past behavior history and provide optimal suggestions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior history data into a generating AI, which can then generate optimal suggestions.

[0040] The suggestion unit can customize the suggested content based on the user's current living situation. For example, if the user is working, the suggestion unit will prioritize suggesting work-related suggestions. For example, the suggestion unit will generate suggestions related to the user's work. The suggestion unit can also prioritize suggesting academic-related suggestions if the user is a student. For example, the suggestion unit will generate suggestions related to the user's studies. The suggestion unit can also prioritize suggesting post-retirement life if the user is retired. For example, the suggestion unit will generate suggestions related to the user's post-retirement life. In this way, the suggestion unit can customize the suggested content based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's current living situation data into a generating AI, which can then customize the suggested content.

[0041] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user lives in an urban area, the suggestion unit can provide suggestions related to urban life. For example, the suggestion unit can generate suggestions related to urban life for the user. The suggestion unit can also provide suggestions related to rural life if the user lives in a rural area. For example, the suggestion unit can generate suggestions related to rural life for the user. The suggestion unit can also provide suggestions related to the culture and lifestyle of the country where the user lives if they live abroad. For example, the suggestion unit can generate suggestions related to life abroad for the user. In this way, the suggestion unit can provide optimal suggestions by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI, which can then generate optimal suggestions.

[0042] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on the content the user frequently posts on social media. For example, the suggestion unit can analyze the content the user frequently posts on social media and generate relevant suggestions. The suggestion unit can also analyze the user's social media friendships and make relevant suggestions. For example, the suggestion unit can analyze the user's interests on social media and make relevant suggestions. For example, the suggestion unit can analyze the user's interests and generate relevant suggestions. In this way, the suggestion unit can analyze the user's social media activity and make relevant suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI, and the generating AI can generate relevant suggestions.

[0043] The support unit can provide optimal advice by referring to the user's past question history during support. For example, the support unit can provide advice for similar questions based on the content of questions the user has asked in the past. For example, the support unit can analyze the user's past question data and generate advice for similar questions. The support unit can also select the most effective advice from the user's past question history. For example, the support unit can analyze the user's past question data and select the most effective advice. The support unit can also analyze the user's past question history and provide new advice. For example, the support unit can generate new advice based on the user's past question data. In this way, the support unit can provide optimal advice by referring to the user's past question history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past question history data into a generating AI, and the generating AI can generate optimal advice.

[0044] The support unit can customize the support provided based on the user's current living situation. For example, if the user is employed, the support unit will prioritize providing work-related support. For example, the support unit will generate work-related support for the user. The support unit can also prioritize providing academic support if the user is a student. For example, the support unit will generate academic support for the user. The support unit can also prioritize providing support related to post-retirement life if the user is retired. For example, the support unit will generate support related to post-retirement life for the user. In this way, the support unit can customize the support provided based on the user's current living situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI, which can then customize the support content.

[0045] The support unit can provide optimal support by considering the user's geographical location information during support. For example, if the user lives in an urban area, the support unit can provide support related to urban life. For example, the support unit can generate support related to urban life for the user. The support unit can also provide support related to rural life if the user lives in a rural area. For example, the support unit can generate support related to rural life for the user. The support unit can also provide support related to the culture and lifestyle of the country where the user lives if they live abroad. For example, the support unit can generate support related to living abroad for the user. In this way, the support unit can provide optimal support by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI, and the generating AI can generate optimal support.

[0046] The support unit can analyze a user's social media activity and provide relevant support during support sessions. For example, the support unit can provide relevant support based on the content a user frequently posts on social media. For example, the support unit can analyze the content a user frequently posts on social media and generate relevant support. The support unit can also analyze a user's social media friendships and provide relevant support. For example, the support unit can analyze a user's interests on social media and provide relevant support. For example, the support unit can analyze a user's interests and generate relevant support. In this way, the support unit can analyze a user's social media activity and provide relevant support. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI, which can then generate relevant support.

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

[0048] The diagnostic unit can acquire the user's biometric data and use it to improve the accuracy of the diagnostic results. For example, the diagnostic unit measures the user's heart rate and skin electrical activity to estimate the user's stress level and emotional state. It can also monitor the user's sleep patterns and exercise levels and use this data as supplementary information for the MBTI personality assessment. Furthermore, the diagnostic unit can record the user's diet and nutritional intake to provide diagnostic results based on their health status. In this way, the diagnostic unit can utilize the user's biometric data to perform a more accurate MBTI personality assessment.

[0049] The support department can analyze past user feedback to improve its support services. For example, it can record how users responded to advice previously provided and incorporate this into future support. Furthermore, if a user has shown a positive response to a particular piece of advice, similar advice can be offered. Additionally, if a user has shown a negative response, the cause can be analyzed, and corrective measures can be implemented. This allows the support department to leverage user feedback to provide more effective support.

[0050] The proposal team can analyze a user's past proposal history to improve the diversity of proposals. For example, it can analyze the content of proposals a user has received in the past to prevent similar proposals from being repeated. It can also make new proposals related to areas in which the user has shown particular interest in past proposals. Furthermore, it can analyze which proposals a user has implemented and which they have not, and prioritize proposals that are easy to implement. In this way, the proposal team can leverage a user's past proposal history to provide more diverse and actionable proposals.

[0051] The suggestion function can adjust its recommendations based on the user's current weather information. For example, it can suggest indoor hobbies the user can enjoy on rainy days, or outdoor activities on sunny days. Furthermore, it can suggest warm drinks or ways to relax indoors on cold days. This allows the suggestion function to provide optimal suggestions by taking the user's current weather information into account.

[0052] The diagnostic unit can generate personalized questions to improve the accuracy of the diagnosis based on the user's past diagnostic results. For example, it can analyze trends in questions the user has answered in the past and add more specific questions. It can also compare the user's past and current diagnostic results and generate questions that reflect the changes. Furthermore, it can provide advice to improve the accuracy of the diagnosis based on the user's past diagnostic results. In this way, the diagnostic unit can perform more accurate diagnoses by utilizing the user's past diagnostic results.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The diagnostic unit receives the user's MBTI personality assessment. The diagnostic unit enables the user to take the MBTI personality assessment on the app, displays the questions, and collects the answers. Furthermore, the diagnostic unit analyzes the user's answers and generates the MBTI personality assessment result. For example, the diagnostic unit uses an algorithm to identify the MBTI personality type based on the user's answers. Step 2: The suggestion department makes lifestyle suggestions based on the diagnostic results obtained by the diagnosis department. The suggestion department uses a generative AI to generate lifestyle suggestions based on the user's personality type. Specifically, it makes suggestions regarding work, hobbies, relationships, health, and personal growth. For example, it makes suggestions for work and hobbies that suit the user's personality type, advice on relationships, suggestions for health management, and suggestions on stress management and appropriate exercise methods. Step 3: The support team provides users with the content suggested by the suggestion team and offers ongoing support in response to user questions. The support team enables users to view the suggested content on the app and ask questions to the generating AI. For example, if a user asks, "I want to find a new hobby," the generating AI will suggest hobbies that suit the user's personality type. If a user asks, "I want to reduce work stress," the generating AI will suggest ways to manage stress.

[0055] (Example of form 2) The AI ​​Life Adviser system according to an embodiment of the present invention is a system in which a generating AI makes personalized lifestyle suggestions based on the user's MBTI personality test results. The AI ​​Life Adviser system works by having the user take an MBTI personality test on an app, and then the generating AI makes lifestyle suggestions tailored to the user's personality based on the results. The suggestions cover a wide range of areas, including work, hobbies, relationships, health, and personal growth. Users can receive continuous support by reviewing the suggestions on the app and asking questions to the generating AI. For example, the user takes an MBTI personality test on the app. The test results form the basis for the personalized lifestyle suggestions made by the generating AI. For example, if the user is an "INTJ" personality type, the generating AI provides appropriate advice on work, hobbies, and relationships based on that personality type. Next, the generating AI makes lifestyle suggestions based on the test results. For example, an INTJ type user might be offered advice on jobs that utilize their analytical skills, hobbies they can pursue independently, and ways to build deep relationships. In terms of health, suggestions are made regarding stress management and appropriate exercise methods. Furthermore, the user can review the suggestions on the app and ask questions to the generating AI. For example, if a user asks, "I want to find a new hobby," the generating AI will suggest hobbies that suit the user's personality type. Similarly, if a user asks, "I want to reduce work-related stress," the generating AI will suggest stress management methods. In this way, the AI ​​Life Adviser system provides personalized lifestyle suggestions and ongoing support based on the user's MBTI personality assessment results, helping users maximize their potential and live fulfilling lives. This enables the AI ​​Life Adviser system to provide personalized lifestyle suggestions and ongoing support based on the user's MBTI personality assessment results.

[0056] The AI ​​Life Adviser system according to this embodiment comprises a diagnostic unit, a suggestion unit, and a support unit. The diagnostic unit receives the user's MBTI personality assessment. The diagnostic unit enables the user to take the MBTI personality assessment on an app, for example. The diagnostic unit displays the questions for the user to answer and collects the answers. For example, the diagnostic unit provides an interface for the user to answer the personality assessment questions. The diagnostic unit also analyzes the user's answers and generates MBTI personality assessment results. For example, the diagnostic unit uses an algorithm to identify the MBTI personality type based on the user's answers. The suggestion unit makes lifestyle suggestions based on the diagnostic results obtained by the diagnostic unit. The suggestion unit generates lifestyle suggestions based on the user's personality type, for example, using a generative AI. The suggestion unit makes suggestions regarding work, hobbies, relationships, health, and self-growth. For example, the suggestion unit makes suggestions for work and hobbies that suit the user's personality type. The suggestion unit also provides advice on relationships and health management based on the user's personality type. For example, the suggestion unit makes suggestions about stress management and appropriate exercise methods based on the user's personality type. The support unit provides the user with the content suggested by the suggestion unit and provides ongoing support in response to the user's questions. The support unit allows the user to check the suggested content on the app, for example. The support unit also allows the user to ask questions to the generating AI. For example, if the user asks the support unit, "I want to find a new hobby," the generating AI will suggest a hobby that suits the user's personality type. Also, if the user asks the support unit, "I want to reduce work-related stress," the generating AI will suggest methods for stress management. In this way, the AI ​​Life Adviser system according to the embodiment can provide personalized lifestyle suggestions and ongoing support based on the user's MBTI personality assessment results.

[0057] The diagnostic unit accepts user MBTI personality assessments. For example, it enables users to take the MBTI personality assessment within an app. Specifically, the diagnostic unit provides an interface for users to answer personality assessment questions. This interface is designed for intuitive user interaction, and questions are provided in various formats, including multiple-choice and open-ended formats. Once a user enters their answers, the diagnostic unit collects them in real time and stores them in a database. Furthermore, the diagnostic unit analyzes the user's responses and generates MBTI personality assessment results. The analysis utilizes natural language processing techniques and machine learning algorithms to analyze user response patterns in detail. For example, based on the user's responses, the diagnostic unit evaluates various indicators such as extraversion, introversion, sensing, intuition, thinking, feeling, judging, and perceiving, ultimately identifying the MBTI personality type. This process is carried out while anonymizing the user's response data and protecting privacy. The assessment results are provided to the user in a visually easy-to-understand format, for example, using graphs and charts to explain the characteristics of the personality type. Furthermore, the diagnostic unit also has a function that allows users to save their past diagnostic results and perform re-diagnosis and comparison. This enables the diagnostic unit to efficiently and accurately assess the user's personality and provide them with useful information.

[0058] The Proposal Department provides lifestyle suggestions based on the diagnostic results obtained by the Diagnostic Department. For example, the Proposal Department uses generative AI to generate lifestyle suggestions based on the user's personality type. Specifically, the Proposal Department takes the user's MBTI personality type as input, and the generative AI generates optimal lifestyle suggestions. The generative AI extracts information from a vast database that matches the user's personality type and provides specific suggestions regarding work, hobbies, relationships, health, and personal growth. For example, if the user's personality type is "INTJ," the Proposal Department suggests jobs that utilize strategic thinking and hobbies that offer a unique perspective. The Proposal Department also provides relationship advice and health management suggestions based on the user's personality type. For example, if the user is "ENFP," the Proposal Department suggests ways to build relationships that leverage their sociable personality and methods for stress management. The generative AI learns from the user's past behavioral data and feedback to continuously improve the accuracy of its suggestions. Furthermore, the Proposal Department provides specific steps and resources to make it easier for the user to implement the suggestions. For example, the Proposal Department provides information on online courses and communities to help the user start a new hobby. Furthermore, the proposal department collects feedback from users after they implement the proposed solutions and incorporates it into future proposals. This allows the proposal department to provide users with personalized lifestyle suggestions and improve their quality of life.

[0059] The support department provides users with the content suggested by the suggestion department and offers ongoing support in response to user questions. Specifically, the support department enables users to view the suggested content within the app. The suggested content is customized based on the user's MBTI personality type and displayed in a visually easy-to-understand format. For example, if a user asks, "I want to find a new hobby," the support department's generating AI will suggest hobbies that suit the user's personality type. Based on the user's personality type and past behavioral data, the generating AI selects the most suitable hobby and provides detailed information and instructions on how to get started. Also, if a user asks, "I want to reduce work-related stress," the support department's generating AI will suggest stress management methods. Based on the user's personality type, the generating AI provides specific stress management techniques and resources. For example, it may suggest methods such as deep breathing, meditation, and exercise, and provides detailed explanations of the effects and methods of practice for each. Furthermore, the support department provides real-time support for any questions or problems that arise when users implement the suggested content. For example, if a user asks about the equipment and resources needed to start a suggested hobby, the support department will provide that information quickly. Furthermore, the support department collects user feedback and continuously improves its suggestions and the quality of its support. This allows the support department to provide users with continuous and personalized support, thereby improving their quality of life.

[0060] The proposal department can make suggestions regarding work, hobbies, relationships, health, and personal growth. For example, the proposal department can suggest appropriate jobs based on the user's personality type. For instance, it can suggest career paths and ways to improve the work environment that suit the user's personality type. The proposal department can also suggest new hobbies based on the user's personality type. For example, it can suggest ways to deepen hobbies that suit the user's personality type. The proposal department can also suggest ways to improve relationships based on the user's personality type. For example, it can suggest methods to improve communication and relationship building that suit the user's personality type. The proposal department can also make suggestions for health management based on the user's personality type. For example, it can suggest ways to improve exercise habits and diet that suit the user's personality type. The proposal department can also make suggestions for personal growth based on the user's personality type. For example, it can suggest methods to improve skills and self-development that suit the user's personality type. In this way, the proposal department can make suggestions across a wide range of fields based on the user's personality.

[0061] The support unit can provide specific advice in response to user questions. For example, if a user asks, "I want to find a new hobby," the generating AI can suggest hobbies that suit the user's personality type. Similarly, if a user asks, "I want to reduce work-related stress," the generating AI can suggest stress management methods. For example, the support unit can suggest stress management methods based on the user's personality type. Furthermore, if a user asks, "I want to improve my relationships," the generating AI can suggest ways to improve communication. For example, the support unit can suggest relationship building methods based on the user's personality type. This allows the support unit to provide specific advice in response to user questions. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input a user's question into a generating AI, which can then output specific advice.

[0062] The diagnostic unit can estimate the user's emotions and adjust the MBTI personality assessment questions based on the estimated emotions. For example, if the user is tense, the diagnostic unit will prioritize questions that help them relax. For example, the diagnostic unit will select questions that help the user relax. If the user is excited, the diagnostic unit can also ask questions that help them concentrate. For example, the diagnostic unit will select questions that help the user concentrate. If the user is tired, the diagnostic unit can also ask simple and easy-to-answer questions. For example, the diagnostic unit will select questions that are simple and easy for the user to answer. In this way, the diagnostic unit can adjust the MBTI personality assessment questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit inputs the user's facial expression data into a generating AI, which can then estimate the user's emotions.

[0063] The diagnostic unit can analyze the user's past diagnostic results and provide feedback to improve the accuracy of the diagnosis. For example, the diagnostic unit can analyze trends in questions the user has answered in the past and add new questions to improve the accuracy of the diagnosis. For example, the diagnostic unit can generate new questions based on the user's past answer data. The diagnostic unit can also compare the user's past and current diagnostic results and provide feedback on the changes. For example, the diagnostic unit can compare the user's past and current diagnostic results and analyze the changes. The diagnostic unit can also provide advice to improve the accuracy of the diagnosis based on the user's past diagnostic results. For example, the diagnostic unit can generate advice to improve the accuracy of the diagnosis based on the user's past diagnostic results. In this way, the diagnostic unit can improve the accuracy of the diagnosis by analyzing the user's past diagnostic results. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input the user's past diagnostic results into a generating AI, and the generating AI can generate feedback to improve the accuracy of the diagnosis.

[0064] The diagnostic unit can customize the order of questions during the diagnostic process based on the user's lifestyle and areas of interest. For example, if the user is interested in work, the diagnostic unit will prioritize work-related questions. For example, the diagnostic unit will select work-related questions for the user. The diagnostic unit can also prioritize health-related questions if the user is interested in health. For example, the diagnostic unit will select health-related questions for the user. The diagnostic unit can also prioritize hobby-related questions if the user is interested in hobbies. For example, the diagnostic unit will select hobby-related questions for the user. In this way, the diagnostic unit can customize the order of questions based on the user's lifestyle and areas of interest. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then customize the order of questions.

[0065] The diagnostic unit can estimate the user's emotions and adjust the display method of the diagnostic results based on the estimated emotions. For example, if the user is nervous, the diagnostic unit can provide a simple and easy-to-read display method. For example, if the user is nervous, the diagnostic unit can provide a simple and easy-to-read graph display. The diagnostic unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the diagnostic unit can provide a detailed text display. The diagnostic unit can also provide a concise display method that gets straight to the point if the user is in a hurry. For example, if the user is in a hurry, the diagnostic unit can provide a concise display method that gets straight to the point. In this way, the diagnostic unit can adjust the display method of the diagnostic results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expression data into a generating AI, which can then estimate the user's emotions and adjust the display method accordingly.

[0066] The diagnostic unit can prioritize questions that are highly relevant to the user's geographical location during the diagnostic process. For example, if the user lives in an urban area, the diagnostic unit will prioritize questions related to urban life. For example, the diagnostic unit will select questions related to urban life if the user lives in a rural area. For example, the diagnostic unit will select questions related to rural life if the user lives in a rural area. Furthermore, if the diagnostic unit lives abroad, it will prioritize questions related to the culture and lifestyle of that country. For example, the diagnostic unit will select questions related to living abroad if the user lives abroad. This allows the diagnostic unit to prioritize questions that are highly relevant to the user's geographical location. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's geographical location information into a generating AI, which can then select highly relevant questions.

[0067] The diagnostic unit can analyze the user's social media activity during the diagnostic process and add relevant questions. For example, the diagnostic unit can add relevant questions based on the content the user frequently posts on social media. For example, the diagnostic unit can analyze the content the user frequently posts on social media and generate relevant questions. The diagnostic unit can also analyze the user's social media friendships and add relevant questions. For example, the diagnostic unit can analyze the user's social media interests and add relevant questions. For example, the diagnostic unit can analyze the user's interests and generate relevant questions. This allows the diagnostic unit to analyze the user's social media activity and add relevant questions. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's social media activity data into a generating AI, which can then generate relevant questions.

[0068] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can present its suggestions in a gentle manner. For example, if the user is relaxed, the suggestion unit can present its suggestions in a gentle manner. For example, if the user is tense, the suggestion unit can present its suggestions in a simple and clear manner. For example, if the user is tense, the suggestion unit can present its suggestions in a simple and clear manner. For example, if the user is excited, the suggestion unit can present its suggestions in an energetic manner. For example, if the user is excited, the suggestion unit can present its suggestions in an energetic manner. In this way, the suggestion unit can adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI, which can then adjust how the proposal is expressed.

[0069] The suggestion unit can provide optimal suggestions by analyzing the user's past behavior history when making suggestions. For example, the suggestion unit can provide similar suggestions based on suggestions the user has previously selected. For example, the suggestion unit can analyze the user's past selection data and generate similar suggestions. The suggestion unit can also select the most effective suggestion from the user's past behavior history. For example, the suggestion unit can analyze the user's past behavior data and select the most effective suggestion. The suggestion unit can also analyze the user's past behavior history and provide new suggestions. For example, the suggestion unit can generate new suggestions based on the user's past behavior data. In this way, the suggestion unit can analyze the user's past behavior history and provide optimal suggestions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior history data into a generating AI, which can then generate optimal suggestions.

[0070] The suggestion unit can customize the suggested content based on the user's current living situation. For example, if the user is working, the suggestion unit will prioritize suggesting work-related suggestions. For example, the suggestion unit will generate suggestions related to the user's work. The suggestion unit can also prioritize suggesting academic-related suggestions if the user is a student. For example, the suggestion unit will generate suggestions related to the user's studies. The suggestion unit can also prioritize suggesting post-retirement life if the user is retired. For example, the suggestion unit will generate suggestions related to the user's post-retirement life. In this way, the suggestion unit can customize the suggested content based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's current living situation data into a generating AI, which can then customize the suggested content.

[0071] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit will prioritize providing suggestions related to stress management. For example, if the user is feeling stressed, the suggestion unit will generate stress management suggestions. The suggestion unit can also prioritize providing suggestions related to new challenges if the user is relaxed. For example, if the user is relaxed, the suggestion unit will generate suggestions related to new challenges. The suggestion unit can also prioritize providing suggestions that help the user utilize their energy if the user is excited. For example, if the suggestion unit is excited, the suggestion unit will generate suggestions that help the user utilize their energy. In this way, the suggestion unit can determine the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generating AI, which can then determine the priority of the proposals.

[0072] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user lives in an urban area, the suggestion unit can provide suggestions related to urban life. For example, the suggestion unit can generate suggestions related to urban life for the user. The suggestion unit can also provide suggestions related to rural life if the user lives in a rural area. For example, the suggestion unit can generate suggestions related to rural life for the user. The suggestion unit can also provide suggestions related to the culture and lifestyle of the country where the user lives if they live abroad. For example, the suggestion unit can generate suggestions related to life abroad for the user. In this way, the suggestion unit can provide optimal suggestions by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI, which can then generate optimal suggestions.

[0073] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on the content the user frequently posts on social media. For example, the suggestion unit can analyze the content the user frequently posts on social media and generate relevant suggestions. The suggestion unit can also analyze the user's social media friendships and make relevant suggestions. For example, the suggestion unit can analyze the user's interests on social media and make relevant suggestions. For example, the suggestion unit can analyze the user's interests and generate relevant suggestions. In this way, the suggestion unit can analyze the user's social media activity and make relevant suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI, and the generating AI can generate relevant suggestions.

[0074] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. For example, if the user is nervous, the support unit can provide advice in a calm voice. The support unit can also provide support in a cheerful voice if the user is relaxed. For example, if the user is relaxed, the support unit can provide advice in a cheerful voice. The support unit can also provide quick and concise support if the user is in a hurry. For example, if the support unit is in a hurry, the support unit can provide quick and concise advice. In this way, the support unit can adjust its support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into the generative AI, and the generative AI can adjust its support methods.

[0075] The support unit can provide optimal advice by referring to the user's past question history during support. For example, the support unit can provide advice for similar questions based on the content of questions the user has asked in the past. For example, the support unit can analyze the user's past question data and generate advice for similar questions. The support unit can also select the most effective advice from the user's past question history. For example, the support unit can analyze the user's past question data and select the most effective advice. The support unit can also analyze the user's past question history and provide new advice. For example, the support unit can generate new advice based on the user's past question data. In this way, the support unit can provide optimal advice by referring to the user's past question history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past question history data into a generating AI, and the generating AI can generate optimal advice.

[0076] The support unit can customize the support provided based on the user's current living situation. For example, if the user is employed, the support unit will prioritize providing work-related support. For example, the support unit will generate work-related support for the user. The support unit can also prioritize providing academic support if the user is a student. For example, the support unit will generate academic support for the user. The support unit can also prioritize providing support related to post-retirement life if the user is retired. For example, the support unit will generate support related to post-retirement life for the user. In this way, the support unit can customize the support provided based on the user's current living situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI, which can then customize the support content.

[0077] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize providing stress management support. For example, if the user is stressed, the support unit will generate stress management support. The support unit can also prioritize providing support related to new challenges if the user is relaxed. For example, if the user is relaxed, the support unit will generate support related to new challenges. The support unit can also prioritize providing support that helps the user harness their energy if the user is excited. For example, if the user is excited, the support unit will generate support that helps the user harness their energy. In this way, the support unit can determine the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI, which can then determine the priority of support.

[0078] The support unit can provide optimal support by considering the user's geographical location information during support. For example, if the user lives in an urban area, the support unit can provide support related to urban life. For example, the support unit can generate support related to urban life for the user. The support unit can also provide support related to rural life if the user lives in a rural area. For example, the support unit can generate support related to rural life for the user. The support unit can also provide support related to the culture and lifestyle of the country where the user lives if they live abroad. For example, the support unit can generate support related to living abroad for the user. In this way, the support unit can provide optimal support by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI, and the generating AI can generate optimal support.

[0079] The support unit can analyze a user's social media activity and provide relevant support during support sessions. For example, the support unit can provide relevant support based on the content a user frequently posts on social media. For example, the support unit can analyze the content a user frequently posts on social media and generate relevant support. The support unit can also analyze a user's social media friendships and provide relevant support. For example, the support unit can analyze a user's interests on social media and provide relevant support. For example, the support unit can analyze a user's interests and generate relevant support. In this way, the support unit can analyze a user's social media activity and provide relevant support. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI, which can then generate relevant support.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The diagnostic unit can acquire the user's biometric data and use it to improve the accuracy of the diagnostic results. For example, the diagnostic unit measures the user's heart rate and skin electrical activity to estimate the user's stress level and emotional state. It can also monitor the user's sleep patterns and exercise levels and use this data as supplementary information for the MBTI personality assessment. Furthermore, the diagnostic unit can record the user's diet and nutritional intake to provide diagnostic results based on their health status. In this way, the diagnostic unit can utilize the user's biometric data to perform a more accurate MBTI personality assessment.

[0082] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is feeling stressed, it can offer suggestions during times when they can relax. If the user is concentrating, it can offer suggestions related to work or learning. Furthermore, if the user is relaxed, it can offer suggestions related to hobbies or entertainment. This allows the suggestion function to offer suggestions at the optimal time, depending on the user's emotions.

[0083] The support department can analyze past user feedback to improve its support services. For example, it can record how users responded to advice previously provided and incorporate this into future support. Furthermore, if a user has shown a positive response to a particular piece of advice, similar advice can be offered. Additionally, if a user has shown a negative response, the cause can be analyzed, and corrective measures can be implemented. This allows the support department to leverage user feedback to provide more effective support.

[0084] The diagnostic unit can estimate the user's emotions and adjust the speed of the diagnosis based on those emotions. For example, if the user is tense, the diagnosis can be slowed down to help them relax. If the user is relaxed, the diagnosis can be sped up. Furthermore, if the user is focused, the diagnosis can be performed at an optimal speed. In this way, the diagnostic unit can adjust the speed of the diagnosis according to the user's emotions.

[0085] The proposal team can analyze a user's past proposal history to improve the diversity of proposals. For example, it can analyze the content of proposals a user has received in the past to prevent similar proposals from being repeated. It can also make new proposals related to areas in which the user has shown particular interest in past proposals. Furthermore, it can analyze which proposals a user has implemented and which they have not, and prioritize proposals that are easy to implement. In this way, the proposal team can leverage a user's past proposal history to provide more diverse and actionable proposals.

[0086] The diagnostic unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is tense, it can prioritize providing positive feedback. If the user is relaxed, it can provide more detailed feedback. Furthermore, if the user is focused, it can provide feedback that includes specific areas for improvement. In this way, the diagnostic unit can provide the most appropriate feedback method according to the user's emotions.

[0087] The suggestion function can adjust its recommendations based on the user's current weather information. For example, it can suggest indoor hobbies the user can enjoy on rainy days, or outdoor activities on sunny days. Furthermore, it can suggest warm drinks or ways to relax indoors on cold days. This allows the suggestion function to provide optimal suggestions by taking the user's current weather information into account.

[0088] The support unit can estimate the user's emotions and adjust the frequency of support based on those estimates. For example, if the user is stressed, support can be provided more frequently. Conversely, if the user is relaxed, the frequency of support can be reduced. Furthermore, if the user is focused, support can be provided at the appropriate time. In this way, the support unit can provide the optimal frequency of support according to the user's emotions.

[0089] The diagnostic unit can generate personalized questions to improve the accuracy of the diagnosis based on the user's past diagnostic results. For example, it can analyze trends in questions the user has answered in the past and add more specific questions. It can also compare the user's past and current diagnostic results and generate questions that reflect the changes. Furthermore, it can provide advice to improve the accuracy of the diagnosis based on the user's past diagnostic results. In this way, the diagnostic unit can perform more accurate diagnoses by utilizing the user's past diagnostic results.

[0090] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing activities. If the user is relaxed, it can suggest new challenges. Furthermore, if the user is excited, it can suggest activities that will help them expend their energy. In this way, the suggestion function can provide optimal suggestions according to the user's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The diagnostic unit receives the user's MBTI personality assessment. The diagnostic unit enables the user to take the MBTI personality assessment on the app, displays the questions, and collects the answers. Furthermore, the diagnostic unit analyzes the user's answers and generates the MBTI personality assessment result. For example, the diagnostic unit uses an algorithm to identify the MBTI personality type based on the user's answers. Step 2: The suggestion department makes lifestyle suggestions based on the diagnostic results obtained by the diagnosis department. The suggestion department uses a generative AI to generate lifestyle suggestions based on the user's personality type. Specifically, it makes suggestions regarding work, hobbies, relationships, health, and personal growth. For example, it makes suggestions for work and hobbies that suit the user's personality type, advice on relationships, suggestions for health management, and suggestions on stress management and appropriate exercise methods. Step 3: The support team provides users with the content suggested by the suggestion team and offers ongoing support in response to user questions. The support team enables users to view the suggested content on the app and ask questions to the generating AI. For example, if a user asks, "I want to find a new hobby," the generating AI will suggest hobbies that suit the user's personality type. If a user asks, "I want to reduce work stress," the generating AI will suggest ways to manage stress.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] Each of the multiple elements described above, including the diagnostic unit, proposal unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the smart device 14, enabling the user to take an MBTI personality test on an app. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, using a generating AI to generate lifestyle suggestions based on the user's personality type. The support unit is implemented by the control unit 46A of the smart device 14, enabling the user to review the suggestions on an app and ask questions to the generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the diagnostic unit, suggestion unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to take an MBTI personality test on an app. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, using a generating AI to generate lifestyle suggestions based on the user's personality type. The support unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to review the suggestions on an app and ask questions to the generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 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.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the 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.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0127] The data processing system 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.

[0128] Each of the multiple elements described above, including the diagnostic unit, suggestion unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to take an MBTI personality test on the app. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, generating lifestyle suggestions based on the user's personality type using a generating AI. The support unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to review the suggestions on the app and ask questions to the generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the diagnostic unit, proposal unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the robot 414, enabling the user to take an MBTI personality test on the app. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, using a generating AI to generate lifestyle suggestions based on the user's personality type. The support unit is implemented by the control unit 46A of the robot 414, enabling the user to review the suggestions on the app and ask questions to the generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Note 1) The diagnostic department accepts user MBTI personality assessments, A proposal unit that makes lifestyle suggestions based on the diagnostic results obtained by the aforementioned diagnostic unit, The system includes a support unit that provides the user with the content proposed by the proposal unit and provides continuous support in response to user questions. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We offer suggestions related to work, hobbies, relationships, health, and personal growth. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is Provide specific advice in response to user questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned diagnostic unit, The system estimates the user's emotions and adjusts the MBTI personality assessment questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned diagnostic unit, We analyze the user's past diagnostic results and provide feedback to improve the accuracy of the diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned diagnostic unit, During the diagnostic process, the order of questions is customized based on the user's lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned diagnostic unit, During the diagnostic process, the system prioritizes asking questions that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned diagnostic unit, During the diagnostic process, we analyze the user's social media activity and add relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, we analyze the user's past behavior history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, customize the proposal based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, we provide the most suitable suggestions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is When providing support, we refer to the user's past question history to offer the best possible advice. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is During support sessions, customize the support content based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is When providing support, we take the user's geographical location into consideration to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is During support, we analyze the user's social media activity to provide relevant support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0165] 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. The diagnostic department accepts user MBTI personality assessments, A proposal unit that makes lifestyle suggestions based on the diagnostic results obtained by the aforementioned diagnostic unit, The system includes a support unit that provides the user with the content proposed by the proposal unit and provides continuous support in response to user questions. A system characterized by the following features.

2. The aforementioned proposal section is, We offer suggestions related to work, hobbies, relationships, health, and personal growth. The system according to feature 1.

3. The aforementioned support unit is Provide specific advice in response to user questions. The system according to feature 1.

4. The aforementioned diagnostic unit, The system estimates the user's emotions and adjusts the MBTI personality assessment questions based on those estimated emotions. The system according to feature 1.

5. The aforementioned diagnostic unit, We analyze the user's past diagnostic results and provide feedback to improve the accuracy of the diagnosis. The system according to feature 1.

6. The aforementioned diagnostic unit, During the diagnostic process, the order of questions is customized based on the user's lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned diagnostic unit, During the diagnostic process, the system prioritizes asking questions that are highly relevant to the user's geographical location. The system according to feature 1.

9. The aforementioned diagnostic unit, During the diagnostic process, we analyze the user's social media activity and add relevant questions. The system according to feature 1.

10. The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system according to feature 1.

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