system

Abstract

The system according to this embodiment aims to accurately determine an individual's aptitude and make appropriate suggestions based on that determination. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects personal information. The analysis unit analyzes the information collected by the collection unit and determines suitability. The proposal unit makes a proposal based on the suitability determination obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately judge an individual's suitability and make an appropriate proposal based on it.

[0005] The system according to the embodiment aims to accurately judge an individual's suitability and make an appropriate proposal based on it.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects an individual's information. The analysis unit analyzes the information collected by the collection unit and judges the suitability. The proposal unit makes a proposal based on the result of the suitability judgment obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can accurately determine an individual's aptitude and make appropriate suggestions based on that determination. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 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 aptitude assessment system according to an embodiment of the present invention is a system for maximizing the potential of individuals. This aptitude assessment system collects information such as an individual's likes and dislikes, strengths and weaknesses, and personality, and the AI ​​determines the individual's aptitude. Furthermore, it provides suggestions for excelling in areas of strength and appropriate support for areas of weakness. This system makes it possible to maximize the abilities of individuals in educational settings and workplaces and realize a society that is flexibly accepting. For example, the aptitude assessment system collects information such as an individual's likes and dislikes, strengths and weaknesses, and personality. In this process, detailed information about the individual is collected using questionnaires, tests, etc. For example, information such as favorite subjects, favorite sports, and hobbies can be collected. Next, the aptitude assessment system uses the collected information to determine the individual's aptitude. The AI ​​analyzes the collected information and identifies the individual's aptitude. For example, it can determine an individual's aptitude based on information about favorite subjects and sports. This makes it possible to clarify each individual's areas of strength and weakness. Furthermore, the aptitude assessment system provides suggestions for excelling in areas of strength and appropriate support for areas of weakness. For example, it can suggest teaching materials and programs for further learning in subjects that the individual excels at, and provide supplementary lessons and support in subjects that the individual struggles with. This allows for the maximization of individual abilities. This system enables a society that maximizes individual potential in educational settings and workplaces, and fosters flexible acceptance. For example, in educational settings, it allows for the provision of educational programs tailored to individual aptitudes, and in workplaces, it allows for the assignment of tasks according to individual aptitudes. This creates an environment where individuals can shine, leading to a society where everyone is happy. Ultimately, this aptitude assessment system allows individuals to unleash their full potential.

[0029] The aptitude assessment system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects personal information. The collection unit can collect detailed personal information, for example, using questionnaires or tests. The collection unit can collect information such as favorite subjects, favorite sports, and hobbies. The collection unit can also collect information using online questionnaires, for example. The collection unit can also collect information using paper-based questionnaires, for example. The collection unit can also collect information through interviews, for example. The analysis unit analyzes the information collected by the collection unit and determines aptitude. The analysis unit can identify individual strengths and weaknesses based on the collected information, for example. The analysis unit can analyze information using data mining techniques, for example. The analysis unit can analyze information using statistical analysis techniques, for example. The analysis unit can analyze information using machine learning algorithms, for example. The proposal unit makes suggestions based on the aptitude assessment results obtained by the analysis unit. The proposal unit can suggest educational materials or programs for in-depth learning in areas of strength, for example. The proposal unit can provide supplementary lessons or support in areas of weakness, for example. The proposal unit can, for example, suggest career paths tailored to individual aptitudes. The proposal unit can, for example, suggest training programs tailored to individual aptitudes. This allows the aptitude assessment system according to the embodiment to maximize each individual's potential.

[0030] The data collection department collects personal information. For example, it can collect detailed personal information using questionnaires and tests. Specifically, it can collect information such as an individual's interests, learning style, and lifestyle through online questionnaires. Online questionnaires are conducted via web browsers and mobile apps, allowing respondents to easily access them from home or any location. Questionnaires incorporate diverse formats, including multiple-choice and open-ended questions, to comprehensively collect detailed information about respondents. When using paper-based questionnaires, they are distributed at schools and workplaces, collected, digitized, and registered in a database. This allows for information collection from respondents who do not have access to online environments. Furthermore, information can also be collected through interviews. Interviews are conducted in person or via video call, with interviewers asking individual questions to collect detailed information about respondents. Interview content is recorded and later transcribed for analysis. The data collection department combines these methods to collect personal information from multiple perspectives and centrally manages it in a database. The collected information is used as foundational data to understand individual characteristics and tendencies.

[0031] The analysis unit analyzes the information collected by the data collection unit to determine aptitude. For example, the analysis unit can identify individual strengths and weaknesses based on the collected information. Specifically, it uses data mining techniques to extract patterns and trends from the collected data. Data mining techniques are methods for discovering useful information from large amounts of data, and employ techniques such as clustering and association analysis. For example, clustering is used to group respondents based on their interests and concerns, and to clarify the characteristics of each group. Association analysis is used to analyze how specific interests relate to other interests. Furthermore, statistical analysis techniques are used to analyze the distribution and correlation of the collected data. Statistical analysis techniques are methods for quantitatively evaluating the characteristics of data by calculating the mean, standard deviation, correlation coefficient, etc. For example, the correlation between preferred subjects and academic performance is analyzed to identify areas of strength. In addition, machine learning algorithms are used to build models that predict aptitude from the collected data. Machine learning algorithms are methods for building predictive models by learning from past data and predicting aptitude for new data. For example, algorithms such as decision trees, random forests, and support vector machines are used to predict individual aptitudes. This allows the analysis unit to analyze the collected data from multiple perspectives and accurately determine individual aptitudes.

[0032] The Proposal Department makes proposals based on the aptitude assessment results obtained by the Analysis Department. For example, the Proposal Department can propose learning materials and programs to deepen learning in areas of strength. Specifically, it can provide advanced learning materials and specialized programs related to subjects of strength to support further development of individual abilities. It can also propose learning opportunities such as online courses, workshops, and seminars to support the acquisition of practical skills. Furthermore, it can provide supplementary lessons and support for areas of weakness. For example, it can provide opportunities for individual instruction and group learning in subjects that are difficult to study to deepen understanding. It can also provide online tutorials and supplementary materials to support learning at home. The Proposal Department can also propose career paths tailored to individual aptitudes. For example, it can propose future occupations and educational options based on areas of strength and interests to support career development. It can also provide opportunities for internships and work experience to support gaining experience in actual workplaces. Furthermore, it can propose training programs tailored to individual aptitudes. For example, it can provide training programs to improve leadership and communication skills to support the overall improvement of individual abilities. This allows the proposal department to make specific proposals tailored to each individual's aptitude and provide support to help them maximize their potential.

[0033] The data collection unit can collect detailed personal information using questionnaires or tests. For example, the data collection unit can collect detailed personal information using questionnaires. For example, the data collection unit can collect information using online questionnaires. For example, the data collection unit can collect information using paper-based questionnaires. The data collection unit can also collect information through interviews. For example, the data collection unit can collect detailed personal information using tests. For example, the data collection unit can collect information using written tests. For example, the data collection unit can collect information using practical tests. For example, the data collection unit can collect information using online tests. This allows for the accurate collection of detailed personal information using questionnaires and tests.

[0034] The analysis unit can identify individual strengths and weaknesses based on the collected information. For example, the analysis unit can identify individual strengths based on the collected information. For example, the analysis unit can identify individual weaknesses based on the collected information. For example, the analysis unit can analyze information using data mining techniques. For example, the analysis unit can analyze information using statistical analysis techniques. For example, the analysis unit can analyze information using machine learning algorithms. This allows for the clarification of individual aptitudes by identifying strengths and weaknesses based on the collected information.

[0035] The proposal department can propose learning materials or programs for in-depth study in areas of expertise, and provide supplementary lessons or support for areas of weakness. For example, the proposal department can propose learning materials for in-depth study in areas of expertise. For example, the proposal department can propose programs for in-depth study in areas of expertise. For example, the proposal department can provide supplementary lessons for areas of weakness. For example, the proposal department can provide support for areas of weakness. For example, the proposal department can propose career paths tailored to individual aptitudes. For example, the proposal department can propose training programs tailored to individual aptitudes. In this way, by proposing in-depth learning in areas of expertise and providing appropriate support for areas of weakness, it is possible to maximize each individual's potential.

[0036] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit may prioritize using survey formats that the user has preferred to answer in the past. For example, the data collection unit may select test formats in which the user has scored highly in the past to improve the accuracy of information collection. For example, the data collection unit may identify the most effective timing for information collection from the user's past behavior history. In this way, by analyzing the user's past behavior history, the optimal information collection method can be selected and the accuracy of information collection can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit prioritizes collecting questions related to areas of interest that the user is currently interested in. For example, the data collection unit selects appropriate questions according to the user's living situation to improve the accuracy of information collection. For example, the data collection unit provides questions that are easy to answer based on the user's current living situation. This allows for the collection of more relevant information by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0038] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. For example, the data collection unit will provide region-specific questions based on the user's geographical location information. For example, if the user is traveling, the data collection unit will prioritize collecting information related to the travel destination. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0039] The data collection unit can analyze the user's social media activity and prioritize the collection of relevant information during data collection. For example, the data collection unit can collect information related to topics that the user frequently mentions on social media. For example, the data collection unit can identify areas of interest from the user's social media activity and provide relevant questions. For example, the data collection unit can analyze the content of the user's social media posts and select appropriate information collection methods. This allows the data collection unit to collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and provide specific suggestions. For example, the analysis unit can perform a concise analysis on less important information and provide an overview. For example, the analysis unit can determine the priority of the analysis according to the importance of the information and perform the analysis efficiently. In this way, by adjusting the level of detail of the analysis based on the importance of the collected information, the analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm specifically for academic information. For example, the analysis unit applies a sports-specific analysis algorithm to sports-related information. For example, the analysis unit applies a hobby-specific analysis algorithm to hobby-related information. By applying different analysis algorithms depending on the category of information, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0042] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information and provide results quickly. For example, the analysis unit may postpone the analysis of older information. For example, the analysis unit may adjust the analysis schedule based on the submission date to perform the analysis efficiently. This allows for efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information and provides results. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the analysis schedule based on the relevance of the information to perform the analysis efficiently. In this way, by adjusting the order of analysis based on the relevance of the information, the analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The proposal department can adjust the level of detail in a proposal based on the importance of its strengths and weaknesses. For example, the proposal department can provide detailed proposals for its strengths to encourage further growth, or provide concise and easy-to-understand proposals and support for its weaknesses. The proposal department can adjust the level of detail in a proposal by considering the balance between its strengths and weaknesses. This allows for the provision of more effective proposals by adjusting the level of detail based on the importance of its strengths and weaknesses. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input importance data for its strengths and weaknesses into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0045] The proposal unit can apply different proposal algorithms depending on the category of areas of expertise and areas of weakness when making a proposal. For example, the proposal unit applies a proposal algorithm specifically for academics to proposals related to academics. For example, the proposal unit applies a proposal algorithm specifically for sports to proposals related to sports. For example, the proposal unit applies a proposal algorithm specifically for hobbies to proposals related to hobbies. By applying different proposal algorithms depending on the category of areas of expertise and areas of weakness, it is possible to provide more effective proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input category data of areas of expertise and areas of weakness into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0046] The proposal department can prioritize proposals based on their strengths and weaknesses and the timing of their submissions. For example, the proposal department might provide detailed proposals early on for areas of strength to encourage further growth. For example, it might provide concise proposals for areas of weakness, postponing them to provide support. The proposal department might adjust the proposal schedule based on submission timing to ensure efficient proposal delivery. This allows for efficient proposal delivery by prioritizing proposals based on their strengths and weaknesses and the timing of their submissions. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department could input submission timing data for areas of strength and weakness into a generating AI and have the generating AI determine the priority of proposals.

[0047] The proposal unit can adjust the order of proposals based on the relationships between areas of expertise and areas of weakness when making proposals. For example, the proposal unit can prioritize providing detailed proposals for areas of expertise that are highly relevant. For example, the proposal unit can postpone providing concise proposals for areas of weakness that are less relevant. The proposal unit can efficiently make proposals by adjusting the order of proposals based on the relationships between areas of expertise and areas of weakness. In this way, proposals can be made efficiently by adjusting the order of proposals based on the relationships between areas of expertise and areas of weakness. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the relationships between areas of expertise and areas of weakness into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The data collection unit collects the user's biometric information, and the analysis unit can determine the user's health status based on that biometric information. For example, the data collection unit collects biometric information such as the user's heart rate, blood pressure, and body temperature. The analysis unit analyzes the collected biometric information to determine the user's health status. The suggestion unit can suggest lifestyle improvements tailored to the user's health status and recommend necessary visits to medical institutions. This allows for an understanding of the user's health status and the provision of appropriate support.

[0050] The data collection unit collects the user's past learning history, and the analysis unit can determine the user's learning tendencies based on that history. For example, the data collection unit collects information such as the user's grades in past classes and exams, and their study time. The analysis unit analyzes the collected learning history to identify the user's strengths and weaknesses in learning methods. The suggestion unit can propose learning plans and materials tailored to the user's learning tendencies. This can improve the user's learning efficiency.

[0051] The data collection unit collects the user's work history, and the analysis unit can determine the user's career path based on that work history. For example, the data collection unit collects information on the job content and projects the user has previously experienced. The analysis unit analyzes the collected work history to identify the user's strengths and skills. The proposal unit can propose jobs and projects that match the user's career path. This allows the system to support the user's career advancement.

[0052] The data collection unit gathers information about the user's hobbies and interests, and the analysis unit uses that information to determine the user's lifestyle. For example, the data collection unit collects information about the user's favorite movies, music, sports, etc. The analysis unit analyzes the collected hobbies and interests to identify the user's lifestyle. The suggestion unit can then suggest hobbies and activities that suit the user's lifestyle. This can improve the user's quality of life.

[0053] The data collection unit collects users' consumption history, and the analysis unit can determine users' consumption trends based on that history. For example, the data collection unit collects information on products and services that users have purchased in the past. The analysis unit analyzes the collected consumption history to identify users' consumption trends. The suggestion unit can suggest products and services that match the user's consumption trends. This allows for the optimization of users' consumption behavior.

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

[0055] Step 1: The collection department collects personal information. The collection department can collect detailed personal information, for example, using questionnaires or tests. The collection department can collect information such as favorite subjects, favorite sports, and hobbies. The collection department can also collect information using online questionnaires, for example. The collection department can also collect information using paper-based questionnaires, for example. The collection department can also collect information through interviews, for example. Step 2: The analysis unit analyzes the information collected by the collection unit and determines suitability. The analysis unit can, for example, identify individual strengths and weaknesses based on the collected information. The analysis unit can, for example, analyze the information using data mining techniques. The analysis unit can, for example, analyze the information using statistical analysis techniques. The analysis unit can, for example, analyze the information using machine learning algorithms. Step 3: The proposal department makes proposals based on the aptitude assessment results obtained by the analysis department. For example, the proposal department can propose educational materials and programs for in-depth learning in areas of strength. For example, the proposal department can provide supplementary lessons and support for areas of weakness. For example, the proposal department can propose career paths tailored to individual aptitudes. For example, the proposal department can propose training programs tailored to individual aptitudes.

[0056] (Example of form 2) The aptitude assessment system according to an embodiment of the present invention is a system for maximizing the potential of individuals. This aptitude assessment system collects information such as an individual's likes and dislikes, strengths and weaknesses, and personality, and the AI ​​determines the individual's aptitude. Furthermore, it provides suggestions for excelling in areas of strength and appropriate support for areas of weakness. This system makes it possible to maximize the abilities of individuals in educational settings and workplaces and realize a society that is flexibly accepting. For example, the aptitude assessment system collects information such as an individual's likes and dislikes, strengths and weaknesses, and personality. In this process, detailed information about the individual is collected using questionnaires, tests, etc. For example, information such as favorite subjects, favorite sports, and hobbies can be collected. Next, the aptitude assessment system uses the collected information to determine the individual's aptitude. The AI ​​analyzes the collected information and identifies the individual's aptitude. For example, it can determine an individual's aptitude based on information about favorite subjects and sports. This makes it possible to clarify each individual's areas of strength and weakness. Furthermore, the aptitude assessment system provides suggestions for excelling in areas of strength and appropriate support for areas of weakness. For example, it can suggest teaching materials and programs for further learning in subjects that the individual excels at, and provide supplementary lessons and support in subjects that the individual struggles with. This allows for the maximization of individual abilities. This system enables a society that maximizes individual potential in educational settings and workplaces, and fosters flexible acceptance. For example, in educational settings, it allows for the provision of educational programs tailored to individual aptitudes, and in workplaces, it allows for the assignment of tasks according to individual aptitudes. This creates an environment where individuals can shine, leading to a society where everyone is happy. Ultimately, this aptitude assessment system allows individuals to unleash their full potential.

[0057] The aptitude assessment system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects personal information. The collection unit can collect detailed personal information, for example, using questionnaires or tests. The collection unit can collect information such as favorite subjects, favorite sports, and hobbies. The collection unit can also collect information using online questionnaires, for example. The collection unit can also collect information using paper-based questionnaires, for example. The collection unit can also collect information through interviews, for example. The analysis unit analyzes the information collected by the collection unit and determines aptitude. The analysis unit can identify individual strengths and weaknesses based on the collected information, for example. The analysis unit can analyze information using data mining techniques, for example. The analysis unit can analyze information using statistical analysis techniques, for example. The analysis unit can analyze information using machine learning algorithms, for example. The proposal unit makes suggestions based on the aptitude assessment results obtained by the analysis unit. The proposal unit can suggest educational materials or programs for in-depth learning in areas of strength, for example. The proposal unit can provide supplementary lessons or support in areas of weakness, for example. The proposal unit can, for example, suggest career paths tailored to individual aptitudes. The proposal unit can, for example, suggest training programs tailored to individual aptitudes. This allows the aptitude assessment system according to the embodiment to maximize each individual's potential.

[0058] The data collection department collects personal information. For example, it can collect detailed personal information using questionnaires and tests. Specifically, it can collect information such as an individual's interests, learning style, and lifestyle through online questionnaires. Online questionnaires are conducted via web browsers and mobile apps, allowing respondents to easily access them from home or any location. Questionnaires incorporate diverse formats, including multiple-choice and open-ended questions, to comprehensively collect detailed information about respondents. When using paper-based questionnaires, they are distributed at schools and workplaces, collected, digitized, and registered in a database. This allows for information collection from respondents who do not have access to online environments. Furthermore, information can also be collected through interviews. Interviews are conducted in person or via video call, with interviewers asking individual questions to collect detailed information about respondents. Interview content is recorded and later transcribed for analysis. The data collection department combines these methods to collect personal information from multiple perspectives and centrally manages it in a database. The collected information is used as foundational data to understand individual characteristics and tendencies.

[0059] The analysis unit analyzes the information collected by the data collection unit to determine aptitude. For example, the analysis unit can identify individual strengths and weaknesses based on the collected information. Specifically, it uses data mining techniques to extract patterns and trends from the collected data. Data mining techniques are methods for discovering useful information from large amounts of data, and employ techniques such as clustering and association analysis. For example, clustering is used to group respondents based on their interests and concerns, and to clarify the characteristics of each group. Association analysis is used to analyze how specific interests relate to other interests. Furthermore, statistical analysis techniques are used to analyze the distribution and correlation of the collected data. Statistical analysis techniques are methods for quantitatively evaluating the characteristics of data by calculating the mean, standard deviation, correlation coefficient, etc. For example, the correlation between preferred subjects and academic performance is analyzed to identify areas of strength. In addition, machine learning algorithms are used to build models that predict aptitude from the collected data. Machine learning algorithms are methods for building predictive models by learning from past data and predicting aptitude for new data. For example, algorithms such as decision trees, random forests, and support vector machines are used to predict individual aptitudes. This allows the analysis unit to analyze the collected data from multiple perspectives and accurately determine individual aptitudes.

[0060] The Proposal Department makes proposals based on the aptitude assessment results obtained by the Analysis Department. For example, the Proposal Department can propose learning materials and programs to deepen learning in areas of strength. Specifically, it can provide advanced learning materials and specialized programs related to subjects of strength to support further development of individual abilities. It can also propose learning opportunities such as online courses, workshops, and seminars to support the acquisition of practical skills. Furthermore, it can provide supplementary lessons and support for areas of weakness. For example, it can provide opportunities for individual instruction and group learning in subjects that are difficult to study to deepen understanding. It can also provide online tutorials and supplementary materials to support learning at home. The Proposal Department can also propose career paths tailored to individual aptitudes. For example, it can propose future occupations and educational options based on areas of strength and interests to support career development. It can also provide opportunities for internships and work experience to support gaining experience in actual workplaces. Furthermore, it can propose training programs tailored to individual aptitudes. For example, it can provide training programs to improve leadership and communication skills to support the overall improvement of individual abilities. This allows the proposal department to make specific proposals tailored to each individual's aptitude and provide support to help them maximize their potential.

[0061] The data collection unit can collect detailed personal information using questionnaires or tests. For example, the data collection unit can collect detailed personal information using questionnaires. For example, the data collection unit can collect information using online questionnaires. For example, the data collection unit can collect information using paper-based questionnaires. The data collection unit can also collect information through interviews. For example, the data collection unit can collect detailed personal information using tests. For example, the data collection unit can collect information using written tests. For example, the data collection unit can collect information using practical tests. For example, the data collection unit can collect information using online tests. This allows for the accurate collection of detailed personal information using questionnaires and tests.

[0062] The analysis unit can identify individual strengths and weaknesses based on the collected information. For example, the analysis unit can identify individual strengths based on the collected information. For example, the analysis unit can identify individual weaknesses based on the collected information. For example, the analysis unit can analyze information using data mining techniques. For example, the analysis unit can analyze information using statistical analysis techniques. For example, the analysis unit can analyze information using machine learning algorithms. This allows for the clarification of individual aptitudes by identifying strengths and weaknesses based on the collected information.

[0063] The proposal department can propose learning materials or programs for in-depth study in areas of expertise, and provide supplementary lessons or support for areas of weakness. For example, the proposal department can propose learning materials for in-depth study in areas of expertise. For example, the proposal department can propose programs for in-depth study in areas of expertise. For example, the proposal department can provide supplementary lessons for areas of weakness. For example, the proposal department can provide support for areas of weakness. For example, the proposal department can propose career paths tailored to individual aptitudes. For example, the proposal department can propose training programs tailored to individual aptitudes. In this way, by proposing in-depth learning in areas of expertise and providing appropriate support for areas of weakness, it is possible to maximize each individual's potential.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can send questionnaires when the user is relaxed to improve the quality of responses. For example, the data collection unit can temporarily suspend information collection when the user is stressed and try again later. For example, the data collection unit can conduct tests when the user is focused to collect accurate information. This allows for more accurate information to be collected by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit may prioritize using survey formats that the user has preferred to answer in the past. For example, the data collection unit may select test formats in which the user has scored highly in the past to improve the accuracy of information collection. For example, the data collection unit may identify the most effective timing for information collection from the user's past behavior history. In this way, by analyzing the user's past behavior history, the optimal information collection method can be selected and the accuracy of information collection can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0066] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit prioritizes collecting questions related to areas of interest that the user is currently interested in. For example, the data collection unit selects appropriate questions according to the user's living situation to improve the accuracy of information collection. For example, the data collection unit provides questions that are easy to answer based on the user's current living situation. This allows for the collection of more relevant information by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit prioritizes collecting important information when the user is relaxed. For example, when the user is stressed, the data collection unit starts with simple questions and gradually collects important information. For example, when the user is focused, the data collection unit prioritizes collecting detailed information. This allows for the priority collection of important information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information to collect.

[0068] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. For example, the data collection unit will provide region-specific questions based on the user's geographical location information. For example, if the user is traveling, the data collection unit will prioritize collecting information related to the travel destination. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0069] The data collection unit can analyze the user's social media activity and prioritize the collection of relevant information during data collection. For example, the data collection unit can collect information related to topics that the user frequently mentions on social media. For example, the data collection unit can identify areas of interest from the user's social media activity and provide relevant questions. For example, the data collection unit can analyze the content of the user's social media posts and select appropriate information collection methods. This allows the data collection unit to collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is focused, the analysis unit provides analysis results using visually easy-to-understand graphs and charts. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and provide specific suggestions. For example, the analysis unit can perform a concise analysis on less important information and provide an overview. For example, the analysis unit can determine the priority of the analysis according to the importance of the information and perform the analysis efficiently. In this way, by adjusting the level of detail of the analysis based on the importance of the collected information, the analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm specifically for academic information. For example, the analysis unit applies a sports-specific analysis algorithm to sports-related information. For example, the analysis unit applies a hobby-specific analysis algorithm to hobby-related information. By applying different analysis algorithms depending on the category of information, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is focused, the analysis unit provides analysis results using visually easy-to-understand graphs and charts. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0074] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information and provide results quickly. For example, the analysis unit may postpone the analysis of older information. For example, the analysis unit may adjust the analysis schedule based on the submission date to perform the analysis efficiently. This allows for efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information and provides results. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the analysis schedule based on the relevance of the information to perform the analysis efficiently. In this way, by adjusting the order of analysis based on the relevance of the information, the analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is focused, the suggestion unit will provide suggestions using visually easy-to-understand graphs or charts. By adjusting the way it presents suggestions based on the user's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0077] The proposal department can adjust the level of detail in a proposal based on the importance of its strengths and weaknesses. For example, the proposal department can provide detailed proposals for its strengths to encourage further growth, or provide concise and easy-to-understand proposals and support for its weaknesses. The proposal department can adjust the level of detail in a proposal by considering the balance between its strengths and weaknesses. This allows for the provision of more effective proposals by adjusting the level of detail based on the importance of its strengths and weaknesses. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input importance data for its strengths and weaknesses into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0078] The proposal unit can apply different proposal algorithms depending on the category of areas of expertise and areas of weakness when making a proposal. For example, the proposal unit applies a proposal algorithm specifically for academics to proposals related to academics. For example, the proposal unit applies a proposal algorithm specifically for sports to proposals related to sports. For example, the proposal unit applies a proposal algorithm specifically for hobbies to proposals related to hobbies. By applying different proposal algorithms depending on the category of areas of expertise and areas of weakness, it is possible to provide more effective proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input category data of areas of expertise and areas of weakness into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is focused, the suggestion unit will provide suggestions using visually easy-to-understand graphs or charts. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0080] The proposal department can prioritize proposals based on their strengths and weaknesses and the timing of their submissions. For example, the proposal department might provide detailed proposals early on for areas of strength to encourage further growth. For example, it might provide concise proposals for areas of weakness, postponing them to provide support. The proposal department might adjust the proposal schedule based on submission timing to ensure efficient proposal delivery. This allows for efficient proposal delivery by prioritizing proposals based on their strengths and weaknesses and the timing of their submissions. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department could input submission timing data for areas of strength and weakness into a generating AI and have the generating AI determine the priority of proposals.

[0081] The proposal unit can adjust the order of proposals based on the relationships between areas of expertise and areas of weakness when making proposals. For example, the proposal unit can prioritize providing detailed proposals for areas of expertise that are highly relevant. For example, the proposal unit can postpone providing concise proposals for areas of weakness that are less relevant. The proposal unit can efficiently make proposals by adjusting the order of proposals based on the relationships between areas of expertise and areas of weakness. In this way, proposals can be made efficiently by adjusting the order of proposals based on the relationships between areas of expertise and areas of weakness. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the relationships between areas of expertise and areas of weakness into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The data collection unit collects the user's biometric information, and the analysis unit can determine the user's health status based on that biometric information. For example, the data collection unit collects biometric information such as the user's heart rate, blood pressure, and body temperature. The analysis unit analyzes the collected biometric information to determine the user's health status. The suggestion unit can suggest lifestyle improvements tailored to the user's health status and recommend necessary visits to medical institutions. This allows for an understanding of the user's health status and the provision of appropriate support.

[0084] The data collection unit collects the user's past learning history, and the analysis unit can determine the user's learning tendencies based on that history. For example, the data collection unit collects information such as the user's grades in past classes and exams, and their study time. The analysis unit analyzes the collected learning history to identify the user's strengths and weaknesses in learning methods. The suggestion unit can propose learning plans and materials tailored to the user's learning tendencies. This can improve the user's learning efficiency.

[0085] The data collection unit collects the user's work history, and the analysis unit can determine the user's career path based on that work history. For example, the data collection unit collects information on the job content and projects the user has previously experienced. The analysis unit analyzes the collected work history to identify the user's strengths and skills. The proposal unit can propose jobs and projects that match the user's career path. This allows the system to support the user's career advancement.

[0086] The data collection unit gathers information about the user's hobbies and interests, and the analysis unit uses that information to determine the user's lifestyle. For example, the data collection unit collects information about the user's favorite movies, music, sports, etc. The analysis unit analyzes the collected hobbies and interests to identify the user's lifestyle. The suggestion unit can then suggest hobbies and activities that suit the user's lifestyle. This can improve the user's quality of life.

[0087] The data collection unit collects users' consumption history, and the analysis unit can determine users' consumption trends based on that history. For example, the data collection unit collects information on products and services that users have purchased in the past. The analysis unit analyzes the collected consumption history to identify users' consumption trends. The suggestion unit can suggest products and services that match the user's consumption trends. This allows for the optimization of users' consumption behavior.

[0088] The data collection unit can estimate the user's emotions and adjust the information collection method based on the estimated emotions. For example, when the user is relaxed, it can send a detailed questionnaire, and when the user is stressed, it can send simple questions. The analysis unit analyzes the changes in the user's emotions based on the collected information, and the suggestion unit can provide support and suggestions that are appropriate to the user's emotions. This enables information collection and suggestions that take the user's emotions into consideration.

[0089] The data collection unit can estimate the user's emotions and prioritize information based on those emotions. For example, when the user is relaxed, it prioritizes collecting important information, and when the user is stressed, it prioritizes collecting simple information. The analysis unit analyzes changes in the user's emotions based on the collected information, and the suggestion unit can provide support and suggestions that are appropriate to the user's emotions. This enables information collection and suggestions that take the user's emotions into consideration.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those estimates. For example, it can send a questionnaire when the user is relaxed and temporarily suspend information collection when the user is stressed. The analysis unit analyzes changes in the user's emotions based on the collected information, and the suggestion unit can provide support and suggestions that are appropriate to the user's emotions. This enables information collection and suggestions that take the user's emotions into consideration.

[0091] The data collection unit can estimate the user's emotions and adjust the information collection method based on the estimated emotions. For example, when the user is relaxed, it can send a detailed questionnaire, and when the user is stressed, it can send simple questions. The analysis unit analyzes the changes in the user's emotions based on the collected information, and the suggestion unit can provide support and suggestions that are appropriate to the user's emotions. This enables information collection and suggestions that take the user's emotions into consideration.

[0092] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those estimates. For example, it can send a questionnaire when the user is relaxed and temporarily suspend information collection when the user is stressed. The analysis unit analyzes changes in the user's emotions based on the collected information, and the suggestion unit can provide support and suggestions that are appropriate to the user's emotions. This enables information collection and suggestions that take the user's emotions into consideration.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The collection department collects personal information. The collection department can collect detailed personal information, for example, using questionnaires or tests. The collection department can collect information such as favorite subjects, favorite sports, and hobbies. The collection department can also collect information using online questionnaires, for example. The collection department can also collect information using paper-based questionnaires, for example. The collection department can also collect information through interviews, for example. Step 2: The analysis unit analyzes the information collected by the collection unit and determines suitability. The analysis unit can, for example, identify individual strengths and weaknesses based on the collected information. The analysis unit can, for example, analyze the information using data mining techniques. The analysis unit can, for example, analyze the information using statistical analysis techniques. The analysis unit can, for example, analyze the information using machine learning algorithms. Step 3: The proposal department makes proposals based on the aptitude assessment results obtained by the analysis department. For example, the proposal department can propose educational materials and programs for in-depth learning in areas of strength. For example, the proposal department can provide supplementary lessons and support for areas of weakness. For example, the proposal department can propose career paths tailored to individual aptitudes. For example, the proposal department can propose training programs tailored to individual aptitudes.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects detailed personal information using questionnaires and tests. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies individual strengths and weaknesses based on the collected information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes educational materials and programs for in-depth learning in strengths and provides supplementary lessons and support in weaknesses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects detailed personal information using questionnaires and tests. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies individual strengths and weaknesses based on the collected information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes educational materials and programs for in-depth learning in strengths and provides supplementary lessons and support in weaknesses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects detailed personal information using questionnaires and tests. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies individual strengths and weaknesses based on the collected information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes educational materials and programs for in-depth learning in strengths and provides supplementary lessons and support in weaknesses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects detailed personal information using questionnaires and tests. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies individual strengths and weaknesses based on the collected information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes educational materials and programs for in-depth learning in strengths and provides supplementary lessons and support in weaknesses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] (Note 1) A collection department that collects personal information, An analysis unit analyzes the information collected by the aforementioned collection unit and determines suitability, The system includes a proposal unit that makes suggestions based on the results of suitability judgment obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect detailed personal information using questionnaires or tests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, identify each individual's strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose learning materials or programs to help you study your strengths in depth, and provide supplementary lessons or support for your weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past behavior history and select the appropriate information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, we analyze users' social media activity and prioritize collecting relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of your strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the categories of areas of expertise and areas of weakness. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission timing of each proposal, considering areas of expertise and areas of weakness. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relationship between areas of expertise and areas of weakness. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects personal information, An analysis unit analyzes the information collected by the aforementioned collection unit and determines suitability, The system includes a proposal unit that makes suggestions based on the results of suitability judgment obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect detailed personal information using questionnaires or tests. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, identify each individual's strengths and weaknesses. The system according to feature 1.

4. The aforementioned proposal section is, We propose learning materials or programs to help you study your strengths in depth, and provide supplementary lessons or support for your weaknesses. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past behavior history and select the appropriate information collection method. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, we analyze users' social media activity and prioritize collecting relevant information. The system according to feature 1.

Images