system

Abstract

An object of the system according to the embodiment is to effectively perform education and testing related to public manners.SOLUTION: A system includes an SNS information analysis part, an education program generation part, a verification test creation part, a certificate issue part, and a preferential treatment provision part. The SNS information analysis unit analyzes the SNS information. The education program generation unit generates an education program related to public manners based on the SNS information analyzed by the SNS information analysis unit. The examination test creation part creates an examination test related to public manners on the basis of the SNS information analyzed by the SNS information analysis part. The certificate issuing part issues a certificate to a person who has completed the education program or a person who has passed the certification test. The preferential treatment providing part provides various preferential treatments by presenting the certificate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide education or certification regarding public manners, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively carry out education and testing on public manners. [Means for solving the problem]

[0006] The system according to the embodiment includes an SNS information analysis unit, an educational program generation unit, a certification exam creation unit, a certificate issuance unit, and a preferential treatment provision unit. The SNS information analysis unit analyzes SNS information. The educational program creation unit generates an educational program on public manners based on the SNS information analyzed by the SNS information analysis unit. The certification exam creation unit creates a certification exam on public manners based on the SNS information analyzed by the SNS information analysis unit. The certificate issuance unit issues a certificate to those who complete the educational program or pass the certification exam. The preferential treatment provision unit provides various preferential treatment upon presentation of the certificate. [Effects of the Invention]

[0007] The system according to the embodiment can effectively carry out education and testing on public manners. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The educational program creation system according to an embodiment of the present invention analyzes social media information, and a generation AI creates educational programs and certification exams related to public manners, issues certificates, and provides preferential treatment. As a result, the educational program creation system can promote the improvement of public manners and raise etiquette awareness throughout society.

[0029] An educational program creation system according to an embodiment includes an SNS information analysis unit, an educational program generation unit, a certification exam creation unit, a certificate issuance unit, and a preferential treatment provision unit. The SNS information analysis unit analyzes SNS information. For example, it collects posts and comments on SNS and analyzes them using text mining technology. The SNS information analysis unit can also estimate the poster's emotions using sentiment analysis technology. For example, it calculates an emotion score using natural language processing technology. The educational program generation unit generates an educational program on public manners based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates a simulation based on examples of bad manners on SNS. The generation AI can also create video learning materials on how to practice good manners. The certification exam creation unit creates a certification exam on public manners based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates multiple-choice questions based on specific examples of bad manners on SNS. The generation AI can also create written questions on how to practice good manners. The certificate issuance unit issues a certificate to those who complete the educational program or pass the certification exam. For example, a digital certificate can be issued and sent to the participant by email. Alternatively, a paper certificate can be mailed. The preferential treatment provision unit can provide various preferential treatments in response to presentation of the certificate. For example, discounts on public transportation or priority entry at specific facilities can be provided. Special benefits can also be provided at commercial facilities. In this way, the educational program creation system according to the embodiment can promote the improvement of public manners and raise awareness of manners throughout society.

[0030] When analyzing SNS information, the SNS information analysis unit takes into account the time of posting and geographical information, and can generate an educational program that reflects etiquette trends for each region and time of day. For example, when the generation AI analyzes SNS information, the SNS information analysis unit takes into account the time of posting and extracts examples of etiquette violations that occur frequently during specific time periods. For example, it creates an educational program based on etiquette violations that occur frequently at night. The SNS information analysis unit also analyzes etiquette trends for each region based on geographical information, and reflects regional etiquette examples in the educational program. For example, it covers etiquette examples that differ between urban and rural areas. The SNS information analysis unit also analyzes a combination of the time of posting and geographical information to generate an educational program specialized for a specific region or time of day. For example, it creates a program based on etiquette violations that occur frequently during rush hour. This allows for more specific and practical education by generating an educational program that reflects etiquette trends for each region and time of day.

[0031] When analyzing SNS information, the SNS information analysis unit evaluates the number of followers and influence of the poster, and can reflect influential examples in the educational program with a focus. For example, when the generation AI analyzes SNS information, the SNS information analysis unit evaluates the number of followers of the poster and prioritizes analysis of influential posts. For example, the SNS information analysis unit reflects etiquette examples of posters with a large number of followers in the educational program. The SNS information analysis unit also evaluates the influence of posters and focuses on influential examples. For example, the SNS information analysis unit creates an educational program based on the posts of influencers. The SNS information analysis unit also selects influential examples based on the number of followers and influence and reflects them in the content of the educational program. For example, it creates content that features influential examples of etiquette of influential posters. In this way, the effectiveness of the educational program is enhanced by focusing on influential examples.

[0032] In addition to analyzing social media information, the SNS information analysis unit also analyzes the comment sections of blogs and news sites, allowing it to generate educational programs from a wider range of information sources. For example, the generation AI in the SNS information analysis unit may analyze the comment sections of blogs in addition to social media information to collect examples of bad manners. For example, examples of bad manners obtained from blog comments may be reflected in the educational program. The SNS information analysis unit may also analyze the comment sections of news sites to collect examples of bad manners from a wider range of information sources. For example, examples of bad manners obtained from comments on news articles may be incorporated into the educational program. The SNS information analysis unit may also comprehensively analyze social media information, blogs, and the comment sections of news sites to reflect a wider variety of examples of bad manners in the educational program. For example, it may comprehensively take into account examples of bad manners obtained from different platforms. This allows the educational program to be generated from a wider range of information sources, thereby increasing the diversity and reliability of the content.

[0033] The SNS information analysis unit can generate educational programs specialized for different age groups or occupations based on the analyzed SNS information. For example, the generation AI in the SNS information analysis unit analyzes SNS information and generates educational programs specialized for different age groups. For example, it creates a program that features etiquette examples for young people. The SNS information analysis unit also generates educational programs specialized by occupation. For example, it creates a program based on etiquette examples for service industry workers. The SNS information analysis unit also analyzes etiquette examples according to age group or occupation and generates educational programs aimed at specific target groups. For example, it creates a program that features etiquette examples for the elderly. In this way, by generating educational programs specialized for different age groups or occupations, it becomes possible to provide education that meets the needs of the participants.

[0034] The certification test creation unit can analyze SNS information, consider the frequency and impact of etiquette violations, and prioritize the creation of questions with high importance. For example, the certification test creation unit uses a generation AI to analyze SNS information and consider the frequency and impact of etiquette violations to create questions with high importance. For example, it creates questions based on frequently seen etiquette violations. The certification test creation unit also evaluates the impact of etiquette violations and prioritizes the creation of questions based on cases with high impact. For example, it covers etiquette violations that have a large social impact. The certification test creation unit also analyzes the frequency and impact of etiquette violations on SNS and creates questions with high importance. For example, it creates questions based on cases with high frequency and impact. In this way, prioritizing the creation of questions with high importance deepens the test taker's understanding.

[0035] When analyzing SNS information, the certification test creation unit takes into account the poster's background information and can create questions from a variety of perspectives. For example, when the generation AI analyzes SNS information, the certification test creation unit takes into account the poster's age and creates questions from the perspective of each age group. For example, it creates questions that reflect the perspectives of young people and the elderly. The certification test creation unit also takes into account the poster's gender and creates questions from the perspective of each gender. For example, it creates questions that reflect the perspectives of men and women. The certification test creation unit also takes into account the poster's occupation and creates questions from the perspective of each occupation. For example, it creates questions that reflect the perspectives of the service industry and the manufacturing industry. In this way, questions from a variety of perspectives are created, thereby deepening the examinee's understanding.

[0036] The certification test creation unit can create certification test questions by analyzing not only SNS information but also observation data from actual public places. For example, the certification test creation unit creates certification test questions by having a generation AI analyze SNS information as well as observation data from actual public places. For example, questions are created based on examples of bad manners in public places. The certification test creation unit also includes the observation data in its analysis and integrates it with SNS information to create questions. For example, examples of bad manners obtained from the observation data are reflected in the certification test. The certification test creation unit also analyzes observation data from actual public places and creates questions by combining it with SNS information. For example, questions are created based on both the observation data and SNS information. In this way, more realistic questions can be created by including observation data from actual public places in the analysis.

[0037] The certification exam creation unit can divide the certification exam questions created by the generation AI into different difficulty levels and provide an exam that suits the skill level of the test taker. For example, the certification exam creation unit divides the certification exam questions created by the generation AI into beginner, intermediate, and advanced difficulty levels. For example, it creates questions ranging from basic etiquette to advanced etiquette. The certification exam creation unit also classifies the questions by difficulty level in order to provide an exam that suits the skill level of the test taker. For example, it prepares easy questions for beginners and difficult questions for advanced test takers. In addition, in order to provide an exam that suits the difficulty level, the generation AI evaluates the difficulty of the questions and classifies them into an appropriate level. For example, it classifies them based on the complexity of the questions and the difficulty of the answers. In this way, by dividing them into different difficulty levels, it is possible to provide an exam that suits the skill level of the test taker.

[0038] The certificate issuance unit can issue personalized certificates by reflecting the individual learning outcomes and characteristics of each student in the certificate design. The certificate issuance unit, for example, reflects the individual learning outcomes of each student in the certificate design and issues personalized certificates. For example, it may include specific skills and knowledge that the student has achieved. The certificate issuance unit also designs certificates that reflect the characteristics of each student. For example, it may incorporate design elements based on the student's interests and concerns. The certificate issuance unit also analyzes student data and includes personalized content in order to issue certificates that reflect the individual learning outcomes and characteristics. For example, it may customize the design based on the student's learning history and evaluation results. In this way, issuing personalized certificates increases the student's motivation.

[0039] The certificate issuance unit records the student's learning history and test results in detail during the certificate issuance process, and can propose future learning plans. For example, during the certificate issuance process, the certificate issuance unit records the student's learning history and test results in detail and proposes future learning plans based on that data. For example, it proposes the next content and skills to learn. The certificate issuance unit also records the learning history and test results in detail and reflects them in the certificate. For example, it records the learning goals and test scores achieved by the student. The certificate issuance unit also builds a system that proposes future learning plans based on the student's learning history and test results. For example, it automatically generates learning plans based on the student's weaknesses and interests. This supports continuous learning by proposing future learning plans based on the student's learning history and test results.

[0040] The certificate issuing unit can digitize certificates and use blockchain technology to guarantee their authenticity. The certificate issuing unit, for example, digitizes certificates and builds a system that uses blockchain technology to guarantee the authenticity of certificates. For example, the certificate issuance history is recorded on the blockchain. The certificate issuing unit also uses blockchain technology to prevent certificate tampering. For example, the digital signature of the certificate is recorded on the blockchain to guarantee authenticity. The certificate issuing unit also combines the digitization of certificates with blockchain technology to improve the reliability of certificates. For example, the issuer and date and time of issuance of the certificate are recorded on the blockchain. In this way, the digitization of certificates and the use of blockchain technology guarantee the authenticity of certificates and prevent tampering.

[0041] The preferential treatment provision unit can customize the content of preferential treatment to suit the student's lifestyle and preferences and provide it individually. For example, the preferential treatment provision unit customizes the content of preferential treatment to suit the student's lifestyle and provides it individually. For example, it provides benefits that can be used during commuting hours. The preferential treatment provision unit also customizes the content of preferential treatment to suit the student's preferences and provides it individually. For example, it provides benefits related to specific hobbies or interests. The preferential treatment provision unit also builds a system that customizes the content of preferential treatment based on the student's lifestyle and preferences and provides them individually. For example, it analyzes student data and selects the optimal benefits. In this way, preferential treatment that suits the student's lifestyle and preferences is provided, thereby increasing student satisfaction.

[0042] The preferential treatment provision unit can collect feedback from students in the process of providing preferential treatment and continuously improve the content of the benefits. For example, the preferential treatment provision unit collects feedback from students in the process of providing preferential treatment and improves the content of the benefits based on the results. For example, it investigates the usage status and satisfaction level of the benefits. The preferential treatment provision unit also builds a system for continuously improving the content of the preferential treatment based on the feedback from students. For example, it analyzes the feedback data and adjusts the content of the benefits. The preferential treatment provision unit also collects opinions and requests from students in the process of providing preferential treatment and improves the content of the benefits based on the results. For example, it conducts surveys and interviews. In this way, the content of the benefits can be improved based on the feedback from the students, thereby increasing student satisfaction.

[0043] The preferential treatment provision department expands the scope of preferential treatment provided to include not only public transportation and commercial facilities but also online services and event participation. The preferential treatment provision department, for example, expands the scope of preferential treatment provided to include online services. For example, it provides discounts on online shopping and streaming services. The preferential treatment provision department also expands the scope of preferential treatment provided to include event participation. For example, it provides priority admission and special benefits at concerts and sporting events. The preferential treatment provision department also builds a system that applies preferential treatment to not only public transportation and commercial facilities but also online services and event participation. For example, it builds partnerships to expand the scope of application of special benefits. This expands the scope of preferential treatment provided to increase participant satisfaction.

[0044] The preferential treatment provision unit can provide region-specific benefits by taking into account the characteristics of each region when providing preferential treatment. The preferential treatment provision unit, for example, provides region-specific benefits by taking into account the characteristics of each region when providing preferential treatment. For example, it provides benefits at local specialties and tourist facilities. The preferential treatment provision unit also builds a system for providing region-specific benefits based on the characteristics of each region. For example, it provides benefits at local events and festivals. The preferential treatment provision unit also provides region-specific benefits by taking into account the needs and characteristics of each region when providing preferential treatment. For example, it provides benefits at local shopping districts and restaurants. In this way, it is possible to provide services that are closely tied to the region by providing benefits that take into account the characteristics of each region.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When analyzing SNS information, the SNS information analysis unit takes into account the time of day the post was made and geographical information, and can generate an educational program that reflects etiquette trends for each region and time of day. For example, it can extract examples of etiquette violations that occur frequently during specific time periods and create an educational program based on etiquette violations that occur frequently at night. It can also analyze etiquette trends for each region based on geographical information and reflect regional etiquette examples in the educational program. For example, it can cover etiquette examples that differ between urban and rural areas. It can also analyze the time of day the post was made in combination with geographical information to generate an educational program specialized for a specific region or time of day. For example, it can create a program based on etiquette violations that occur frequently during rush hour. This allows for more specific and practical education by generating an educational program that reflects etiquette trends for each region and time of day.

[0047] When analyzing SNS information, the SNS information analysis unit evaluates the number of followers and influence of posters, and can reflect influential examples in the educational program with a focus. For example, etiquette examples from posters with a large number of followers can be reflected in the educational program. The influence of posters can also be evaluated, and influential examples can be highlighted with a focus. For example, an educational program can be created based on the posts of influencers. In addition, influential examples can be selected based on the number of followers and influence, and reflected in the content of the educational program. For example, content can be created that features influential examples from influential posters. In this way, the effectiveness of the educational program can be enhanced by focusing on influential examples.

[0048] In addition to analyzing SNS information, the SNS information analysis unit also analyzes the comment sections of blogs and news sites, allowing it to generate educational programs from a wider range of information sources. For example, it can analyze the comment sections of blogs to collect examples of bad manners. For example, examples of bad manners obtained from blog comments can be reflected in the educational programs. It can also analyze the comment sections of news sites to collect examples of bad manners from a wider range of information sources. For example, it can incorporate examples of bad manners obtained from comments on news articles into the educational programs. It can also analyze SNS information, blogs, and the comment sections of news sites in an integrated manner to reflect a wider variety of examples of bad manners in the educational programs. For example, it can comprehensively take into account examples of bad manners obtained from different platforms. This allows it to generate educational programs from a wider range of information sources, thereby increasing the diversity and reliability of the content.

[0049] The SNS information analysis unit can generate educational programs specialized for different age groups and occupations based on the analyzed SNS information. For example, it generates educational programs specialized for different age groups. For example, it creates a program that features etiquette examples for young people. It also generates educational programs specialized by occupation. For example, it creates a program based on etiquette examples for service industry workers. It also analyzes etiquette examples according to age group and occupation to generate educational programs aimed at specific target groups. For example, it creates a program that features etiquette examples for the elderly. In this way, by generating educational programs specialized for different age groups and occupations, it becomes possible to provide education that meets the needs of the participants.

[0050] The certification test creation department can analyze SNS information, consider the frequency and impact of etiquette violations, and prioritize the creation of questions with high importance. For example, it creates questions with high importance by considering the frequency and impact of etiquette violations. For example, it creates questions based on frequently seen etiquette violations. It also evaluates the impact of etiquette violations and prioritizes the creation of questions based on cases with high impact. For example, it focuses on etiquette violations that have a large social impact. It also analyzes the frequency and impact of etiquette violations on SNS and creates questions with high importance. For example, it creates questions based on cases with high frequency and impact. By prioritizing the creation of questions with high importance, it deepens the test taker's understanding.

[0051] When analyzing SNS information, the certification test creation department can take into account the poster's background information and create questions from a variety of perspectives. For example, it can take into account the poster's age and create questions from the perspective of each age group. For example, it can create questions that reflect the perspectives of young people and the elderly. It can also take into account the poster's gender and create questions from the perspective of each gender. For example, it can create questions that reflect the perspectives of men and women. It can also take into account the poster's occupation and create questions from the perspective of each occupation. For example, it can create questions that reflect the perspectives of the service industry and the manufacturing industry. This allows the test takers to deepen their understanding by creating questions from a variety of perspectives.

[0052] The certification test creation unit can create certification test questions by analyzing not only SNS information but also observation data from actual public places. For example, the unit analyzes observation data from actual public places to create certification test questions. For example, questions are created based on examples of bad manners in public places. The unit also includes the observation data in its analysis and integrates it with SNS information to create questions. For example, examples of bad manners obtained from the observation data are reflected in the certification test. The unit also analyzes observation data from actual public places and combines it with SNS information to create questions. For example, questions are created based on both the observation data and SNS information. In this way, by including observation data from actual public places in its analysis, more realistic questions can be created.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The SNS information analysis unit analyzes SNS information. For example, it collects posts and comments on SNS and analyzes them using text mining technology. It can also estimate the poster's emotions using sentiment analysis technology. For example, it calculates an emotion score using natural language processing technology. Step 2: The educational program generation unit generates an educational program on public manners based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates a simulation based on examples of bad manners on SNS. It can also create video teaching materials on how to practice good manners. Step 3: The test creation unit creates a test on public etiquette based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates multiple-choice questions based on specific examples of SNS etiquette violations. It can also create written questions on how to practice good etiquette. Step 4: The Certificate Issuance Department issues a certificate to those who complete the educational program or pass the certification exam. For example, a digital certificate can be issued and sent to the student via email. Alternatively, a paper certificate can be sent by mail. Step 5: The incentives department offers various incentives in exchange for presenting the certificate, such as discounts on public transport, priority entry at certain establishments, or special offers at commercial establishments.

[0055] (Example 2) The educational program creation system according to an embodiment of the present invention analyzes social media information, and a generation AI creates educational programs and certification exams related to public manners, issues certificates, and provides preferential treatment. As a result, the educational program creation system can promote the improvement of public manners and raise etiquette awareness throughout society.

[0056] An educational program creation system according to an embodiment includes an SNS information analysis unit, an educational program generation unit, a certification exam creation unit, a certificate issuance unit, and a preferential treatment provision unit. The SNS information analysis unit analyzes SNS information. For example, it collects posts and comments on SNS and analyzes them using text mining technology. The SNS information analysis unit can also estimate the poster's emotions using sentiment analysis technology. For example, it calculates an emotion score using natural language processing technology. The educational program generation unit generates an educational program on public manners based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates a simulation based on examples of bad manners on SNS. The generation AI can also create video learning materials on how to practice good manners. The certification exam creation unit creates a certification exam on public manners based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates multiple-choice questions based on specific examples of bad manners on SNS. The generation AI can also create written questions on how to practice good manners. The certificate issuance unit issues a certificate to those who complete the educational program or pass the certification exam. For example, a digital certificate can be issued and sent to the participant by email. Alternatively, a paper certificate can be mailed. The preferential treatment provision unit can provide various preferential treatments in response to presentation of the certificate. For example, discounts on public transportation or priority entry at specific facilities can be provided. Special benefits can also be provided at commercial facilities. In this way, the educational program creation system according to the embodiment can promote the improvement of public manners and raise awareness of manners throughout society.

[0057] When analyzing SNS information, the SNS information analysis unit can estimate the poster's emotions and prioritize emotionally influential examples to be reflected in the educational program. For example, when the generation AI analyzes SNS information, the SNS information analysis unit estimates the poster's emotions and prioritizes extracting examples that have a strong emotional impact. For example, it focuses on analyzing posts that strongly express emotions such as anger or sadness and reflects them in the educational program. The SNS information analysis unit also estimates the poster's emotions and selects examples that are likely to resonate with the poster emotionally. For example, based on posts that express strong emotions such as joy or surprise, it incorporates examples of positive etiquette into the educational program. The SNS information analysis unit also uses the emotion estimation function to prioritize analyzing emotionally influential examples and reflect them in the content of the educational program. For example, it creates educational content with an emotional impact based on posts with a high emotional score. This prioritizes reflecting emotionally influential examples in the educational program, making it easier to attract students' attention.

[0058] When analyzing SNS information, the SNS information analysis unit takes into account the time of posting and geographical information, and can generate an educational program that reflects etiquette trends for each region and time of day. For example, when the generation AI analyzes SNS information, the SNS information analysis unit takes into account the time of posting and extracts examples of etiquette violations that occur frequently during specific time periods. For example, it creates an educational program based on etiquette violations that occur frequently at night. The SNS information analysis unit also analyzes etiquette trends for each region based on geographical information, and reflects regional etiquette examples in the educational program. For example, it covers etiquette examples that differ between urban and rural areas. The SNS information analysis unit also analyzes a combination of the time of posting and geographical information to generate an educational program specialized for a specific region or time of day. For example, it creates a program based on etiquette violations that occur frequently during rush hour. This allows for more specific and practical education by generating an educational program that reflects etiquette trends for each region and time of day.

[0059] When analyzing SNS information, the SNS information analysis unit evaluates the number of followers and influence of the poster, and can reflect influential examples in the educational program with a focus. For example, when the generation AI analyzes SNS information, the SNS information analysis unit evaluates the number of followers of the poster and prioritizes analysis of influential posts. For example, the SNS information analysis unit reflects etiquette examples of posters with a large number of followers in the educational program. The SNS information analysis unit also evaluates the influence of posters and focuses on influential examples. For example, the SNS information analysis unit creates an educational program based on the posts of influencers. The SNS information analysis unit also selects influential examples based on the number of followers and influence and reflects them in the content of the educational program. For example, it creates content that features influential examples of etiquette of influential posters. In this way, the effectiveness of the educational program is enhanced by focusing on influential examples.

[0060] In addition to analyzing social media information, the SNS information analysis unit also analyzes the comment sections of blogs and news sites, allowing it to generate educational programs from a wider range of information sources. For example, the generation AI in the SNS information analysis unit may analyze the comment sections of blogs in addition to social media information to collect examples of bad manners. For example, examples of bad manners obtained from blog comments may be reflected in the educational program. The SNS information analysis unit may also analyze the comment sections of news sites to collect examples of bad manners from a wider range of information sources. For example, examples of bad manners obtained from comments on news articles may be incorporated into the educational program. The SNS information analysis unit may also comprehensively analyze social media information, blogs, and the comment sections of news sites to reflect a wider variety of examples of bad manners in the educational program. For example, it may comprehensively take into account examples of bad manners obtained from different platforms. This allows the educational program to be generated from a wider range of information sources, thereby increasing the diversity and reliability of the content.

[0061] The SNS information analysis unit can generate educational programs specialized for different age groups or occupations based on the analyzed SNS information. For example, the generation AI in the SNS information analysis unit analyzes SNS information and generates educational programs specialized for different age groups. For example, it creates a program that features etiquette examples for young people. The SNS information analysis unit also generates educational programs specialized by occupation. For example, it creates a program based on etiquette examples for service industry workers. The SNS information analysis unit also analyzes etiquette examples according to age group or occupation and generates educational programs aimed at specific target groups. For example, it creates a program that features etiquette examples for the elderly. In this way, by generating educational programs specialized for different age groups or occupations, it becomes possible to provide education that meets the needs of the participants.

[0062] The SNS information analysis unit can use the emotion estimation function to evaluate the emotional impact that the content of the educational program has on students and optimize it to elicit positive emotions. The SNS information analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact that the content of the educational program has on students. For example, it analyzes whether the content of the program elicits positive emotions in students. The SNS information analysis unit also evaluates the content of the educational program using the emotion estimation function and optimizes it to elicit positive emotions. For example, it creates content that makes students feel joy and satisfaction. The SNS information analysis unit also adjusts the content of the educational program based on the emotion estimation data and optimizes it to elicit positive emotions in students. For example, it prioritizes content with a high emotion score. In this way, optimizing the content to elicit positive emotions increases students' motivation to learn.

[0063] The certification test creation unit can analyze SNS information and use an emotion estimation function to create questions that are easy to empathize with emotionally. For example, the certification test creation unit uses a generation AI to analyze SNS information and an emotion estimation function to create questions that are easy to empathize with emotionally. For example, questions are created based on posts with high emotion scores. The certification test creation unit also uses the emotion estimation function to select questions that are easy to empathize with emotionally and create them as certification test questions. For example, questions that elicit positive emotions are created preferentially. The certification test creation unit also creates questions that are easy to empathize with emotionally based on the results of emotion analysis of SNS information. For example, questions are created based on examples with high emotion scores. In this way, questions that are easy to empathize with emotionally deepen the test taker's understanding.

[0064] The certification test creation unit can analyze SNS information, consider the frequency and impact of etiquette violations, and prioritize the creation of questions with high importance. For example, the certification test creation unit uses a generation AI to analyze SNS information and consider the frequency and impact of etiquette violations to create questions with high importance. For example, it creates questions based on frequently seen etiquette violations. The certification test creation unit also evaluates the impact of etiquette violations and prioritizes the creation of questions based on cases with high impact. For example, it covers etiquette violations that have a large social impact. The certification test creation unit also analyzes the frequency and impact of etiquette violations on SNS and creates questions with high importance. For example, it creates questions based on cases with high frequency and impact. In this way, prioritizing the creation of questions with high importance deepens the test taker's understanding.

[0065] When analyzing SNS information, the certification test creation unit takes into account the poster's background information and can create questions from a variety of perspectives. For example, when the generation AI analyzes SNS information, the certification test creation unit takes into account the poster's age and creates questions from the perspective of each age group. For example, it creates questions that reflect the perspectives of young people and the elderly. The certification test creation unit also takes into account the poster's gender and creates questions from the perspective of each gender. For example, it creates questions that reflect the perspectives of men and women. The certification test creation unit also takes into account the poster's occupation and creates questions from the perspective of each occupation. For example, it creates questions that reflect the perspectives of the service industry and the manufacturing industry. In this way, questions from a variety of perspectives are created, thereby deepening the examinee's understanding.

[0066] The certification test creation unit can create certification test questions by analyzing not only SNS information but also observation data from actual public places. For example, the certification test creation unit creates certification test questions by having a generation AI analyze SNS information as well as observation data from actual public places. For example, questions are created based on examples of bad manners in public places. The certification test creation unit also includes the observation data in its analysis and integrates it with SNS information to create questions. For example, examples of bad manners obtained from the observation data are reflected in the certification test. The certification test creation unit also analyzes observation data from actual public places and creates questions by combining it with SNS information. For example, questions are created based on both the observation data and SNS information. In this way, more realistic questions can be created by including observation data from actual public places in the analysis.

[0067] The certification exam creation unit can divide the certification exam questions created by the generation AI into different difficulty levels and provide an exam that suits the skill level of the test taker. For example, the certification exam creation unit divides the certification exam questions created by the generation AI into beginner, intermediate, and advanced difficulty levels. For example, it creates questions ranging from basic etiquette to advanced etiquette. The certification exam creation unit also classifies the questions by difficulty level in order to provide an exam that suits the skill level of the test taker. For example, it prepares easy questions for beginners and difficult questions for advanced test takers. In addition, in order to provide an exam that suits the difficulty level, the generation AI evaluates the difficulty of the questions and classifies them into an appropriate level. For example, it classifies them based on the complexity of the questions and the difficulty of the answers. In this way, by dividing them into different difficulty levels, it is possible to provide an exam that suits the skill level of the test taker.

[0068] The certification exam creation unit can use the emotion estimation function to evaluate the stress level that certification exam questions impose on test takers and adjust the difficulty level appropriately. The certification exam creation unit, for example, uses the emotion estimation function to evaluate the stress level that certification exam questions impose on test takers. For example, it analyzes the emotion scores when solving questions to measure the stress level. The certification exam creation unit also evaluates the stress level of test takers and adjusts the difficulty level appropriately. For example, it adjusts questions with high stress levels to be less difficult. The certification exam creation unit also adjusts the difficulty of certification exam questions based on the emotion estimation data to provide test takers with a test of appropriate difficulty. For example, it prioritizes selecting questions with low stress levels. In this way, providing test takers with a test of appropriate difficulty reduces stress and improves learning effectiveness.

[0069] The certificate issuing unit can add an evaluation indicating the student's emotional growth to the certificate and visualize that growth using the emotion estimation function. The certificate issuing unit, for example, adds an evaluation indicating the student's emotional growth to the certificate and visualizes that growth using the emotion estimation function. For example, it compares emotion scores before and after the course and displays a graph showing the growth. The certificate issuing unit also uses the emotion estimation function to evaluate the student's emotional growth and reflects the result in the certificate. For example, it adds a chart showing emotional changes during the course to the certificate. In addition, the certificate issuing unit performs an evaluation based on emotion estimation data to visualize the student's emotional growth and includes the result in the certificate. For example, it includes a visual showing the fluctuations in emotion scores in the certificate. This visualizes the student's emotional growth, making it possible to more clearly demonstrate the results of learning.

[0070] The certificate issuance unit can issue personalized certificates by reflecting the individual learning outcomes and characteristics of each student in the certificate design. The certificate issuance unit, for example, reflects the individual learning outcomes of each student in the certificate design and issues personalized certificates. For example, it may include specific skills and knowledge that the student has achieved. The certificate issuance unit also designs certificates that reflect the characteristics of each student. For example, it may incorporate design elements based on the student's interests and concerns. The certificate issuance unit also analyzes student data and includes personalized content in order to issue certificates that reflect the individual learning outcomes and characteristics. For example, it may customize the design based on the student's learning history and evaluation results. In this way, issuing personalized certificates increases the student's motivation.

[0071] The certificate issuance unit records the student's learning history and test results in detail during the certificate issuance process, and can propose future learning plans. For example, during the certificate issuance process, the certificate issuance unit records the student's learning history and test results in detail and proposes future learning plans based on that data. For example, it proposes the next content and skills to learn. The certificate issuance unit also records the learning history and test results in detail and reflects them in the certificate. For example, it records the learning goals and test scores achieved by the student. The certificate issuance unit also builds a system that proposes future learning plans based on the student's learning history and test results. For example, it automatically generates learning plans based on the student's weaknesses and interests. This supports continuous learning by proposing future learning plans based on the student's learning history and test results.

[0072] The certificate issuing unit can digitize certificates and use blockchain technology to guarantee their authenticity. The certificate issuing unit, for example, digitizes certificates and builds a system that uses blockchain technology to guarantee the authenticity of certificates. For example, the certificate issuance history is recorded on the blockchain. The certificate issuing unit also uses blockchain technology to prevent certificate tampering. For example, the digital signature of the certificate is recorded on the blockchain to guarantee authenticity. The certificate issuing unit also combines the digitization of certificates with blockchain technology to improve the reliability of certificates. For example, the issuer and date and time of issuance of the certificate are recorded on the blockchain. In this way, the digitization of certificates and the use of blockchain technology guarantee the authenticity of certificates and prevent tampering.

[0073] The certificate issuing unit can add graphs or charts showing the emotional changes of the student during the learning process to the certificate, making it easier to understand visually. For example, the certificate issuing unit can add graphs showing the emotional changes of the student during the learning process to the certificate, making it easier to understand visually. For example, it can display a graph showing the fluctuations in the emotional score during the learning period. The certificate issuing unit can also add a chart showing the emotional changes of the student to the certificate based on the emotion estimation data. For example, it can display a line graph showing the fluctuations in the student's emotional score. The certificate issuing unit can also add visual elements showing the emotional changes to the certificate, making it easier to understand visually the student's learning process. For example, it can display a heat map showing the fluctuations in the emotional score. This visually shows the emotional changes of the student during the learning process, making it possible to more clearly demonstrate the results of the learning.

[0074] The certificate issuance unit can use the emotion estimation function to evaluate the sense of accomplishment felt by the student when receiving the certificate and attach a message to reinforce that feeling. The certificate issuance unit, for example, uses the emotion estimation function to evaluate the sense of accomplishment felt by the student when receiving the certificate and attaches a message to reinforce that feeling. For example, it displays an encouraging message to enhance the sense of accomplishment. The certificate issuance unit also evaluates the student's emotions when receiving the certificate and generates a message to reinforce the sense of accomplishment based on the evaluation result. For example, it displays a customized message according to the student's emotion score. The certificate issuance unit also builds a system that attaches a message to reinforce the sense of accomplishment felt by the student when receiving the certificate based on the emotion estimation data. For example, it displays a special message if the emotion score is high. In this way, attaching a message to reinforce the student's sense of accomplishment increases their motivation to learn.

[0075] The preferential treatment provision unit can use the emotion estimation function to identify and provide the benefit that the student will enjoy most. The preferential treatment provision unit, for example, uses the emotion estimation function to identify the benefit that the student will enjoy most and provides that benefit. For example, it provides preferentially benefits with a high emotion score. The preferential treatment provision unit also builds a system that selects and provides the benefit that the student will enjoy most based on the student's emotional response. For example, it automatically generates optimal benefits based on past data. The preferential treatment provision unit also identifies the benefit that the student will enjoy most based on the emotion estimation data and provides that benefit. For example, it selects preferentially benefits with a high emotion score. In this way, by providing the benefit that the student will enjoy most, the student's satisfaction is increased.

[0076] The preferential treatment provision unit can customize the content of preferential treatment to suit the student's lifestyle and preferences and provide it individually. For example, the preferential treatment provision unit customizes the content of preferential treatment to suit the student's lifestyle and provides it individually. For example, it provides benefits that can be used during commuting hours. The preferential treatment provision unit also customizes the content of preferential treatment to suit the student's preferences and provides it individually. For example, it provides benefits related to specific hobbies or interests. The preferential treatment provision unit also builds a system that customizes the content of preferential treatment based on the student's lifestyle and preferences and provides them individually. For example, it analyzes student data and selects the optimal benefits. In this way, preferential treatment that suits the student's lifestyle and preferences is provided, thereby increasing student satisfaction.

[0077] The preferential treatment provision unit can collect feedback from students in the process of providing preferential treatment and continuously improve the content of the benefits. For example, the preferential treatment provision unit collects feedback from students in the process of providing preferential treatment and improves the content of the benefits based on the results. For example, it investigates the usage status and satisfaction level of the benefits. The preferential treatment provision unit also builds a system for continuously improving the content of the preferential treatment based on the feedback from students. For example, it analyzes the feedback data and adjusts the content of the benefits. The preferential treatment provision unit also collects opinions and requests from students in the process of providing preferential treatment and improves the content of the benefits based on the results. For example, it conducts surveys and interviews. In this way, the content of the benefits can be improved based on the feedback from the students, thereby increasing student satisfaction.

[0078] The preferential treatment provision department expands the scope of preferential treatment provided to include not only public transportation and commercial facilities but also online services and event participation. The preferential treatment provision department, for example, expands the scope of preferential treatment provided to include online services. For example, it provides discounts on online shopping and streaming services. The preferential treatment provision department also expands the scope of preferential treatment provided to include event participation. For example, it provides priority admission and special benefits at concerts and sporting events. The preferential treatment provision department also builds a system that applies preferential treatment to not only public transportation and commercial facilities but also online services and event participation. For example, it builds partnerships to expand the scope of application of special benefits. This expands the scope of preferential treatment provided to increase participant satisfaction.

[0079] The preferential treatment provision unit can provide region-specific benefits by taking into account the characteristics of each region when providing preferential treatment. The preferential treatment provision unit, for example, provides region-specific benefits by taking into account the characteristics of each region when providing preferential treatment. For example, it provides benefits at local specialties and tourist facilities. The preferential treatment provision unit also builds a system for providing region-specific benefits based on the characteristics of each region. For example, it provides benefits at local events and festivals. The preferential treatment provision unit also provides region-specific benefits by taking into account the needs and characteristics of each region when providing preferential treatment. For example, it provides benefits at local shopping districts and restaurants. In this way, it is possible to provide services that are closely tied to the region by providing benefits that take into account the characteristics of each region.

[0080] The preferential treatment provision unit can use the emotion estimation function to evaluate the satisfaction felt by students when preferential treatment is provided and introduce measures to maximize that satisfaction. The preferential treatment provision unit, for example, uses the emotion estimation function to evaluate the satisfaction felt by students when preferential treatment is provided and introduces measures to maximize satisfaction based on the results. For example, it adjusts the content of the benefits and the method of providing them. The preferential treatment provision unit also evaluates the satisfaction with the preferential treatment based on the students' emotional reactions and introduces measures based on the results. For example, it prioritizes providing benefits that result in high satisfaction. The preferential treatment provision unit also builds a system that evaluates the satisfaction felt by students when preferential treatment is provided based on the emotion estimation data and introduces measures to maximize that satisfaction. For example, it adjusts the customization of the benefits and the timing of their provision. In this way, measures to maximize student satisfaction are introduced, thereby increasing student satisfaction.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When analyzing SNS information, the SNS information analysis unit can estimate the emotions of the poster and prioritize emotionally influential examples to be reflected in the educational program. For example, it can focus on analyzing posts that strongly express emotions such as anger or sadness and reflect these in the educational program. The SNS information analysis unit also estimates the emotions of the poster and selects examples that are likely to resonate with the poster emotionally. For example, it can incorporate positive etiquette examples into the educational program based on posts that express strong emotions such as joy or surprise. The SNS information analysis unit also uses the emotion estimation function to prioritize analyzing emotionally influential examples and reflect these in the content of the educational program. For example, it can create educational content with an emotional impact based on posts with a high emotional score. This allows emotionally influential examples to be prioritized and reflected in the educational program, making it easier to attract students' attention.

[0083] When analyzing SNS information, the SNS information analysis unit takes into account the time of day the post was made and geographical information, and can generate an educational program that reflects etiquette trends for each region and time of day. For example, it can extract examples of etiquette violations that occur frequently during specific time periods and create an educational program based on etiquette violations that occur frequently at night. It can also analyze etiquette trends for each region based on geographical information and reflect regional etiquette examples in the educational program. For example, it can cover etiquette examples that differ between urban and rural areas. It can also analyze the time of day the post was made in combination with geographical information to generate an educational program specialized for a specific region or time of day. For example, it can create a program based on etiquette violations that occur frequently during rush hour. This allows for more specific and practical education by generating an educational program that reflects etiquette trends for each region and time of day.

[0084] When analyzing SNS information, the SNS information analysis unit evaluates the number of followers and influence of posters, and can reflect influential examples in the educational program with a focus. For example, etiquette examples from posters with a large number of followers can be reflected in the educational program. The influence of posters can also be evaluated, and influential examples can be highlighted with a focus. For example, an educational program can be created based on the posts of influencers. In addition, influential examples can be selected based on the number of followers and influence, and reflected in the content of the educational program. For example, content can be created that features influential examples from influential posters. In this way, the effectiveness of the educational program can be enhanced by focusing on influential examples.

[0085] In addition to analyzing SNS information, the SNS information analysis unit also analyzes the comment sections of blogs and news sites, allowing it to generate educational programs from a wider range of information sources. For example, it can analyze the comment sections of blogs to collect examples of bad manners. For example, examples of bad manners obtained from blog comments can be reflected in the educational programs. It can also analyze the comment sections of news sites to collect examples of bad manners from a wider range of information sources. For example, it can incorporate examples of bad manners obtained from comments on news articles into the educational programs. It can also analyze SNS information, blogs, and the comment sections of news sites in an integrated manner to reflect a wider variety of examples of bad manners in the educational programs. For example, it can comprehensively take into account examples of bad manners obtained from different platforms. This allows it to generate educational programs from a wider range of information sources, thereby increasing the diversity and reliability of the content.

[0086] The SNS information analysis unit can generate educational programs specialized for different age groups and occupations based on the analyzed SNS information. For example, it generates educational programs specialized for different age groups. For example, it creates a program that features etiquette examples for young people. It also generates educational programs specialized by occupation. For example, it creates a program based on etiquette examples for service industry workers. It also analyzes etiquette examples according to age group and occupation to generate educational programs aimed at specific target groups. For example, it creates a program that features etiquette examples for the elderly. In this way, by generating educational programs specialized for different age groups and occupations, it becomes possible to provide education that meets the needs of the participants.

[0087] The SNS information analysis unit uses the emotion estimation function to evaluate the emotional impact that the content of an educational program has on participants and can optimize it to elicit positive emotions. For example, it analyzes whether the content of an educational program elicits positive emotions in participants. It also evaluates the content of the educational program using the emotion estimation function and optimizes it to elicit positive emotions. For example, it creates content that makes participants feel joy and satisfaction. It also adjusts the content of the educational program based on the emotion estimation data and optimizes it to elicit positive emotions in participants. For example, it prioritizes content with a high emotion score. This optimizes the content to elicit positive emotions, thereby increasing participants' motivation to learn.

[0088] The certification test creation unit can analyze SNS information and use the emotion estimation function to create questions that are easy to empathize with emotionally. For example, it creates questions based on posts with high emotion scores. It also uses the emotion estimation function to select questions that are easy to empathize with emotionally and creates them as certification test questions. For example, it prioritizes the creation of questions that elicit positive emotions. It also creates questions that are easy to empathize with emotionally based on the results of emotion analysis of SNS information. For example, it creates questions based on examples with high emotion scores. In this way, it creates questions that are easy to empathize with emotionally, thereby deepening the test taker's understanding.

[0089] The certification test creation department can analyze SNS information, consider the frequency and impact of etiquette violations, and prioritize the creation of questions with high importance. For example, it creates questions with high importance by considering the frequency and impact of etiquette violations. For example, it creates questions based on frequently seen etiquette violations. It also evaluates the impact of etiquette violations and prioritizes the creation of questions based on cases with high impact. For example, it focuses on etiquette violations that have a large social impact. It also analyzes the frequency and impact of etiquette violations on SNS and creates questions with high importance. For example, it creates questions based on cases with high frequency and impact. By prioritizing the creation of questions with high importance, it deepens the test taker's understanding.

[0090] When analyzing SNS information, the certification test creation department can take into account the poster's background information and create questions from a variety of perspectives. For example, it can take into account the poster's age and create questions from the perspective of each age group. For example, it can create questions that reflect the perspectives of young people and the elderly. It can also take into account the poster's gender and create questions from the perspective of each gender. For example, it can create questions that reflect the perspectives of men and women. It can also take into account the poster's occupation and create questions from the perspective of each occupation. For example, it can create questions that reflect the perspectives of the service industry and the manufacturing industry. This allows the test takers to deepen their understanding by creating questions from a variety of perspectives.

[0091] The certification test creation unit can create certification test questions by analyzing not only SNS information but also observation data from actual public places. For example, the unit analyzes observation data from actual public places to create certification test questions. For example, questions are created based on examples of bad manners in public places. The unit also includes the observation data in its analysis and integrates it with SNS information to create questions. For example, examples of bad manners obtained from the observation data are reflected in the certification test. The unit also analyzes observation data from actual public places and combines it with SNS information to create questions. For example, questions are created based on both the observation data and SNS information. In this way, by including observation data from actual public places in its analysis, more realistic questions can be created.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The SNS information analysis unit analyzes SNS information. For example, it collects posts and comments on SNS and analyzes them using text mining technology. It can also estimate the poster's emotions using sentiment analysis technology. For example, it calculates an emotion score using natural language processing technology. Step 2: The educational program generation unit generates an educational program on public manners based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates a simulation based on examples of bad manners on SNS. It can also create video teaching materials on how to practice good manners. Step 3: The test creation unit creates a test on public etiquette based on the SNS information analyzed by the SNS information analysis unit. For example, the generation AI creates multiple-choice questions based on specific examples of SNS etiquette violations. It can also create written questions on how to practice good etiquette. Step 4: The Certificate Issuance Department issues a certificate to those who complete the educational program or pass the certification exam. For example, a digital certificate can be issued and sent to the student via email. Alternatively, a paper certificate can be sent by mail. Step 5: The incentives department offers various incentives in exchange for presenting the certificate, such as discounts on public transport, priority entry at certain establishments, or special offers at commercial establishments.

[0094] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

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

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an SNS information analysis unit that analyzes SNS information; an educational program generation unit that generates an educational program regarding public manners based on the SNS information analyzed by the SNS information analysis unit; a certification test creation unit that creates a certification test on public manners based on the SNS information analyzed by the SNS information analysis unit; a certificate issuing department that issues a certificate to a person who has completed the educational program or passed the certification examination; and a preferential treatment provision unit that provides various preferential treatments upon presentation of the certificate. A system characterized by:

2. The SNS information analysis unit When analyzing the SNS information, the emotions of the poster are estimated, and emotionally influential cases are reflected preferentially in the educational program.

2. The system of claim 1.

3. The SNS information analysis unit When analyzing the SNS information, the time of day and geographical information of the posting are taken into consideration, and the educational program is generated that reflects the manners trends for each region and time of day.

2. The system of claim 1.

4. The SNS information analysis unit When analyzing the SNS information, the number of followers and influence of the poster will be evaluated, and the most influential cases will be reflected in the educational program.

2. The system of claim 1.

5. The SNS information analysis unit In addition to analyzing the social media information, the comment sections of blogs and news sites will also be analyzed to generate the educational program from a wider range of information sources.

2. The system of claim 1.

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