System

Abstract

An object of a system according to an embodiment is to enable a high school student to specifically imagine a career or a future career.SOLUTION: A system according to an embodiment includes a reception unit, a module for experiencing campus life, a module for conducting academic research, a module for building human relationships, and a module for experiencing job hunting. The reception unit receives a user's selection. The module for experiencing the campus life experiences the campus life on the basis of the information received by the reception unit. The module for performing academic research performs academic research based on the information provided by the module for experiencing campus life. The module for establishing human relationships establishes human relationships based on the information provided by the module for conducting academic research. The module for experiencing job hunting experiences job hunting based on the information provided by the module for building human relationships.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 has the problem that it is difficult for high school students to visualize their future careers and future education options.

[0005] The system according to the embodiment aims to enable high school students to concretely imagine their future education and career. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a module for experiencing campus life, a module for conducting academic research, a module for building relationships, and a module for experiencing job hunting. The reception unit receives a user's selection. The module for experiencing campus life experiences campus life based on information received by the reception unit. The module for conducting academic research conducts academic research based on information provided by the module for experiencing campus life. The module for building relationships builds relationships based on information provided by the module for conducting academic research. The module for experiencing job hunting experiences job hunting based on information provided by the module for building relationships. [Effects of the Invention]

[0007] The system according to the embodiment can enable high school students to concretely imagine their future education and career. [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) A system according to an embodiment of the present invention is a simulation game for high school students that allows them to experience university life and future careers. This system allows users to select different universities and majors and experience virtual campus life, academic research, relationships, and even job hunting. For example, if a user selects a specific major, the virtual campus life allows them to participate in classes and research projects for that major. The user can also build relationships with other students and professors, thereby experiencing realistic aspects of university life. Next, the user can deepen their expertise through academic research. For example, if a user majors in a specific field, they can conduct experiments in a virtual laboratory and present their research results. This allows the user to confirm their interests and aptitudes. Furthermore, the user can experience job hunting. For example, if a user majors in a specific field, they can experience an internship at a virtual company and learn about the job hunting process. This allows the user to gain a concrete image of their future career. This system can help high school students deepen their understanding of their future. For example, through virtual university life, users can confirm their interests and aptitudes and gain a concrete image of their future university and career. It can also be used as a career selection support tool that combines education and entertainment to improve the learning effect of users.

[0029] A simulation game system according to an embodiment includes a reception unit, a campus life experience unit, an academic research unit, a relationship building unit, and a job hunting experience unit. The reception unit accepts a user's selection. For example, it accepts information about the university and major selected by the user. The campus life experience unit provides virtual classes and research projects based on the user's selected major. For example, if the user selects a specific major, the user can participate in classes and research projects in that major. The academic research unit provides experiments in a virtual laboratory and presentations of research results based on the user's selected major. For example, if the user is majoring in a specific field, the user can conduct experiments in a virtual laboratory and present their research results. The relationship building unit provides relationships with other students and professors based on the user's selected major. For example, the user can build relationships with other students and professors, thereby experiencing realistic aspects of university life. The job hunting experience unit provides internships at virtual companies and the job hunting process based on the user's selected major. For example, if the user is majoring in a specific field, the user can experience an internship at a virtual company and learn about the job hunting process. As a result, the simulation game system according to the embodiment allows the user to experience university life and future careers.

[0030] The reception unit can receive information about the university and major selected by the user. The reception unit receives, for example, the information about the university and major selected by the user. The information about the university and major includes, for example, the name of the university, the name of the major, the faculty, the department, etc., but is not limited to these examples. This allows the information about the university and major selected by the user to be received.

[0031] The campus life experience club can provide virtual classes and research projects based on the major selected by the user. The campus life experience club provides virtual classes and research projects based on, for example, the major selected by the user. Examples of virtual classes and research projects include, but are not limited to, class syllabi, project themes, and evaluation criteria. This allows the user to experience virtual classes and research projects based on the major selected by the user.

[0032] The academic research department can provide experiments in a virtual laboratory and presentations of research results based on the major selected by the user. The academic research department can provide, for example, experiments in a virtual laboratory and presentations of research results based on the major selected by the user. The experiments in a virtual laboratory and presentations of research results can include, for example, but are not limited to, experimental procedures, data collection methods, presentation formats, etc. This allows the user to experience experiments in a virtual laboratory and presentations of research results based on the major selected by the user.

[0033] The relationship building section can provide relationships with other students and professors based on the major selected by the user. The relationship building section provides, for example, relationships with other students and professors based on the major selected by the user. Relationships with other students and professors include, for example, but are not limited to, social opportunities, communication tools, and relationship depth. This allows the user to experience relationships with other students and professors based on the major selected by the user.

[0034] The job hunting experience section can provide an internship or job hunting process at a virtual company based on the major selected by the user. The job hunting experience section provides, for example, an internship or job hunting process at a virtual company based on the major selected by the user. The internship or job hunting process at a virtual company includes, for example, but is not limited to, the content of the internship, the interview process, evaluation criteria, etc. This allows the user to experience the internship or job hunting process at a virtual company based on the major selected by the user.

[0035] The reception unit can analyze the user's past selection history and suggest the optimal option. The reception unit, for example, analyzes the user's past selection history and suggests the optimal option. The past selection history includes, for example, the selected university and major, the frequency of selection, and the reason for selection, but is not limited to these examples. For example, similar options are suggested based on the history of universities and majors selected by the user in the past. Related universities and majors are suggested based on fields in which the user has shown interest in the past. Specific trends are analyzed from the user's past selection history and the optimal option is suggested. In this way, the optimal option can be suggested based on the user's past selection history.

[0036] The reception unit can filter options based on the user's current academic performance and interests. The reception unit filters options based on, for example, the user's current academic performance and interests. Current academic performance and interests include, but are not limited to, examples such as a report card, subjects of interest, and hobbies. For example, if the user has high academic performance, universities and majors with a high level of difficulty are presented preferentially. If the user's interests are concentrated in a specific field, universities and majors related to that field are presented. The user's academic performance and interests are comprehensively taken into consideration, and the most suitable options are filtered and presented. This makes it possible to present the most suitable options based on the user's academic performance and interests.

[0037] The reception unit can provide an optimal selection means depending on the user's input method. The reception unit provides, for example, an optimal selection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, options for universities and majors are presented using voice recognition technology. If the user selects text input, options are presented using keyword search. If the user selects image input, related universities and majors are presented using image analysis technology. This makes it possible to provide an optimal selection means depending on the user's input method.

[0038] The reception unit can prioritize presenting highly relevant universities and majors in consideration of the user's geographical location information. The reception unit, for example, prioritizes presenting highly relevant universities and majors in consideration of the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location information services, etc. For example, universities close to the user's current location are prioritized. Regional majors are presented based on the user's geographical location information. Universities and majors that emphasize commuting convenience are presented in consideration of the user's geographical location information. This makes it possible to present the most suitable universities and majors based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and present relevant options. The reception unit, for example, analyzes the user's social media activity and presents relevant options. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, relevant options are presented based on the universities and majors the user follows on social media. The content of the user's social media posts is analyzed to present universities and majors that may be of interest. Related universities and majors are presented based on the activity of the user's friends on social media. In this way, optimal options can be presented based on the user's social media activity.

[0040] The reception unit can customize the options by reflecting the user's past feedback. The reception unit, for example, customizes the options by reflecting the user's past feedback. Past feedback includes, for example, the user's ratings, comments, areas for improvement, etc., but is not limited to these examples. For example, universities and majors that the user has given high ratings to in the past are presented preferentially. Options that reflect areas for improvement are presented based on the user's past feedback. The user's past feedback is comprehensively analyzed, and the optimal options are customized and presented. In this way, the optimal options can be customized based on the user's past feedback.

[0041] The campus life experience section can provide details of specific classes and projects based on the user's selected major. The campus life experience section can provide details of specific classes and projects based on the user's selected major, for example. Details of specific classes and projects include, but are not limited to, the content of the classes, the objectives of the projects, and evaluation criteria. For example, the section can provide syllabi of classes related to the user's selected major. The section can provide details of research projects related to the user's selected major. The section can provide information about specific professors and laboratories based on the user's selected major. This allows the section to provide details of specific classes and projects based on the user's selected major.

[0042] The campus life experience section can customize the experience content by referring to the user's past campus life experiences. The campus life experience section, for example, customizes the experience content by referring to the user's past campus life experiences. Past campus life experiences include, but are not limited to, events experienced, activities participated in, and experiences gained. For example, related experience content is provided based on classes and projects the user has participated in in the past. Events that the user may be interested in are provided based on the user's past campus life experiences. The user's past campus life experiences are comprehensively analyzed, and the optimal experience content is customized and provided. This allows the optimal experience content to be customized based on the user's past campus life experiences.

[0043] The campus life experience unit can adjust the difficulty of classes and projects to be experienced based on the user's academic performance. The campus life experience unit adjusts the difficulty of classes and projects to be experienced based on, for example, the user's academic performance. The difficulty of classes and projects includes, but is not limited to, the difficulty of assignments, evaluation criteria, and progress speed. For example, if the user's academic performance is high, high-difficulty classes and projects are provided. If the user's academic performance is low, basic classes and projects are provided. Classes and projects of optimal difficulty are provided by comprehensively considering the user's academic performance. This makes it possible to provide classes and projects of optimal difficulty based on the user's academic performance.

[0044] The campus life experience section can provide highly relevant campus events by taking into account the user's geographical location information. The campus life experience section, for example, provides highly relevant campus events by taking into account the user's geographical location information. Highly relevant campus events include, but are not limited to, the type of event, the location, and participation conditions. For example, campus events close to the user's current location can be provided preferentially. Regional campus events can be provided based on the user's geographical location information. Campus events that emphasize the convenience of commuting can be provided by taking into account the user's geographical location information. This allows the most suitable campus events to be provided based on the user's geographical location information.

[0045] The campus life experience club can analyze the user's social media activity and suggest relevant campus events. The campus life experience club, for example, analyzes the user's social media activity and suggests relevant campus events. Examples of relevant campus events include, but are not limited to, the event theme, participant profiles, and past participation history. For example, relevant events are provided based on campus events the user follows on social media. The content of the user's social media posts is analyzed to provide campus events that may be of interest. Relevant campus events are provided based on the activity of the user's friends on social media. This allows the club to suggest optimal campus events based on the user's social media activity.

[0046] The campus life experience section can customize the experience content by reflecting the user's past feedback. The campus life experience section, for example, customizes the experience content by reflecting the user's past feedback. The experience content includes, for example, the user's interests, past experiences, feedback, etc., but is not limited to these examples. For example, campus events that the user has previously rated highly can be provided preferentially. Experience content that reflects areas for improvement can be provided based on the user's past feedback. The user's past feedback can be comprehensively analyzed to customize and provide the optimal experience content. This allows the optimal experience content to be customized based on the user's past feedback.

[0047] The academic research department can provide details of a specific research project based on the user's selected major. The academic research department can provide details of a specific research project based on the user's selected major, for example. Details of a specific research project include, but are not limited to, the project's objectives, progress, and evaluation criteria. For example, details of a research project related to the user's selected major can be provided. Information about laboratories related to the user's selected major can be provided. Information about specific professors and research teams can be provided based on the user's selected major. This makes it possible to provide details of a specific research project based on the user's selected major.

[0048] The academic research department can customize the research content by referring to the user's past research results. The academic research department, for example, customizes the research content by referring to the user's past research results. Past research results include, but are not limited to, published papers, experimental results, evaluations, etc. For example, related research content is provided based on the user's previously published research results. Research topics that may be of interest to the user are provided based on the user's past research results. The user's past research results are comprehensively analyzed, and the optimal research content is customized and provided. This allows the optimal research content to be customized based on the user's past research results.

[0049] The academic research department can adjust the difficulty of the research project based on the user's academic performance. The academic research department adjusts the difficulty of the research project based on, for example, the user's academic performance. The difficulty of the research project includes, for example, the difficulty of the assignment, the evaluation criteria, the progress speed, etc., but is not limited to such examples. For example, if the user's academic performance is high, a high-difficulty research project is provided. If the user's academic performance is low, a basic research project is provided. A research project of optimal difficulty is provided by comprehensively considering the user's academic performance. This makes it possible to provide a research project of optimal difficulty based on the user's academic performance.

[0050] The academic research department can provide highly relevant research themes by taking into account the user's geographical location information. The academic research department, for example, provides highly relevant research themes by taking into account the user's geographical location information. Highly relevant research themes include, but are not limited to, the type of theme, the purpose and subject of the research, for example. For example, research facilities and projects close to the user's current location can be provided preferentially. Regional research themes can be provided based on the user's geographical location information. Research themes that emphasize commuting convenience can be provided by taking into account the user's geographical location information. This allows the optimal research themes to be provided based on the user's geographical location information.

[0051] The academic research department can analyze the user's social media activity and suggest related research topics. The academic research department, for example, analyzes the user's social media activity and suggests related research topics. Related research topics include, but are not limited to, the type of topic, the purpose of the research, and the subject. For example, related research topics are provided based on researchers and projects that the user follows on social media. Research topics that may interest the user are provided by analyzing the content of the user's social media posts. Related research topics are provided based on the activities of the user's friends on social media. This makes it possible to suggest optimal research topics based on the user's social media activity.

[0052] The academic research department can customize the research content by reflecting the user's past feedback. The academic research department, for example, customizes the research content by reflecting the user's past feedback. The research content includes, for example, the user's interests, past research results, feedback, etc., but is not limited to these examples. For example, research topics that the user has previously rated highly can be provided preferentially. Research content that reflects areas for improvement can be provided based on the user's past feedback. The user's past feedback can be comprehensively analyzed, and the optimal research content can be customized and provided. This makes it possible to customize the optimal research content based on the user's past feedback.

[0053] The relationship building section can provide relationships with specific students and professors based on the user's selected major. The relationship building section can provide relationships with specific students and professors based on the user's selected major, for example. Relationships with specific students and professors include, but are not limited to, the type of relationship, a place for interaction, and communication tools. For example, relationships with students related to the user's selected major can be provided. Relationships with professors related to the user's selected major can be provided. Information on specific research teams and club activities can be provided based on the user's selected major. This makes it possible to provide relationships with specific students and professors based on the user's selected major.

[0054] The relationship building unit can customize the relationship content by referring to the user's past relationship experiences. The relationship building unit, for example, customizes the relationship content by referring to the user's past relationship experiences. Past relationship experiences include, for example, relationships experienced, places of interaction, experiences gained, etc., but are not limited to these examples. For example, related relationship content is provided based on the relationships the user has built in the past. Relationship content that is likely to be of interest to the user based on the user's past relationship experiences is provided. The user's past relationship experiences are comprehensively analyzed, and the optimal relationship content is customized and provided. This makes it possible to customize the optimal relationship content based on the user's past relationship experiences.

[0055] The relationship building unit can adjust the difficulty of related people based on the user's academic performance. The relationship building unit adjusts the difficulty of related people based on, for example, the user's academic performance. The difficulty of related people includes, for example, the person's role, the depth of the relationship, the frequency of interaction, etc., but is not limited to these examples. For example, if the user's academic performance is high, a relationship with a high-difficulty person is provided. If the user's academic performance is low, a relationship with a basic person is provided. A relationship with a person of optimal difficulty is provided by comprehensively considering the user's academic performance. In this way, a relationship with a person of optimal difficulty can be provided based on the user's academic performance.

[0056] The relationship building unit can provide highly relevant people by taking into account the user's geographical location information. The relationship building unit provides highly relevant people by taking into account, for example, the user's geographical location information. Highly relevant people include, but are not limited to, for example, the type of person, role, and depth of relationship. For example, relationships with people close to the user's current location can be provided preferentially. Relationships with people specific to the area can be provided based on the user's geographical location information. Relationships with people who prioritize convenience for commuting can be provided by taking into account the user's geographical location information. This makes it possible to provide relationships with optimal people based on the user's geographical location information.

[0057] The relationship building unit can analyze the user's social media activity and suggest related people. The relationship building unit, for example, analyzes the user's social media activity and suggests related people. Related people include, but are not limited to, the type of person, role, and depth of relationship. For example, the unit provides relationships with people the user follows on social media. The unit analyzes the content of the user's social media posts and suggests relationships with people who may be of interest. The unit provides relationships with related people based on the activity of the user's friends on social media. This makes it possible to suggest relationships with optimal people based on the user's social media activity.

[0058] The relationship building unit can customize the relationship content by reflecting the user's past feedback. The relationship building unit customizes the relationship content by reflecting, for example, the user's past feedback. The relationship content includes, for example, the user's interests, past relationship experiences, feedback, etc., but is not limited to these examples. For example, relationships with people who the user has given high ratings to in the past are provided preferentially. Relationship content that reflects areas for improvement is provided based on the user's past feedback. The user's past feedback is comprehensively analyzed, and the optimal relationship content is customized and provided. This makes it possible to customize the optimal relationship content based on the user's past feedback.

[0059] The job hunting experience section can provide details of specific companies and internships based on the user's selected major. The job hunting experience section can provide details of specific companies and internships based on the user's selected major, for example. Details of specific companies and internships include, but are not limited to, company information, internship content, evaluation criteria, etc. For example, information on companies related to the user's selected major can be provided. Details of internships related to the user's selected major can be provided. Information on specific industries and job types can be provided based on the user's selected major. This makes it possible to provide details of specific companies and internships based on the user's selected major.

[0060] The job hunting experience section can customize the experience content by referring to the user's past job hunting experiences. The job hunting experience section, for example, customizes the experience content by referring to the user's past job hunting experiences. Past job hunting experiences include, for example, companies visited, interview content, and experiences gained, but are not limited to these examples. For example, related experience content is provided based on internships and company visits the user has participated in in the past. Companies and job types that may be of interest to the user are provided based on the user's past job hunting experiences. The user's past job hunting experiences are comprehensively analyzed, and the optimal experience content is customized and provided. This allows the optimal experience content to be customized based on the user's past job hunting experiences.

[0061] The job hunting experience section can adjust the difficulty of the job hunting experience based on the user's academic performance. The job hunting experience section adjusts the difficulty of the job hunting experience based on, for example, the user's academic performance. The difficulty of the job hunting experience includes, for example, the difficulty of the interview, the evaluation criteria, the progress speed, etc., but is not limited to these examples. For example, if the user's academic performance is high, a high level of difficulty in the job hunting experience is provided. If the user's academic performance is low, a basic level in the job hunting experience is provided. The user's academic performance is taken into consideration comprehensively to provide a job hunting experience of an optimal level of difficulty. This makes it possible to provide a job hunting experience of an optimal level of difficulty based on the user's academic performance.

[0062] The job hunting experience section can provide highly relevant companies and internships by taking into account the user's geographical location information. The job hunting experience section, for example, provides highly relevant companies and internships by taking into account the user's geographical location information. Highly relevant companies and internships include, but are not limited to, the type of company, the content of the internship, and evaluation criteria. For example, companies and internships close to the user's current location are provided preferentially. Regional companies and internships are provided based on the user's geographical location information. Companies and internships that emphasize commuting convenience are provided by taking into account the user's geographical location information. This makes it possible to provide optimal companies and internships based on the user's geographical location information.

[0063] The job hunting experience section can analyze the user's social media activity and suggest related companies and internships. The job hunting experience section, for example, analyzes the user's social media activity and suggests related companies and internships. Related companies and internships include, but are not limited to, the type of company, the content of the internship, and evaluation criteria. For example, related experience content is provided based on the companies and internships the user follows on social media. The user's social media posts are analyzed to provide companies and internships that may be of interest. Related companies and internships are provided based on the activity of the user's friends on social media. This makes it possible to suggest optimal companies and internships based on the user's social media activity.

[0064] The job hunting experience section can customize the experience content by reflecting the user's past feedback. The job hunting experience section, for example, customizes the experience content by reflecting the user's past feedback. The experience content includes, for example, the user's interests, past experiences, feedback, etc., but is not limited to these examples. For example, companies and internships that the user has previously given high ratings are provided preferentially. Experience content that reflects areas for improvement is provided based on the user's past feedback. The user's past feedback is comprehensively analyzed, and the optimal experience content is customized and provided. In this way, the optimal experience content can be customized based on the user's past feedback.

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

[0066] The reception unit not only accepts the user's selection, but also analyzes the user's past selection history and proposes optimal options. For example, similar options can be proposed based on the user's history of past university and major selections. Also, related universities and majors can be proposed based on fields in which the user has shown interest in the past. Furthermore, specific trends can be analyzed from the user's past selection history to propose optimal options. This makes it possible to propose optimal options based on the user's past selection history.

[0067] The reception unit can also filter options based on the user's current academic performance and interests. For example, if the user has high academic performance, it can prioritize the presentation of highly competitive universities and majors. Also, if the user's interests are concentrated in a particular field, it can present universities and majors related to that field. Furthermore, it can comprehensively consider the user's academic performance and interests and filter and present the most suitable options. This makes it possible to present the most suitable options based on the user's academic performance and interests.

[0068] The reception unit can also provide the optimal selection means depending on the user's input method. For example, if the user selects voice input, options for universities and majors can be presented using voice recognition technology. If the user selects text input, options can be presented using keyword search. Furthermore, if the user selects image input, related universities and majors can be presented using image analysis technology. This makes it possible to provide the optimal selection means depending on the user's input method.

[0069] The campus life experience section can also customize the experience content by referring to the user's past campus life experiences. For example, it can provide related experience content based on classes and projects the user has participated in in the past. It can also provide events that the user may be interested in based on the user's past campus life experiences. Furthermore, it can comprehensively analyze the user's past campus life experiences and provide the most suitable customized experience content. This allows the most suitable experience content to be customized based on the user's past campus life experiences.

[0070] The academic research department can also customize the research content by referring to the user's past research results. For example, it can provide related research content based on the research results the user has published in the past. It can also provide research topics that may be of interest to the user based on the user's past research results. Furthermore, it can comprehensively analyze the user's past research results and provide the most suitable customized research content. This allows it to customize the most suitable research content based on the user's past research results.

[0071] The relationship building unit can also customize relationship content by referring to the user's past relationship experiences. For example, it can provide related relationship content based on the relationships the user has built in the past. It can also provide relationship content that is likely to be of interest to the user based on the user's past relationship experiences. Furthermore, it can comprehensively analyze the user's past relationship experiences and customize and provide optimal relationship content. This makes it possible to customize optimal relationship content based on the user's past relationship experiences.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives the user's selection, for example, the information about the university and major selected by the user. Step 2: The campus life experience module provides virtual classes and research projects based on the user's chosen major. For example, if a user selects a specific major, they can participate in classes and research projects for that major. Step 3: The academic research module provides virtual laboratory experiments and research presentations based on the user's selected major. For example, if a user is majoring in a specific field, they can conduct experiments in a virtual laboratory and present their research results. Step 4: Build RelationshipsThe module provides users with relationships with other students and professors based on their chosen major. For example, users can build relationships with other students and professors, allowing them to experience a realistic side of university life. Step 5: The job hunting experience module provides internships and job hunting processes at virtual companies based on the user's selected major. For example, if a user is majoring in a specific field, they can experience an internship at a virtual company and learn about the job hunting process.

[0074] (Example 2) A system according to an embodiment of the present invention is a simulation game for high school students that allows them to experience university life and future careers. This system allows users to select different universities and majors and experience virtual campus life, academic research, relationships, and even job hunting. For example, if a user selects a specific major, the virtual campus life allows them to participate in classes and research projects for that major. The user can also build relationships with other students and professors, thereby experiencing realistic aspects of university life. Next, the user can deepen their expertise through academic research. For example, if a user majors in a specific field, they can conduct experiments in a virtual laboratory and present their research results. This allows the user to confirm their interests and aptitudes. Furthermore, the user can experience job hunting. For example, if a user majors in a specific field, they can experience an internship at a virtual company and learn about the job hunting process. This allows the user to gain a concrete image of their future career. This system can help high school students deepen their understanding of their future. For example, through virtual university life, users can confirm their interests and aptitudes and gain a concrete image of their future university and career. It can also be used as a career selection support tool that combines education and entertainment to improve the learning effect of users.

[0075] A simulation game system according to an embodiment includes a reception unit, a campus life experience unit, an academic research unit, a relationship building unit, and a job hunting experience unit. The reception unit accepts a user's selection. For example, it accepts information about the university and major selected by the user. The campus life experience unit provides virtual classes and research projects based on the user's selected major. For example, if the user selects a specific major, the user can participate in classes and research projects in that major. The academic research unit provides experiments in a virtual laboratory and presentations of research results based on the user's selected major. For example, if the user is majoring in a specific field, the user can conduct experiments in a virtual laboratory and present their research results. The relationship building unit provides relationships with other students and professors based on the user's selected major. For example, the user can build relationships with other students and professors, thereby experiencing realistic aspects of university life. The job hunting experience unit provides internships at virtual companies and the job hunting process based on the user's selected major. For example, if the user is majoring in a specific field, the user can experience an internship at a virtual company and learn about the job hunting process. As a result, the simulation game system according to the embodiment allows the user to experience university life and future careers.

[0076] The reception unit can receive information about the university and major selected by the user. The reception unit receives, for example, the information about the university and major selected by the user. The information about the university and major includes, for example, the name of the university, the name of the major, the faculty, the department, etc., but is not limited to these examples. This allows the information about the university and major selected by the user to be received.

[0077] The campus life experience club can provide virtual classes and research projects based on the major selected by the user. The campus life experience club provides virtual classes and research projects based on, for example, the major selected by the user. Examples of virtual classes and research projects include, but are not limited to, class syllabi, project themes, and evaluation criteria. This allows the user to experience virtual classes and research projects based on the major selected by the user.

[0078] The academic research department can provide experiments in a virtual laboratory and presentations of research results based on the major selected by the user. The academic research department can provide, for example, experiments in a virtual laboratory and presentations of research results based on the major selected by the user. The experiments in a virtual laboratory and presentations of research results can include, for example, but are not limited to, experimental procedures, data collection methods, presentation formats, etc. This allows the user to experience experiments in a virtual laboratory and presentations of research results based on the major selected by the user.

[0079] The relationship building section can provide relationships with other students and professors based on the major selected by the user. The relationship building section provides, for example, relationships with other students and professors based on the major selected by the user. Relationships with other students and professors include, for example, but are not limited to, social opportunities, communication tools, and relationship depth. This allows the user to experience relationships with other students and professors based on the major selected by the user.

[0080] The job hunting experience section can provide an internship or job hunting process at a virtual company based on the major selected by the user. The job hunting experience section provides, for example, an internship or job hunting process at a virtual company based on the major selected by the user. The internship or job hunting process at a virtual company includes, for example, but is not limited to, the content of the internship, the interview process, evaluation criteria, etc. This allows the user to experience the internship or job hunting process at a virtual company based on the major selected by the user.

[0081] The reception unit can estimate the user's emotions and present university and major options based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and presents university and major options based on the estimated user emotions. User emotions include, but are not limited to, anxiety, excitement, and uncertainty. For example, if the user is feeling anxious, university and major options that are relaxing are presented preferentially. If the user is excited, university and major options that are challenging and stimulating are presented. If the user is unsure, a wide range of options are presented to support the selection. In this way, optimal university and major options can be presented based on the user's emotions.

[0082] The reception unit can analyze the user's past selection history and suggest the optimal option. The reception unit, for example, analyzes the user's past selection history and suggests the optimal option. The past selection history includes, for example, the selected university and major, the frequency of selection, and the reason for selection, but is not limited to these examples. For example, similar options are suggested based on the history of universities and majors selected by the user in the past. Related universities and majors are suggested based on fields in which the user has shown interest in the past. Specific trends are analyzed from the user's past selection history and the optimal option is suggested. In this way, the optimal option can be suggested based on the user's past selection history.

[0083] The reception unit can filter options based on the user's current academic performance and interests. The reception unit filters options based on, for example, the user's current academic performance and interests. Current academic performance and interests include, but are not limited to, examples such as a report card, subjects of interest, and hobbies. For example, if the user has high academic performance, universities and majors with a high level of difficulty are presented preferentially. If the user's interests are concentrated in a specific field, universities and majors related to that field are presented. The user's academic performance and interests are comprehensively taken into consideration, and the most suitable options are filtered and presented. This makes it possible to present the most suitable options based on the user's academic performance and interests.

[0084] The reception unit can provide an optimal selection means depending on the user's input method. The reception unit provides, for example, an optimal selection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, options for universities and majors are presented using voice recognition technology. If the user selects text input, options are presented using keyword search. If the user selects image input, related universities and majors are presented using image analysis technology. This makes it possible to provide an optimal selection means depending on the user's input method.

[0085] The reception unit can estimate the user's emotion and determine the priority of options based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of options based on the estimated user's emotion. The priority of options includes, but is not limited to, for example, the intensity of emotion, past selection history, and current situation. For example, if the user is feeling stressed, options that are relaxing are preferentially presented. If the user is excited, options that are challenging are preferentially presented. If the user is unsure, a wide range of options are presented to support the user in making a selection. In this way, the priority of options can be determined based on the user's emotion.

[0086] The reception unit can prioritize presenting highly relevant universities and majors in consideration of the user's geographical location information. The reception unit, for example, prioritizes presenting highly relevant universities and majors in consideration of the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location information services, etc. For example, universities close to the user's current location are prioritized. Regional majors are presented based on the user's geographical location information. Universities and majors that emphasize commuting convenience are presented in consideration of the user's geographical location information. This makes it possible to present the most suitable universities and majors based on the user's geographical location information.

[0087] The reception unit can analyze the user's social media activity and present relevant options. The reception unit, for example, analyzes the user's social media activity and presents relevant options. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, relevant options are presented based on the universities and majors the user follows on social media. The content of the user's social media posts is analyzed to present universities and majors that may be of interest. Related universities and majors are presented based on the activity of the user's friends on social media. In this way, optimal options can be presented based on the user's social media activity.

[0088] The reception unit can customize the options by reflecting the user's past feedback. The reception unit, for example, customizes the options by reflecting the user's past feedback. Past feedback includes, for example, the user's ratings, comments, areas for improvement, etc., but is not limited to these examples. For example, universities and majors that the user has given high ratings to in the past are presented preferentially. Options that reflect areas for improvement are presented based on the user's past feedback. The user's past feedback is comprehensively analyzed, and the optimal options are customized and presented. In this way, the optimal options can be customized based on the user's past feedback.

[0089] The campus life experiencing unit can estimate the user's emotions and adjust the campus life scenario based on the estimated user emotions. The campus life experiencing unit, for example, estimates the user's emotions and adjusts the campus life scenario based on the estimated user emotions. The campus life scenario includes, but is not limited to, the flow of the scenario, the order of events, and adjustment criteria. For example, if the user is nervous, a relaxing scenario is provided. If the user is excited, a challenging scenario is provided. If the user is unsure, a wide range of scenarios is provided to support the user's selection. This makes it possible to provide an optimal campus life scenario based on the user's emotions.

[0090] The campus life experience section can provide details of specific classes and projects based on the user's selected major. The campus life experience section can provide details of specific classes and projects based on the user's selected major, for example. Details of specific classes and projects include, but are not limited to, the content of the classes, the objectives of the projects, and evaluation criteria. For example, the section can provide syllabi of classes related to the user's selected major. The section can provide details of research projects related to the user's selected major. The section can provide information about specific professors and laboratories based on the user's selected major. This allows the section to provide details of specific classes and projects based on the user's selected major.

[0091] The campus life experience section can customize the experience content by referring to the user's past campus life experiences. The campus life experience section, for example, customizes the experience content by referring to the user's past campus life experiences. Past campus life experiences include, but are not limited to, events experienced, activities participated in, and experiences gained. For example, related experience content is provided based on classes and projects the user has participated in in the past. Events that the user may be interested in are provided based on the user's past campus life experiences. The user's past campus life experiences are comprehensively analyzed, and the optimal experience content is customized and provided. This allows the optimal experience content to be customized based on the user's past campus life experiences.

[0092] The campus life experience unit can adjust the difficulty of classes and projects to be experienced based on the user's academic performance. The campus life experience unit adjusts the difficulty of classes and projects to be experienced based on, for example, the user's academic performance. The difficulty of classes and projects includes, but is not limited to, the difficulty of assignments, evaluation criteria, and progress speed. For example, if the user's academic performance is high, high-difficulty classes and projects are provided. If the user's academic performance is low, basic classes and projects are provided. Classes and projects of optimal difficulty are provided by comprehensively considering the user's academic performance. This makes it possible to provide classes and projects of optimal difficulty based on the user's academic performance.

[0093] The campus life experiencing unit can estimate the user's emotions and adjust the order of events to be experienced based on the estimated user emotions. The campus life experiencing unit, for example, estimates the user's emotions and adjusts the order of events to be experienced based on the estimated user emotions. The order of events to be experienced can include, but is not limited to, the importance of the events, the user's interests, changes in emotions, etc. For example, if the user is nervous, a relaxing event can be provided first. If the user is excited, a challenging event can be provided first. If the user is unsure, a wide range of events can be provided to support the user's selection. This makes it possible to provide events in an optimal order based on the user's emotions.

[0094] The campus life experience section can provide highly relevant campus events by taking into account the user's geographical location information. The campus life experience section, for example, provides highly relevant campus events by taking into account the user's geographical location information. Highly relevant campus events include, but are not limited to, the type of event, the location, and participation conditions. For example, campus events close to the user's current location can be provided preferentially. Regional campus events can be provided based on the user's geographical location information. Campus events that emphasize the convenience of commuting can be provided by taking into account the user's geographical location information. This allows the most suitable campus events to be provided based on the user's geographical location information.

[0095] The campus life experience club can analyze the user's social media activity and suggest relevant campus events. The campus life experience club, for example, analyzes the user's social media activity and suggests relevant campus events. Examples of relevant campus events include, but are not limited to, the event theme, participant profiles, and past participation history. For example, relevant events are provided based on campus events the user follows on social media. The content of the user's social media posts is analyzed to provide campus events that may be of interest. Relevant campus events are provided based on the activity of the user's friends on social media. This allows the club to suggest optimal campus events based on the user's social media activity.

[0096] The campus life experience section can customize the experience content by reflecting the user's past feedback. The campus life experience section, for example, customizes the experience content by reflecting the user's past feedback. The experience content includes, for example, the user's interests, past experiences, feedback, etc., but is not limited to these examples. For example, campus events that the user has previously rated highly can be provided preferentially. Experience content that reflects areas for improvement can be provided based on the user's past feedback. The user's past feedback can be comprehensively analyzed to customize and provide the optimal experience content. This allows the optimal experience content to be customized based on the user's past feedback.

[0097] The academic research unit can estimate the user's emotions and adjust the research topic based on the estimated user's emotions. The academic research unit, for example, estimates the user's emotions and adjusts the research topic based on the estimated user's emotions. Research topics include, but are not limited to, the purpose, subject, and method of the research. For example, if the user is excited, a challenging research topic is provided. If the user is relaxed, a basic research topic is provided. If the user is unsure, a wide range of research topics is provided to support the user's selection. This makes it possible to provide the optimal research topic based on the user's emotions.

[0098] The academic research department can provide details of a specific research project based on the user's selected major. The academic research department can provide details of a specific research project based on the user's selected major, for example. Details of a specific research project include, but are not limited to, the project's objectives, progress, and evaluation criteria. For example, details of a research project related to the user's selected major can be provided. Information about laboratories related to the user's selected major can be provided. Information about specific professors and research teams can be provided based on the user's selected major. This makes it possible to provide details of a specific research project based on the user's selected major.

[0099] The academic research department can customize the research content by referring to the user's past research results. The academic research department, for example, customizes the research content by referring to the user's past research results. Past research results include, but are not limited to, published papers, experimental results, evaluations, etc. For example, related research content is provided based on the user's previously published research results. Research topics that may be of interest to the user are provided based on the user's past research results. The user's past research results are comprehensively analyzed, and the optimal research content is customized and provided. This allows the optimal research content to be customized based on the user's past research results.

[0100] The academic research department can adjust the difficulty of the research project based on the user's academic performance. The academic research department adjusts the difficulty of the research project based on, for example, the user's academic performance. The difficulty of the research project includes, for example, the difficulty of the assignment, the evaluation criteria, the progress speed, etc., but is not limited to such examples. For example, if the user's academic performance is high, a high-difficulty research project is provided. If the user's academic performance is low, a basic research project is provided. A research project of optimal difficulty is provided by comprehensively considering the user's academic performance. This makes it possible to provide a research project of optimal difficulty based on the user's academic performance.

[0101] The academic research unit can estimate the user's emotions and prioritize research projects based on the estimated user emotions. The academic research unit, for example, estimates the user's emotions and prioritizes research projects based on the estimated user emotions. The prioritization of research projects includes, but is not limited to, project importance, user interests, and emotional changes. For example, if the user is excited, challenging research projects are provided preferentially. If the user is relaxed, basic research projects are provided preferentially. If the user is unsure, a wide range of research projects are provided and selection is supported. This makes it possible to provide research projects in an optimal order based on the user's emotions.

[0102] The academic research department can provide highly relevant research themes by taking into account the user's geographical location information. The academic research department, for example, provides highly relevant research themes by taking into account the user's geographical location information. Highly relevant research themes include, but are not limited to, the type of theme, the purpose and subject of the research, for example. For example, research facilities and projects close to the user's current location can be provided preferentially. Regional research themes can be provided based on the user's geographical location information. Research themes that emphasize commuting convenience can be provided by taking into account the user's geographical location information. This allows the optimal research themes to be provided based on the user's geographical location information.

[0103] The academic research department can analyze the user's social media activity and suggest related research topics. The academic research department, for example, analyzes the user's social media activity and suggests related research topics. Related research topics include, but are not limited to, the type of topic, the purpose of the research, and the subject. For example, related research topics are provided based on researchers and projects that the user follows on social media. Research topics that may interest the user are provided by analyzing the content of the user's social media posts. Related research topics are provided based on the activities of the user's friends on social media. This makes it possible to suggest optimal research topics based on the user's social media activity.

[0104] The academic research department can customize the research content by reflecting the user's past feedback. The academic research department, for example, customizes the research content by reflecting the user's past feedback. The research content includes, for example, the user's interests, past research results, feedback, etc., but is not limited to these examples. For example, research topics that the user has previously rated highly can be provided preferentially. Research content that reflects areas for improvement can be provided based on the user's past feedback. The user's past feedback can be comprehensively analyzed, and the optimal research content can be customized and provided. This makes it possible to customize the optimal research content based on the user's past feedback.

[0105] The relationship building unit can estimate the user's emotions and adjust the relationship scenario based on the estimated user emotions. The relationship building unit, for example, estimates the user's emotions and adjusts the relationship scenario based on the estimated user emotions. The relationship scenario includes, but is not limited to, for example, a scenario flow, relationship depth, and adjustment criteria. For example, if the user is nervous, a relaxing relationship scenario is provided. If the user is excited, a challenging relationship scenario is provided. If the user is unsure, a wide range of relationship scenarios are provided to support selection. This makes it possible to provide the optimal relationship scenario based on the user's emotions.

[0106] The relationship building section can provide relationships with specific students and professors based on the user's selected major. The relationship building section can provide relationships with specific students and professors based on the user's selected major, for example. Relationships with specific students and professors include, but are not limited to, the type of relationship, a place for interaction, and communication tools. For example, relationships with students related to the user's selected major can be provided. Relationships with professors related to the user's selected major can be provided. Information on specific research teams and club activities can be provided based on the user's selected major. This makes it possible to provide relationships with specific students and professors based on the user's selected major.

[0107] The relationship building unit can customize the relationship content by referring to the user's past relationship experiences. The relationship building unit, for example, customizes the relationship content by referring to the user's past relationship experiences. Past relationship experiences include, for example, relationships experienced, places of interaction, experiences gained, etc., but are not limited to these examples. For example, related relationship content is provided based on the relationships the user has built in the past. Relationship content that is likely to be of interest to the user based on the user's past relationship experiences is provided. The user's past relationship experiences are comprehensively analyzed, and the optimal relationship content is customized and provided. This makes it possible to customize the optimal relationship content based on the user's past relationship experiences.

[0108] The relationship building unit can adjust the difficulty of related people based on the user's academic performance. The relationship building unit adjusts the difficulty of related people based on, for example, the user's academic performance. The difficulty of related people includes, for example, the person's role, the depth of the relationship, the frequency of interaction, etc., but is not limited to these examples. For example, if the user's academic performance is high, a relationship with a high-difficulty person is provided. If the user's academic performance is low, a relationship with a basic person is provided. A relationship with a person of optimal difficulty is provided by comprehensively considering the user's academic performance. In this way, a relationship with a person of optimal difficulty can be provided based on the user's academic performance.

[0109] The relationship building unit can estimate the user's emotions and determine the priority of related people based on the estimated user emotions. The relationship building unit, for example, estimates the user's emotions and determines the priority of related people based on the estimated user emotions. The priority of related people includes, but is not limited to, the importance of the person, the user's interests, changes in emotions, etc. For example, if the user is nervous, relationships with people who are relaxing are provided preferentially. If the user is excited, relationships with people who are challenging are provided preferentially. If the user is unsure, relationships with a wide range of people are provided and selection is supported. This makes it possible to provide relationships with people in an optimal order based on the user's emotions.

[0110] The relationship building unit can provide highly relevant people by taking into account the user's geographical location information. The relationship building unit provides highly relevant people by taking into account, for example, the user's geographical location information. Highly relevant people include, but are not limited to, for example, the type of person, role, and depth of relationship. For example, relationships with people close to the user's current location can be provided preferentially. Relationships with people specific to the area can be provided based on the user's geographical location information. Relationships with people who prioritize convenience for commuting can be provided by taking into account the user's geographical location information. This makes it possible to provide relationships with optimal people based on the user's geographical location information.

[0111] The relationship building unit can analyze the user's social media activity and suggest related people. The relationship building unit, for example, analyzes the user's social media activity and suggests related people. Related people include, but are not limited to, the type of person, role, and depth of relationship. For example, the unit provides relationships with people the user follows on social media. The unit analyzes the content of the user's social media posts and suggests relationships with people who may be of interest. The unit provides relationships with related people based on the activity of the user's friends on social media. This makes it possible to suggest relationships with optimal people based on the user's social media activity.

[0112] The relationship building unit can customize the relationship content by reflecting the user's past feedback. The relationship building unit customizes the relationship content by reflecting, for example, the user's past feedback. The relationship content includes, for example, the user's interests, past relationship experiences, feedback, etc., but is not limited to these examples. For example, relationships with people who the user has given high ratings to in the past are provided preferentially. Relationship content that reflects areas for improvement is provided based on the user's past feedback. The user's past feedback is comprehensively analyzed, and the optimal relationship content is customized and provided. This makes it possible to customize the optimal relationship content based on the user's past feedback.

[0113] The job hunting experience unit can estimate the user's emotions and adjust the job hunting scenario based on the estimated user emotions. The job hunting experience unit, for example, estimates the user's emotions and adjusts the job hunting scenario based on the estimated user emotions. The job hunting scenario includes, but is not limited to, the flow of the scenario, the order of interviews, and adjustment criteria. For example, if the user is nervous, a relaxing job hunting scenario can be provided. If the user is excited, a challenging job hunting scenario can be provided. If the user is unsure, a wide range of job hunting scenarios can be provided to support the user's selection. This makes it possible to provide the optimal job hunting scenario based on the user's emotions.

[0114] The job hunting experience section can provide details of specific companies and internships based on the user's selected major. The job hunting experience section can provide details of specific companies and internships based on the user's selected major, for example. Details of specific companies and internships include, but are not limited to, company information, internship content, evaluation criteria, etc. For example, information on companies related to the user's selected major can be provided. Details of internships related to the user's selected major can be provided. Information on specific industries and job types can be provided based on the user's selected major. This makes it possible to provide details of specific companies and internships based on the user's selected major.

[0115] The job hunting experience section can customize the experience content by referring to the user's past job hunting experiences. The job hunting experience section, for example, customizes the experience content by referring to the user's past job hunting experiences. Past job hunting experiences include, for example, companies visited, interview content, and experiences gained, but are not limited to these examples. For example, related experience content is provided based on internships and company visits the user has participated in in the past. Companies and job types that may be of interest to the user are provided based on the user's past job hunting experiences. The user's past job hunting experiences are comprehensively analyzed, and the optimal experience content is customized and provided. This allows the optimal experience content to be customized based on the user's past job hunting experiences.

[0116] The job hunting experience section can adjust the difficulty of the job hunting experience based on the user's academic performance. The job hunting experience section adjusts the difficulty of the job hunting experience based on, for example, the user's academic performance. The difficulty of the job hunting experience includes, for example, the difficulty of the interview, the evaluation criteria, the progress speed, etc., but is not limited to these examples. For example, if the user's academic performance is high, a high level of difficulty in the job hunting experience is provided. If the user's academic performance is low, a basic level in the job hunting experience is provided. The user's academic performance is taken into consideration comprehensively to provide a job hunting experience of an optimal level of difficulty. This makes it possible to provide a job hunting experience of an optimal level of difficulty based on the user's academic performance.

[0117] The job hunting experience unit can estimate the user's emotions and determine the priority of job hunting activities based on the estimated user emotions. The job hunting experience unit, for example, estimates the user's emotions and determines the priority of job hunting activities based on the estimated user emotions. Job hunting priorities include, but are not limited to, the importance of companies, the user's interests, and changes in emotions. For example, if the user is nervous, job hunting activities that are relaxing are provided preferentially. If the user is excited, job hunting activities that are challenging are provided preferentially. If the user is unsure, a wide range of job hunting activities are provided and selection is supported. This makes it possible to provide job hunting activities in an optimal order based on the user's emotions.

[0118] The job hunting experience section can provide highly relevant companies and internships by taking into account the user's geographical location information. The job hunting experience section, for example, provides highly relevant companies and internships by taking into account the user's geographical location information. Highly relevant companies and internships include, but are not limited to, the type of company, the content of the internship, and evaluation criteria. For example, companies and internships close to the user's current location are provided preferentially. Regional companies and internships are provided based on the user's geographical location information. Companies and internships that emphasize commuting convenience are provided by taking into account the user's geographical location information. This makes it possible to provide optimal companies and internships based on the user's geographical location information.

[0119] The job hunting experience section can analyze the user's social media activity and suggest related companies and internships. The job hunting experience section, for example, analyzes the user's social media activity and suggests related companies and internships. Related companies and internships include, but are not limited to, the type of company, the content of the internship, and evaluation criteria. For example, related experience content is provided based on the companies and internships the user follows on social media. The user's social media posts are analyzed to provide companies and internships that may be of interest. Related companies and internships are provided based on the activity of the user's friends on social media. This makes it possible to suggest optimal companies and internships based on the user's social media activity.

[0120] The job hunting experience section can customize the experience content by reflecting the user's past feedback. The job hunting experience section, for example, customizes the experience content by reflecting the user's past feedback. The experience content includes, for example, the user's interests, past experiences, feedback, etc., but is not limited to these examples. For example, companies and internships that the user has previously given high ratings are provided preferentially. Experience content that reflects areas for improvement is provided based on the user's past feedback. The user's past feedback is comprehensively analyzed, and the optimal experience content is customized and provided. In this way, the optimal experience content can be customized based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, campus life experience unit, academic research unit, human relationship building unit, and job hunting experience unit, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts a user's selection via the reception device 38 of the smart device 14. The campus life experience unit provides virtual classes and research projects via the output device 40 of the smart device 14. The academic research unit provides virtual laboratory experiments and research results presentations via the specific processing unit 290 of the data processing device 12. The human relationship building unit provides human relationships with other students and professors via the camera 42 and microphone 38B of the smart device 14. The job hunting experience unit provides internships at virtual companies and the job hunting process via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, campus life experience unit, academic research unit, interpersonal relationship building unit, and job-hunting experience unit, is realized by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts a user's selection via the microphone 238 of the smart glasses 214. The campus life experience unit provides virtual classes and research projects via the speaker 240 of the smart glasses 214. The academic research unit provides virtual laboratory experiments and research result presentations via the specific processing unit 290 of the data processing device 12. The interpersonal relationship building unit provides interpersonal relationships with other students and professors via the camera 42 and microphone 238 of the smart glasses 214. The job-hunting experience unit provides internships at virtual companies and the job-hunting process via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, campus life experience unit, academic research unit, human relationship building unit, and job search experience unit, is realized by, for example, at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit accepts a user's selection via the microphone 238 of the headset terminal 314. The campus life experience unit provides virtual classes and research projects via the display 343 of the headset terminal 314. The academic research unit provides virtual laboratory experiments and research result presentations via the specific processing unit 290 of the data processing device 12. The human relationship building unit provides human relationships with other students and professors via the camera 42 and microphone 238 of the headset terminal 314. The job search experience unit provides internships at virtual companies and the job search process via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, the campus life experience unit, the academic research unit, the interpersonal relationship building unit, and the job search experience unit, is realized by, for example, at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts a user's selection via the microphone 238 of the robot 414. The campus life experience unit provides virtual classes and research projects via the speaker 240 of the robot 414. The academic research unit provides virtual laboratory experiments and research result presentations via the specific processing unit 290 of the data processing device 12. The interpersonal relationship building unit provides interpersonal relationships with other students and professors via the camera 42 and microphone 238 of the robot 414. The job search experience unit provides internships at virtual companies and the job search process via the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The reception unit not only accepts the user's selection, but also analyzes the user's past selection history and proposes optimal options. For example, similar options can be proposed based on the user's history of past university and major selections. Also, related universities and majors can be proposed based on fields in which the user has shown interest in the past. Furthermore, specific trends can be analyzed from the user's past selection history to propose optimal options. This makes it possible to propose optimal options based on the user's past selection history.

[0123] The reception unit can also filter options based on the user's current academic performance and interests. For example, if the user has high academic performance, it can prioritize the presentation of highly competitive universities and majors. Also, if the user's interests are concentrated in a particular field, it can present universities and majors related to that field. Furthermore, it can comprehensively consider the user's academic performance and interests and filter and present the most suitable options. This makes it possible to present the most suitable options based on the user's academic performance and interests.

[0124] The campus life experience unit can also estimate the user's emotions and adjust the campus life scenario based on the estimated user emotions. For example, if the user is nervous, a relaxing scenario can be provided. If the user is excited, a challenging scenario can be provided. Furthermore, if the user is unsure, a wide range of scenarios can be provided to support the user's selection. This makes it possible to provide the optimal campus life scenario based on the user's emotions.

[0125] The academic research department can also estimate the user's emotions and adjust the research topic based on the estimated user's emotions. For example, if the user is excited, a challenging research topic can be offered. If the user is relaxed, a basic research topic can be offered. Furthermore, if the user is unsure, a wide range of research topics can be offered and selection can be supported. This makes it possible to offer the most suitable research topic based on the user's emotions.

[0126] The interpersonal relationship building unit can also estimate the user's emotions and adjust interpersonal relationship scenarios based on the estimated user's emotions. For example, if the user is nervous, a relaxing interpersonal relationship scenario can be provided. If the user is excited, a challenging interpersonal relationship scenario can be provided. Furthermore, if the user is unsure, a wide range of interpersonal relationship scenarios can be provided to support the user's selection. This makes it possible to provide the optimal interpersonal relationship scenario based on the user's emotions.

[0127] The job hunting experience section can also estimate the user's emotions and adjust the job hunting scenario based on the estimated user emotions. For example, if the user is nervous, a relaxing job hunting scenario can be provided. Alternatively, if the user is excited, a challenging job hunting scenario can be provided. Furthermore, if the user is unsure, a wide range of job hunting scenarios can be provided to support the user's selection. This makes it possible to provide the optimal job hunting scenario based on the user's emotions.

[0128] The reception unit can also provide the optimal selection means depending on the user's input method. For example, if the user selects voice input, options for universities and majors can be presented using voice recognition technology. If the user selects text input, options can be presented using keyword search. Furthermore, if the user selects image input, related universities and majors can be presented using image analysis technology. This makes it possible to provide the optimal selection means depending on the user's input method.

[0129] The campus life experience section can also customize the experience content by referring to the user's past campus life experiences. For example, it can provide related experience content based on classes and projects the user has participated in in the past. It can also provide events that the user may be interested in based on the user's past campus life experiences. Furthermore, it can comprehensively analyze the user's past campus life experiences and provide the most suitable customized experience content. This allows the most suitable experience content to be customized based on the user's past campus life experiences.

[0130] The academic research department can also customize the research content by referring to the user's past research results. For example, it can provide related research content based on the research results the user has published in the past. It can also provide research topics that may be of interest to the user based on the user's past research results. Furthermore, it can comprehensively analyze the user's past research results and provide the most suitable customized research content. This allows it to customize the most suitable research content based on the user's past research results.

[0131] The relationship building unit can also customize relationship content by referring to the user's past relationship experiences. For example, it can provide related relationship content based on the relationships the user has built in the past. It can also provide relationship content that is likely to be of interest to the user based on the user's past relationship experiences. Furthermore, it can comprehensively analyze the user's past relationship experiences and customize and provide optimal relationship content. This makes it possible to customize optimal relationship content based on the user's past relationship experiences.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The reception unit receives the user's selection, for example, the information about the university and major selected by the user. Step 2: The campus life experience module provides virtual classes and research projects based on the user's chosen major. For example, if a user selects a specific major, they can participate in classes and research projects for that major. Step 3: The academic research module provides virtual laboratory experiments and research presentations based on the user's selected major. For example, if a user is majoring in a specific field, they can conduct experiments in a virtual laboratory and present their research results. Step 4: Build RelationshipsThe module provides users with relationships with other students and professors based on their chosen major. For example, users can build relationships with other students and professors, allowing them to experience a realistic side of university life. Step 5: The job hunting experience module provides internships and job hunting processes at virtual companies based on the user's selected major. For example, if a user is majoring in a specific field, they can experience an internship at a virtual company and learn about the job hunting process.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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. a reception unit that receives a user's selection; a module for experiencing campus life based on the information received by the reception unit; a module for conducting academic research based on the information provided by the module for experiencing campus life; a module for building relationships based on information provided by the module for conducting academic research; a job hunting experience module based on the information provided by the relationship building module; A system characterized by:

2. The reception unit Accepts information about the university and major selected by the user 2. The system of claim 1.

3. The club that offers the experience of campus life is: Offer virtual classes and research projects based on the user's chosen major 2. The system of claim 1.

4. The department conducting the academic research will: Providing virtual laboratory experiments and research presentations based on the user's chosen major 2. The system of claim 1.

5. The human relationship building section includes: Provides connections to other students and professors based on the user's chosen major 2. The system of claim 1.

6. The job-hunting experience club is: Providing a virtual internship and job search process based on the user's chosen major 2. The system of claim 1.

7. The reception unit Inferring user emotions and presenting university and major options based on the inferred user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past selection history and suggest the best option 2. The system of claim 1.

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