System

Abstract

An object of the system according to the embodiment is to virtually experience the daily life of different occupations and determine the aptitude.SOLUTION: A system according to an embodiment includes a scenario generation unit, a virtual experience providing unit, a suitability evaluation unit, a feedback providing unit, and an occupation suggestion unit. The scenario generation unit generates a scenario for each occupation using the generation AI. The virtual experience providing unit provides a virtual experience based on the occupation scenario generated by the scenario generation unit. The aptitude evaluation unit performs aptitude evaluation by analyzing the result of the virtual experience provided by the virtual experience providing unit. The feedback providing unit provides feedback based on a result of the aptitude evaluation performed by the aptitude evaluation unit. The occupation proposal unit performs the occupation proposal based on the result of the feedback provided by the feedback providing unit.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 limited means for people to experience the daily life of different occupations and assess their suitability, leaving room for improvement.

[0005] The system according to the embodiment aims to virtually experience the daily life of different occupations and determine aptitude for them. [Means for solving the problem]

[0006] The system according to the embodiment includes a scenario generation unit, a virtual experience provision unit, an aptitude evaluation unit, a feedback provision unit, and a career suggestion unit. The scenario generation unit generates a scenario for each career using a generation AI. The virtual experience provision unit provides a virtual experience based on the career scenario generated by the scenario generation unit. The aptitude evaluation unit analyzes the results of the virtual experience provided by the virtual experience provision unit and performs an aptitude evaluation. The feedback provision unit provides feedback based on the results of the aptitude evaluation performed by the aptitude evaluation unit. The career suggestion unit suggests a career based on the results of the feedback provided by the feedback provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to virtually experience the daily life of different occupations and determine their aptitude. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The virtual experience system according to an embodiment of the present invention allows users to virtually experience the daily life of different occupations, and a generation AI generates scenarios, evaluates aptitude, and provides feedback. This allows users to realistically experience the daily life of different occupations and identify the occupation that best suits them.

[0029] A virtual experience system according to an embodiment includes a scenario generation unit, a virtual experience provision unit, an aptitude evaluation unit, a feedback provision unit, and a career suggestion unit. The scenario generation unit generates a scenario for each career using a generation AI. For example, the generation AI generates a scenario including daily work and tasks for a career selected by a user. The generation AI also generates a scenario based on a prompt including the name of the career the user wants to experience and detailed instructions. The virtual experience provision unit provides a virtual experience based on the career scenario generated by the scenario generation unit. For example, the virtual experience provision unit allows the user to immerse themselves in the work environment of the selected career and experience actual work by wearing a VR headset. The virtual experience provision unit also provides interactive tasks and problem-solving situations according to the progress of the work. The aptitude evaluation unit analyzes the results of the virtual experience provided by the virtual experience provision unit to perform an aptitude evaluation. For example, the aptitude evaluation unit analyzes how the user performed the task and the extent to which the user demonstrated problem-solving ability and communication skills. The feedback provision unit provides feedback based on the results of the aptitude evaluation performed by the aptitude evaluation unit. For example, the feedback providing unit provides specific feedback such as, "You have a high aptitude as a doctor. In particular, you have excellent communication skills with patients." The career suggestion unit makes a career suggestion based on the results of the feedback provided by the feedback providing unit. For example, the career suggestion unit makes a suggestion such as, "You also have a high aptitude as an engineer. Why not try an engineering job experience next?" In this way, the virtual experience system according to the embodiment allows the user to realistically experience the daily life of different jobs and identify the job that best suits them.

[0030] The scenario generation unit can generate more personalized career scenarios based on the user's past work experience and skill set. For example, the scenario generation unit uses a generation AI to analyze data such as the user's resume and LinkedIn profile and generate scenarios based on the user's past work experience and skill set. For example, if the user previously worked as an engineer, the scenario generation unit provides a scenario that makes use of that experience. Furthermore, the scenario generation unit uses the generation AI to generate scenarios that include tasks and work related to that occupation based on the skills and experience entered by the user. For example, a user with experience in project management is provided with a scenario that includes project management tasks. Furthermore, the scenario generation unit uses the generation AI to generate scenarios that simulate career paths in specific occupations, taking into account the user's past work experience and skill set. For example, the scenario generation unit provides a scenario that allows the user to experience a career path from engineer to project manager. This makes it possible to provide personalized scenarios that take into account the user's past work experience and skill set.

[0031] The scenario generation unit can generate career scenarios from a global perspective, incorporating work habits from different cultures and regions. For example, the generation AI collects work habits from different cultures and regions from a database and generates scenarios based on them. For example, it provides a scenario that reflects Japanese corporate culture and American startup culture. The scenario generation unit also generates scenarios that incorporate work habits from different regions for a career selected by the user. For example, it provides a scenario that allows the user to experience the differences between a European engineering team and an Asian engineering team. The scenario generation unit also generates scenarios that allow the user to experience a career from a global perspective, taking into account work habits from different cultures and regions. For example, it provides a scenario in which the user participates in an international project. This makes it possible to provide scenarios from a global perspective that incorporate work habits from different cultures and regions.

[0032] The scenario generation unit can generate scenarios that include historical background and future prospects related to the occupation in addition to occupational scenarios. For example, the scenario generation unit uses a generation AI to collect the historical background of an occupation from a database and generate a scenario based on that information. For example, it provides a scenario that allows users to experience the history and evolution of the medical profession. The scenario generation unit also considers the future prospects of an occupation and generates a scenario that allows users to experience the future of that occupation. For example, it provides a scenario that reflects future engineering technologies and trends. The scenario generation unit also generates scenarios that include historical background and future prospects related to an occupation, allowing users to understand the overall picture of that occupation. For example, it provides a scenario that allows users to experience the past, present, and future of the teaching occupation. This makes it possible to provide scenarios that include the historical background and future prospects of an occupation.

[0033] The scenario generation unit generates a career scenario that combines multiple occupations in accordance with the user's selection, thereby providing a composite career experience. In the scenario generation unit, for example, the generation AI generates a scenario that combines multiple occupations in accordance with the user's selection. For example, a scenario that combines the occupations of doctor and researcher is provided. The scenario generation unit also combines multiple occupations selected by the user to generate a scenario that provides a composite career experience. For example, a scenario that combines the occupations of engineer and project manager is provided. In addition, the scenario generation unit generates a scenario that combines multiple occupations in accordance with the user's selection, thereby allowing the user to experience the skills and tasks of different occupations. For example, a scenario that combines the occupations of chef and restaurant manager is provided. This makes it possible to provide a composite career experience that combines multiple occupations.

[0034] The virtual experience providing unit can simulate tools and equipment used by the user in their actual occupation during the virtual experience, thereby providing a more realistic experience. For example, the virtual experience providing unit simulates tools and equipment used by the user in their actual occupation during the virtual experience. For example, it simulates medical equipment used by doctors and CAD software used by engineers. The virtual experience providing unit also reproduces tools and equipment related to the occupation selected by the user in a virtual environment to provide a realistic experience. For example, it simulates cooking utensils used by chefs and educational tools used by teachers. The virtual experience providing unit can also simulate tools and equipment used by the user in their actual occupation during the virtual experience, allowing the user to experience the specific tasks of that occupation. For example, it simulates measuring equipment and project management tools used by engineers. This allows the user to simulate tools and equipment used in their actual occupation and provide a more realistic experience.

[0035] The virtual experience providing unit may add a multi-user function in which a user cooperates with other virtual users to accomplish a task during a virtual experience. The virtual experience providing unit may add, for example, a multi-user function in which a user cooperates with other virtual users to accomplish a task during a virtual experience. For example, a scenario in which a doctor and a nurse cooperate to treat a patient is provided. The virtual experience providing unit may also add a multi-user function in which a user cooperates with other virtual users to accomplish a task, thereby providing a realistic occupational experience. For example, a scenario in which an engineer and a designer cooperate to progress a project is provided. The virtual experience providing unit may also add a multi-user function in which a user cooperates with other virtual users to accomplish a task during a virtual experience, thereby allowing the user to experience teamwork. For example, a scenario in which a teacher and an assistant cooperate to advance a lesson is provided. This may provide a multi-user function in which a user cooperates with other virtual users to accomplish a task.

[0036] The virtual experience providing unit can enable a virtual experience to be experienced seamlessly on different devices (smartphone, tablet, PC). The virtual experience providing unit, for example, builds a system that enables a virtual experience to be experienced seamlessly on different devices. For example, the same experience is provided on a smartphone, tablet, and PC. The virtual experience providing unit also ensures that the virtual experience is not interrupted even when a user uses different devices. For example, an experience started on a smartphone can be continued on a PC. The virtual experience providing unit also develops a system that synchronizes data between devices so that a virtual experience can be experienced seamlessly on different devices. For example, progress on a tablet is synchronized with a PC. This allows a virtual experience to be provided seamlessly on different devices.

[0037] The virtual experience providing unit can provide educational content and training modules related to the occupation selected by the user during the virtual experience. The virtual experience providing unit, for example, builds a system that provides educational content and training modules related to the occupation selected by the user during the virtual experience. For example, a medical knowledge training module is provided during a doctor's job experience. The virtual experience providing unit also provides educational content and training modules related to the occupation selected by the user during the virtual experience, allowing the user to learn the skills required for that occupation. For example, a programming training module is provided during an engineer's job experience. The virtual experience providing unit also provides educational content and training modules related to the occupation selected by the user during the virtual experience, allowing the user to acquire the knowledge required for that occupation. For example, an educational theory training module is provided during a teacher's job experience. In this way, educational content and training modules related to the occupation selected by the user can be provided.

[0038] The aptitude evaluation unit analyzes the user's behavioral data and can evaluate not only occupational aptitude but also the user's strengths and weaknesses. For example, the aptitude evaluation unit constructs a system in which the generation AI analyzes the user's behavioral data and evaluates not only occupational aptitude but also the user's strengths and weaknesses. For example, it evaluates the user's task execution ability and communication skills. Furthermore, the aptitude evaluation unit evaluates the generation AI's aptitude for the occupation based on the user's behavioral data and further identifies the user's strengths and weaknesses. For example, it evaluates the tasks that the user is good at and the tasks that the user is not good at. Furthermore, the aptitude evaluation unit analyzes the user's behavioral data and evaluates not only occupational aptitude but also the user's strengths and weaknesses. For example, it evaluates the user's problem-solving ability and leadership skills. This allows the system to provide a more detailed aptitude evaluation by also evaluating the user's strengths and weaknesses.

[0039] The aptitude evaluation unit can compare the user's aptitude evaluation results with data from other users and provide a relative evaluation. For example, the aptitude evaluation unit constructs a system in which a generation AI compares the user's aptitude evaluation results with data from other users and provides a relative evaluation. For example, the evaluation is made by comparing the user with other users who have experience in the same occupation. The aptitude evaluation unit also compares the user's aptitude evaluation results with data from other users and provides a relative evaluation. For example, the user's task execution ability and communication skills are compared with those of other users. The aptitude evaluation unit also compares the user's aptitude evaluation results with data from other users and provides a relative evaluation. For example, the user's problem-solving ability and leadership skills are compared with those of other users. This makes it possible to compare the user's aptitude evaluation results with data from other users and provide a relative evaluation.

[0040] The aptitude assessment unit can integrate the results of the aptitude assessment with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment. For example, the aptitude assessment unit builds a system that integrates the results of the aptitude assessment with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment. For example, data from multiple work experiences is integrated and evaluated. The aptitude assessment unit also integrates the results of the aptitude assessment based on data from other work experiences that the user has had in the past to provide a comprehensive assessment. For example, the aptitude assessment unit integrates and evaluates work experience data for doctors and engineers. The aptitude assessment unit also integrates the results of the aptitude assessment with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment. For example, the aptitude assessment unit integrates and evaluates work experience data for teachers and chefs. This allows the aptitude assessment to be integrated with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment.

[0041] The aptitude assessment unit visualizes the results of the aptitude assessment in a manner that is useful for the user's career planning, and can support the user in making a future career choice. The aptitude assessment unit, for example, builds a system that visualizes the results of the aptitude assessment and provides them in a manner that is useful for the user's career planning. For example, the assessment results are displayed using graphs or charts. The aptitude assessment unit also visualizes the results of the user's aptitude assessment and supports the user in making a future career choice. For example, the aptitude assessment unit visually displays the user's strengths and weaknesses to help with career planning. The aptitude assessment unit also visualizes the results of the aptitude assessment and provides them in a manner that is useful for the user's career planning. For example, the aptitude assessment unit displays a list of occupations based on the user's aptitude. This allows the results of the aptitude assessment to be visualized and provided in a manner that is useful for the user's career planning.

[0042] The feedback providing unit can provide specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. The feedback providing unit, for example, builds a system in which the generation AI provides specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. For example, it provides a training plan for overcoming the user's weaknesses. The feedback providing unit also provides specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. For example, it provides advice for improving the user's communication skills. The feedback providing unit also provides specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. For example, it provides advice for improving the user's problem-solving ability. In this way, it is possible to provide specific points for improvement and advice for skill improvement based on the user's aptitude assessment results.

[0043] The feedback providing unit can compare the user's feedback with data of other users and provide relative feedback. The feedback providing unit, for example, constructs a system in which the generation AI compares the user's feedback with data of other users and provides relative feedback. For example, the feedback is provided by comparing with other users who have experienced the same occupation. The feedback providing unit also compares the user's feedback with data of other users and provides relative feedback. For example, the user's task execution ability or communication skills are compared with other users. The feedback providing unit also compares the user's feedback with data of other users and provides relative feedback. For example, the user's problem-solving ability or leadership skills are compared with other users. This allows the generation AI to compare the user's feedback with data of other users and provide relative feedback.

[0044] The feedback providing unit can combine the feedback with comments and advice from experts related to the occupation selected by the user. For example, the feedback providing unit builds a system that combines the feedback with comments and advice from experts related to the occupation selected by the user. For example, advice from a medical expert is provided for the occupational experience of a doctor. The feedback providing unit also combines the feedback with comments and advice from experts related to the occupation selected by the user. For example, advice from a technical expert is provided for the occupational experience of an engineer. The feedback providing unit also combines the feedback with comments and advice from experts related to the occupation selected by the user. For example, advice from an education expert is provided for the occupational experience of a teacher. In this way, it is possible to provide feedback combined with comments and advice from experts related to the occupation selected by the user.

[0045] The feedback providing unit can integrate the feedback with other feedback the user has received in the past and provide an overall evaluation. The feedback providing unit, for example, builds a system that integrates the feedback with other feedback the user has received in the past and provides an overall evaluation. For example, the feedback providing unit integrates feedback for multiple work experiences and provides an evaluation. The feedback providing unit also integrates feedback based on other feedback the user has received in the past and provides an overall evaluation. For example, the feedback providing unit integrates feedback for the work experiences of a doctor and an engineer and provides an evaluation. The feedback providing unit also integrates the feedback with other feedback the user has received in the past and provides an overall evaluation. For example, the feedback providing unit integrates feedback for the work experiences of a teacher and a chef and provides an evaluation. This allows the feedback to be integrated with other feedback the user has received in the past and provides an overall evaluation.

[0046] The career suggestion unit can recommend an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, the career suggestion unit builds a system in which a generation AI recommends an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, it proposes the most suitable occupation based on the user's skill set. Furthermore, the career suggestion unit recommends an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, it proposes an occupation that reflects the user's interests. Furthermore, the career suggestion unit recommends an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, it proposes an occupation that combines the user's skills and interests. In this way, it is possible to recommend an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results.

[0047] The career suggestion unit can provide a simulation of long-term career choices to support the user's career planning. The career suggestion unit, for example, builds a system in which a generation AI provides a simulation of long-term career choices to support the user's career planning. For example, it simulates the user's career path. The career suggestion unit also provides a simulation of long-term career choices by the generation AI based on the user's aptitude assessment results. For example, it provides a simulation based on the user's career goals. The career suggestion unit also provides a simulation of long-term career choices by the generation AI to support the user's career planning. For example, it simulates a career path that reflects the user's skills and interests. This makes it possible to provide a simulation of long-term career choices to support the user's career planning.

[0048] The career suggestion unit can integrate career selection support with the user's past work experiences to provide comprehensive career selection support. The career suggestion unit, for example, builds a system that integrates career selection support with the user's past work experiences to provide comprehensive career selection support. For example, it integrates data on multiple work experiences to provide support. The career suggestion unit also integrates career selection support based on data on the user's past work experiences to provide comprehensive support. For example, it integrates work experience data for doctors and engineers to provide support. The career suggestion unit also integrates career selection support with the user's past work experiences to provide comprehensive career selection support. For example, it integrates work experience data for teachers and chefs to provide support. This allows the system to integrate with the user's past work experiences to provide comprehensive career selection support.

[0049] The career suggestion unit can customize the career selection support based on the user's lifestyle and values. The career suggestion unit, for example, builds a system that customizes career selection support based on the user's lifestyle and values. For example, it suggests careers that match the user's values. The career suggestion unit also customizes the career selection support based on the user's lifestyle and values. For example, it suggests careers that match the user's values. The career suggestion unit also customizes the career selection support based on the user's lifestyle and values. For example, it suggests careers that match the user's values. This makes it possible to provide career selection support that is customized based on the user's lifestyle and values.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The virtual experience system can also include a health management unit that monitors the user's health and provides appropriate rest timing. For example, it can monitor the user's heart rate and stress level in real time and prompt the user to take a break if excessive stress is detected. The health management unit can also evaluate the impact of a long virtual experience on the user's health based on the user's health data and provide appropriate advice. Furthermore, the health management unit can provide guidance to help the user maintain proper posture during the virtual experience, reducing physical strain. This allows the user to maintain their health while providing a more comfortable virtual experience.

[0052] The virtual experience system can also include a learning support unit that provides customized educational content tailored to the user's learning style. For example, visually-inclined users can be provided with content that makes heavy use of graphics and video. Audio-guided and podcast-style content can also be provided for auditory learners. Furthermore, the learning support unit can monitor the user's progress in real time and provide additional resources and support as needed. This maximizes the user's learning effectiveness and provides a more effective virtual experience.

[0053] The virtual experience system may further include a social interaction unit for improving the user's social skills. For example, the system may provide a scenario in which the user cooperates with other virtual users to complete a task. The social interaction unit may also provide a chat function or a video call function for the user to communicate with other users during the virtual experience. The social interaction unit may also evaluate the user's communication skills and teamwork abilities and provide feedback. This may improve the user's social skills and provide a more realistic work experience.

[0054] The virtual experience system may further include a feedback analysis unit that improves the scenario based on user feedback. For example, the feedback analysis unit may analyze the feedback provided by the user and identify areas for improvement in the scenario. The feedback analysis unit may also add new scenarios or tasks based on the user feedback. Furthermore, the feedback analysis unit may analyze the user feedback in real time and dynamically adjust the scenario. This may provide a scenario that reflects the user feedback, thereby providing a more satisfying virtual experience.

[0055] The virtual experience system may further include a feedback analysis unit that improves the scenario based on user feedback. For example, the feedback analysis unit may analyze the feedback provided by the user and identify areas for improvement in the scenario. The feedback analysis unit may also add new scenarios or tasks based on the user feedback. Furthermore, the feedback analysis unit may analyze the user feedback in real time and dynamically adjust the scenario. This may provide a scenario that reflects the user feedback, thereby providing a more satisfying virtual experience.

[0056] The virtual experience system can also include a health management unit that monitors the user's health and provides appropriate rest timing. For example, it can monitor the user's heart rate and stress level in real time and prompt the user to take a break if excessive stress is detected. The health management unit can also evaluate the impact of a long virtual experience on the user's health based on the user's health data and provide appropriate advice. Furthermore, the health management unit can provide guidance to help the user maintain proper posture during the virtual experience, reducing physical strain. This allows the user to maintain their health while providing a more comfortable virtual experience.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The scenario generation unit uses the generation AI to generate a scenario for each occupation. For example, the generation AI generates a scenario that includes the daily work and tasks of an occupation selected by the user. The generation AI also generates a scenario based on the name of the occupation the user wants to experience and prompts that include detailed instructions. Step 2: The virtual experience provider provides a virtual experience based on the occupational scenario generated by the scenario generator. For example, the virtual experience provider allows the user to immerse themselves in the work environment of the occupation they selected by wearing a VR headset and experience actual work. The virtual experience provider also provides interactive tasks and problem-solving situations according to the progress of the work. Step 3: The aptitude evaluation unit analyzes the results of the virtual experience provided by the virtual experience providing unit and performs an aptitude evaluation. For example, the aptitude evaluation unit analyzes how the user performed the task and the extent to which the user demonstrated problem-solving ability and communication skills. Step 4: The feedback providing unit provides feedback based on the results of the aptitude evaluation performed by the aptitude evaluation unit. For example, the feedback providing unit provides specific feedback such as, "You have high aptitude as a doctor. In particular, you have excellent communication skills with patients." Step 5: The career suggestion unit makes career suggestions based on the feedback provided by the feedback providing unit. For example, the career suggestion unit may suggest, "You have a high aptitude for engineering. How about trying out an engineering work experience next?"

[0059] (Example 2) The virtual experience system according to an embodiment of the present invention allows users to virtually experience the daily life of different occupations, and a generation AI generates scenarios, evaluates aptitude, and provides feedback. This allows users to realistically experience the daily life of different occupations and identify the occupation that best suits them.

[0060] A virtual experience system according to an embodiment includes a scenario generation unit, a virtual experience provision unit, an aptitude evaluation unit, a feedback provision unit, and a career suggestion unit. The scenario generation unit generates a scenario for each career using a generation AI. For example, the generation AI generates a scenario including daily work and tasks for a career selected by a user. The generation AI also generates a scenario based on a prompt including the name of the career the user wants to experience and detailed instructions. The virtual experience provision unit provides a virtual experience based on the career scenario generated by the scenario generation unit. For example, the virtual experience provision unit allows the user to immerse themselves in the work environment of the selected career and experience actual work by wearing a VR headset. The virtual experience provision unit also provides interactive tasks and problem-solving situations according to the progress of the work. The aptitude evaluation unit analyzes the results of the virtual experience provided by the virtual experience provision unit to perform an aptitude evaluation. For example, the aptitude evaluation unit analyzes how the user performed the task and the extent to which the user demonstrated problem-solving ability and communication skills. The feedback provision unit provides feedback based on the results of the aptitude evaluation performed by the aptitude evaluation unit. For example, the feedback providing unit provides specific feedback such as, "You have a high aptitude as a doctor. In particular, you have excellent communication skills with patients." The career suggestion unit makes a career suggestion based on the results of the feedback provided by the feedback providing unit. For example, the career suggestion unit makes a suggestion such as, "You also have a high aptitude as an engineer. Why not try an engineering job experience next?" In this way, the virtual experience system according to the embodiment allows the user to realistically experience the daily life of different jobs and identify the job that best suits them.

[0061] The scenario generation unit can generate more personalized career scenarios based on the user's past work experience and skill set. For example, the scenario generation unit uses a generation AI to analyze data such as the user's resume and LinkedIn profile and generate scenarios based on the user's past work experience and skill set. For example, if the user previously worked as an engineer, the scenario generation unit provides a scenario that makes use of that experience. Furthermore, the scenario generation unit uses the generation AI to generate scenarios that include tasks and work related to that occupation based on the skills and experience entered by the user. For example, a user with experience in project management is provided with a scenario that includes project management tasks. Furthermore, the scenario generation unit uses the generation AI to generate scenarios that simulate career paths in specific occupations, taking into account the user's past work experience and skill set. For example, the scenario generation unit provides a scenario that allows the user to experience a career path from engineer to project manager. This makes it possible to provide personalized scenarios that take into account the user's past work experience and skill set.

[0062] The scenario generation unit can generate career scenarios from a global perspective, incorporating work habits from different cultures and regions. For example, the generation AI collects work habits from different cultures and regions from a database and generates scenarios based on them. For example, it provides a scenario that reflects Japanese corporate culture and American startup culture. The scenario generation unit also generates scenarios that incorporate work habits from different regions for a career selected by the user. For example, it provides a scenario that allows the user to experience the differences between a European engineering team and an Asian engineering team. The scenario generation unit also generates scenarios that allow the user to experience a career from a global perspective, taking into account work habits from different cultures and regions. For example, it provides a scenario in which the user participates in an international project. This makes it possible to provide scenarios from a global perspective that incorporate work habits from different cultures and regions.

[0063] The scenario generation unit uses the emotion estimation function to generate an occupation scenario that is most interesting to the user, thereby improving satisfaction with the experience. For example, the scenario generation unit uses the emotion estimation function to analyze information entered by the user and past behavioral data to generate a scenario that is likely to be of most interest to the user. For example, it provides a scenario related to an occupation in which the user has shown interest in the past. The scenario generation unit also analyzes the user's emotional responses in real time and dynamically adjusts the scenario based on the results. For example, it increases tasks in which the user has shown interest and reduces tasks in which the user has no interest. The scenario generation unit also uses the emotion estimation function to generate a scenario that is likely to be of most interest to the user, thereby improving satisfaction with the experience. For example, it preferentially provides scenarios in which the user has shown positive emotions. This makes it possible to provide scenarios that attract the user's interest and improve satisfaction with the experience.

[0064] The scenario generation unit can generate scenarios that include historical background and future prospects related to the occupation in addition to occupational scenarios. For example, the scenario generation unit uses a generation AI to collect the historical background of an occupation from a database and generate a scenario based on that information. For example, it provides a scenario that allows users to experience the history and evolution of the medical profession. The scenario generation unit also considers the future prospects of an occupation and generates a scenario that allows users to experience the future of that occupation. For example, it provides a scenario that reflects future engineering technologies and trends. The scenario generation unit also generates scenarios that include historical background and future prospects related to an occupation, allowing users to understand the overall picture of that occupation. For example, it provides a scenario that allows users to experience the past, present, and future of the teaching occupation. This makes it possible to provide scenarios that include the historical background and future prospects of an occupation.

[0065] The scenario generation unit generates a career scenario that combines multiple occupations in accordance with the user's selection, thereby providing a composite career experience. In the scenario generation unit, for example, the generation AI generates a scenario that combines multiple occupations in accordance with the user's selection. For example, a scenario that combines the occupations of doctor and researcher is provided. The scenario generation unit also combines multiple occupations selected by the user to generate a scenario that provides a composite career experience. For example, a scenario that combines the occupations of engineer and project manager is provided. In addition, the scenario generation unit generates a scenario that combines multiple occupations in accordance with the user's selection, thereby allowing the user to experience the skills and tasks of different occupations. For example, a scenario that combines the occupations of chef and restaurant manager is provided. This makes it possible to provide a composite career experience that combines multiple occupations.

[0066] The scenario generation unit can use the emotion estimation function to monitor the stress or excitement felt by the user during the experience in real time and dynamically adjust the occupation scenario. For example, the scenario generation unit uses the emotion estimation function to monitor the stress or excitement felt by the user during the experience in real time and dynamically adjust the scenario based on the data. For example, if the user feels stressed, the difficulty of the task can be lowered. The scenario generation unit also analyzes the user's emotional reactions in real time and builds a system that dynamically adjusts the scenario. For example, if the user feels excited, a more challenging task can be added. The scenario generation unit also uses the emotion estimation function to monitor the stress or excitement felt by the user during the experience and dynamically adjust the scenario. For example, the scenario generation unit adjusts the progress of the scenario based on the user's emotional data. This allows the scenario to be dynamically adjusted according to the user's emotional state, providing an optimal experience.

[0067] The virtual experience providing unit can simulate tools and equipment used by the user in their actual occupation during the virtual experience, thereby providing a more realistic experience. For example, the virtual experience providing unit simulates tools and equipment used by the user in their actual occupation during the virtual experience. For example, it simulates medical equipment used by doctors and CAD software used by engineers. The virtual experience providing unit also reproduces tools and equipment related to the occupation selected by the user in a virtual environment to provide a realistic experience. For example, it simulates cooking utensils used by chefs and educational tools used by teachers. The virtual experience providing unit can also simulate tools and equipment used by the user in their actual occupation during the virtual experience, allowing the user to experience the specific tasks of that occupation. For example, it simulates measuring equipment and project management tools used by engineers. This allows the user to simulate tools and equipment used in their actual occupation and provide a more realistic experience.

[0068] The virtual experience providing unit may add a multi-user function in which a user cooperates with other virtual users to accomplish a task during a virtual experience. The virtual experience providing unit may add, for example, a multi-user function in which a user cooperates with other virtual users to accomplish a task during a virtual experience. For example, a scenario in which a doctor and a nurse cooperate to treat a patient is provided. The virtual experience providing unit may also add a multi-user function in which a user cooperates with other virtual users to accomplish a task, thereby providing a realistic occupational experience. For example, a scenario in which an engineer and a designer cooperate to progress a project is provided. The virtual experience providing unit may also add a multi-user function in which a user cooperates with other virtual users to accomplish a task during a virtual experience, thereby allowing the user to experience teamwork. For example, a scenario in which a teacher and an assistant cooperate to advance a lesson is provided. This may provide a multi-user function in which a user cooperates with other virtual users to accomplish a task.

[0069] The virtual experience providing unit can use the emotion estimation function to dynamically change the experience content based on the user's emotional state, thereby providing an optimal experience. The virtual experience providing unit, for example, uses the emotion estimation function to build a system that dynamically changes the experience content according to the user's emotional state. For example, if the user feels stressed, the difficulty of the task is lowered. The virtual experience providing unit also analyzes the user's emotional response in real time and dynamically changes the experience content. For example, if the user feels excited, a more challenging task is added. The virtual experience providing unit also uses the emotion estimation function to dynamically change the experience content according to the user's emotional state, thereby providing an optimal experience. For example, the progress of the scenario is adjusted based on the user's emotional data. This allows the experience content to be dynamically changed according to the user's emotional state, thereby providing an optimal experience.

[0070] The virtual experience providing unit can enable a virtual experience to be experienced seamlessly on different devices (smartphone, tablet, PC). The virtual experience providing unit, for example, builds a system that enables a virtual experience to be experienced seamlessly on different devices. For example, the same experience is provided on a smartphone, tablet, and PC. The virtual experience providing unit also ensures that the virtual experience is not interrupted even when a user uses different devices. For example, an experience started on a smartphone can be continued on a PC. The virtual experience providing unit also develops a system that synchronizes data between devices so that a virtual experience can be experienced seamlessly on different devices. For example, progress on a tablet is synchronized with a PC. This allows a virtual experience to be provided seamlessly on different devices.

[0071] The virtual experience providing unit can provide educational content and training modules related to the occupation selected by the user during the virtual experience. The virtual experience providing unit, for example, builds a system that provides educational content and training modules related to the occupation selected by the user during the virtual experience. For example, a medical knowledge training module is provided during a doctor's job experience. The virtual experience providing unit also provides educational content and training modules related to the occupation selected by the user during the virtual experience, allowing the user to learn the skills required for that occupation. For example, a programming training module is provided during an engineer's job experience. The virtual experience providing unit also provides educational content and training modules related to the occupation selected by the user during the virtual experience, allowing the user to acquire the knowledge required for that occupation. For example, an educational theory training module is provided during a teacher's job experience. In this way, educational content and training modules related to the occupation selected by the user can be provided.

[0072] The virtual experience providing unit can add an emotion log function that uses the emotion estimation function to record the emotions the user feels during the experience and allow them to review later. The virtual experience providing unit, for example, uses the emotion estimation function to record the emotions the user feels during the experience in real time and save the data as an emotion log. For example, it records moments of joy or stress the user felt during the experience. The virtual experience providing unit also adds an emotion log function that records the emotions the user feels during the experience and allows them to review later. For example, it displays the emotion log after the experience ends, allowing the user to see what emotions the user felt in each situation. The virtual experience providing unit also uses the emotion estimation function to build a system that records the emotions the user feels during the experience and provides feedback based on the data. For example, it identifies situations in which the user felt stress and suggests areas for improvement. This makes it possible to provide an emotion log function that records the emotions the user feels during the experience and allows them to review later.

[0073] The aptitude evaluation unit analyzes the user's behavioral data and can evaluate not only occupational aptitude but also the user's strengths and weaknesses. For example, the aptitude evaluation unit constructs a system in which the generation AI analyzes the user's behavioral data and evaluates not only occupational aptitude but also the user's strengths and weaknesses. For example, it evaluates the user's task execution ability and communication skills. Furthermore, the aptitude evaluation unit evaluates the generation AI's aptitude for the occupation based on the user's behavioral data and further identifies the user's strengths and weaknesses. For example, it evaluates the tasks that the user is good at and the tasks that the user is not good at. Furthermore, the aptitude evaluation unit analyzes the user's behavioral data and evaluates not only occupational aptitude but also the user's strengths and weaknesses. For example, it evaluates the user's problem-solving ability and leadership skills. This allows the system to provide a more detailed aptitude evaluation by also evaluating the user's strengths and weaknesses.

[0074] The aptitude evaluation unit can compare the user's aptitude evaluation results with data from other users and provide a relative evaluation. For example, the aptitude evaluation unit constructs a system in which a generation AI compares the user's aptitude evaluation results with data from other users and provides a relative evaluation. For example, the evaluation is made by comparing the user with other users who have experience in the same occupation. The aptitude evaluation unit also compares the user's aptitude evaluation results with data from other users and provides a relative evaluation. For example, the user's task execution ability and communication skills are compared with those of other users. The aptitude evaluation unit also compares the user's aptitude evaluation results with data from other users and provides a relative evaluation. For example, the user's problem-solving ability and leadership skills are compared with those of other users. This makes it possible to compare the user's aptitude evaluation results with data from other users and provide a relative evaluation.

[0075] The aptitude evaluation unit uses the emotion estimation function to analyze the user's emotional response and can include emotion-based aptitude in the aptitude evaluation. The aptitude evaluation unit, for example, uses the emotion estimation function to analyze the user's emotional response and build a system that also includes emotional aptitude in the evaluation. For example, tasks in which the user showed positive emotions are reflected in the evaluation. The aptitude evaluation unit also analyzes the user's emotional response in real time and includes emotional aptitude in the evaluation based on the results. For example, situations in which the user felt stressed are reflected in the evaluation. The aptitude evaluation unit also uses the emotion estimation function to analyze the user's emotional response and include emotional aptitude in the evaluation. For example, tasks in which the user felt excited are reflected in the evaluation. In this way, the user's emotional response can be analyzed and emotional aptitude can be included in the evaluation.

[0076] The aptitude assessment unit can integrate the results of the aptitude assessment with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment. For example, the aptitude assessment unit builds a system that integrates the results of the aptitude assessment with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment. For example, data from multiple work experiences is integrated and evaluated. The aptitude assessment unit also integrates the results of the aptitude assessment based on data from other work experiences that the user has had in the past to provide a comprehensive assessment. For example, the aptitude assessment unit integrates and evaluates work experience data for doctors and engineers. The aptitude assessment unit also integrates the results of the aptitude assessment with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment. For example, the aptitude assessment unit integrates and evaluates work experience data for teachers and chefs. This allows the aptitude assessment to be integrated with other work experiences that the user has had in the past to provide a comprehensive aptitude assessment.

[0077] The aptitude assessment unit visualizes the results of the aptitude assessment in a manner that is useful for the user's career planning, and can support the user in making a future career choice. The aptitude assessment unit, for example, builds a system that visualizes the results of the aptitude assessment and provides them in a manner that is useful for the user's career planning. For example, the assessment results are displayed using graphs or charts. The aptitude assessment unit also visualizes the results of the user's aptitude assessment and supports the user in making a future career choice. For example, the aptitude assessment unit visually displays the user's strengths and weaknesses to help with career planning. The aptitude assessment unit also visualizes the results of the aptitude assessment and provides them in a manner that is useful for the user's career planning. For example, the aptitude assessment unit displays a list of occupations based on the user's aptitude. This allows the results of the aptitude assessment to be visualized and provided in a manner that is useful for the user's career planning.

[0078] The aptitude evaluation unit can use the emotion estimation function to identify the occupation for which the user has the most positive emotion and emphasize the aptitude for that occupation. The aptitude evaluation unit, for example, uses the emotion estimation function to identify the occupation for which the user has the most positive emotion and builds a system to emphasize the aptitude for that occupation. For example, the occupation for which the user felt joy is reflected in the evaluation. The aptitude evaluation unit also analyzes the user's emotional response in real time to identify the occupation for which the user has the most positive emotion. For example, the occupation for which the user felt excited is reflected in the evaluation. The aptitude evaluation unit also uses the emotion estimation function to identify the occupation for which the user has the most positive emotion and emphasize the aptitude for that occupation. For example, the occupation for which the user felt satisfaction is reflected in the evaluation. This makes it possible to identify the occupation for which the user has the most positive emotion and emphasize the aptitude for that occupation.

[0079] The feedback providing unit can provide specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. The feedback providing unit, for example, builds a system in which the generation AI provides specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. For example, it provides a training plan for overcoming the user's weaknesses. The feedback providing unit also provides specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. For example, it provides advice for improving the user's communication skills. The feedback providing unit also provides specific points for improvement and advice for skill improvement based on the user's aptitude assessment results. For example, it provides advice for improving the user's problem-solving ability. In this way, it is possible to provide specific points for improvement and advice for skill improvement based on the user's aptitude assessment results.

[0080] The feedback providing unit can compare the user's feedback with data of other users and provide relative feedback. The feedback providing unit, for example, constructs a system in which the generation AI compares the user's feedback with data of other users and provides relative feedback. For example, the feedback is provided by comparing with other users who have experienced the same occupation. The feedback providing unit also compares the user's feedback with data of other users and provides relative feedback. For example, the user's task execution ability or communication skills are compared with other users. The feedback providing unit also compares the user's feedback with data of other users and provides relative feedback. For example, the user's problem-solving ability or leadership skills are compared with other users. This allows the generation AI to compare the user's feedback with data of other users and provide relative feedback.

[0081] The feedback providing unit uses the emotion estimation function to analyze the emotional reaction of the user when receiving feedback and can emphasize positive feedback. The feedback providing unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when receiving feedback and builds a system that emphasizes positive feedback. For example, it emphasizes feedback that makes the user feel happy. The feedback providing unit also analyzes the user's emotional reaction in real time and emphasizes positive feedback. For example, it emphasizes feedback that makes the user feel satisfied. The feedback providing unit also uses the emotion estimation function to analyze the emotional reaction of the user when receiving feedback and emphasizes positive feedback. For example, it emphasizes feedback that makes the user feel excited. In this way, it is possible to analyze the emotional reaction of the user when receiving feedback and emphasize positive feedback.

[0082] The feedback providing unit can combine the feedback with comments and advice from experts related to the occupation selected by the user. For example, the feedback providing unit builds a system that combines the feedback with comments and advice from experts related to the occupation selected by the user. For example, advice from a medical expert is provided for the occupational experience of a doctor. The feedback providing unit also combines the feedback with comments and advice from experts related to the occupation selected by the user. For example, advice from a technical expert is provided for the occupational experience of an engineer. The feedback providing unit also combines the feedback with comments and advice from experts related to the occupation selected by the user. For example, advice from an education expert is provided for the occupational experience of a teacher. In this way, it is possible to provide feedback combined with comments and advice from experts related to the occupation selected by the user.

[0083] The feedback providing unit can integrate the feedback with other feedback the user has received in the past and provide an overall evaluation. The feedback providing unit, for example, builds a system that integrates the feedback with other feedback the user has received in the past and provides an overall evaluation. For example, the feedback providing unit integrates feedback for multiple work experiences and provides an evaluation. The feedback providing unit also integrates feedback based on other feedback the user has received in the past and provides an overall evaluation. For example, the feedback providing unit integrates feedback for the work experiences of a doctor and an engineer and provides an evaluation. The feedback providing unit also integrates the feedback with other feedback the user has received in the past and provides an overall evaluation. For example, the feedback providing unit integrates feedback for the work experiences of a teacher and a chef and provides an evaluation. This allows the feedback to be integrated with other feedback the user has received in the past and provides an overall evaluation.

[0084] The feedback providing unit can use the emotion estimation function to identify the feedback format in which the user feels the most positive emotion and provide feedback in that format. The feedback providing unit, for example, uses the emotion estimation function to identify the feedback format in which the user feels the most positive emotion and builds a system for providing feedback in that format. For example, the feedback providing unit preferentially provides the feedback format in which the user feels joy. The feedback providing unit also analyzes the user's emotional response in real time to identify the feedback format in which the user feels the most positive emotion. For example, the feedback providing unit provides the feedback format in which the user feels satisfied. The feedback providing unit also uses the emotion estimation function to identify the feedback format in which the user feels the most positive emotion and provide feedback in that format. For example, the feedback providing unit provides the feedback format in which the user feels excited. In this way, the feedback format in which the user feels the most positive emotion can be identified and feedback can be provided in that format.

[0085] The career suggestion unit can recommend an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, the career suggestion unit builds a system in which a generation AI recommends an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, it proposes the most suitable occupation based on the user's skill set. Furthermore, the career suggestion unit recommends an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, it proposes an occupation that reflects the user's interests. Furthermore, the career suggestion unit recommends an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results. For example, it proposes an occupation that combines the user's skills and interests. In this way, it is possible to recommend an occupation that is most suitable for the user's interests and skills based on the user's aptitude assessment results.

[0086] The career suggestion unit can provide a simulation of long-term career choices to support the user's career planning. The career suggestion unit, for example, builds a system in which a generation AI provides a simulation of long-term career choices to support the user's career planning. For example, it simulates the user's career path. The career suggestion unit also provides a simulation of long-term career choices by the generation AI based on the user's aptitude assessment results. For example, it provides a simulation based on the user's career goals. The career suggestion unit also provides a simulation of long-term career choices by the generation AI to support the user's career planning. For example, it simulates a career path that reflects the user's skills and interests. This makes it possible to provide a simulation of long-term career choices to support the user's career planning.

[0087] The career suggestion unit can use the emotion estimation function to identify the career for which the user feels the most positive emotion and preferentially suggest that career. The career suggestion unit, for example, uses the emotion estimation function to identify the career for which the user feels the most positive emotion and builds a system that preferentially suggests that career. For example, it proposes a career for which the user feels joy. The career suggestion unit also analyzes the user's emotional response in real time to identify the career for which the user feels the most positive emotion. For example, it proposes a career for which the user feels satisfied. The career suggestion unit also uses the emotion estimation function to identify the career for which the user feels the most positive emotion and preferentially suggest that career. For example, it proposes a career for which the user feels excited. This allows the career for which the user feels the most positive emotion to be identified and preferentially suggest that career.

[0088] The career suggestion unit can integrate career selection support with the user's past work experiences to provide comprehensive career selection support. The career suggestion unit, for example, builds a system that integrates career selection support with the user's past work experiences to provide comprehensive career selection support. For example, it integrates data on multiple work experiences to provide support. The career suggestion unit also integrates career selection support based on data on the user's past work experiences to provide comprehensive support. For example, it integrates work experience data for doctors and engineers to provide support. The career suggestion unit also integrates career selection support with the user's past work experiences to provide comprehensive career selection support. For example, it integrates work experience data for teachers and chefs to provide support. This allows the system to integrate with the user's past work experiences to provide comprehensive career selection support.

[0089] The career suggestion unit can customize the career selection support based on the user's lifestyle and values. The career suggestion unit, for example, builds a system that customizes career selection support based on the user's lifestyle and values. For example, it suggests careers that match the user's values. The career suggestion unit also customizes the career selection support based on the user's lifestyle and values. For example, it suggests careers that match the user's values. The career suggestion unit also customizes the career selection support based on the user's lifestyle and values. For example, it suggests careers that match the user's values. This makes it possible to provide career selection support that is customized based on the user's lifestyle and values.

[0090] The career suggestion unit uses the emotion estimation function to generate a career selection scenario in which the user feels the most positive emotion, and can suggest a career based on that scenario. For example, the career suggestion unit uses the emotion estimation function to generate a career selection scenario in which the user feels the most positive emotion, and builds a system that suggests a career based on that scenario. For example, the career suggestion unit suggests a career based on a scenario in which the user felt joy. The career suggestion unit also analyzes the user's emotional response in real time to generate a career selection scenario in which the user feels the most positive emotion. For example, the career suggestion unit suggests a career based on a scenario in which the user felt satisfied. The career suggestion unit also uses the emotion estimation function to generate a career selection scenario in which the user feels the most positive emotion, and can suggest a career based on that scenario. For example, the career suggestion unit suggests a career based on a scenario in which the user felt excited. In this way, it is possible to generate a career selection scenario in which the user feels the most positive emotion, and can suggest a career based on that scenario.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The virtual experience system can also include a health management unit that monitors the user's health and provides appropriate rest timing. For example, it can monitor the user's heart rate and stress level in real time and prompt the user to take a break if excessive stress is detected. The health management unit can also evaluate the impact of a long virtual experience on the user's health based on the user's health data and provide appropriate advice. Furthermore, the health management unit can provide guidance to help the user maintain proper posture during the virtual experience, reducing physical strain. This allows the user to maintain their health while providing a more comfortable virtual experience.

[0093] The virtual experience system can also include a learning support unit that provides customized educational content tailored to the user's learning style. For example, visually-inclined users can be provided with content that makes heavy use of graphics and video. Audio-guided and podcast-style content can also be provided for auditory learners. Furthermore, the learning support unit can monitor the user's progress in real time and provide additional resources and support as needed. This maximizes the user's learning effectiveness and provides a more effective virtual experience.

[0094] The virtual experience system may further include a social interaction unit for improving the user's social skills. For example, the system may provide a scenario in which the user cooperates with other virtual users to complete a task. The social interaction unit may also provide a chat function or a video call function for the user to communicate with other users during the virtual experience. The social interaction unit may also evaluate the user's communication skills and teamwork abilities and provide feedback. This may improve the user's social skills and provide a more realistic work experience.

[0095] The virtual experience system may further include a relaxation unit that uses the user's emotion estimation function to provide the user with the most relaxing environment. For example, if the user feels stressed, it may display relaxing music or scenery. The relaxation unit may also suggest the most appropriate relaxation method based on the user's emotion data. For example, it may preferentially provide relaxation methods that the user expresses positive emotions in. Furthermore, by providing a user with a relaxing environment, the relaxation unit can improve satisfaction with the virtual experience. This reduces the user's stress and provides a more comfortable virtual experience.

[0096] The virtual experience system may further include an entertainment unit that uses the user's emotion estimation function to provide a scenario that is most exciting to the user. For example, a scenario including action or adventure may be provided based on a scenario that the user finds exciting. The entertainment unit may also suggest optimal entertainment content based on the user's emotion data. For example, entertainment content for which the user shows positive emotions may be provided preferentially. Furthermore, the entertainment unit may improve satisfaction with the virtual experience by providing a scenario that the user finds most enjoyable. This may draw out the user's excitement and provide a more enjoyable virtual experience.

[0097] The virtual experience system may further include a feedback analysis unit that improves the scenario based on user feedback. For example, the feedback analysis unit may analyze the feedback provided by the user and identify areas for improvement in the scenario. The feedback analysis unit may also add new scenarios or tasks based on the user feedback. Furthermore, the feedback analysis unit may analyze the user feedback in real time and dynamically adjust the scenario. This may provide a scenario that reflects the user feedback, thereby providing a more satisfying virtual experience.

[0098] The virtual experience system may further include a relaxation unit that uses the user's emotion estimation function to provide the user with the most relaxing environment. For example, if the user feels stressed, it may display relaxing music or scenery. The relaxation unit may also suggest the most appropriate relaxation method based on the user's emotion data. For example, it may preferentially provide relaxation methods that the user expresses positive emotions in. Furthermore, by providing a user with a relaxing environment, the relaxation unit can improve satisfaction with the virtual experience. This reduces the user's stress and provides a more comfortable virtual experience.

[0099] The virtual experience system may further include an entertainment unit that uses the user's emotion estimation function to provide a scenario that is most exciting to the user. For example, a scenario including action or adventure may be provided based on a scenario that the user finds exciting. The entertainment unit may also suggest optimal entertainment content based on the user's emotion data. For example, entertainment content for which the user shows positive emotions may be provided preferentially. Furthermore, the entertainment unit may improve satisfaction with the virtual experience by providing a scenario that the user finds most enjoyable. This may draw out the user's excitement and provide a more enjoyable virtual experience.

[0100] The virtual experience system may further include a feedback analysis unit that improves the scenario based on user feedback. For example, the feedback analysis unit may analyze the feedback provided by the user and identify areas for improvement in the scenario. The feedback analysis unit may also add new scenarios or tasks based on the user feedback. Furthermore, the feedback analysis unit may analyze the user feedback in real time and dynamically adjust the scenario. This may provide a scenario that reflects the user feedback, thereby providing a more satisfying virtual experience.

[0101] The virtual experience system can also include a health management unit that monitors the user's health and provides appropriate rest timing. For example, it can monitor the user's heart rate and stress level in real time and prompt the user to take a break if excessive stress is detected. The health management unit can also evaluate the impact of a long virtual experience on the user's health based on the user's health data and provide appropriate advice. Furthermore, the health management unit can provide guidance to help the user maintain proper posture during the virtual experience, reducing physical strain. This allows the user to maintain their health while providing a more comfortable virtual experience.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The scenario generation unit uses the generation AI to generate a scenario for each occupation. For example, the generation AI generates a scenario that includes the daily work and tasks of an occupation selected by the user. The generation AI also generates a scenario based on the name of the occupation the user wants to experience and prompts that include detailed instructions. Step 2: The virtual experience provider provides a virtual experience based on the occupational scenario generated by the scenario generator. For example, the virtual experience provider allows the user to immerse themselves in the work environment of the occupation they selected by wearing a VR headset and experience actual work. The virtual experience provider also provides interactive tasks and problem-solving situations according to the progress of the work. Step 3: The aptitude evaluation unit analyzes the results of the virtual experience provided by the virtual experience providing unit and performs an aptitude evaluation. For example, the aptitude evaluation unit analyzes how the user performed the task and the extent to which the user demonstrated problem-solving ability and communication skills. Step 4: The feedback providing unit provides feedback based on the results of the aptitude evaluation performed by the aptitude evaluation unit. For example, the feedback providing unit provides specific feedback such as, "You have high aptitude as a doctor. In particular, you have excellent communication skills with patients." Step 5: The career suggestion unit makes career suggestions based on the feedback provided by the feedback providing unit. For example, the career suggestion unit may suggest, "You have a high aptitude for engineering. How about trying out an engineering work experience next?"

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

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

[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 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.

[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 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 scenario generation unit that generates scenarios for each occupation using generation AI; a virtual experience providing unit that provides a virtual experience based on the occupation scenario generated by the scenario generating unit; an aptitude evaluation unit that analyzes the results of the virtual experience provided by the virtual experience providing unit and performs aptitude evaluation; a feedback providing unit that provides feedback based on the result of the aptitude evaluation performed by the aptitude evaluation unit; a career suggestion unit that suggests a career based on the feedback provided by the feedback providing unit; A system characterized by:

2. The scenario generation unit Generate more personalized career scenarios based on the user's past work experience and skill set.

2. The system of claim 1.

3. The scenario generation unit Generate the above mentioned occupational scenarios from a global perspective, incorporating occupational habits from different cultures and regions.

2. The system of claim 1.

4. The scenario generation unit To generate the occupation scenario that the user is most interested in, and to improve the satisfaction of the experience 2. The system of claim 1.

5. The scenario generation unit In addition to the above occupational scenarios, generate scenarios that include historical background and future prospects related to the occupations.

2. The system of claim 1.

6. The scenario generation unit A job scenario that combines a plurality of jobs is generated in response to a user's selection, and a plurality of job experiences are provided.

2. The system of claim 1.

Images