System

Abstract

An object of the system according to the embodiment is to propose an appropriate occupation or work based on personal information such as an individual's skill, work experience, and qualification.SOLUTION: A system according to an embodiment includes a personal information input unit, an analysis unit, and a proper job type proposal unit. The personal information input unit inputs personal information such as skills, work experience, and qualifications of the user. The analysis unit analyzes the personal information input by the personal information input unit. The appropriate occupation proposal unit outputs an appropriate occupation or work based on the information analyzed by the analysis 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 had the problem of making it difficult to find suitable jobs or occupations based on personal information such as an individual's skills, work experience, and qualifications.

[0005] The system according to the embodiment aims to suggest suitable occupations and jobs based on personal information such as an individual's skills, work experience, and qualifications. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal information input unit, an analysis unit, and an appropriate occupation suggestion unit. The personal information input unit inputs personal information such as a user's skills, work experience, and qualifications. The analysis unit analyzes the personal information input by the personal information input unit. The appropriate occupation suggestion unit outputs appropriate occupations and tasks based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest suitable occupations and jobs based on personal information such as an individual's skills, work experience, and qualifications. [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 aptitude assessment system according to an embodiment of the present invention is a system in which a user's personal information is input, the generation AI analyzes the information, and outputs suitable occupations and tasks. As a result, the aptitude assessment system can suggest suitable occupations and tasks based on the user's personal information, such as skills, work experience, and qualifications.

[0029] The appropriate occupation diagnosis system according to the embodiment includes a personal information input unit, an analysis unit, and an appropriate occupation suggestion unit. The personal information input unit inputs personal information such as a user's skills, work experience, and qualifications. For example, the personal information input unit provides an interface for the user to input their skills, work experience, and qualifications. The personal information input unit can also store the information input by the user in a database. For example, the information input by the user is stored in cloud storage. The analysis unit analyzes the personal information input by the personal information input unit. For example, a generation AI analyzes the user's skills, work experience, and qualifications to identify appropriate occupations and jobs. The analysis unit can also list appropriate occupation candidates based on the user's personal information. For example, the generation AI proposes multiple appropriate occupations based on the user's skill set. The appropriate occupation suggestion unit outputs appropriate occupations and jobs based on the information analyzed by the analysis unit. For example, the generation AI proposes appropriate occupations and jobs to the user. The appropriate occupation suggestion unit can also provide detailed information about appropriate occupations to the user. For example, the generation AI explains the job content and required skills of the occupations proposed by the generation AI. As a result, the aptitude job diagnosis system according to the embodiment can suggest aptitude jobs based on the user's personal information. For example, the user can find the most suitable job based on their own skills and work experience. It can also introduce suitable personnel based on the skill sets required by companies.

[0030] The personal information input unit can perform a more accurate diagnosis of an appropriate occupation by including the user's daily behavioral patterns and hobbies and preferences. The personal information input unit, for example, includes the user's daily behavioral patterns in the personal information input by the user. For example, the user inputs the activities they perform each day and the time of day they perform the activities. The personal information input unit also improves the accuracy of the appropriate occupation diagnosis by inputting the user's hobbies and preferences. For example, the user inputs the hobbies they have and the activities they are interested in. The personal information input unit also analyzes the user's behavioral patterns and hobbies and preferences and suggests an appropriate occupation based on the results. For example, if the user likes outdoor activities, the unit suggests an occupation that involves a lot of fieldwork. This improves the accuracy of the appropriate occupation diagnosis by taking the user's daily behavioral patterns and hobbies and preferences into consideration.

[0031] The analysis unit can analyze past work history and examples of project success and failure to identify suitable occupations in more detail. The analysis unit, for example, inputs the user's past work history and identifies suitable occupations based on that. For example, the user inputs details of the types of work experience they have had in the past. The analysis unit also inputs examples of the user's project success and failure and identifies suitable occupations based on that. For example, the user inputs what projects they have been involved in in the past and the results they achieved. The analysis unit also analyzes the user's work history and examples of project success and failure, and suggests suitable occupations based on that. For example, the analysis unit analyzes the characteristics of the user's successful projects and suggests occupations with similar characteristics. This allows for more detailed identification of suitable occupations by taking into account the user's past work history and examples of project success and failure.

[0032] The personal information input unit inputs personal information using voice input and image analysis, thereby reducing the burden on the user. The personal information input unit, for example, allows the user to provide personal information by voice input. For example, a system is constructed in which information is input by the user simply speaking into a microphone. The personal information input unit also automatically extracts the user's personal information using image analysis. For example, information is input by the user simply uploading an image of their resume or qualification certificate. The personal information input unit also combines voice input and image analysis to reduce the burden on the user. For example, information is automatically input by the user uploading related images while speaking. In this way, the burden on the user can be reduced by using voice input and image analysis.

[0033] The personal information input unit can accept reviews by experts in different industries and fields to improve the accuracy of the input information. The personal information input unit, for example, builds a system in which experts in different industries review the information input by the user. For example, experts in technical, design, and marketing fields check the information. The personal information input unit also accepts reviews by the experts to improve the accuracy of the input information. For example, the experts provide advice based on the user's skills and experience. The personal information input unit also scrutinizes the information input by the user with the cooperation of experts in different fields. For example, multiple experts jointly review the information to confirm the accuracy of the information. In this way, the accuracy of the input information is improved by accepting reviews by experts in different industries and fields.

[0034] The suitable occupation suggestion unit can include a future career path and a specific action plan for skill improvement in the output of the suitable occupation. For example, the suitable occupation suggestion unit includes a future career path in the output of the suitable occupation. For example, it suggests steps for the user to build a career as a marketing manager. The suitable occupation suggestion unit also provides a specific action plan for skill improvement. For example, it suggests training and courses for the user to acquire the necessary skills. The suitable occupation suggestion unit also integrates the career path and action plan for skill improvement to support the user's long-term growth. For example, it provides the skill set required for the occupation the user is aiming for. In this way, the inclusion of a future career path and a specific action plan for skill improvement can support the user's long-term growth.

[0035] The appropriate occupation suggestion unit can suggest occupations that take into consideration the user's lifestyle and values. The appropriate occupation suggestion unit, for example, suggests occupations that take into consideration the user's lifestyle. For example, if the user wishes to work remotely, it suggests occupations that allow remote work. The appropriate occupation suggestion unit also suggests occupations that reflect the user's values. For example, if the user places importance on contributing to society, it suggests occupations at NPOs or social enterprises. The appropriate occupation suggestion unit also suggests occupations that are most suitable for the user, taking into consideration the user's lifestyle and values ​​comprehensively. For example, if the user wishes to live a balanced life, it suggests occupations that offer a good work-life balance. In this way, more appropriate occupations can be suggested by taking into consideration the user's lifestyle and values.

[0036] The appropriate occupation suggestion unit visualizes and provides the output of appropriate occupations, making it easier for users to understand intuitively. The appropriate occupation suggestion unit, for example, visualizes the output of appropriate occupations. For example, it displays an overview of the occupation and the required skills in graphs and charts. The appropriate occupation suggestion unit also uses the visualized output to enable users to understand intuitively. For example, it shows the career path of the occupation in a flowchart. The appropriate occupation suggestion unit also provides the visualized output interactively, allowing users to access detailed information. For example, it adds a function to display details of the occupation by clicking. In this way, visualizing the output of appropriate occupations makes it easier for users to understand intuitively.

[0037] The appropriate job suggestion unit can suggest jobs that correspond to different languages ​​and cultural areas, and provide an appropriate job diagnosis from a global perspective. The appropriate job suggestion unit, for example, suggests jobs that correspond to different languages. For example, it suggests appropriate jobs in multiple languages ​​such as English, French, and Chinese. The appropriate job suggestion unit also suggests jobs that correspond to cultural areas. For example, it suggests jobs that take into account the culture and working environment of each country. The appropriate job suggestion unit also provides an appropriate job diagnosis from a global perspective, and suggests jobs that allow the user to be active internationally. For example, it suggests jobs at international companies and multinational companies. This makes it possible to provide an appropriate job diagnosis from a global perspective by supporting different languages ​​and cultural areas.

[0038] The appropriate job proposal unit can match employee skills based on the appropriate job proposed by the generation AI in order to optimize internal personnel allocation. The appropriate job proposal unit, for example, matches employee skills based on the appropriate job proposed by the generation AI in order to optimize internal personnel allocation. For example, it analyzes an employee's skill set and assigns them to the most appropriate department. The appropriate job proposal unit also builds a system that matches employee skills based on the appropriate job proposed by the generation AI. For example, it registers an employee's skills and experience in a database and suggests an appropriate job. The appropriate job proposal unit also refers to the appropriate job proposed by the generation AI when matching employees' skills. For example, it assigns an employee to the most appropriate project based on their skills and experience. This makes it possible to effectively utilize internal resources by matching employees' skills based on the appropriate job proposed by the generation AI in order to optimize internal personnel allocation.

[0039] The Appropriate Job Suggestion Department can maximize team performance by referring to the appropriate job types proposed by the generation AI when forming internal project teams. For example, the Appropriate Job Suggestion Department refers to the appropriate job types proposed by the generation AI when forming internal project teams. For example, it selects the most suitable members according to the project requirements. The Appropriate Job Suggestion Department also maximizes project team performance based on the appropriate job types proposed by the generation AI. For example, it considers the skill sets of each member and assigns roles. The Appropriate Job Suggestion Department also builds a system that refers to the appropriate job types proposed by the generation AI when forming project teams. For example, it adjusts the allocation of members according to the progress of the project. In this way, it is possible to maximize team performance by referring to the appropriate job types proposed by the generation AI when forming internal project teams.

[0040] The Appropriate Occupation Suggestion Department can design employee career paths based on the appropriate occupations proposed by the generation AI when improving internal business processes. For example, when improving internal business processes, the Appropriate Occupation Suggestion Department designs employee career paths based on the appropriate occupations proposed by the generation AI. For example, it proposes long-term career plans based on employees' skills and experience. The Appropriate Occupation Suggestion Department also builds a system that designs employee career paths based on the appropriate occupations proposed by the generation AI. For example, it provides career plans based on employees' growth goals. The Appropriate Occupation Suggestion Department also references the appropriate occupations proposed by the generation AI when designing employee career paths. For example, it provides the skill sets required for the occupation the employee is aiming for. This makes it possible to support employee growth by designing employee career paths based on the appropriate occupations proposed by the generation AI when improving internal business processes.

[0041] The appropriate job proposal unit can provide a curriculum based on the appropriate job proposed by the generation AI in an in-house training program. The appropriate job proposal unit, for example, provides a curriculum based on the appropriate job proposed by the generation AI in an in-house training program. For example, it proposes training for employees to acquire the necessary skills. The appropriate job proposal unit also designs a curriculum for a training program based on the appropriate job proposed by the generation AI. For example, it provides training content according to the skills and experience of employees. The appropriate job proposal unit also refers to the appropriate job proposed by the generation AI in order to optimize the in-house training program. For example, it provides a curriculum for employees to acquire the skills necessary for the job they are aiming for. In this way, by providing a curriculum based on the appropriate job proposed by the generation AI in an in-house training program, it is possible to support employee skill development.

[0042] The appropriate job proposal unit provides an appropriate job diagnosis service using generative AI as a recruitment platform for companies, thereby supporting their recruitment activities. For example, the appropriate job proposal unit provides an appropriate job diagnosis service using generative AI as a recruitment platform for companies. For example, it introduces appropriate personnel based on the skill sets that companies are looking for. The appropriate job proposal unit also builds a recruitment platform for companies and introduces personnel based on appropriate jobs suggested by the generative AI. For example, it proposes personnel that match the company's recruitment requirements. The appropriate job proposal unit also provides an appropriate job diagnosis service using generative AI to companies, thereby supporting their recruitment activities. For example, it introduces appropriate personnel based on the skills and experience that companies are looking for. In this way, by providing an appropriate job diagnosis service using generative AI as a recruitment platform for companies, it is possible to support companies' recruitment activities.

[0043] The appropriate occupation suggestion unit provides an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals, thereby supporting the career development of individuals. For example, the appropriate occupation suggestion unit provides an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals. For example, an individual finds the most suitable occupation based on their own skills and experience. The appropriate occupation suggestion unit also builds a career consulting service for individuals and supports career development based on the appropriate occupation suggested by the generative AI. For example, it designs the individual's career path. The appropriate occupation suggestion unit also provides an appropriate occupation diagnosis service using generative AI to individuals and supports the individual's career development. For example, it provides the skill set required for the occupation that the individual is aiming for. In this way, the appropriate occupation suggestion service using generative AI can be provided as a career consulting service for individuals to support the individual's career development.

[0044] The appropriate occupation suggestion unit can provide an appropriate occupation diagnosis service using generative AI to educational institutions and support students in their career choices. The appropriate occupation suggestion unit, for example, provides an appropriate occupation diagnosis service using generative AI to educational institutions. For example, students find the best career based on their skills and interests. The appropriate occupation suggestion unit also builds an appropriate occupation diagnosis service for educational institutions and supports students in their career choices based on the appropriate occupations suggested by the generative AI. For example, it provides a career plan based on the student's growth goals. The appropriate occupation suggestion unit also provides an appropriate occupation diagnosis service using generative AI to educational institutions and supports students in their career choices. For example, it provides the skill set required for the occupation the student is aiming for. In this way, by providing an appropriate occupation diagnosis service using generative AI to educational institutions, it is possible to support students in their career choices.

[0045] The appropriate job proposal unit provides an appropriate job diagnosis service using generative AI for remote work and freelancers, thereby accommodating diverse work styles. The appropriate job proposal unit, for example, provides an appropriate job diagnosis service using generative AI for remote work and freelancers. For example, it suggests jobs that allow remote work and jobs that are suitable for freelancers. The appropriate job proposal unit also builds an appropriate job diagnosis service for remote work and freelancers, and accommodates diverse work styles based on the appropriate jobs suggested by the generative AI. For example, it provides the skill sets necessary for remote work. The appropriate job proposal unit also provides an appropriate job diagnosis service using generative AI for remote work and freelancers, thereby accommodating diverse work styles. For example, it provides a career plan for succeeding as a freelancer. In this way, by providing an appropriate job diagnosis service using generative AI for remote work and freelancers, it is possible to accommodate diverse work styles.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The suitable occupation suggestion unit can suggest occupations that take into account the user's lifestyle and values. For example, if the user wishes to work remotely, it can suggest occupations that allow remote work. If the user places importance on contributing to society, it can also suggest occupations with NPOs or social enterprises. Furthermore, if the user desires a balanced life, it can suggest occupations that offer a good work-life balance. This allows for more appropriate occupation suggestions by taking into account the user's lifestyle and values.

[0048] The suitable occupation suggestion unit can include future career paths and specific action plans for skill development in the suitable occupation output. For example, it can suggest steps for a user to advance in their career as a marketing manager. It can also suggest training and courses for the user to acquire the necessary skills. Furthermore, it can integrate action plans for career paths and skill development to support the user's long-term growth. This makes it possible to support the user's long-term growth by including specific action plans for future career paths and skill development.

[0049] The suitable occupation suggestion unit can visualize and provide the output of suitable occupations, making it easier for users to understand intuitively. For example, it can display an overview of the occupation and the skills required in a graph or chart. It can also show the career path of the occupation in a flowchart. Furthermore, it is possible to provide the visualized output interactively, allowing users to access detailed information. In this way, visualizing the output of suitable occupations makes it easier for users to understand intuitively.

[0050] The aptitude job suggestion unit can propose jobs that correspond to different languages ​​and cultural spheres, and provide aptitude job diagnosis from a global perspective. For example, it can propose aptitude jobs in multiple languages, such as English, French, and Chinese. It can also propose jobs that take into account the culture and working environment of each country. It can also propose jobs for international companies and multinational corporations. This makes it possible to provide aptitude job diagnosis from a global perspective by supporting different languages ​​and cultural spheres.

[0051] The Appropriate Occupation Proposal Department can provide an appropriate occupation diagnosis service using generative AI as a recruitment platform for companies, thereby supporting their recruitment activities. For example, it can introduce appropriate personnel based on the skill set that the company is looking for. It can also suggest personnel that match the company's recruitment requirements. Furthermore, it can provide an appropriate occupation diagnosis service using generative AI to companies, thereby supporting their recruitment activities. In this way, by providing an appropriate occupation diagnosis service using generative AI as a recruitment platform for companies, it can support companies' recruitment activities.

[0052] The appropriate occupation suggestion unit can provide an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals, thereby supporting their career development. For example, individuals can find the most suitable occupation based on their own skills and experience. It can also design their own career path. Furthermore, it is possible to provide an appropriate occupation diagnosis service using generative AI to individuals and support their career development. In this way, by providing an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals, it is possible to support individuals' career development.

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

[0054] Step 1: The personal information input unit inputs personal information such as the user's skills, work experience, and qualifications. For example, it provides an interface for the user to input their own skills, work experience, and qualifications. The personal information input unit can also save the information entered by the user in a database. For example, it saves the information entered by the user in cloud storage. Step 2: The analysis unit analyzes the personal information entered by the personal information input unit. For example, the generation AI analyzes the user's skills, work experience, and qualifications to identify suitable occupations and tasks. The analysis unit can also create a list of suitable occupation candidates based on the user's personal information. For example, the generation AI suggests multiple suitable occupations based on the user's skill set. Step 3: The appropriate occupation suggestion unit outputs suitable occupations and tasks based on the information analyzed by the analysis unit. For example, the generation AI suggests suitable occupations and tasks to the user. The appropriate occupation suggestion unit can also provide the user with detailed information about suitable occupations. For example, it explains the work content and required skills of the occupations suggested by the generation AI.

[0055] (Example 2) The aptitude assessment system according to an embodiment of the present invention is a system in which a user's personal information is input, the generation AI analyzes the information, and outputs suitable occupations and tasks. As a result, the aptitude assessment system can suggest suitable occupations and tasks based on the user's personal information, such as skills, work experience, and qualifications.

[0056] The appropriate occupation diagnosis system according to the embodiment includes a personal information input unit, an analysis unit, and an appropriate occupation suggestion unit. The personal information input unit inputs personal information such as a user's skills, work experience, and qualifications. For example, the personal information input unit provides an interface for the user to input their skills, work experience, and qualifications. The personal information input unit can also store the information input by the user in a database. For example, the information input by the user is stored in cloud storage. The analysis unit analyzes the personal information input by the personal information input unit. For example, a generation AI analyzes the user's skills, work experience, and qualifications to identify appropriate occupations and jobs. The analysis unit can also list appropriate occupation candidates based on the user's personal information. For example, the generation AI proposes multiple appropriate occupations based on the user's skill set. The appropriate occupation suggestion unit outputs appropriate occupations and jobs based on the information analyzed by the analysis unit. For example, the generation AI proposes appropriate occupations and jobs to the user. The appropriate occupation suggestion unit can also provide detailed information about appropriate occupations to the user. For example, the generation AI explains the job content and required skills of the occupations proposed by the generation AI. As a result, the aptitude job diagnosis system according to the embodiment can suggest aptitude jobs based on the user's personal information. For example, the user can find the most suitable job based on their own skills and work experience. It can also introduce suitable personnel based on the skill sets required by companies.

[0057] The personal information input unit can perform a more accurate diagnosis of an appropriate occupation by including the user's daily behavioral patterns and hobbies and preferences. The personal information input unit, for example, includes the user's daily behavioral patterns in the personal information input by the user. For example, the user inputs the activities they perform each day and the time of day they perform the activities. The personal information input unit also improves the accuracy of the appropriate occupation diagnosis by inputting the user's hobbies and preferences. For example, the user inputs the hobbies they have and the activities they are interested in. The personal information input unit also analyzes the user's behavioral patterns and hobbies and preferences and suggests an appropriate occupation based on the results. For example, if the user likes outdoor activities, the unit suggests an occupation that involves a lot of fieldwork. This improves the accuracy of the appropriate occupation diagnosis by taking the user's daily behavioral patterns and hobbies and preferences into consideration.

[0058] The analysis unit can analyze past work history and examples of project success and failure to identify suitable occupations in more detail. The analysis unit, for example, inputs the user's past work history and identifies suitable occupations based on that. For example, the user inputs details of the types of work experience they have had in the past. The analysis unit also inputs examples of the user's project success and failure and identifies suitable occupations based on that. For example, the user inputs what projects they have been involved in in the past and the results they achieved. The analysis unit also analyzes the user's work history and examples of project success and failure, and suggests suitable occupations based on that. For example, the analysis unit analyzes the characteristics of the user's successful projects and suggests occupations with similar characteristics. This allows for more detailed identification of suitable occupations by taking into account the user's past work history and examples of project success and failure.

[0059] The analysis unit uses the emotion estimation function to analyze the emotional state of the user when entering information and can suggest occupations that take into account the user's levels of stress and motivation. The analysis unit, for example, analyzes the user's emotional state when entering personal information. For example, it analyzes the user's facial expressions and voice to estimate the user's levels of stress and motivation. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggests appropriate occupations based on that analysis. For example, it places emphasis on information entered when the user is relaxed. The analysis unit also takes into account the user's emotional state and suggests occupations that are appropriate for the user's levels of stress and motivation. For example, if the user is prone to stress, it suggests occupations that are less stressful. In this way, by taking the user's emotional state into consideration, it is possible to suggest occupations that are appropriate for the user's levels of stress and motivation.

[0060] The personal information input unit inputs personal information using voice input and image analysis, thereby reducing the burden on the user. The personal information input unit, for example, allows the user to provide personal information by voice input. For example, a system is constructed in which information is input by the user simply speaking into a microphone. The personal information input unit also automatically extracts the user's personal information using image analysis. For example, information is input by the user simply uploading an image of their resume or qualification certificate. The personal information input unit also combines voice input and image analysis to reduce the burden on the user. For example, information is automatically input by the user uploading related images while speaking. In this way, the burden on the user can be reduced by using voice input and image analysis.

[0061] The personal information input unit can accept reviews by experts in different industries and fields to improve the accuracy of the input information. The personal information input unit, for example, builds a system in which experts in different industries review the information input by the user. For example, experts in technical, design, and marketing fields check the information. The personal information input unit also accepts reviews by the experts to improve the accuracy of the input information. For example, the experts provide advice based on the user's skills and experience. The personal information input unit also scrutinizes the information input by the user with the cooperation of experts in different fields. For example, multiple experts jointly review the information to confirm the accuracy of the information. In this way, the accuracy of the input information is improved by accepting reviews by experts in different industries and fields.

[0062] The analysis unit can use the emotion estimation function to provide real-time feedback on the emotions the user feels when inputting data, thereby providing input support that draws out positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotions the user feels when inputting data, and provide feedback that draws out positive emotions. For example, it can display an encouraging message when the user is relaxed. The analysis unit can also monitor the user's emotional state in real time and provide input support that draws out positive emotions. For example, it can suggest ways to relax when the user is feeling stressed. The analysis unit can also provide advice to help the user maintain positive emotions when inputting data, based on the emotion estimation data. For example, it can make suggestions to create an environment that makes it easier for the user to concentrate. In this way, input support that draws out positive emotions can be provided by providing real-time feedback on the user's emotions.

[0063] The suitable occupation suggestion unit can include a future career path and a specific action plan for skill improvement in the output of the suitable occupation. For example, the suitable occupation suggestion unit includes a future career path in the output of the suitable occupation. For example, it suggests steps for the user to build a career as a marketing manager. The suitable occupation suggestion unit also provides a specific action plan for skill improvement. For example, it suggests training and courses for the user to acquire the necessary skills. The suitable occupation suggestion unit also integrates the career path and action plan for skill improvement to support the user's long-term growth. For example, it provides the skill set required for the occupation the user is aiming for. In this way, the inclusion of a future career path and a specific action plan for skill improvement can support the user's long-term growth.

[0064] The appropriate occupation suggestion unit can suggest occupations that take into consideration the user's lifestyle and values. The appropriate occupation suggestion unit, for example, suggests occupations that take into consideration the user's lifestyle. For example, if the user wishes to work remotely, it suggests occupations that allow remote work. The appropriate occupation suggestion unit also suggests occupations that reflect the user's values. For example, if the user places importance on contributing to society, it suggests occupations at NPOs or social enterprises. The appropriate occupation suggestion unit also suggests occupations that are most suitable for the user, taking into consideration the user's lifestyle and values ​​comprehensively. For example, if the user wishes to live a balanced life, it suggests occupations that offer a good work-life balance. In this way, more appropriate occupations can be suggested by taking into consideration the user's lifestyle and values.

[0065] The appropriate occupation suggestion unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving the output and improve the content of the suggestion based on the feedback. The appropriate occupation suggestion unit, for example, analyzes the emotional reaction of the user when receiving the output of the appropriate occupation. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The appropriate occupation suggestion unit also uses the emotion estimation function to monitor the user's emotional reaction in real time and improve the content of the suggestion. For example, it prioritizes suggesting occupations to which the user has a positive reaction. The appropriate occupation suggestion unit also continuously improves the content of the suggested appropriate occupations based on the user's emotional reaction data. For example, it reevaluates an occupation to which the user has a negative reaction and suggests a different occupation. In this way, by analyzing the user's emotional reaction and improving the content of the suggestion based on feedback, it is possible to suggest occupations that provide greater satisfaction.

[0066] The appropriate occupation suggestion unit visualizes and provides the output of appropriate occupations, making it easier for users to understand intuitively. The appropriate occupation suggestion unit, for example, visualizes the output of appropriate occupations. For example, it displays an overview of the occupation and the required skills in graphs and charts. The appropriate occupation suggestion unit also uses the visualized output to enable users to understand intuitively. For example, it shows the career path of the occupation in a flowchart. The appropriate occupation suggestion unit also provides the visualized output interactively, allowing users to access detailed information. For example, it adds a function to display details of the occupation by clicking. In this way, visualizing the output of appropriate occupations makes it easier for users to understand intuitively.

[0067] The appropriate job suggestion unit can suggest jobs that correspond to different languages ​​and cultural areas, and provide an appropriate job diagnosis from a global perspective. The appropriate job suggestion unit, for example, suggests jobs that correspond to different languages. For example, it suggests appropriate jobs in multiple languages ​​such as English, French, and Chinese. The appropriate job suggestion unit also suggests jobs that correspond to cultural areas. For example, it suggests jobs that take into account the culture and working environment of each country. The appropriate job suggestion unit also provides an appropriate job diagnosis from a global perspective, and suggests jobs that allow the user to be active internationally. For example, it suggests jobs at international companies and multinational companies. This makes it possible to provide an appropriate job diagnosis from a global perspective by supporting different languages ​​and cultural areas.

[0068] The appropriate job suggestion unit can use the emotion estimation function to adjust in real time the job suggestions that will evoke the most positive emotions for the user. The appropriate job suggestion unit, for example, uses the emotion estimation function to adjust in real time the job suggestions that will evoke the most positive emotions for the user. For example, it analyzes the user's facial expressions and voice and prioritizes suggesting jobs that elicit a positive reaction. The appropriate job suggestion unit also monitors the user's emotional reactions in real time and dynamically adjusts the suggestions. For example, it reevaluates a job to which the user elicited a negative reaction and suggests a different job. The appropriate job suggestion unit also builds a system that suggests a job that will evoke the most positive emotions for the user based on the emotion estimation data. For example, it suggests an optimal job based on the user's emotion score. As a result, by adjusting in real time the job suggestions that will evoke the most positive emotions for the user, user satisfaction is improved.

[0069] The appropriate job proposal unit can match employee skills based on the appropriate job proposed by the generation AI in order to optimize internal personnel allocation. The appropriate job proposal unit, for example, matches employee skills based on the appropriate job proposed by the generation AI in order to optimize internal personnel allocation. For example, it analyzes an employee's skill set and assigns them to the most appropriate department. The appropriate job proposal unit also builds a system that matches employee skills based on the appropriate job proposed by the generation AI. For example, it registers an employee's skills and experience in a database and suggests an appropriate job. The appropriate job proposal unit also refers to the appropriate job proposed by the generation AI when matching employees' skills. For example, it assigns an employee to the most appropriate project based on their skills and experience. This makes it possible to effectively utilize internal resources by matching employees' skills based on the appropriate job proposed by the generation AI in order to optimize internal personnel allocation.

[0070] The Appropriate Job Suggestion Department can maximize team performance by referring to the appropriate job types proposed by the generation AI when forming internal project teams. For example, the Appropriate Job Suggestion Department refers to the appropriate job types proposed by the generation AI when forming internal project teams. For example, it selects the most suitable members according to the project requirements. The Appropriate Job Suggestion Department also maximizes project team performance based on the appropriate job types proposed by the generation AI. For example, it considers the skill sets of each member and assigns roles. The Appropriate Job Suggestion Department also builds a system that refers to the appropriate job types proposed by the generation AI when forming project teams. For example, it adjusts the allocation of members according to the progress of the project. In this way, it is possible to maximize team performance by referring to the appropriate job types proposed by the generation AI when forming internal project teams.

[0071] The analysis unit can use the emotion estimation function to monitor the emotional state of employees and improve the accuracy of suitable job suggestions. The analysis unit, for example, uses the emotion estimation function to monitor the emotional state of employees. For example, it analyzes the employee's facial expressions and voice to estimate the level of stress and motivation. The analysis unit also monitors the employee's emotional state in real time to improve the accuracy of suitable job suggestions. For example, it places emphasis on information entered when the employee is relaxed. The analysis unit also makes suitable job suggestions that take the employee's emotional state into consideration based on the emotion estimation data. For example, if an employee is prone to stress, it will suggest a job that is less stressful. In this way, by monitoring the employee's emotional state, the accuracy of suitable job suggestions is improved.

[0072] The Appropriate Occupation Suggestion Department can design employee career paths based on the appropriate occupations proposed by the generation AI when improving internal business processes. For example, when improving internal business processes, the Appropriate Occupation Suggestion Department designs employee career paths based on the appropriate occupations proposed by the generation AI. For example, it proposes long-term career plans based on employees' skills and experience. The Appropriate Occupation Suggestion Department also builds a system that designs employee career paths based on the appropriate occupations proposed by the generation AI. For example, it provides career plans based on employees' growth goals. The Appropriate Occupation Suggestion Department also references the appropriate occupations proposed by the generation AI when designing employee career paths. For example, it provides the skill sets required for the occupation the employee is aiming for. This makes it possible to support employee growth by designing employee career paths based on the appropriate occupations proposed by the generation AI when improving internal business processes.

[0073] The appropriate job proposal unit can provide a curriculum based on the appropriate job proposed by the generation AI in an in-house training program. The appropriate job proposal unit, for example, provides a curriculum based on the appropriate job proposed by the generation AI in an in-house training program. For example, it proposes training for employees to acquire the necessary skills. The appropriate job proposal unit also designs a curriculum for a training program based on the appropriate job proposed by the generation AI. For example, it provides training content according to the skills and experience of employees. The appropriate job proposal unit also refers to the appropriate job proposed by the generation AI in order to optimize the in-house training program. For example, it provides a curriculum for employees to acquire the skills necessary for the job they are aiming for. In this way, by providing a curriculum based on the appropriate job proposed by the generation AI in an in-house training program, it is possible to support employee skill development.

[0074] The analysis unit uses the emotion estimation function to provide feedback on the emotional state of employees in real time, maximizing the effectiveness of business improvement. The analysis unit, for example, uses the emotion estimation function to provide feedback on the emotional state of employees in real time. For example, it analyzes the employee's facial expressions and voice to estimate the level of stress and motivation. The analysis unit also monitors the employee's emotional state in real time to maximize the effectiveness of business improvement. For example, it makes suggestions for business improvement when the employee is relaxed. The analysis unit also makes business improvement that takes into account the employee's emotional state based on the emotion estimation data. For example, if an employee is prone to stress, it proposes business improvement measures that reduce stress. In this way, the effectiveness of business improvement can be maximized by providing feedback on the employee's emotional state in real time.

[0075] The appropriate job proposal unit provides an appropriate job diagnosis service using generative AI as a recruitment platform for companies, thereby supporting their recruitment activities. For example, the appropriate job proposal unit provides an appropriate job diagnosis service using generative AI as a recruitment platform for companies. For example, it introduces appropriate personnel based on the skill sets that companies are looking for. The appropriate job proposal unit also builds a recruitment platform for companies and introduces personnel based on appropriate jobs suggested by the generative AI. For example, it proposes personnel that match the company's recruitment requirements. The appropriate job proposal unit also provides an appropriate job diagnosis service using generative AI to companies, thereby supporting their recruitment activities. For example, it introduces appropriate personnel based on the skills and experience that companies are looking for. In this way, by providing an appropriate job diagnosis service using generative AI as a recruitment platform for companies, it is possible to support companies' recruitment activities.

[0076] The appropriate occupation suggestion unit provides an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals, thereby supporting the career development of individuals. For example, the appropriate occupation suggestion unit provides an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals. For example, an individual finds the most suitable occupation based on their own skills and experience. The appropriate occupation suggestion unit also builds a career consulting service for individuals and supports career development based on the appropriate occupation suggested by the generative AI. For example, it designs the individual's career path. The appropriate occupation suggestion unit also provides an appropriate occupation diagnosis service using generative AI to individuals and supports the individual's career development. For example, it provides the skill set required for the occupation that the individual is aiming for. In this way, the appropriate occupation suggestion service using generative AI can be provided as a career consulting service for individuals to support the individual's career development.

[0077] The appropriate job suggestion unit uses the emotion estimation function to analyze the user's emotional state and suggest the most appropriate job, thereby improving satisfaction with the service. The appropriate job suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional state. For example, it analyzes the user's facial expressions and voice to estimate the level of stress and motivation. The appropriate job suggestion unit also monitors the user's emotional state in real time and suggests the most appropriate job. For example, it suggests an appropriate job when the user is relaxed. The appropriate job suggestion unit also suggests a job that takes the user's emotional state into consideration based on the emotion estimation data. For example, if the user is prone to stress, it suggests a job that is less stressful. In this way, satisfaction with the service can be improved by using the emotion estimation function to analyze the user's emotional state and suggest the most appropriate job.

[0078] The appropriate occupation suggestion unit can provide an appropriate occupation diagnosis service using generative AI to educational institutions and support students in their career choices. The appropriate occupation suggestion unit, for example, provides an appropriate occupation diagnosis service using generative AI to educational institutions. For example, students find the best career based on their skills and interests. The appropriate occupation suggestion unit also builds an appropriate occupation diagnosis service for educational institutions and supports students in their career choices based on the appropriate occupations suggested by the generative AI. For example, it provides a career plan based on the student's growth goals. The appropriate occupation suggestion unit also provides an appropriate occupation diagnosis service using generative AI to educational institutions and supports students in their career choices. For example, it provides the skill set required for the occupation the student is aiming for. In this way, by providing an appropriate occupation diagnosis service using generative AI to educational institutions, it is possible to support students in their career choices.

[0079] The appropriate job proposal unit provides an appropriate job diagnosis service using generative AI for remote work and freelancers, thereby accommodating diverse work styles. The appropriate job proposal unit, for example, provides an appropriate job diagnosis service using generative AI for remote work and freelancers. For example, it suggests jobs that allow remote work and jobs that are suitable for freelancers. The appropriate job proposal unit also builds an appropriate job diagnosis service for remote work and freelancers, and accommodates diverse work styles based on the appropriate jobs suggested by the generative AI. For example, it provides the skill sets necessary for remote work. The appropriate job proposal unit also provides an appropriate job diagnosis service using generative AI for remote work and freelancers, thereby accommodating diverse work styles. For example, it provides a career plan for succeeding as a freelancer. In this way, by providing an appropriate job diagnosis service using generative AI for remote work and freelancers, it is possible to accommodate diverse work styles.

[0080] The analysis unit can use the emotion estimation function to monitor the user's emotional reactions in real time and utilize the results to improve the service. The analysis unit, for example, uses the emotion estimation function to monitor the user's emotional reactions in real time. For example, it analyzes the user's facial expressions and voice to estimate the level of stress and motivation. The analysis unit also monitors the user's emotional reactions in real time and utilizes the results to improve the service. For example, it suggests services when the user is relaxed. The analysis unit also improves the service by taking the user's emotional reactions into consideration based on the emotion estimation data. For example, if the user is prone to stress, it suggests services that cause less stress. In this way, monitoring the user's emotional reactions in real time can be utilized to improve the service.

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

[0082] The suitable occupation suggestion unit can suggest occupations that take into account the user's lifestyle and values. For example, if the user wishes to work remotely, it can suggest occupations that allow remote work. If the user places importance on contributing to society, it can also suggest occupations with NPOs or social enterprises. Furthermore, if the user desires a balanced life, it can suggest occupations that offer a good work-life balance. This allows for more appropriate occupation suggestions by taking into account the user's lifestyle and values.

[0083] The analysis unit uses the emotion estimation function to analyze the emotional state of the user when entering information, and can suggest jobs that take into account the user's level of stress and motivation. For example, it places emphasis on information entered when the user is relaxed. Also, if the user is prone to stress, it can suggest jobs that are less stressful. Furthermore, it is possible to analyze the user's emotional state in real time and provide feedback that elicits positive emotions. This allows the system to suggest jobs that take into account the user's emotional state, according to the user's level of stress and motivation.

[0084] The suitable occupation suggestion unit can include future career paths and specific action plans for skill development in the suitable occupation output. For example, it can suggest steps for a user to advance in their career as a marketing manager. It can also suggest training and courses for the user to acquire the necessary skills. Furthermore, it can integrate action plans for career paths and skill development to support the user's long-term growth. This makes it possible to support the user's long-term growth by including specific action plans for future career paths and skill development.

[0085] The analysis unit uses the emotion estimation function to provide real-time feedback on the emotions the user feels when entering text, enabling input support that elicits positive emotions. For example, it can display an encouraging message when the user is relaxed. It can also suggest ways to relax when the user is feeling stressed. It can also make suggestions for creating an environment that makes it easier for the user to concentrate. This allows input support that elicits positive emotions by providing real-time feedback on the user's emotions.

[0086] The suitable occupation suggestion unit can visualize and provide the output of suitable occupations, making it easier for users to understand intuitively. For example, it can display an overview of the occupation and the skills required in a graph or chart. It can also show the career path of the occupation in a flowchart. Furthermore, it is possible to provide the visualized output interactively, allowing users to access detailed information. In this way, visualizing the output of suitable occupations makes it easier for users to understand intuitively.

[0087] The appropriate occupation suggestion unit uses the emotion estimation function to analyze the emotional response of the user when receiving the output and can improve the content of the suggestions based on the feedback. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. It can also prioritize suggestions of occupations to which the user has responded positively. Furthermore, it can continuously improve the content of the appropriate occupation suggestions based on the user's emotional response data. This allows for more satisfying occupation suggestions by analyzing the user's emotional response and improving the content of the suggestions based on feedback.

[0088] The aptitude job suggestion unit can propose jobs that correspond to different languages ​​and cultural spheres, and provide aptitude job diagnosis from a global perspective. For example, it can propose aptitude jobs in multiple languages, such as English, French, and Chinese. It can also propose jobs that take into account the culture and working environment of each country. It can also propose jobs for international companies and multinational corporations. This makes it possible to provide aptitude job diagnosis from a global perspective by supporting different languages ​​and cultural spheres.

[0089] The analysis unit can use the emotion estimation function to monitor the emotional state of employees and improve the accuracy of suitable job suggestions. For example, it can analyze the employee's facial expressions and voice to estimate the level of stress and motivation. It can also place emphasis on information entered when the employee is relaxed. It can also take into account the employee's emotional state and suggest jobs according to their level of stress and motivation. In this way, by monitoring the emotional state of employees, the accuracy of suitable job suggestions can be improved.

[0090] The Appropriate Occupation Proposal Department can provide an appropriate occupation diagnosis service using generative AI as a recruitment platform for companies, thereby supporting their recruitment activities. For example, it can introduce appropriate personnel based on the skill set that the company is looking for. It can also suggest personnel that match the company's recruitment requirements. Furthermore, it can provide an appropriate occupation diagnosis service using generative AI to companies, thereby supporting their recruitment activities. In this way, by providing an appropriate occupation diagnosis service using generative AI as a recruitment platform for companies, it can support companies' recruitment activities.

[0091] The appropriate occupation suggestion unit can provide an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals, thereby supporting their career development. For example, individuals can find the most suitable occupation based on their own skills and experience. It can also design their own career path. Furthermore, it is possible to provide an appropriate occupation diagnosis service using generative AI to individuals and support their career development. In this way, by providing an appropriate occupation diagnosis service using generative AI as a career consulting service for individuals, it is possible to support individuals' career development.

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

[0093] Step 1: The personal information input unit inputs personal information such as the user's skills, work experience, and qualifications. For example, it provides an interface for the user to input their own skills, work experience, and qualifications. The personal information input unit can also save the information entered by the user in a database. For example, it saves the information entered by the user in cloud storage. Step 2: The analysis unit analyzes the personal information entered by the personal information input unit. For example, the generation AI analyzes the user's skills, work experience, and qualifications to identify suitable occupations and tasks. The analysis unit can also create a list of suitable occupation candidates based on the user's personal information. For example, the generation AI suggests multiple suitable occupations based on the user's skill set. Step 3: The appropriate occupation suggestion unit outputs suitable occupations and tasks based on the information analyzed by the analysis unit. For example, the generation AI suggests suitable occupations and tasks to the user. The appropriate occupation suggestion unit can also provide the user with detailed information about suitable occupations. For example, it explains the work content and required skills of the occupations suggested by the generation AI.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

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

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

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

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

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

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

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

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

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

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

Claims

1. a personal information input section for inputting personal information such as skills, work experience, and qualifications of the user; an analysis unit that analyzes the personal information input by the personal information input unit; and an appropriate occupation suggestion unit that outputs appropriate occupations and tasks based on the information analyzed by the analysis unit. A system characterized by:

2. The personal information input unit By including the user's daily behavioral patterns and hobbies and preferences, we can more accurately diagnose suitable occupations.

2. The system of claim 1.

3. The analysis unit Analyze users' past work history and project successes and failures to identify suitable job types in more detail 2. The system of claim 1.

4. The analysis unit Analyzes the user's emotional state when entering information and suggests jobs that take into account their level of stress and motivation 2. The system of claim 1.

5. The personal information input unit Personal information is entered using voice input and image analysis, reducing the burden on users.

2. The system of claim 1.

Images