system

Abstract

An object of the system according to the embodiment is to visualize careers and skills of employees of a company, professional workers of a partner company, OB seekers, and the like, and to match optimal human resources.SOLUTION: A system includes a carrier analysis unit, a skill matching unit, a point giving unit, and an evaluation update unit. The career analysis unit analyzes careers and skills of employees and groups of a company, professionals of a partner company, and OB seekers. A skill matching part performs matching of human resources meeting needs on the basis of the carriers and skill data analyzed by the carrier analysis part. A point imparting part imparts points corresponding to the introduction, the consultation acceptance and the contribution degree. The evaluation update section updates the experience value and the evaluation based on the point given by the point giving section.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 had the challenge of making it difficult to effectively visualize the careers and skills of company employees, professionals at partner companies, and aspiring alumni, and to match them with the most suitable talent.

[0005] The system of the embodiment aims to visualize the careers and skills of company employees, professionals at partner companies, and aspiring alumni, and to match them with the most suitable talent. [Means for solving the problem]

[0006] The system according to the embodiment includes a career analysis unit, a skill matching unit, a point assignment unit, and an evaluation update unit. The career analysis unit analyzes the careers and skills of company employees, professionals in the group and partner companies, and those seeking alumni employment. The skill matching unit matches personnel to meet needs based on the career and skill data analyzed by the career analysis unit. The point assignment unit assigns points according to introductions, consultation acceptance, and contribution level. The evaluation update unit updates experience points and evaluations based on the points assigned by the point assignment unit. [Effects of the Invention]

[0007] The system of the embodiment visualizes the careers and skills of company employees, professionals at partner companies, and aspiring alumni, and can match them with the most suitable talent. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 talent resource platform system according to an embodiment of the present invention visualizes the careers and skills of a company's employees and group members, professional positions at partner companies, and alumni candidates, and builds a talent resource platform. This system allows users to register ad hoc tasks or minor problems, and AI automatically matches registered personnel to those who fit their needs. This allows the talent resource platform system to efficiently utilize a company's human resources and quickly match the most suitable personnel.

[0029] A talent resource platform system according to an embodiment includes a career analysis unit, a skill matching unit, a point assignment unit, and an evaluation update unit. The career analysis unit analyzes the careers and skills of company employees, professionals at group and partner companies, and alumni candidates. For example, the career analysis unit registers each individual's work history, skill set, and past project experience in a database, and a generation AI analyzes and visually displays them. The career analysis unit can also analyze an individual's learning history and self-development activities to generate a comprehensive skill map. The skill matching unit matches talent to meet needs based on the career and skill data analyzed by the career analysis unit. For example, when the skill matching unit registers spot work or minor issues on the platform, the generation AI automatically recommends talent that meets those needs. The point assignment unit assigns points based on referrals, consultation acceptance, and contributions. For example, points are assigned to talent for achieving results in a specific project. The evaluation update unit updates experience points and evaluations based on the points assigned by the point assignment unit. For example, the evaluation update unit updates the evaluation of the talent based on the points and reflects the updated evaluation in the next matching process. As a result, the talent resource platform system according to the embodiment can efficiently utilize the talent resources of the company and quickly match the most suitable talent.

[0030] The career analysis unit can analyze each person's work history, skill set, and project experience and display them visually. For example, the career analysis unit extracts each person's emotional success experiences in past projects, and the generation AI visually displays them based on their emotional scores. For example, the joy and sense of accomplishment felt when a project was successful can be quantified and displayed in a graph or chart. The career analysis unit also uses an emotion estimation function to extract emotional success experiences in past projects, and the generation AI creates a career map based on that data. For example, it highlights projects with many successful experiences. The career analysis unit also uses an emotion estimation function to analyze emotional success experiences in past projects and visually display the results. For example, projects with high emotional scores can be displayed in different colors. This allows each person's skills and experience to be understood at a glance.

[0031] The career analysis unit can analyze learning history and self-development activities to generate a comprehensive skill map. For example, the career analysis unit registers each person's learning history and self-development activities in a database, and the generation AI analyzes them to generate a comprehensive skill map. For example, training courses taken and qualifications obtained are reflected in the skill map. The career analysis unit also uses the generation AI to analyze an individual's learning history and self-development activities and create a comprehensive skill map based on that data. For example, the skill map displays online course attendance history and self-study results. The career analysis unit also builds a system that analyzes learning history and self-development activities and uses the generation AI to generate a comprehensive skill map. For example, the system visually displays skill growth based on data from learning history and self-development activities. This makes it possible to generate a comprehensive skill map that reflects an individual's learning history and self-development activities.

[0032] The career analysis unit can analyze detailed project deliverables and evaluation comments and display specific skill sets. For example, the career analysis unit registers detailed project deliverables and evaluation comments for each person in a database, and the generation AI analyzes them to display specific skill sets. For example, skills are displayed in detail based on project deliverables and evaluation comments. The career analysis unit also builds a system in which the generation AI analyzes detailed deliverables and evaluation comments for each project and displays specific skill sets based on that data. For example, the project deliverables and evaluation comments are reflected in a skill map. The career analysis unit also analyzes detailed project deliverables and evaluation comments, and the generation AI displays specific skill sets. For example, skill details are visually displayed based on project deliverables and evaluation comments. This makes it possible to display specific skill sets that reflect the detailed deliverables and evaluation comments for each project.

[0033] The career analysis unit can integrate data from different industries or occupations to enable cross-industry skill matching. For example, the career analysis unit integrates data from different industries and occupations to build a system in which generation AI performs cross-industry skill matching. For example, technical and design skills are integrated and displayed. The career analysis unit also analyzes data from different industries and occupations to enable generation AI to perform cross-industry skill matching. For example, skills from different industries are combined to generate a new skill set. The career analysis unit also integrates data from different industries and occupations to develop a system in which generation AI performs cross-industry skill matching. For example, skills from different industries are integrated and displayed. This makes it possible to integrate data from different industries and occupations and achieve cross-industry skill matching.

[0034] The career analysis department updates career and skill data in real time, allowing it to always reflect the latest skills and experience. The career analysis department, for example, updates career and skill data in real time, building a system in which the generation AI always reflects the latest skills and experience. For example, the results of a new project are instantly reflected in the skill map. The career analysis department also analyzes career and skill data in real time, allowing the generation AI to reflect the latest skills and experience. For example, the skill map is instantly updated when a new skill is acquired. The career analysis department also develops a system in which the career and skill data is updated in real time, allowing the generation AI to always reflect the latest skills and experience. For example, new experience is instantly reflected in the skill map. This allows career and skill data to be updated in real time, allowing the latest information to always be reflected.

[0035] The skill matching unit can improve the accuracy of matching by reflecting the success rate of past projects and user feedback. For example, the skill matching unit builds a system in which the generation AI improves the accuracy of matching by reflecting the success rate of past projects and user feedback in the matching algorithm. For example, matching is performed based on data from projects with high success rates. In addition, the skill matching unit has the generation AI analyze the success rate of past projects and user feedback and reflect this in the matching algorithm. For example, it prioritizes matching with personnel who have received a lot of positive user feedback. In addition, the skill matching unit develops a system in which the generation AI improves the accuracy of matching by reflecting the success rate of past projects and user feedback in the matching algorithm. For example, matching is performed based on data from projects with high success rates. This makes it possible to improve the accuracy of matching by reflecting the success rate of past projects and user feedback.

[0036] The skill matching unit can perform matching along a career path based on the user's long-term career goals. The skill matching unit, for example, builds a system in which a generation AI analyzes the user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. The skill matching unit also considers the user's long-term career goals and performs matching along a career path. For example, it recommends personnel with skills that match the user's career goals. The skill matching unit also develops a system in which a generation AI analyzes the user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. This makes it possible to perform matching along a career path taking the user's long-term career goals into consideration.

[0037] The point assigning unit can set criteria for awarding points based on the user's long-term career goals and progress in skill improvement. For example, the point assigning unit builds a system that sets criteria for awarding points based on the user's long-term career goals and progress in skill improvement. For example, points are awarded when a skill that matches the career goal is acquired. Furthermore, the point assigning unit sets criteria for awarding points by using a generation AI that analyzes the user's career goals and progress in skill improvement. For example, points are awarded when a project that matches the career goal is completed. Furthermore, the point assigning unit develops a system that sets criteria for awarding points based on the user's long-term career goals and progress in skill improvement. For example, points are awarded when a skill that matches the career goal is acquired. This makes it possible to set criteria for awarding points based on the user's long-term career goals and progress in skill improvement.

[0038] The point assigning unit analyzes the user's past evaluation history and feedback, and can perform fairer evaluations. The point assigning unit, for example, constructs a system in which a generation AI analyzes the user's past evaluation history and feedback, and performs fairer evaluations when assigning points. For example, points are assigned based on the past evaluation history. The point assigning unit also analyzes the user's past evaluation history and feedback, and the generation AI performs fair evaluations when assigning points. For example, points are assigned based on the past feedback. The point assigning unit also develops a system in which a generation AI analyzes the user's past evaluation history and feedback, and performs fairer evaluations when assigning points. For example, points are assigned based on the past evaluation history. This makes it possible to perform fairer evaluations by analyzing the user's past evaluation history and feedback.

[0039] The consultation content and matching record analysis unit can analyze past consultation content and matching record over time to identify long-term trends and patterns. For example, the consultation content and matching record analysis unit constructs a system in which a generation AI analyzes past consultation content and matching record over time to identify long-term trends and patterns. For example, it identifies a trend of increasing demand for a specific skill set. The consultation content and matching record analysis unit also analyzes past consultation content and matching record over time to identify long-term trends and patterns. For example, it analyzes changes in skill demand in a specific industry. The consultation content and matching record analysis unit also develops a system in which a generation AI analyzes past consultation content and matching record over time to identify long-term trends and patterns. For example, it identifies a trend of increasing demand for a specific skill set. This makes it possible to analyze past consultation content and matching record over time to identify long-term trends and patterns.

[0040] The analysis unit for consultation content and matching results can compare the content with data from different industries or job types to gain cross-industry insights. For example, the analysis unit for consultation content and matching results can construct a system in which a generation AI compares consultation content and matching results with data from different industries or job types to gain cross-industry insights. For example, it can identify commonalities in skill demand across different industries. Furthermore, the analysis unit for consultation content and matching results can compare consultation content and matching results with data from different industries or job types to gain cross-industry insights. For example, it can identify commonalities in skill demand across different industries. Furthermore, the analysis unit for consultation content and matching results can develop a system in which a generation AI compares consultation content and matching results with data from different industries or job types to gain cross-industry insights. For example, it can identify commonalities in skill demand across different industries. This can be compared with data from different industries or job types to gain cross-industry insights.

[0041] The analysis unit for consultation content and matching results can compare the data with data from different regions or cultural spheres and conduct analysis from a global perspective. For example, the analysis unit for consultation content and matching results can construct a system in which a generation AI compares consultation content and matching results with data from different regions or cultural spheres and conducts analysis from a global perspective. For example, it can identify differences in skill demand between regions. Furthermore, the analysis unit for consultation content and matching results can compare consultation content and matching results with data from different regions or cultural spheres and conducts analysis from a global perspective. For example, it can identify differences in skill demand between cultural spheres. Furthermore, the analysis unit for consultation content and matching results can develop a system in which a generation AI compares consultation content and matching results with data from different regions or cultural spheres and conducts analysis from a global perspective. For example, it can identify differences in skill demand between regions. This can enable comparison with data from different regions or cultural spheres and conduct analysis from a global perspective.

[0042] The analysis unit for consultation content and matching results can integrate with other datasets to gain new insights. For example, the analysis unit for consultation content and matching results can build a system in which the generation AI integrates the consultation content and matching results with other datasets (e.g., patent data or market data) to gain new insights. For example, it can identify new skill demand based on patent data. The analysis unit for consultation content and matching results can also integrate the consultation content and matching results with other datasets to gain new insights. For example, it can identify new skill demand based on market data. The analysis unit for consultation content and matching results can also develop a system in which the generation AI integrates the consultation content and matching results with other datasets to gain new insights. For example, it can identify new skill demand based on patent data. This can then be integrated with other datasets to gain new insights.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The talent resource platform system may further include a health management unit that monitors the user's health condition. For example, the health management unit may acquire data from the user's fitness tracker or smartwatch and analyze the user's health condition. The health management unit may also provide appropriate rest and exercise advice based on the user's health condition. For example, the health management unit may send a notification urging the user to take a break if the user has been working for a long period of time. The health management unit may also analyze the user's stress level based on the user's health data and provide resources for stress reduction. For example, the health management unit may provide music or meditation guides for relaxation. In this way, the user's health condition can be monitored and appropriate advice and resources can be provided.

[0045] The career analysis unit can analyze a user's hobbies and interests and suggest new opportunities related to their career or skills. For example, for a user who enjoys programming as a hobby, it can suggest related projects and learning resources. The career analysis unit can also suggest the possibility of a career change to a different industry based on the user's interests. For example, it can suggest design-related projects to an engineer who is interested in design. The career analysis unit can also analyze a user's hobbies and interests and, based on that, provide opportunities to acquire new skills. For example, it can suggest opportunities to learn food tech-related skills to a user whose hobby is cooking. This makes it possible to suggest careers and skills that make use of the user's hobbies and interests.

[0046] The career analysis unit can integrate data from different industries or occupations to enable cross-industry skill matching. For example, technical and design skills can be integrated and displayed. The career analysis unit also allows the generation AI to analyze data from different industries and occupations to perform cross-industry skill matching. For example, skills from different industries can be combined to generate a new skill set. The career analysis unit also develops a system that integrates data from different industries and occupations to enable the generation AI to perform cross-industry skill matching. For example, skills from different industries can be integrated and displayed. This makes it possible to integrate data from different industries and occupations to achieve cross-industry skill matching.

[0047] The skill matching unit can reflect the success rate of past projects and user feedback to improve the accuracy of matching. For example, a system can be constructed in which the matching algorithm reflects the success rate of past projects and user feedback, and the generation AI improves the accuracy of matching. For example, matching is performed based on data from projects with a high success rate. The skill matching unit also has the generation AI analyze the success rate of past projects and user feedback and reflect this in the matching algorithm. For example, it prioritizes matching with talent who have a lot of positive user feedback. The skill matching unit also has the matching algorithm reflect the success rate of past projects and user feedback, and the generation AI develops a system in which the matching algorithm reflects the success rate of past projects and user feedback, and the generation AI improves the accuracy of matching. For example, matching is performed based on data from projects with a high success rate. This makes it possible to reflect the success rate of past projects and user feedback and improve the accuracy of matching.

[0048] The skill matching unit can perform matching along a career path based on the user's long-term career goals. For example, a system is constructed in which a generation AI analyzes a user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. The skill matching unit also considers the user's long-term career goals and performs matching along a career path. For example, it recommends personnel with skills that match the user's career goals. The skill matching unit also develops a system in which a generation AI analyzes a user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. This makes it possible to perform matching along a career path taking the user's long-term career goals into consideration.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The Career Analysis Department analyzes the careers and skills of company employees, professionals in the group and partner companies, and alumni applicants. For example, the Career Analysis Department registers each person's work history, skill set, and past project experience in a database, and the generation AI analyzes and displays this visually. The Career Analysis Department can also analyze individuals' learning history and self-development activities to generate a comprehensive skill map. Step 2: The Skill Matching Department matches personnel to meet needs based on the career and skill data analyzed by the Career Analysis Department. For example, when the Skill Matching Department registers a one-off job or minor problem on the platform, the generation AI automatically recommends personnel that meet the needs. Step 3: The point-assigning unit assigns points according to the introduction, consultation acceptance, and contribution. For example, if a person achieves results in a specific project, points are assigned to that person. Step 4: The evaluation update unit updates the experience points and evaluation based on the points assigned by the point assignment unit. For example, the evaluation update unit updates the evaluation of the talent based on the points and reflects this in the next matching.

[0051] (Example 2) The talent resource platform system according to an embodiment of the present invention visualizes the careers and skills of a company's employees and group members, professional positions at partner companies, and alumni candidates, and builds a talent resource platform. This system allows users to register ad hoc tasks or minor problems, and AI automatically matches registered personnel to those who fit their needs. This allows the talent resource platform system to efficiently utilize a company's human resources and quickly match the most suitable personnel.

[0052] A talent resource platform system according to an embodiment includes a career analysis unit, a skill matching unit, a point assignment unit, and an evaluation update unit. The career analysis unit analyzes the careers and skills of company employees, professionals at group and partner companies, and alumni candidates. For example, the career analysis unit registers each individual's work history, skill set, and past project experience in a database, and a generation AI analyzes and visually displays them. The career analysis unit can also analyze an individual's learning history and self-development activities to generate a comprehensive skill map. The skill matching unit matches talent to meet needs based on the career and skill data analyzed by the career analysis unit. For example, when the skill matching unit registers spot work or minor issues on the platform, the generation AI automatically recommends talent that meets those needs. The point assignment unit assigns points based on referrals, consultation acceptance, and contributions. For example, points are assigned to talent for achieving results in a specific project. The evaluation update unit updates experience points and evaluations based on the points assigned by the point assignment unit. For example, the evaluation update unit updates the evaluation of the talent based on the points and reflects the updated evaluation in the next matching process. As a result, the talent resource platform system according to the embodiment can efficiently utilize the talent resources of the company and quickly match the most suitable talent.

[0053] The career analysis unit can analyze each person's work history, skill set, and project experience and display them visually. For example, the career analysis unit extracts each person's emotional success experiences in past projects, and the generation AI visually displays them based on their emotional scores. For example, the joy and sense of accomplishment felt when a project was successful can be quantified and displayed in a graph or chart. The career analysis unit also uses an emotion estimation function to extract emotional success experiences in past projects, and the generation AI creates a career map based on that data. For example, it highlights projects with many successful experiences. The career analysis unit also uses an emotion estimation function to analyze emotional success experiences in past projects and visually display the results. For example, projects with high emotional scores can be displayed in different colors. This allows each person's skills and experience to be understood at a glance.

[0054] The career analysis unit can analyze learning history and self-development activities to generate a comprehensive skill map. For example, the career analysis unit registers each person's learning history and self-development activities in a database, and the generation AI analyzes them to generate a comprehensive skill map. For example, training courses taken and qualifications obtained are reflected in the skill map. The career analysis unit also uses the generation AI to analyze an individual's learning history and self-development activities and create a comprehensive skill map based on that data. For example, the skill map displays online course attendance history and self-study results. The career analysis unit also builds a system that analyzes learning history and self-development activities and uses the generation AI to generate a comprehensive skill map. For example, the system visually displays skill growth based on data from learning history and self-development activities. This makes it possible to generate a comprehensive skill map that reflects an individual's learning history and self-development activities.

[0055] The career analysis unit can analyze detailed project deliverables and evaluation comments and display specific skill sets. For example, the career analysis unit registers detailed project deliverables and evaluation comments for each person in a database, and the generation AI analyzes them to display specific skill sets. For example, skills are displayed in detail based on project deliverables and evaluation comments. The career analysis unit also builds a system in which the generation AI analyzes detailed deliverables and evaluation comments for each project and displays specific skill sets based on that data. For example, the project deliverables and evaluation comments are reflected in a skill map. The career analysis unit also analyzes detailed project deliverables and evaluation comments, and the generation AI displays specific skill sets. For example, skill details are visually displayed based on project deliverables and evaluation comments. This makes it possible to display specific skill sets that reflect the detailed deliverables and evaluation comments for each project.

[0056] The career analysis unit can integrate data from different industries or occupations to enable cross-industry skill matching. For example, the career analysis unit integrates data from different industries and occupations to build a system in which generation AI performs cross-industry skill matching. For example, technical and design skills are integrated and displayed. The career analysis unit also analyzes data from different industries and occupations to enable generation AI to perform cross-industry skill matching. For example, skills from different industries are combined to generate a new skill set. The career analysis unit also integrates data from different industries and occupations to develop a system in which generation AI performs cross-industry skill matching. For example, skills from different industries are integrated and displayed. This makes it possible to integrate data from different industries and occupations and achieve cross-industry skill matching.

[0057] The career analysis department updates career and skill data in real time, allowing it to always reflect the latest skills and experience. The career analysis department, for example, updates career and skill data in real time, building a system in which the generation AI always reflects the latest skills and experience. For example, the results of a new project are instantly reflected in the skill map. The career analysis department also analyzes career and skill data in real time, allowing the generation AI to reflect the latest skills and experience. For example, the skill map is instantly updated when a new skill is acquired. The career analysis department also develops a system in which the career and skill data is updated in real time, allowing the generation AI to always reflect the latest skills and experience. For example, new experience is instantly reflected in the skill map. This allows career and skill data to be updated in real time, allowing the latest information to always be reflected.

[0058] The career analysis unit uses the emotion estimation function to analyze the emotions of a user when entering their career and skills, and can provide positive feedback. The career analysis unit, for example, uses the emotion estimation function to analyze the emotions of a user when entering their career and skills, and builds a system that provides positive feedback. For example, an encouraging message is displayed based on the emotion score at the time of entry. The career analysis unit also analyzes the user's emotions in real time and provides positive feedback. For example, an encouraging message is displayed if the emotion score at the time of entry is low. The career analysis unit also uses the emotion estimation function to develop a system that analyzes the emotions of a user when entering their career and skills, and provides positive feedback. For example, appropriate feedback is provided based on the emotion score at the time of entry. This makes it possible to provide positive feedback when a user enters their career and skills.

[0059] The skill matching unit can perform matching at the optimal timing based on the user's emotional state. The skill matching unit, for example, builds a system in which the generation AI uses an emotion estimation function to analyze the user's emotional state and perform matching at the optimal timing. For example, matching is performed when the user is in a positive emotional state. The skill matching unit also analyzes the user's emotional state in real time and the generation AI performs matching at the optimal timing. For example, matching is performed when the user is not feeling stressed. The skill matching unit also uses the emotion estimation function to develop a system in which the generation AI takes the user's emotional state into consideration and performs matching at the optimal timing. For example, matching is performed when the user is relaxed. This makes it possible to perform matching at the optimal timing taking the user's emotional state into consideration.

[0060] The skill matching unit can improve the accuracy of matching by reflecting the success rate of past projects and user feedback. For example, the skill matching unit builds a system in which the generation AI improves the accuracy of matching by reflecting the success rate of past projects and user feedback in the matching algorithm. For example, matching is performed based on data from projects with high success rates. In addition, the skill matching unit has the generation AI analyze the success rate of past projects and user feedback and reflect this in the matching algorithm. For example, it prioritizes matching with personnel who have received a lot of positive user feedback. In addition, the skill matching unit develops a system in which the generation AI improves the accuracy of matching by reflecting the success rate of past projects and user feedback in the matching algorithm. For example, matching is performed based on data from projects with high success rates. This makes it possible to improve the accuracy of matching by reflecting the success rate of past projects and user feedback.

[0061] The skill matching unit can perform matching along a career path based on the user's long-term career goals. The skill matching unit, for example, builds a system in which a generation AI analyzes the user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. The skill matching unit also considers the user's long-term career goals and performs matching along a career path. For example, it recommends personnel with skills that match the user's career goals. The skill matching unit also develops a system in which a generation AI analyzes the user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. This makes it possible to perform matching along a career path taking the user's long-term career goals into consideration.

[0062] The point assigning unit can assign points based on the user's emotional state at a timing that maximizes motivation. For example, the point assigning unit constructs a system in which the generation AI uses an emotion estimation function to analyze the user's emotional state and assign points at a timing that maximizes motivation. For example, points are assigned when the user is in a positive emotional state. The point assigning unit also analyzes the user's emotional state in real time and assigns points at a timing that maximizes motivation. For example, points are assigned when the user is not feeling stressed. The point assigning unit also develops a system in which the generation AI uses the emotion estimation function to consider the user's emotional state and assign points at a timing that maximizes motivation. For example, points are assigned when the user is relaxed. This makes it possible to assign points at a timing that maximizes motivation in consideration of the user's emotional state.

[0063] The point assigning unit can set criteria for awarding points based on the user's long-term career goals and progress in skill improvement. For example, the point assigning unit builds a system that sets criteria for awarding points based on the user's long-term career goals and progress in skill improvement. For example, points are awarded when a skill that matches the career goal is acquired. Furthermore, the point assigning unit sets criteria for awarding points by using a generation AI that analyzes the user's career goals and progress in skill improvement. For example, points are awarded when a project that matches the career goal is completed. Furthermore, the point assigning unit develops a system that sets criteria for awarding points based on the user's long-term career goals and progress in skill improvement. For example, points are awarded when a skill that matches the career goal is acquired. This makes it possible to set criteria for awarding points based on the user's long-term career goals and progress in skill improvement.

[0064] The point assigning unit analyzes the user's past evaluation history and feedback, and can perform fairer evaluations. The point assigning unit, for example, constructs a system in which a generation AI analyzes the user's past evaluation history and feedback, and performs fairer evaluations when assigning points. For example, points are assigned based on the past evaluation history. The point assigning unit also analyzes the user's past evaluation history and feedback, and the generation AI performs fair evaluations when assigning points. For example, points are assigned based on the past feedback. The point assigning unit also develops a system in which a generation AI analyzes the user's past evaluation history and feedback, and performs fairer evaluations when assigning points. For example, points are assigned based on the past evaluation history. This makes it possible to perform fairer evaluations by analyzing the user's past evaluation history and feedback.

[0065] The point assigning unit uses the emotion estimation function to monitor the user's emotional reactions after points are assigned, and can use the information to improve the system. The point assigning unit, for example, uses the emotion estimation function to monitor the user's emotional reactions after points are assigned, and builds a system that uses the information to improve the system. For example, it identifies the timing for assigning points that results in a high percentage of positive emotional reactions. The point assigning unit also uses a generation AI to analyze the user's emotional reactions in real time, and improves the system based on the emotional data after points are assigned. For example, it reviews the criteria for assigning points that result in a high percentage of negative emotional reactions. The point assigning unit also uses the emotion estimation function to monitor the user's emotional reactions after points are assigned, and the generation AI uses the information to improve the system. For example, it identifies the timing for assigning points that results in a high percentage of positive emotional reactions. This makes it possible to monitor the user's emotional reactions after points are assigned, and use the information to improve the system.

[0066] The consultation content and matching record analysis unit uses the emotion estimation function to analyze the user's emotional response to the consultation content and matching record, allowing for more accurate analysis. For example, the consultation content and matching record analysis unit uses the emotion estimation function to build a system in which the generation AI analyzes the user's emotional response to the consultation content and matching record, allowing for more accurate analysis. For example, the consultation content and matching record analysis unit uses the emotion estimation function to analyze the user's emotional response to the consultation content and matching record in real time, allowing the generation AI to perform a more accurate analysis. For example, the matching record with a high number of negative emotional responses is reviewed. For example, the consultation content and matching record analysis unit uses the emotion estimation function to build a system in which the generation AI analyzes the user's emotional response to the consultation content and matching record, allowing for more accurate analysis. For example, the consultation content and matching record with a high number of positive emotional responses is identified. This allows for more accurate analysis of the user's emotional response to the consultation content and matching record.

[0067] The consultation content and matching record analysis unit can analyze past consultation content and matching record over time to identify long-term trends and patterns. For example, the consultation content and matching record analysis unit constructs a system in which a generation AI analyzes past consultation content and matching record over time to identify long-term trends and patterns. For example, it identifies a trend of increasing demand for a specific skill set. The consultation content and matching record analysis unit also analyzes past consultation content and matching record over time to identify long-term trends and patterns. For example, it analyzes changes in skill demand in a specific industry. The consultation content and matching record analysis unit also develops a system in which a generation AI analyzes past consultation content and matching record over time to identify long-term trends and patterns. For example, it identifies a trend of increasing demand for a specific skill set. This makes it possible to analyze past consultation content and matching record over time to identify long-term trends and patterns.

[0068] The analysis unit for consultation content and matching results can compare the content with data from different industries or job types to gain cross-industry insights. For example, the analysis unit for consultation content and matching results can construct a system in which a generation AI compares consultation content and matching results with data from different industries or job types to gain cross-industry insights. For example, it can identify commonalities in skill demand across different industries. Furthermore, the analysis unit for consultation content and matching results can compare consultation content and matching results with data from different industries or job types to gain cross-industry insights. For example, it can identify commonalities in skill demand across different industries. Furthermore, the analysis unit for consultation content and matching results can develop a system in which a generation AI compares consultation content and matching results with data from different industries or job types to gain cross-industry insights. For example, it can identify commonalities in skill demand across different industries. This can be compared with data from different industries or job types to gain cross-industry insights.

[0069] The analysis unit for consultation content and matching results can compare the data with data from different regions or cultural spheres and conduct analysis from a global perspective. For example, the analysis unit for consultation content and matching results can construct a system in which a generation AI compares consultation content and matching results with data from different regions or cultural spheres and conducts analysis from a global perspective. For example, it can identify differences in skill demand between regions. Furthermore, the analysis unit for consultation content and matching results can compare consultation content and matching results with data from different regions or cultural spheres and conducts analysis from a global perspective. For example, it can identify differences in skill demand between cultural spheres. Furthermore, the analysis unit for consultation content and matching results can develop a system in which a generation AI compares consultation content and matching results with data from different regions or cultural spheres and conducts analysis from a global perspective. For example, it can identify differences in skill demand between regions. This can enable comparison with data from different regions or cultural spheres and conduct analysis from a global perspective.

[0070] The analysis unit for consultation content and matching results can integrate with other datasets to gain new insights. For example, the analysis unit for consultation content and matching results can build a system in which the generation AI integrates the consultation content and matching results with other datasets (e.g., patent data or market data) to gain new insights. For example, it can identify new skill demand based on patent data. The analysis unit for consultation content and matching results can also integrate the consultation content and matching results with other datasets to gain new insights. For example, it can identify new skill demand based on market data. The analysis unit for consultation content and matching results can also develop a system in which the generation AI integrates the consultation content and matching results with other datasets to gain new insights. For example, it can identify new skill demand based on patent data. This can then be integrated with other datasets to gain new insights.

[0071] The consultation content and matching record analysis unit uses an emotion estimation function to monitor users' emotional reactions to consultation content and matching record in real time, thereby improving the accuracy of the analysis results. For example, the consultation content and matching record analysis unit uses the emotion estimation function to monitor users' emotional reactions to consultation content and matching record in real time, thereby building a system in which the generation AI improves the accuracy of the analysis results. For example, the consultation content that has a high percentage of positive emotional reactions is identified. Furthermore, the consultation content and matching record analysis unit uses the generation AI to analyze users' emotional reactions in real time, thereby improving the accuracy of the analysis results of consultation content and matching record. For example, the matching record that has a high percentage of negative emotional reactions is reviewed. Furthermore, the consultation content and matching record analysis unit uses the emotion estimation function to monitor users' emotional reactions to consultation content and matching record in real time, thereby developing a system in which the generation AI improves the accuracy of the analysis results. For example, the consultation content that has a high percentage of positive emotional reactions is identified. This allows users' emotional reactions to consultation content and matching record to be monitored in real time, thereby improving the accuracy of the analysis results.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The talent resource platform system may further include a health management unit that monitors the user's health condition. For example, the health management unit may acquire data from the user's fitness tracker or smartwatch and analyze the user's health condition. The health management unit may also provide appropriate rest and exercise advice based on the user's health condition. For example, the health management unit may send a notification urging the user to take a break if the user has been working for a long period of time. The health management unit may also analyze the user's stress level based on the user's health data and provide resources for stress reduction. For example, the health management unit may provide music or meditation guides for relaxation. In this way, the user's health condition can be monitored and appropriate advice and resources can be provided.

[0074] The career analysis unit can analyze a user's hobbies and interests and suggest new opportunities related to their career or skills. For example, for a user who enjoys programming as a hobby, it can suggest related projects and learning resources. The career analysis unit can also suggest the possibility of a career change to a different industry based on the user's interests. For example, it can suggest design-related projects to an engineer who is interested in design. The career analysis unit can also analyze a user's hobbies and interests and, based on that, provide opportunities to acquire new skills. For example, it can suggest opportunities to learn food tech-related skills to a user whose hobby is cooking. This makes it possible to suggest careers and skills that make use of the user's hobbies and interests.

[0075] The career analysis unit uses the user's emotion estimation function to extract emotionally successful experiences from past projects, and the generation AI creates a career map based on that data. For example, projects with many successful experiences are highlighted. The career analysis unit also uses the emotion estimation function to analyze emotionally successful experiences from past projects, and visually displays the results. For example, projects with high emotional scores are displayed in different colors. This allows each person's skills and experience to be understood at a glance.

[0076] The career analysis unit uses the user's emotion estimation function to analyze the user's emotions when entering their career or skills, and can provide positive feedback. For example, an encouraging message is displayed based on the emotion score at the time of entry. The career analysis unit also analyzes the user's emotions in real time and provides positive feedback. For example, an encouraging message is displayed if the emotion score at the time of entry is low. The career analysis unit also uses the emotion estimation function to develop a system that analyzes the user's emotions when entering their career or skills, and provides positive feedback. For example, appropriate feedback is provided based on the emotion score at the time of entry. This makes it possible to provide positive feedback when the user enters their career or skills.

[0077] The career analysis unit can integrate data from different industries or occupations to enable cross-industry skill matching. For example, technical and design skills can be integrated and displayed. The career analysis unit also allows the generation AI to analyze data from different industries and occupations to perform cross-industry skill matching. For example, skills from different industries can be combined to generate a new skill set. The career analysis unit also develops a system that integrates data from different industries and occupations to enable the generation AI to perform cross-industry skill matching. For example, skills from different industries can be integrated and displayed. This makes it possible to integrate data from different industries and occupations to achieve cross-industry skill matching.

[0078] The career analysis unit uses the user's emotion estimation function to analyze the user's emotions when entering their career or skills, and can provide positive feedback. For example, an encouraging message is displayed based on the emotion score at the time of entry. The career analysis unit also analyzes the user's emotions in real time and provides positive feedback. For example, an encouraging message is displayed if the emotion score at the time of entry is low. The career analysis unit also uses the emotion estimation function to develop a system that analyzes the user's emotions when entering their career or skills, and provides positive feedback. For example, appropriate feedback is provided based on the emotion score at the time of entry. This makes it possible to provide positive feedback when the user enters their career or skills.

[0079] The skill matching unit can perform matching at the optimal timing based on the user's emotional state. For example, a system is constructed in which the generation AI uses an emotion estimation function to analyze the user's emotional state and perform matching at the optimal timing. For example, matching is performed when the user is in a positive emotional state. The skill matching unit also analyzes the user's emotional state in real time, and the generation AI performs matching at the optimal timing. For example, matching is performed when the user is not feeling stressed. The skill matching unit also uses an emotion estimation function to develop a system in which the generation AI takes the user's emotional state into consideration and performs matching at the optimal timing. For example, matching is performed when the user is relaxed. This makes it possible to perform matching at the optimal timing, taking the user's emotional state into consideration.

[0080] The skill matching unit can reflect the success rate of past projects and user feedback to improve the accuracy of matching. For example, a system can be constructed in which the matching algorithm reflects the success rate of past projects and user feedback, and the generation AI improves the accuracy of matching. For example, matching is performed based on data from projects with a high success rate. The skill matching unit also has the generation AI analyze the success rate of past projects and user feedback and reflect this in the matching algorithm. For example, it prioritizes matching with talent who have a lot of positive user feedback. The skill matching unit also has the matching algorithm reflect the success rate of past projects and user feedback, and the generation AI develops a system in which the matching algorithm reflects the success rate of past projects and user feedback, and the generation AI improves the accuracy of matching. For example, matching is performed based on data from projects with a high success rate. This makes it possible to reflect the success rate of past projects and user feedback and improve the accuracy of matching.

[0081] The skill matching unit can perform matching along a career path based on the user's long-term career goals. For example, a system is constructed in which a generation AI analyzes a user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. The skill matching unit also considers the user's long-term career goals and performs matching along a career path. For example, it recommends personnel with skills that match the user's career goals. The skill matching unit also develops a system in which a generation AI analyzes a user's long-term career goals and performs matching along a career path. For example, it recommends projects that match the user's career goals. This makes it possible to perform matching along a career path taking the user's long-term career goals into consideration.

[0082] The point assigning unit can assign points based on the user's emotional state at a timing that maximizes motivation. For example, a system is constructed in which the generation AI uses an emotion estimation function to analyze the user's emotional state and assign points at a timing that maximizes motivation. For example, points are assigned when the user is in a positive emotional state. The point assigning unit also analyzes the user's emotional state in real time and assigns points at a timing that maximizes motivation. For example, points are assigned when the user is not feeling stressed. The point assigning unit also uses the emotion estimation function to develop a system in which the generation AI considers the user's emotional state and assigns points at a timing that maximizes motivation. For example, points are assigned when the user is relaxed. This makes it possible to assign points at a timing that maximizes motivation, taking into account the user's emotional state.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The Career Analysis Department analyzes the careers and skills of company employees, professionals in the group and partner companies, and alumni applicants. For example, the Career Analysis Department registers each person's work history, skill set, and past project experience in a database, and the generation AI analyzes and displays this visually. The Career Analysis Department can also analyze individuals' learning history and self-development activities to generate a comprehensive skill map. Step 2: The Skill Matching Department matches personnel to meet needs based on the career and skill data analyzed by the Career Analysis Department. For example, when the Skill Matching Department registers a one-off job or minor problem on the platform, the generation AI automatically recommends personnel that meet the needs. Step 3: The point-assigning unit assigns points according to the introduction, consultation acceptance, and contribution. For example, if a person achieves results in a specific project, points are assigned to that person. Step 4: The evaluation update unit updates the experience points and evaluation based on the points assigned by the point assignment unit. For example, the evaluation update unit updates the evaluation of the talent based on the points and reflects this in the next matching.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. Career Analysis Department, which analyzes the careers and skills of company employees, professionals in the group and partner companies, and alumni applicants; a skill matching unit that matches personnel to needs based on the career and skill data analyzed by the career analysis unit; a point granting unit that grants points according to introductions, consultation acceptances, and contributions; and an evaluation update unit that updates the experience value and the evaluation based on the points assigned by the point assigning unit. A system characterized by:

2. The carrier analysis unit Analyze learning history and self-development activities to generate a comprehensive skills map 2. The system of claim 1.

3. The carrier analysis unit Integrate data from different industries or occupations to enable cross-industry skills matching 2. The system of claim 1.

4. The skill matching unit Improve matching accuracy by incorporating the success rate of past projects and user feedback 2. The system of claim 1.

5. The point giving unit The points are awarded based on the user's emotional state at a timing that maximizes motivation.

2. The system of claim 1.

6. The analysis department for consultation content and matching results is Analyzing the user's emotional response to the consultation content and the matching results to perform a more accurate analysis 2. The system of claim 1.

7. The carrier analysis unit Analyzing emotions when a user inputs the career and skills and providing positive feedback 2. The system of claim 1.

8. The skill matching unit The matching is performed at the optimal timing based on the user's emotional state.

2. The system of claim 1.

Images