System

Abstract

An object of the system according to the embodiment is to analyze a skill set and experience of a job applicant and recommend an unexpected suitable job.SOLUTION: A system according to an embodiment includes a skill set analyzer, a commonality finder, and a recommender. The skill set analysis unit analyzes a skill set and experience of the job applicant by using the generated AI. The commonality finder finds commonalities with various industries and professions based on the skill sets and experiences analyzed by the skill set analyzer. The recommendation unit recommends an unexpected suitable job on the basis of the commonality found by the commonality finding unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for job seekers to find unexpected suitable jobs that utilize their skill sets and experience, which can result in missed opportunities for suitable job matching.

[0005] The system according to the embodiment aims to analyze the skill sets and experience of job seekers and recommend unexpected suitable jobs. [Means for solving the problem]

[0006] The system according to the embodiment includes a skillset analysis unit, a commonality discovery unit, and a recommendation unit. The skillset analysis unit uses a generative AI to analyze a job seeker's skill set and experience. The commonality discovery unit finds commonalities with various industries and occupations based on the skill set and experience analyzed by the skillset analysis unit. The recommendation unit recommends unexpected suitable jobs based on the commonalities found by the commonality discovery unit. [Effects of the Invention]

[0007] The system according to the embodiment analyzes the skill sets and experience of job seekers and can recommend unexpected suitable jobs. [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 suitable career recommendation system according to an embodiment of the present invention analyzes the skill sets and experience of job seekers, and uses a generative AI to find commonalities and recommend unexpected suitable jobs. This allows job seekers to realize that their skill sets can be utilized in other industries, thereby expanding new career possibilities.

[0029] A suitable career recommendation system according to an embodiment includes a skillset analysis unit, a commonality discovery unit, and a recommendation unit. The skillset analysis unit uses a generation AI to analyze a job seeker's skill set and experience. For example, the skillset analysis unit receives the job seeker's work history and project experience as input information, and the generation AI analyzes that information. The skillset analysis unit can also analyze the job seeker's skill set in detail and extract specific skills and experience. For example, the generation AI analyzes the job seeker's programming skills and management skills and extracts those skills in a form that can be utilized in other industries. The commonality discovery unit finds commonalities with various industries and occupations based on the skillset and experience analyzed by the skillset analysis unit. For example, the commonality discovery unit uses the generation AI to find commonalities that can be utilized in the medical and education industries based on the job seeker's skill set. The commonality discovery unit can also use the generation AI to analyze commonalities between different industries and occupations and discover commonalities that are suitable for the job seeker. For example, the generation AI finds project management positions in other industries based on the job seeker's project management skills. The recommendation unit recommends unexpected suitable jobs based on the commonalities discovered by the commonalities discovery unit. For example, the recommendation unit recommends project management positions in the medical industry or IT coordinator positions in the education industry based on the job seeker's skill set. The recommendation unit can also recommend suitable jobs in other industries based on the job seeker's skill set. For example, the generation AI recommends sales positions or customer support positions based on the job seeker's communication skills. As a result, the suitable job recommendation system according to the embodiment analyzes the job seeker's skill set and experience and recommends unexpected suitable jobs, thereby expanding new career possibilities. For example, the job seeker may realize that their skill set can be utilized in other industries, opening up new career options. Additionally, the recommendation department will support job seekers in their job search by using a generation AI to recommend suitable jobs based on the job seeker's skill set.

[0030] The commonality discovery unit can analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can extract the factors behind a job seeker's past successful projects in a form that can be utilized in other industries. The commonality discovery unit can also analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can extract the factors behind a job seeker's past achievements in a form that can be utilized in other industries. The commonality discovery unit can also analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can extract the skills and knowledge a job seeker has acquired in the past in a form that can be utilized in other industries. By extracting the job seeker's past success factors, it is possible to support success in other industries and occupations.

[0031] When analyzing a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, when the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, the generation AI finds commonalities in the hobby activities of a job seeker that can be utilized in other industries. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, the generation AI finds commonalities in the fields that a job seeker is interested in that can be utilized in other industries. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, the generation AI finds commonalities in the skills a job seeker has acquired from a hobby that can be utilized in other industries. This allows for more personalized job recommendations by taking into account job seekers' hobbies and interests.

[0032] When analyzing a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, when the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, the generation AI finds commonalities in a way that the job seeker's skills can be utilized in other countries. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, the generation AI finds commonalities in a way that the job seeker's experience can be utilized in other cultural spheres. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, generative AI can identify commonalities between job seekers and their knowledge so that it can be applied to other regions, thereby expanding the job seeker's career possibilities by recommending suitable jobs from a global perspective.

[0033] When analyzing a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, when the generation AI analyzes a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, the generation AI can find commonalities even between young job seekers. When the generation AI analyzes a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, the generation AI can find commonalities even between middle-aged and older job seekers. When the generation AI analyzes a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, the generation AI can find commonalities even between senior job seekers. This allows the system to cater to a wide range of job seekers by recommending suitable jobs to people of different ages and generations.

[0034] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, the generation AI may recommend occupations in which other job seekers with similar skill sets have been successful. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, the generation AI may recommend occupations in which other job seekers with similar experience have been successful. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, the generation AI may recommend occupations in which other job seekers with similar backgrounds have been successful. This allows us to recommend suitable jobs with a high probability of success by referring to past successful job change cases.

[0035] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, the generation AI recommends occupations that match the work style and values ​​that the job seeker values. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, the generation AI recommends occupations that match the job seeker's desired work-life balance. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, the generation AI recommends occupations that match the values ​​and goals that the job seeker holds dear. This allows for more personalized job recommendations by taking into account the job seeker's lifestyle and values.

[0036] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to the trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, the generation AI recommends occupations in emerging industries that are expected to grow. Furthermore, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, the generation AI recommends occupations in industries where technological innovation is progressing. Furthermore, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, the generation AI recommends occupations that are in high demand. This allows the system to recommend suitable jobs with promising future prospects by referencing trend information from different industries and occupations.

[0037] The recommendation unit can refer to job information in different regions and countries when making recommendations based on the job seeker's skill set and experience, and recommend international career possibilities. For example, the generation AI can recommend career opportunities overseas. The recommendation unit can also refer to job information in different regions and countries when making recommendations based on the job seeker's skill set and experience, and recommend international career possibilities. For example, the generation AI can recommend jobs at global companies. The recommendation unit can also refer to job information in different regions and countries when making recommendations based on the job seeker's skill set and experience, and recommend international career possibilities. For example, the generation AI can recommend jobs that allow participation in international projects. This allows us to recommend international career possibilities by referencing job listings in different regions and countries.

[0038] When recommending job information, the recommendation unit can refer to the job seeker's past application history and feedback to recommend the most suitable job information. For example, when the generation AI recommends job information, the recommendation unit refers to the job seeker's past application history and feedback to recommend the most suitable job information. For example, the generation AI recommends job information similar to job information that the person has applied for in the past. In addition, when the generation AI recommends job information, the recommendation unit can also refer to the job seeker's past application history and feedback to recommend the most suitable job information. For example, the generation AI recommends the most suitable job information based on feedback from job information that the person has applied for in the past. In addition, when the generation AI recommends job information, the recommendation unit can also refer to the job seeker's past application history and feedback to recommend the most suitable job information. For example, the generation AI recommends the most suitable job information based on feedback from job information that the person has applied for in the past. For example, the generation AI recommends the most suitable job information based on the results of job information that the person has applied for in the past. In this way, the most suitable job information can be recommended by referring to the job seeker's past application history and feedback.

[0039] When recommending job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, when the generation AI recommends job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, the generation AI recommends job information in different industries that can utilize the job seeker's skill set. In addition, when the generation AI recommends job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, the generation AI recommends job information in different occupations that can utilize the job seeker's experience. In addition, when the generation AI recommends job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, the generation AI recommends job information in different industries that can utilize the job seeker's knowledge. This allows job seekers to browse job listings in different industries and occupations, revealing new career possibilities.

[0040] When recommending job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, when the generation AI recommends job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, the generation AI recommends career opportunities overseas. Also, when the generation AI recommends job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, the generation AI recommends jobs at global companies. Also, when the generation AI recommends job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, the generation AI recommends jobs that allow participation in international projects. In this way, by referring to job information in different regions and countries, international career possibilities can be recommended.

[0041] When performing the application procedures on behalf of a job seeker, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents. For example, when the generation AI performs the application procedures on behalf of a job seeker, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents. For example, the generation AI creates the optimal application documents based on the job seeker's skill set and experience. In addition, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents when the generation AI performs the application procedures on behalf of a job seeker. For example, the generation AI creates the optimal application documents based on the job seeker's past achievements and projects. In addition, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents when the generation AI performs the application procedures on behalf of a job seeker. For example, the generation AI creates the optimal application documents based on the job seeker's knowledge and qualifications. In this way, by analyzing the job seeker's resume in detail and automatically generating the optimal application documents, the accuracy of the application procedures can be improved.

[0042] When performing the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, when the generation AI performs the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, the generation AI performs the application procedure that other job seekers with a similar skill set have successfully completed. In addition, when the generation AI performs the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, the generation AI performs the application procedure that other job seekers with similar experience have successfully completed. In addition, when the generation AI performs the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, the generation AI performs the application procedure that other job seekers with a similar background have successfully completed. In this way, by referring to past successful application cases, the application procedure can be performed with a high probability of success.

[0043] When performing application procedures on behalf of a job seeker, the recommendation unit can refer to the application procedures for different industries and occupations and perform the optimal application procedures. For example, when the generation AI performs application procedures on behalf of a job seeker, the recommendation unit can refer to application procedures for different industries and occupations and perform the optimal application procedures. For example, the generation AI performs application procedures for different industries that can utilize the job seeker's skill set. In addition, when the generation AI performs application procedures on behalf of a job seeker, the recommendation unit can refer to application procedures for different industries and occupations and perform the optimal application procedures. For example, the generation AI performs application procedures for different occupations that can utilize the job seeker's experience. In addition, when the generation AI performs application procedures on behalf of a job seeker, the recommendation unit can refer to application procedures for different industries and occupations and perform the optimal application procedures. For example, the generation AI performs application procedures for different industries that can utilize the job seeker's knowledge. In this way, the optimal application procedures can be performed by referring to application procedures for different industries and occupations.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The suitable career recommendation system can further include a health monitoring unit that monitors the user's health condition and recommends suitable careers based on the user's health condition. For example, the health monitoring unit measures the user's heart rate and stress level and recommends suitable careers based on this data. The health monitoring unit can also analyze the user's sleep patterns and recommend careers with appropriate working hours and working styles. Furthermore, the health monitoring unit can take the user's exercise habits into consideration and recommend careers that are less physically demanding. In this way, by recommending suitable careers that take the user's health condition into consideration, it is possible to support the user in maintaining both their health and their career.

[0046] The suitable career recommendation system can further include a learning analysis unit that analyzes the user's learning history and recommends suitable careers that will increase the user's motivation to learn. For example, the learning analysis unit can analyze the online courses the user has taken in the past and the qualifications the user has obtained, and recommend careers that will allow the user to utilize that knowledge. The learning analysis unit can also recommend careers that will allow the user to further improve their skills based on the user's learning history in areas of interest. Furthermore, the learning analysis unit can take the user's learning style into consideration and recommend careers that allow for self-study. In this way, the system can increase the user's motivation to learn by recommending suitable careers that take the user's learning history into consideration.

[0047] The suitable career recommendation system can further include a social media analysis unit that analyzes the user's social media activity and recommends suitable careers that utilize the user's influence on social media. For example, the social media analysis unit can analyze the number of followers and content of the user's posts to recommend careers in marketing or public relations. The social media analysis unit can also recommend careers such as community manager or influencer marketing based on the engagement rate of the user's posts. Furthermore, the social media analysis unit can take into account the user's social media activity time and recommend careers that allow for remote work. This allows the user's influence to be maximized by recommending suitable careers that take into account the user's social media activity.

[0048] The suitable career recommendation system can further include an environment analysis unit that analyzes the user's living environment and recommends occupations that are suitable for that living environment. For example, the environment analysis unit analyzes the climate and traffic conditions in the user's place of residence and recommends occupations with easy commute times. The environment analysis unit can also consider the user's home environment and recommend occupations that allow telecommuting. Furthermore, the environment analysis unit can analyze the user's daily rhythm and recommend occupations with flexible working hours. This can improve the user's quality of life by recommending suitable occupations that take the user's living environment into consideration.

[0049] The suitable career recommendation system can further include a hobby analysis unit that analyzes the user's hobbies and special skills and recommends careers that can utilize those hobbies and special skills. For example, the hobby analysis unit analyzes the activities the user engages in as hobbies and recommends careers that can utilize those skills. The hobby analysis unit can also recommend careers that can utilize the user's special skills based on the skills the user possesses as special skills. Furthermore, the hobby analysis unit can analyze job information in industries related to the user's hobbies and special skills and recommend suitable careers. This can increase user satisfaction by recommending suitable careers that take the user's hobbies and special skills into consideration.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The skillset analysis unit uses the generative AI to analyze the job seeker's skill set and experience. For example, the skillset analysis unit receives the job seeker's work history and project experience as input information, and the generative AI analyzes that information. The skillset analysis unit can also perform a detailed analysis of the job seeker's skill set and extract specific skills and experience. For example, the generative AI analyzes the job seeker's programming skills and management skills and extracts those skills in a form that can be used in other industries. Step 2: The commonality discovery unit finds commonalities with various industries and occupations based on the skill sets and experience analyzed by the skill set analysis unit. For example, the commonality discovery unit allows the generation AI to find commonalities that can be utilized in the medical and education industries based on the job seeker's skill set. The commonality discovery unit can also allow the generation AI to analyze commonalities between different industries and occupations and find commonalities that are suitable for the job seeker. For example, the generation AI can find project management jobs in other industries based on the job seeker's project management skills. Step 3: The recommendation unit recommends unexpected suitable jobs based on the commonalities discovered by the commonalities discovery unit. For example, the recommendation unit may recommend project management positions in the medical industry or IT coordinator positions in the education industry based on the job seeker's skill set. The recommendation unit may also recommend suitable jobs in other industries based on the job seeker's skill set. For example, the generation AI may recommend sales or customer support positions based on the job seeker's communication skills.

[0052] (Example 2) The suitable career recommendation system according to an embodiment of the present invention analyzes the skill sets and experience of job seekers, and uses a generative AI to find commonalities and recommend unexpected suitable jobs. This allows job seekers to realize that their skill sets can be utilized in other industries, thereby expanding new career possibilities.

[0053] A suitable career recommendation system according to an embodiment includes a skillset analysis unit, a commonality discovery unit, and a recommendation unit. The skillset analysis unit uses a generation AI to analyze a job seeker's skill set and experience. For example, the skillset analysis unit receives the job seeker's work history and project experience as input information, and the generation AI analyzes that information. The skillset analysis unit can also analyze the job seeker's skill set in detail and extract specific skills and experience. For example, the generation AI analyzes the job seeker's programming skills and management skills and extracts those skills in a form that can be utilized in other industries. The commonality discovery unit finds commonalities with various industries and occupations based on the skillset and experience analyzed by the skillset analysis unit. For example, the commonality discovery unit uses the generation AI to find commonalities that can be utilized in the medical and education industries based on the job seeker's skill set. The commonality discovery unit can also use the generation AI to analyze commonalities between different industries and occupations and discover commonalities that are suitable for the job seeker. For example, the generation AI finds project management positions in other industries based on the job seeker's project management skills. The recommendation unit recommends unexpected suitable jobs based on the commonalities discovered by the commonalities discovery unit. For example, the recommendation unit recommends project management positions in the medical industry or IT coordinator positions in the education industry based on the job seeker's skill set. The recommendation unit can also recommend suitable jobs in other industries based on the job seeker's skill set. For example, the generation AI recommends sales positions or customer support positions based on the job seeker's communication skills. As a result, the suitable job recommendation system according to the embodiment analyzes the job seeker's skill set and experience and recommends unexpected suitable jobs, thereby expanding new career possibilities. For example, the job seeker may realize that their skill set can be utilized in other industries, opening up new career options. Additionally, the recommendation department will support job seekers in their job search by using a generation AI to recommend suitable jobs based on the job seeker's skill set.

[0054] When analyzing a job seeker's skill set and experience, the skill set analysis unit can use the emotion estimation function to analyze the job seeker's feelings about their career and prioritize discover commonalities that elicit positive emotions. For example, when the generation AI analyzes a job seeker's skill set and experience, the skill set analysis unit can use the emotion estimation function to analyze the job seeker's feelings about their career and prioritize discover commonalities that elicit positive emotions. For example, the generation AI can find commonalities that can apply the project management skills in which the job seeker has had success in the past to other industries. When analyzing a job seeker's skill set and experience, the generation AI can use the emotion estimation function to analyze the job seeker's feelings about their career and prioritize discover commonalities that elicit positive emotions. For example, the generation AI can find commonalities that can apply the work content that the job seeker found satisfying in the past to other industries. When the generation AI analyzes a job seeker's skill set and experience, the skill set analysis unit can use the emotion estimation function to analyze the job seeker's feelings about their career and prioritize discover commonalities that elicit positive emotions. For example, generative AI can identify commonalities between the work a job seeker has previously found rewarding and the work they have done in other industries. This prioritizes the discovery of commonalities that evoke positive emotions in job seekers, thereby increasing their motivation.

[0055] The commonality discovery unit can analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can extract the factors behind a job seeker's past successful projects in a form that can be utilized in other industries. The commonality discovery unit can also analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can extract the factors behind a job seeker's past achievements in a form that can be utilized in other industries. The commonality discovery unit can also analyze a job seeker's past projects and work content in detail to extract success factors in a specific industry or occupation. For example, the generation AI can extract the skills and knowledge a job seeker has acquired in the past in a form that can be utilized in other industries. By extracting the job seeker's past success factors, it is possible to support success in other industries and occupations.

[0056] When analyzing a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, when the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, the generation AI finds commonalities in the hobby activities of a job seeker that can be utilized in other industries. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, the generation AI finds commonalities in the fields that a job seeker is interested in that can be utilized in other industries. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit can also take into account the job seeker's hobbies and interests to discover personalized commonalities. For example, the generation AI finds commonalities in the skills a job seeker has acquired from a hobby that can be utilized in other industries. This allows for more personalized job recommendations by taking into account job seekers' hobbies and interests.

[0057] When analyzing a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, when the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, the generation AI finds commonalities in a way that the job seeker's skills can be utilized in other countries. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, the generation AI finds commonalities in a way that the job seeker's experience can be utilized in other cultural spheres. When the generation AI analyzes a job seeker's skill set and experience, the commonality discovery unit discovers commonalities with industries and occupations in different cultural spheres and regions, and can recommend suitable jobs from a global perspective. For example, generative AI can identify commonalities between job seekers and their knowledge so that it can be applied to other regions, thereby expanding the job seeker's career possibilities by recommending suitable jobs from a global perspective.

[0058] When analyzing a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, when the generation AI analyzes a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, the generation AI can find commonalities even between young job seekers. When the generation AI analyzes a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, the generation AI can find commonalities even between middle-aged and older job seekers. When the generation AI analyzes a job seeker's skill sets and experience, the commonality discovery unit can discover commonalities between job seekers of different age groups and generations, and recommend suitable jobs for each generation. For example, the generation AI can find commonalities even between senior job seekers. This allows the system to cater to a wide range of job seekers by recommending suitable jobs to people of different ages and generations.

[0059] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending suitable jobs that elicit positive emotions. For example, when the generation AI makes recommendations based on the job seeker's skill set and experience, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending suitable jobs that elicit positive emotions. For example, the generation AI recommends suitable jobs based on work content in which the job seeker has had past success. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can also use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending suitable jobs that elicit positive emotions. For example, the generation AI recommends suitable jobs based on work content in which the job seeker has found satisfaction in the past. In addition, when the generation AI makes recommendations based on the job seeker's skill set and experience, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending suitable jobs that elicit positive emotions. For example, generative AI can recommend suitable jobs based on the work that job seekers have found rewarding in the past. This can increase job seekers' motivation by prioritizing suitable jobs that evoke positive emotions in job seekers.

[0060] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, the generation AI may recommend occupations in which other job seekers with similar skill sets have been successful. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, the generation AI may recommend occupations in which other job seekers with similar experience have been successful. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to past successful job changes and recommend suitable jobs with a high probability of success. For example, the generation AI may recommend occupations in which other job seekers with similar backgrounds have been successful. This allows us to recommend suitable jobs with a high probability of success by referring to past successful job change cases.

[0061] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, the generation AI recommends occupations that match the work style and values ​​that the job seeker values. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, the generation AI recommends occupations that match the job seeker's desired work-life balance. In addition, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can take into account the job seeker's lifestyle and values ​​to recommend personalized suitable jobs. For example, the generation AI recommends occupations that match the values ​​and goals that the job seeker holds dear. This allows for more personalized job recommendations by taking into account the job seeker's lifestyle and values.

[0062] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to the trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, the generation AI recommends occupations in emerging industries that are expected to grow. Furthermore, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, the generation AI recommends occupations in industries where technological innovation is progressing. Furthermore, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can refer to trend information for different industries and occupations to recommend suitable jobs with future prospects. For example, the generation AI recommends occupations that are in high demand. This allows the system to recommend suitable jobs with promising future prospects by referencing trend information from different industries and occupations.

[0063] The recommendation unit can refer to job information in different regions and countries when making recommendations based on the job seeker's skill set and experience, and recommend international career possibilities. For example, the generation AI can recommend career opportunities overseas. The recommendation unit can also refer to job information in different regions and countries when making recommendations based on the job seeker's skill set and experience, and recommend international career possibilities. For example, the generation AI can recommend jobs at global companies. The recommendation unit can also refer to job information in different regions and countries when making recommendations based on the job seeker's skill set and experience, and recommend international career possibilities. For example, the generation AI can recommend jobs that allow participation in international projects. This allows us to recommend international career possibilities by referencing job listings in different regions and countries.

[0064] When making recommendations based on a job seeker's skill set and experience, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and recommend suitable jobs that elicit positive emotions. For example, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and recommend suitable jobs that elicit positive emotions. For example, the generation AI can recommend suitable jobs based on work content in which the job seeker has had past success. Also, when the generation AI makes recommendations based on a job seeker's skill set and experience, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and recommend suitable jobs that elicit positive emotions. For example, the generation AI can recommend suitable jobs based on work content in which the job seeker has had past satisfaction. Additionally, when the Generative AI makes recommendations based on a job seeker's skill set and experience, the Recommendation Department can also use its emotion estimation function to monitor the job seeker's emotions in real time and recommend suitable jobs that elicit positive emotions. For example, the Generative AI can recommend suitable jobs based on work that the job seeker found rewarding in the past. This allows the system to monitor the job seeker's emotions in real time and recommend suitable jobs that elicit positive emotions, thereby increasing the job seeker's motivation.

[0065] When recommending job information, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending job information that elicits positive emotions. For example, when the generation AI recommends job information, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending job information that elicits positive emotions. For example, the generation AI recommends job information that includes work content that the job seeker has had success with in the past. In addition, when the generation AI recommends job information, the recommendation unit can also use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending job information that elicits positive emotions. For example, the generation AI recommends job information that includes work content that the job seeker has found satisfying in the past. In addition, when the generation AI recommends job information, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize recommending job information that elicits positive emotions. For example, the generation AI recommends job information that includes work content that the job seeker has found rewarding in the past. This will increase job seekers' motivation by prioritizing job listings that evoke positive emotions in job seekers.

[0066] When recommending job information, the recommendation unit can refer to the job seeker's past application history and feedback to recommend the most suitable job information. For example, when the generation AI recommends job information, the recommendation unit refers to the job seeker's past application history and feedback to recommend the most suitable job information. For example, the generation AI recommends job information similar to job information that the person has applied for in the past. In addition, when the generation AI recommends job information, the recommendation unit can also refer to the job seeker's past application history and feedback to recommend the most suitable job information. For example, the generation AI recommends the most suitable job information based on feedback from job information that the person has applied for in the past. In addition, when the generation AI recommends job information, the recommendation unit can also refer to the job seeker's past application history and feedback to recommend the most suitable job information. For example, the generation AI recommends the most suitable job information based on feedback from job information that the person has applied for in the past. For example, the generation AI recommends the most suitable job information based on the results of job information that the person has applied for in the past. In this way, the most suitable job information can be recommended by referring to the job seeker's past application history and feedback.

[0067] When recommending job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, when the generation AI recommends job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, the generation AI recommends job information in different industries that can utilize the job seeker's skill set. In addition, when the generation AI recommends job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, the generation AI recommends job information in different occupations that can utilize the job seeker's experience. In addition, when the generation AI recommends job information, the recommendation unit can refer to job information in different industries and occupations to present new career possibilities to the job seeker. For example, the generation AI recommends job information in different industries that can utilize the job seeker's knowledge. This allows job seekers to browse job listings in different industries and occupations, revealing new career possibilities.

[0068] When recommending job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, when the generation AI recommends job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, the generation AI recommends career opportunities overseas. Also, when the generation AI recommends job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, the generation AI recommends jobs at global companies. Also, when the generation AI recommends job information, the recommendation unit can refer to job information in different regions and countries and recommend international career possibilities. For example, the generation AI recommends jobs that allow participation in international projects. In this way, by referring to job information in different regions and countries, international career possibilities can be recommended.

[0069] When recommending job information, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and recommend job information that elicits positive emotions. For example, when the generation AI recommends job information, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and recommend job information that elicits positive emotions. For example, the generation AI recommends job information that includes work content that the job seeker has had success with in the past. In addition, when the generation AI recommends job information, the recommendation unit can also use the emotion estimation function to monitor the job seeker's emotions in real time and recommend job information that elicits positive emotions. For example, the generation AI recommends job information that includes work content that the job seeker has found satisfying in the past. In addition, when the generation AI recommends job information, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and recommend job information that elicits positive emotions. For example, generative AI can recommend job postings that include work that job seekers have found rewarding in the past. This allows job seekers' emotions to be monitored in real time, and job postings that evoke positive emotions can be recommended, thereby increasing their motivation.

[0070] When performing application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize application procedures that elicit positive emotions. For example, when the generation AI performs application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize application procedures that elicit positive emotions. For example, the generation AI prioritizes application procedures that the job seeker has had successful experiences with. Furthermore, when the generation AI performs application procedures on behalf of the generation AI, the recommendation unit can also use the emotion estimation function to analyze the job seeker's emotions and prioritize application procedures that elicit positive emotions. For example, the generation AI prioritizes application procedures that the job seeker has found satisfying in the past. Furthermore, when the generation AI performs application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to analyze the job seeker's emotions and prioritize application procedures that elicit positive emotions. For example, the generation AI prioritizes application procedures that the job seeker has found rewarding in the past. In this way, by analyzing the job seeker's emotions and prioritizing application procedures that elicit positive emotions, the job seeker's motivation can be increased.

[0071] When performing the application procedures on behalf of a job seeker, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents. For example, when the generation AI performs the application procedures on behalf of a job seeker, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents. For example, the generation AI creates the optimal application documents based on the job seeker's skill set and experience. In addition, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents when the generation AI performs the application procedures on behalf of a job seeker. For example, the generation AI creates the optimal application documents based on the job seeker's past achievements and projects. In addition, the recommendation unit can analyze the job seeker's resume in detail and automatically generate the optimal application documents when the generation AI performs the application procedures on behalf of a job seeker. For example, the generation AI creates the optimal application documents based on the job seeker's knowledge and qualifications. In this way, by analyzing the job seeker's resume in detail and automatically generating the optimal application documents, the accuracy of the application procedures can be improved.

[0072] When performing the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, when the generation AI performs the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, the generation AI performs the application procedure that other job seekers with a similar skill set have successfully completed. In addition, when the generation AI performs the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, the generation AI performs the application procedure that other job seekers with similar experience have successfully completed. In addition, when the generation AI performs the application procedure on behalf of the generation AI, the recommendation unit can refer to past successful application cases and perform the application procedure with a high probability of success. For example, the generation AI performs the application procedure that other job seekers with a similar background have successfully completed. In this way, by referring to past successful application cases, the application procedure can be performed with a high probability of success.

[0073] When performing application procedures on behalf of a job seeker, the recommendation unit can refer to the application procedures for different industries and occupations and perform the optimal application procedures. For example, when the generation AI performs application procedures on behalf of a job seeker, the recommendation unit can refer to application procedures for different industries and occupations and perform the optimal application procedures. For example, the generation AI performs application procedures for different industries that can utilize the job seeker's skill set. In addition, when the generation AI performs application procedures on behalf of a job seeker, the recommendation unit can refer to application procedures for different industries and occupations and perform the optimal application procedures. For example, the generation AI performs application procedures for different occupations that can utilize the job seeker's experience. In addition, when the generation AI performs application procedures on behalf of a job seeker, the recommendation unit can refer to application procedures for different industries and occupations and perform the optimal application procedures. For example, the generation AI performs application procedures for different industries that can utilize the job seeker's knowledge. In this way, the optimal application procedures can be performed by referring to application procedures for different industries and occupations.

[0074] When performing application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and perform the application procedures in a way that elicits positive emotions. For example, when the generation AI performs application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and perform the application procedures in a way that elicits positive emotions. For example, the generation AI prioritizes application procedures that the job seeker has had successful experiences with in the past. In addition, when the generation AI performs application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and perform the application procedures in a way that elicits positive emotions. For example, the generation AI prioritizes application procedures that the job seeker has had a sense of satisfaction with in the past. In addition, when the generation AI performs application procedures on behalf of the generation AI, the recommendation unit can use the emotion estimation function to monitor the job seeker's emotions in real time and perform the application procedures in a way that elicits positive emotions. For example, the generation AI prioritizes application procedures that the job seeker has found rewarding in the past. This allows job seekers' emotions to be monitored in real time, and the application process can be designed to elicit positive emotions, thereby increasing their motivation.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The suitable career recommendation system can further include a health monitoring unit that monitors the user's health condition and recommends suitable careers based on the user's health condition. For example, the health monitoring unit measures the user's heart rate and stress level and recommends suitable careers based on this data. The health monitoring unit can also analyze the user's sleep patterns and recommend careers with appropriate working hours and working styles. Furthermore, the health monitoring unit can take the user's exercise habits into consideration and recommend careers that are less physically demanding. In this way, by recommending suitable careers that take the user's health condition into consideration, it is possible to support the user in maintaining both their health and their career.

[0077] The suitable career recommendation system can further include a learning analysis unit that analyzes the user's learning history and recommends suitable careers that will increase the user's motivation to learn. For example, the learning analysis unit can analyze the online courses the user has taken in the past and the qualifications the user has obtained, and recommend careers that will allow the user to utilize that knowledge. The learning analysis unit can also recommend careers that will allow the user to further improve their skills based on the user's learning history in areas of interest. Furthermore, the learning analysis unit can take the user's learning style into consideration and recommend careers that allow for self-study. In this way, the system can increase the user's motivation to learn by recommending suitable careers that take the user's learning history into consideration.

[0078] The suitable career recommendation system can further include a social media analysis unit that analyzes the user's social media activity and recommends suitable careers that utilize the user's influence on social media. For example, the social media analysis unit can analyze the number of followers and content of the user's posts to recommend careers in marketing or public relations. The social media analysis unit can also recommend careers such as community manager or influencer marketing based on the engagement rate of the user's posts. Furthermore, the social media analysis unit can take into account the user's social media activity time and recommend careers that allow for remote work. This allows the user's influence to be maximized by recommending suitable careers that take into account the user's social media activity.

[0079] The suitable career recommendation system can further include an environment analysis unit that analyzes the user's living environment and recommends occupations that are suitable for that living environment. For example, the environment analysis unit analyzes the climate and traffic conditions in the user's place of residence and recommends occupations with easy commute times. The environment analysis unit can also consider the user's home environment and recommend occupations that allow telecommuting. Furthermore, the environment analysis unit can analyze the user's daily rhythm and recommend occupations with flexible working hours. This can improve the user's quality of life by recommending suitable occupations that take the user's living environment into consideration.

[0080] The suitable career recommendation system can further include a hobby analysis unit that analyzes the user's hobbies and special skills and recommends careers that can utilize those hobbies and special skills. For example, the hobby analysis unit analyzes the activities the user engages in as hobbies and recommends careers that can utilize those skills. The hobby analysis unit can also recommend careers that can utilize the user's special skills based on the skills the user possesses as special skills. Furthermore, the hobby analysis unit can analyze job information in industries related to the user's hobbies and special skills and recommend suitable careers. This can increase user satisfaction by recommending suitable careers that take the user's hobbies and special skills into consideration.

[0081] The suitable career recommendation system can further estimate the user's emotions and, based on the estimated emotions, recommend occupations that will reduce the user's stress level. For example, the emotion estimation function can be used to recommend occupations that avoid work content that the user has experienced stress in the past. The emotion estimation function can also be used to recommend occupations in which the user can work in a relaxing environment. Furthermore, the emotion estimation function can also be used to recommend occupations that include work content that the user has positive emotions about. This can support the user's mental health by recommending occupations that will reduce the user's stress level.

[0082] The suitable career recommendation system can further estimate the user's emotions and recommend occupations that will increase the user's motivation based on the estimated emotions. For example, the emotion estimation function can be used to recommend occupations that include work content that the user has found rewarding in the past. The emotion estimation function can also be used to recommend occupations that will give the user a sense of accomplishment. The emotion estimation function can also be used to recommend occupations that include work content that the user finds enjoyable. In this way, by recommending occupations that will increase the user's motivation, the user's job satisfaction can be improved.

[0083] The suitable career recommendation system can further estimate the user's emotions and, based on the estimated emotions, recommend occupations that will reduce the user's career anxiety. For example, the emotion estimation function can be used to recommend occupations that avoid work content that the user has felt anxious about in the past. The emotion estimation function can also be used to recommend occupations in which the user can work in an environment that gives the user a sense of security. Furthermore, the emotion estimation function can also be used to recommend occupations that include work content that the user can feel confident about. In this way, by recommending occupations that will reduce the user's career anxiety, it is possible to support the user's mental stability.

[0084] The suitable career recommendation system can further estimate the user's emotions and, based on the estimated emotions, recommend occupations that will elicit positive emotions about the user's career. For example, the emotion estimation function can be used to recommend occupations that include work content that the user has felt positive about in the past. The emotion estimation function can also be used to recommend occupations that will give the user a sense of satisfaction. Furthermore, the emotion estimation function can also be used to recommend occupations that include work content that the user finds rewarding. In this way, by recommending occupations that will elicit positive emotions about the user's career, it is possible to increase the user's motivation.

[0085] The suitable career recommendation system can further estimate the user's emotions and, based on the estimated emotions, recommend occupations that will reduce the user's negative emotions about their career. For example, the emotion estimation function can be used to recommend occupations that avoid work content that the user has had negative emotions about in the past. The emotion estimation function can also be used to recommend occupations in which the user can work in an environment that is less stressful. Furthermore, the emotion estimation function can also be used to recommend occupations that include work content that allows the user to relax. In this way, by recommending occupations that will reduce the user's negative emotions about their career, it is possible to support the user's mental health.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The skillset analysis unit uses the generative AI to analyze the job seeker's skill set and experience. For example, the skillset analysis unit receives the job seeker's work history and project experience as input information, and the generative AI analyzes that information. The skillset analysis unit can also perform a detailed analysis of the job seeker's skill set and extract specific skills and experience. For example, the generative AI analyzes the job seeker's programming skills and management skills and extracts those skills in a form that can be used in other industries. Step 2: The commonality discovery unit finds commonalities with various industries and occupations based on the skill sets and experience analyzed by the skill set analysis unit. For example, the commonality discovery unit allows the generation AI to find commonalities that can be utilized in the medical and education industries based on the job seeker's skill set. The commonality discovery unit can also allow the generation AI to analyze commonalities between different industries and occupations and find commonalities that are suitable for the job seeker. For example, the generation AI can find project management jobs in other industries based on the job seeker's project management skills. Step 3: The recommendation unit recommends unexpected suitable jobs based on the commonalities discovered by the commonalities discovery unit. For example, the recommendation unit may recommend project management positions in the medical industry or IT coordinator positions in the education industry based on the job seeker's skill set. The recommendation unit may also recommend suitable jobs in other industries based on the job seeker's skill set. For example, the generation AI may recommend sales or customer support positions based on the job seeker's communication skills.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A skillset analysis unit that uses generative AI to analyze the skillset and experience of job seekers; a commonality finding unit that finds commonalities with various industries and occupations based on the skill set and experience analyzed by the skill set analysis unit; a recommendation unit that recommends unexpected suitable jobs based on the common points found by the common point discovery unit. A system characterized by:

2. The skill set analysis unit When analyzing the job seeker's skill set and experience, analyze the job seeker's feelings about their career and prioritize commonalities that elicit positive emotions. The system of claim 1 .

3. The common point discovery unit When analyzing the job seeker's skill set and experience, we identify commonalities between industries and occupations in different cultures and regions, and recommend suitable jobs from a global perspective. The system of claim 1 .

4. The recommendation unit When making recommendations based on the job seeker's skill set and experience, the system analyzes the job seeker's emotions and prioritizes recommending suitable jobs that evoke positive emotions. The system of claim 1 .

5. The recommendation unit When making recommendations based on the job seeker's skill set and experience, the system references trend information from different industries and occupations to recommend suitable jobs with promising future prospects. The system of claim 1 .

6. The recommendation unit When recommending job information, the emotions of the job seeker are analyzed, and the job information that elicits positive emotions is preferentially recommended. The system of claim 1 .

7. The recommendation unit When carrying out application procedures on behalf of the job seeker, analyze the job seeker's emotions and prioritize the application procedures that elicit positive emotions. The system of claim 1 .

8. The recommendation unit When carrying out application procedures on behalf of a job seeker, monitor the job seeker's emotions in real time and carry out the application procedures in a way that elicits positive emotions. The system of claim 1 .

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