system

Abstract

The system according to this embodiment aims to propose the most suitable occupation for an individual, introduce them to potential employers, and act as an agent for the hiring process. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a search unit, and a generation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the most suitable occupation based on the analysis results obtained by the analysis unit. The search unit searches for job postings for the occupations proposed by the proposal unit. The generation unit generates a resume when the user indicates an intention to apply based on the job postings found by the search unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process of proposing the most suitable occupation for an individual, introducing job transfer destinations, and acting as an agent for the procedures in the employment steps is not sufficiently automated and there is room for improvement.

[0005] The system according to the embodiment aims to propose the most suitable occupation for an individual, introduce job transfer destinations, and act as an agent for the procedures in the employment steps.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a search unit, and a generation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests the most suitable occupation based on the analysis results obtained by the analysis unit. The search unit searches for job postings for the occupations suggested by the suggestion unit. The generation unit generates a resume when the user indicates an intention to apply based on the job postings found by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable occupation for an individual, introduce them to potential employers, and act as an agent for the hiring process. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The career suggestion system according to an embodiment of the present invention is a system that suggests the most suitable occupation for an individual, introduces potential employers, and acts as an agent for the hiring process. This career suggestion system begins with the user registering information such as their personality, experience and skills, family structure, and lifestyle. Next, the AI ​​analyzes this information and searches for a suitable occupation based on past data. Furthermore, it searches for job openings in the market for the suggested occupation in conjunction with existing job sites and presents a list of job postings. If the user indicates an intention to apply, the AI ​​automatically generates a resume based on the diagnosis results of personality, skills, etc., and sends it to the company. This mechanism allows users to find an occupation that suits their aptitudes and streamlines their job search. For example, the user registers information such as their personality, experience and skills, family structure, and lifestyle. At this time, tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, quick thinking, athletic ability, memorization ability, information processing, empathy, interpersonal skills, language skills, clerical skills, and PC skills are conducted to determine the individual's skills and personality. Experience in sports, music, studying, etc., is also registered. Furthermore, users register their family structure and lifestyle. For example, they register information such as whether they live alone, with a partner, with three children, or are caring for grandparents. They also register their lifestyle, such as whether they are an early riser who goes to the gym three times a week and spends their evenings relaxing at home, or whether they are not a morning person but enjoy drinking with colleagues and friends almost every day. Next, the AI ​​analyzes this information and searches for a suitable job based on past data. For example, based on the results of personality tests and SPI comprehensive aptitude tests, as well as information such as family structure and lifestyle, the AI ​​makes suggestions such as, "○○ is a good fit for you!" It also provides advice such as, "You are ○○% ready to start right now, and you would be even more suitable if you develop skills such as ○○ and △△!" Furthermore, it searches for job openings in the market for the suggested occupation in conjunction with existing job sites and presents a list of job postings. For example, it searches for job postings related to the suggested occupation and presents them to the user. If the user indicates their intention to apply, the AI ​​automatically generates a resume based on the personality and skills assessment results and sends it to the company.This system eliminates the need for users to create their own resumes, and because it incorporates the results of official diagnostic tests, employers can confidently use it as an evaluation criterion. This mechanism allows users to find a job that suits their aptitudes, making the job search process more efficient. Furthermore, using a career agent can be expected to improve both life satisfaction and contribution to society. For example, when a user finds a job that allows them to fully utilize their abilities, their job satisfaction increases, and their contribution to society also increases. In this way, the career suggestion system streamlines the job search process for users and helps them find the optimal job.

[0029] The occupation suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a search unit, and a generation unit. The collection unit collects user information. For example, the collection unit collects information such as the user's personality, experience and skills, family structure, and lifestyle. The collection unit can conduct tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, quick thinking tests, athletic ability tests, memorization skills, information processing tests, empathy tests, interpersonal skills tests, language skills tests, clerical skills tests, and PC skills tests to determine an individual's skills and personality. The collection unit can also register experiences such as sports, music, and studying. The collection unit can also register family structure and lifestyle. For example, the collection unit can register information such as living alone, living with a partner, living with three children, and caring for grandparents. The collection unit can also register lifestyles such as being an early riser who goes to the gym three times a week and spends evenings relaxing at home, or being a late riser but enjoying drinking parties and going out for drinks with colleagues and friends almost every day. The analysis unit analyzes the information collected by the collection unit. The Analysis Department searches for ideal careers by referencing past data based on information such as the results of personality assessment tests and SPI comprehensive aptitude tests, as well as family structure and lifestyle. The Analysis Department uses AI to analyze the collected information and propose the most suitable occupation. The Proposal Department proposes the most suitable occupation based on the analysis results obtained by the Analysis Department. The Proposal Department can make suggestions such as, "You are suited to XX!" The Proposal Department can also provide advice such as, "You are X% ready to start right now, and if you develop skills such as XX and YY, you will be even more suitable!" The Search Department searches for job postings for occupations proposed by the Proposal Department. The Search Department searches for job postings related to the proposed occupations by linking with existing job sites and presents a list of job postings. The Search Department can efficiently search for job postings related to the proposed occupations using AI. The Generation Department generates a resume when the user indicates their intention to apply. The Generation Department automatically generates a resume based on the results of personality and skills assessments and sends it to companies. The generation unit can use AI to generate an optimal resume based on the user's information. This allows the job suggestion system according to the embodiment to streamline the user's job search and help them find the most suitable job.

[0030] The data collection unit collects user information. For example, it collects information such as the user's personality, experience and skills, family structure, and lifestyle. Specifically, it administers various tests such as personality assessment tests, SPI comprehensive aptitude tests, and IQ intelligence quotient tests to gain a detailed understanding of the user's personality and skills. These tests are conducted online, and the user's responses are automatically sent to the data collection unit. The data collection unit can also register the user's experiences in areas such as sports, music, and academics. For example, it collects information such as what sports the user has played in the past, what musical instruments they can play, and what subjects they excel at. This allows for a comprehensive understanding of the user's multifaceted skills and interests. The data collection unit can also register family structure and lifestyle. For example, it collects information such as whether the user lives alone, with a partner, with children, or is caring for grandparents. Furthermore, it can register detailed information about the user's lifestyle. For example, it collects information such as whether the user is a morning person who goes to the gym three times a week, or a night owl who enjoys drinking and goes out drinking with colleagues or friends almost every day. This allows the data collection unit to gain a detailed understanding of users' lifestyles and family structures, which can then be used to inform career recommendations. The collected information is stored in a central database, making it accessible to the analysis and recommendation units. The data collection unit centrally manages user information and can collaborate with other systems and departments as needed. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The Analysis Department analyzes the information collected by the Data Collection Department. For example, the Analysis Department searches for ideal careers by referencing past data based on information such as the results of personality tests and SPI comprehensive aptitude tests, family structure, and lifestyle. Specifically, it uses AI to analyze the collected information and suggest the most suitable occupation. The AI ​​uses machine learning algorithms to identify the occupation best suited to the user's personality, skills, and lifestyle. For example, if the results of a personality test indicate a sociable person with strong interpersonal skills, it might determine that sales or customer support positions are suitable. Similarly, if the results of the SPI comprehensive aptitude test indicate strong logical thinking skills, it might determine that engineering or analyst positions are suitable. Furthermore, considering family structure and lifestyle, it suggests occupations that allow for a good work-life balance. For example, for users with children, it might suggest occupations that allow for telecommuting or flexible working hours. The Analysis Department can also utilize past data and statistical information to analyze long-term career paths and occupational trends. This allows it to suggest what skills users should acquire and what career paths they should choose in the future. The analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term carrier management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The Proposal Department suggests the most suitable occupation based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can make suggestions such as, "This job is perfect for you!" Specifically, it suggests the most suitable occupation based on the user's personality, skills, and lifestyle. Using AI, the Proposal Department identifies the most suitable occupation based on user information and generates suggestions. For example, if a user is sociable and has strong interpersonal skills, it might suggest sales or customer support positions. If a user has excellent logical thinking skills, it might suggest engineering or analyst positions. Furthermore, the Proposal Department can provide advice such as, "You're ready to start right now (X%), and you'd be even more suitable if you develop skills like XX and YY!" This allows it to specifically show users what skills they should acquire and what career path they should choose. The Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise its suggestions based on user reactions and feedback to the suggested occupations, proposing more appropriate jobs. The Proposal Department can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through email and SMS. This allows the proposal department to provide users with job suggestions quickly and reliably, supporting their job search activities.

[0033] The search unit searches for job postings for occupations suggested by the suggestion unit. For example, the search unit searches for job postings related to the suggested occupation in conjunction with existing job sites and presents the job details in a list. Specifically, it uses AI to efficiently search for job postings related to the suggested occupation. The AI ​​uses natural language processing technology to extract relevant job postings from job sites and present them to the user. For example, if the suggested occupation is a sales position, it searches for job postings related to sales positions and displays a list of detailed information such as work location, salary, and working hours. The search unit can present the most suitable job postings considering the user's desired conditions. For example, if the user wants to work from home, it will prioritize displaying job postings that allow for remote work. The search unit can also evaluate the update frequency and reliability of job postings and provide the latest and most reliable information. This allows the search unit to help users efficiently search for job postings and apply for the most suitable occupation. Furthermore, the search unit can analyze the user's search history and application history and suggest more appropriate job postings based on the user's interests and tendencies. This allows the search unit to streamline the user's job search and find the most suitable occupation.

[0034] The generation unit generates a resume when a user expresses interest in applying. For example, it automatically generates a resume based on personality and skill assessment results and sends it to the company. Specifically, it uses AI to generate the optimal resume based on the user's information. The AI ​​considers the user's personality, skills, and experience to create a resume that matches the ideal candidate profile sought by the company. For example, if a user applies for a sales position, it generates a resume emphasizing sales and interpersonal skills. Similarly, if a user applies for an engineering position, it generates a resume emphasizing technical skills and project experience. The generation unit centrally manages user information and can update the resume content as needed. For example, if a user acquires a new skill, that information is reflected in the resume. Furthermore, the generation unit provides multiple resume templates, allowing users to select the most suitable template based on their preferences and the requirements of the company they are applying to. This enables the generation unit to efficiently create resumes and support applications. Additionally, the generation unit manages the user's application history and collects feedback from the companies they apply to. This allows users to understand their application status and use it as a reference for the next step. The generation unit streamlines the user's job search process and provides support to help them find the most suitable job.

[0035] The data collection unit can conduct tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, tests for quick reflexes, athletic ability, memorization ability, information processing ability, empathy ability, interpersonal skills, language skills, clerical skills, and PC skills to assess an individual's skills and personality. For example, the data collection unit can conduct personality assessment tests to determine the user's personality in detail. The data collection unit can also conduct SPI comprehensive aptitude tests to evaluate the user's aptitude. The data collection unit can conduct IQ intelligence quotient tests to measure the user's intelligence quotient. The data collection unit can measure quick reflexes and evaluate the user's quick reflexes. The data collection unit can measure athletic ability and evaluate the user's athletic ability. The data collection unit can measure memorization ability and evaluate the user's memorization ability. The data collection unit can measure information processing ability and evaluate the user's information processing ability. The data collection unit can measure empathy ability and evaluate the user's empathy ability. The data collection unit can measure interpersonal skills and evaluate the user's interpersonal skills. The data collection unit can measure language skills and evaluate the user's language skills. The data collection unit can also measure and evaluate the user's administrative skills. The data collection unit can also measure and evaluate the user's PC skills. This allows for a more detailed assessment of an individual's skills and personality, enabling more appropriate career suggestions. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the results of a personality assessment test into a generating AI, which can then analyze the personality and output the results.

[0036] The data collection unit can register experiences such as sports, music, and studying. For example, the data collection unit can register a user's sports experience. The data collection unit can also register a user's music experience. The data collection unit can also register a user's study experience. The data collection unit can register a user's sports experience in detail and reflect it in career suggestions. The data collection unit can register a user's music experience in detail and reflect it in career suggestions. The data collection unit can register a user's study experience in detail and reflect it in career suggestions. This improves the accuracy of career suggestions by registering a variety of user experiences. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's sports experience into a generating AI, which can then analyze the experience and output results.

[0037] The data collection unit can register family structure and lifestyle. For example, the data collection unit can register the user's family structure. The data collection unit can also register the user's lifestyle. The data collection unit can register the user's family structure in detail and reflect it in career suggestions. The data collection unit can also register information such as living alone, living with a partner, living with three children, or caring for grandparents. The data collection unit can also register lifestyles such as being an early riser who goes to the gym three times a week and spends evenings relaxing at home, or being a late riser but enjoying drinking parties and going out for drinks with colleagues or friends almost every day. This makes it possible to provide career suggestions that take into account the user's living situation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's family structure and lifestyle into a generating AI, which can analyze the information and output results.

[0038] The analysis department can search for ideal careers by referencing past data based on the collected information. For example, the analysis department searches for ideal careers by referencing past data based on information such as the results of personality assessment tests and SPI comprehensive aptitude tests, family structure, and lifestyle. The analysis department uses AI to perform analysis to suggest the most suitable occupation based on the collected information. For example, the analysis department searches for the most suitable occupation for the user by referring to data such as past work history and success stories. This makes it possible to make more appropriate occupation suggestions by referring to past data. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected information into a generating AI, which can analyze the information and suggest the most suitable occupation.

[0039] The suggestion department can make suggestions such as "○○ is a good fit for you!" based on the analysis results. For example, the suggestion department can suggest the most suitable occupation to the user based on the analysis results. The suggestion department can use AI to suggest the most suitable occupation to the user based on the analysis results. For example, the suggestion department can use generative AI to make suggestions such as "○○ is a good fit for you!" This makes it possible to make specific occupation suggestions based on the analysis results. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input the analysis results into generative AI, and the generative AI can suggest the most suitable occupation.

[0040] The suggestion department can provide advice such as, "You are X% ready to start right now, and you would be even more suitable if you acquire skills such as XX and YY!" The suggestion department can, for example, provide specific advice to the user. The suggestion department can use AI to provide specific advice to the user. The suggestion department can, for example, use generative AI to provide advice such as, "You are X% ready to start right now, and you would be even more suitable if you acquire skills such as XX and YY!" By providing specific advice to the user, it can serve as a reference for career selection. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input user information into a generative AI, and the generative AI can provide specific advice.

[0041] The search unit can search for job postings related to a proposed occupation in conjunction with existing job sites and present the job details in a list. For example, the search unit can search for job postings related to a proposed occupation and present them to the user. The search unit can use AI to efficiently search for job postings related to a proposed occupation. For example, the search unit can use a generation AI to search for job postings related to a proposed occupation and present the job details in a list. This allows for efficient searching and presentation of job postings related to a proposed occupation. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input job postings related to a proposed occupation into a generation AI, which can then search for the job postings and output the results.

[0042] The generation unit can automatically generate a resume based on the results of a personality and skills assessment when a user expresses interest in applying, and send it to the company. For example, the generation unit generates a resume based on the user's information. The generation unit can use AI to generate an optimal resume based on the user's information. For example, the generation unit can use a generation AI to automatically generate a resume based on the results of a personality and skills assessment and send it to the company. This streamlines the job search process by automatically generating and sending a resume to the company when a user expresses interest in applying. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user information into a generation AI, which can then generate a resume and output the results.

[0043] The data collection unit can analyze the user's past information provision history and select the optimal data collection method. For example, the data collection unit may prioritize information provision methods that the user has frequently used in the past (such as surveys and interviews). The data collection unit can adjust the data collection method based on the level of detail of the information the user has provided in the past. The data collection unit can select the most efficient data collection method from the user's past information provision history. In this way, the optimal data collection method can be selected by analyzing the user's past information provision history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past information provision history into a generating AI, which can then analyze the information and select the optimal data collection method.

[0044] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information relevant to the user's current living situation (work, family, etc.). The data collection unit can filter information based on the user's areas of interest (hobbies, interests, etc.). The data collection unit can adjust the scope of information collection according to the user's living situation and areas of interest. This allows for the collection of more relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current living situation and areas of interest into a generating AI, which can then filter the information and output the results.

[0045] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can prioritize collecting information related to places the user has visited in the past. The data collection unit can prioritize collecting information related to places the user plans to visit in the future. By collecting information while considering the user's geographical location, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the information and output the results.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. The data collection unit can collect relevant information based on accounts followed by the user on social media. The data collection unit can collect relevant information based on groups the user participates in on social media. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into a generating AI, which can then analyze the information and output results.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can perform a detailed analysis on information of high importance, and a simplified analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the information. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the importance of the collected information into a generating AI, which can then analyze the information and output the results.

[0048] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a psychological analysis algorithm to personality information, a technical analysis algorithm to skills information, and a sociological analysis algorithm to lifestyle information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of information into a generating AI, which can then analyze the information and output the results.

[0049] The analysis unit can determine the priority of analysis based on when the information was submitted. For example, the analysis unit may prioritize the analysis of recently submitted information. The analysis unit may postpone the analysis of older information. The analysis unit can adjust the priority of analysis in stages according to the submission date. This allows for more efficient analysis by determining the priority of analysis based on when the information was submitted. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the information into a generating AI, which can then analyze the information and output the results.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis step by step according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the information into a generating AI, and the generating AI can analyze the information and output the results.

[0051] The proposal unit can adjust the level of detail of its proposals based on the importance of the occupations. For example, the proposal unit can provide detailed proposals for highly important occupations and simplified proposals for less important occupations. The proposal unit can adjust the level of detail of its proposals in stages according to the importance of the occupations. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the occupations. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of occupations into a generating AI, which can then analyze the information and output results.

[0052] The proposal unit can apply different proposal algorithms depending on the occupation category when making a proposal. For example, the proposal unit can apply a technical proposal algorithm to technical occupations. For creative occupations, it can apply a creative proposal algorithm. For service occupations, it can apply a proposal algorithm that emphasizes interpersonal skills. By applying different proposal algorithms depending on the occupation category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the occupation category into a generating AI, which can analyze the information and output the result.

[0053] The proposal department can determine the priority of proposals based on when the occupations were submitted. For example, the proposal department may prioritize recently submitted occupations. It may also postpone the submission of older occupations. The proposal department can adjust the priority of proposals in stages according to the submission date. This allows for more efficient proposals by determining the priority of proposals based on the submission date of occupations. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the submission date of occupations into a generating AI, which can then analyze the information and output results.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the occupations. For example, the proposal unit may prioritize proposing occupations with high relevance. The proposal unit may postpone proposing occupations with low relevance. The proposal unit can adjust the order of proposals in stages according to the relevance of the occupations. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the occupations. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the relevance of occupations into a generating AI, which can then analyze the information and output results.

[0055] The search unit can improve the accuracy of searches by considering the interrelationships between occupations. For example, the search unit can group related occupations and display the search results. The search unit can adjust the priority of search results based on the interrelationships between occupations. The search unit can improve the accuracy of search results by considering the interrelationships between occupations. This allows for the provision of more appropriate search results by improving search accuracy by considering the interrelationships between occupations. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the interrelationships between occupations into a generating AI, which can then analyze the information and output results.

[0056] The search unit can perform searches while considering the attribute information of the job submitter. For example, the search unit can prioritize relevant occupations based on the submitter's age and gender. The search unit can prioritize relevant occupations based on the submitter's work history and skills. The search unit can prioritize relevant occupations based on the submitter's place of residence and lifestyle. By considering the attribute information of the job submitter, the search unit can provide more appropriate search results. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the attribute information of the job submitter into a generating AI, which can analyze the information and output results.

[0057] The search unit can perform searches while considering the geographical distribution of occupations. For example, the search unit can prioritize searching for occupations close to the user's place of residence. The search unit can prioritize searching for occupations in the user's desired region. The search unit can adjust the priority of search results based on the geographical distribution of occupations. This allows for more appropriate search results by considering the geographical distribution of occupations. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the geographical distribution of occupations into a generating AI, which can then analyze the information and output results.

[0058] The search unit can improve the accuracy of its searches by referring to relevant literature on occupations during the search process. For example, the search unit can improve the accuracy of search results by referring to literature on relevant occupations. The search unit can adjust search results based on the latest research and reports on occupations. The search unit can improve the accuracy of search results by analyzing relevant literature on occupations. This allows for the provision of more appropriate search results by improving search accuracy through the referencing of relevant literature on occupations. Some or all of the above processes in the search unit may be performed using AI or not. For example, the search unit can input relevant literature on occupations into a generating AI, which can then analyze the information and output results.

[0059] The generation unit can generate an optimal resume by analyzing the user's past work experience during resume generation. For example, the generation unit generates an optimal resume based on the user's past work experience. The generation unit can adjust the content of the resume based on the level of detail of the user's work experience. The generation unit can analyze the user's work experience and generate the most effective resume. This allows for the provision of a more appropriate resume by analyzing the user's past work experience and generating an optimal resume. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past work experience into a generation AI, which can analyze the information and output the results.

[0060] The generation unit can customize the content of a resume based on the user's current living situation when generating a resume. For example, the generation unit can customize the content of a resume based on the user's current living situation (family, work, etc.). The generation unit can adjust the content of a resume according to the user's lifestyle. The generation unit can generate an optimal resume by taking into account the user's current living situation. This allows for the provision of a more appropriate resume by customizing the content of the resume based on the user's current living situation. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's current living situation into a generation AI, which can then analyze the information and output the results.

[0061] The generation unit can generate an optimal resume by considering the user's geographical location information when generating a resume. For example, the generation unit can include relevant information in the resume based on the user's place of residence. The generation unit can also include information about the region the user desires in the resume. The generation unit can generate an optimal resume by considering the user's geographical location information. This allows for the provision of a more appropriate resume by generating an optimal resume that considers the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into a generation AI, which can then analyze the information and output the results.

[0062] The generation unit can analyze the user's social media activity and suggest resume content when generating a resume. For example, the generation unit can suggest resume content based on information the user has shared on social media. The generation unit can suggest resume content based on accounts the user follows on social media. The generation unit can suggest resume content based on groups the user participates in on social media. This allows for the provision of a more appropriate resume by analyzing the user's social media activity and suggesting resume content. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity into a generation AI, which can then analyze the information and output the results.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can analyze the user's past work history and select the most suitable information collection method. For example, it can prioritize information delivery methods that the user has frequently used in the past (such as surveys and interviews). It can also adjust the collection method based on the level of detail the user has provided in the past. This allows the system to select the most suitable information collection method by analyzing the user's past work history.

[0065] The analysis department can apply different analysis algorithms depending on the category of the collected information. For example, a psychological analysis algorithm can be applied to personality information, a technical analysis algorithm to skills information, and a sociological analysis algorithm to lifestyle information. By applying different analysis algorithms according to the category of information, more appropriate analysis results can be provided.

[0066] The proposal department can prioritize proposals based on when the occupation was submitted. For example, recently submitted occupations can be given priority. Older submitted occupations can be postponed. The priority of proposals can be adjusted in stages according to the submission date. This allows for more efficient proposals by prioritizing proposals based on when the occupation was submitted.

[0067] The search function can perform searches while considering the geographical distribution of occupations. For example, it can prioritize searches for occupations close to the user's place of residence. It can also prioritize searches for occupations in the user's desired region. The priority of search results can be adjusted based on the geographical distribution of occupations. As a result, by considering the geographical distribution of occupations when performing searches, more appropriate search results can be provided.

[0068] The generation unit can analyze the user's social media activity and suggest resume content when generating a resume. For example, it can suggest resume content based on information the user has shared on social media. It can suggest resume content based on accounts the user follows on social media. It can suggest resume content based on groups the user participates in on social media. By analyzing the user's social media activity and suggesting resume content accordingly, it can provide a more appropriate resume.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects user information. The data collection unit collects information such as the user's personality, experience and skills, family structure, and lifestyle. The data collection unit can assess an individual's skills and personality by conducting tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, quick thinking tests, athletic ability tests, memorization tests, information processing tests, empathy tests, interpersonal skills tests, language skills tests, clerical skills tests, and PC skills tests. The data collection unit can also register experiences such as sports, music, and studying. The data collection unit can also register family structure and lifestyle. For example, the data collection unit can register information such as living alone, living with a partner, living with three children, or caring for grandparents. The data collection unit can also register lifestyles such as being a morning person who goes to the gym three times a week and spends evenings relaxing at home, or being a morning person but enjoying drinking parties and going out for drinks with colleagues and friends almost every day. Step 2: The Analysis Department analyzes the information collected by the Collection Department. The Analysis Department searches for an ideal career by referring to past data, based on information such as the results of personality tests and SPI comprehensive aptitude tests, family structure, and lifestyle. The Analysis Department uses AI to perform analysis based on the collected information to suggest the most suitable occupation. Step 3: The Proposal Department proposes the most suitable occupation based on the analysis results obtained by the Analysis Department. The Proposal Department can make suggestions such as, "You are well-suited for XX!" The Proposal Department can also provide advice such as, "You are X% ready to start right now, and you would be even more suitable if you develop skills such as XX and YY!" Step 4: The search unit searches for job postings for the occupation suggested by the suggestion unit. For example, the search unit searches for job postings related to the suggested occupation in conjunction with existing job sites and presents the job details in a list. The search unit can efficiently search for job postings related to the suggested occupation using AI. Step 5: The generation unit generates a resume when the user expresses interest in applying. The generation unit automatically generates a resume based on, for example, the results of personality and skills assessments, and sends it to the company. The generation unit can use AI to generate the optimal resume based on the user's information.

[0071] (Example of form 2) The career suggestion system according to an embodiment of the present invention is a system that suggests the most suitable occupation for an individual, introduces potential employers, and acts as an agent for the hiring process. This career suggestion system begins with the user registering information such as their personality, experience and skills, family structure, and lifestyle. Next, the AI ​​analyzes this information and searches for a suitable occupation based on past data. Furthermore, it searches for job openings in the market for the suggested occupation in conjunction with existing job sites and presents a list of job postings. If the user indicates an intention to apply, the AI ​​automatically generates a resume based on the diagnosis results of personality, skills, etc., and sends it to the company. This mechanism allows users to find an occupation that suits their aptitudes and streamlines their job search. For example, the user registers information such as their personality, experience and skills, family structure, and lifestyle. At this time, tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, quick thinking, athletic ability, memorization ability, information processing, empathy, interpersonal skills, language skills, clerical skills, and PC skills are conducted to determine the individual's skills and personality. Experience in sports, music, studying, etc., is also registered. Furthermore, users register their family structure and lifestyle. For example, they register information such as whether they live alone, with a partner, with three children, or are caring for grandparents. They also register their lifestyle, such as whether they are an early riser who goes to the gym three times a week and spends their evenings relaxing at home, or whether they are not a morning person but enjoy drinking with colleagues and friends almost every day. Next, the AI ​​analyzes this information and searches for a suitable job based on past data. For example, based on the results of personality tests and SPI comprehensive aptitude tests, as well as information such as family structure and lifestyle, the AI ​​makes suggestions such as, "○○ is a good fit for you!" It also provides advice such as, "You are ○○% ready to start right now, and you would be even more suitable if you develop skills such as ○○ and △△!" Furthermore, it searches for job openings in the market for the suggested occupation in conjunction with existing job sites and presents a list of job postings. For example, it searches for job postings related to the suggested occupation and presents them to the user. If the user indicates their intention to apply, the AI ​​automatically generates a resume based on the personality and skills assessment results and sends it to the company.This system eliminates the need for users to create their own resumes, and because it incorporates the results of official diagnostic tests, employers can confidently use it as an evaluation criterion. This mechanism allows users to find a job that suits their aptitudes, making the job search process more efficient. Furthermore, using a career agent can be expected to improve both life satisfaction and contribution to society. For example, when a user finds a job that allows them to fully utilize their abilities, their job satisfaction increases, and their contribution to society also increases. In this way, the career suggestion system streamlines the job search process for users and helps them find the optimal job.

[0072] The occupation suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a search unit, and a generation unit. The collection unit collects user information. For example, the collection unit collects information such as the user's personality, experience and skills, family structure, and lifestyle. The collection unit can conduct tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, quick thinking tests, athletic ability tests, memorization skills, information processing tests, empathy tests, interpersonal skills tests, language skills tests, clerical skills tests, and PC skills tests to determine an individual's skills and personality. The collection unit can also register experiences such as sports, music, and studying. The collection unit can also register family structure and lifestyle. For example, the collection unit can register information such as living alone, living with a partner, living with three children, and caring for grandparents. The collection unit can also register lifestyles such as being an early riser who goes to the gym three times a week and spends evenings relaxing at home, or being a late riser but enjoying drinking parties and going out for drinks with colleagues and friends almost every day. The analysis unit analyzes the information collected by the collection unit. The Analysis Department searches for ideal careers by referencing past data based on information such as the results of personality assessment tests and SPI comprehensive aptitude tests, as well as family structure and lifestyle. The Analysis Department uses AI to analyze the collected information and propose the most suitable occupation. The Proposal Department proposes the most suitable occupation based on the analysis results obtained by the Analysis Department. The Proposal Department can make suggestions such as, "You are suited to XX!" The Proposal Department can also provide advice such as, "You are X% ready to start right now, and if you develop skills such as XX and YY, you will be even more suitable!" The Search Department searches for job postings for occupations proposed by the Proposal Department. The Search Department searches for job postings related to the proposed occupations by linking with existing job sites and presents a list of job postings. The Search Department can efficiently search for job postings related to the proposed occupations using AI. The Generation Department generates a resume when the user indicates their intention to apply. The Generation Department automatically generates a resume based on the results of personality and skills assessments and sends it to companies. The generation unit can use AI to generate an optimal resume based on the user's information. This allows the job suggestion system according to the embodiment to streamline the user's job search and help them find the most suitable job.

[0073] The data collection unit collects user information. For example, it collects information such as the user's personality, experience and skills, family structure, and lifestyle. Specifically, it administers various tests such as personality assessment tests, SPI comprehensive aptitude tests, and IQ intelligence quotient tests to gain a detailed understanding of the user's personality and skills. These tests are conducted online, and the user's responses are automatically sent to the data collection unit. The data collection unit can also register the user's experiences in areas such as sports, music, and academics. For example, it collects information such as what sports the user has played in the past, what musical instruments they can play, and what subjects they excel at. This allows for a comprehensive understanding of the user's multifaceted skills and interests. The data collection unit can also register family structure and lifestyle. For example, it collects information such as whether the user lives alone, with a partner, with children, or is caring for grandparents. Furthermore, it can register detailed information about the user's lifestyle. For example, it collects information such as whether the user is a morning person who goes to the gym three times a week, or a night owl who enjoys drinking and goes out drinking with colleagues or friends almost every day. This allows the data collection unit to gain a detailed understanding of users' lifestyles and family structures, which can then be used to inform career recommendations. The collected information is stored in a central database, making it accessible to the analysis and recommendation units. The data collection unit centrally manages user information and can collaborate with other systems and departments as needed. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0074] The Analysis Department analyzes the information collected by the Data Collection Department. For example, the Analysis Department searches for ideal careers by referencing past data based on information such as the results of personality tests and SPI comprehensive aptitude tests, family structure, and lifestyle. Specifically, it uses AI to analyze the collected information and suggest the most suitable occupation. The AI ​​uses machine learning algorithms to identify the occupation best suited to the user's personality, skills, and lifestyle. For example, if the results of a personality test indicate a sociable person with strong interpersonal skills, it might determine that sales or customer support positions are suitable. Similarly, if the results of the SPI comprehensive aptitude test indicate strong logical thinking skills, it might determine that engineering or analyst positions are suitable. Furthermore, considering family structure and lifestyle, it suggests occupations that allow for a good work-life balance. For example, for users with children, it might suggest occupations that allow for telecommuting or flexible working hours. The Analysis Department can also utilize past data and statistical information to analyze long-term career paths and occupational trends. This allows it to suggest what skills users should acquire and what career paths they should choose in the future. The analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term carrier management and anomaly detection, thereby improving the reliability and security of the entire system.

[0075] The Proposal Department suggests the most suitable occupation based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can make suggestions such as, "This job is perfect for you!" Specifically, it suggests the most suitable occupation based on the user's personality, skills, and lifestyle. Using AI, the Proposal Department identifies the most suitable occupation based on user information and generates suggestions. For example, if a user is sociable and has strong interpersonal skills, it might suggest sales or customer support positions. If a user has excellent logical thinking skills, it might suggest engineering or analyst positions. Furthermore, the Proposal Department can provide advice such as, "You're ready to start right now (X%), and you'd be even more suitable if you develop skills like XX and YY!" This allows it to specifically show users what skills they should acquire and what career path they should choose. The Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise its suggestions based on user reactions and feedback to the suggested occupations, proposing more appropriate jobs. The Proposal Department can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through email and SMS. This allows the proposal department to provide users with job suggestions quickly and reliably, supporting their job search activities.

[0076] The search unit searches for job postings for occupations suggested by the suggestion unit. For example, the search unit searches for job postings related to the suggested occupation in conjunction with existing job sites and presents the job details in a list. Specifically, it uses AI to efficiently search for job postings related to the suggested occupation. The AI ​​uses natural language processing technology to extract relevant job postings from job sites and present them to the user. For example, if the suggested occupation is a sales position, it searches for job postings related to sales positions and displays a list of detailed information such as work location, salary, and working hours. The search unit can present the most suitable job postings considering the user's desired conditions. For example, if the user wants to work from home, it will prioritize displaying job postings that allow for remote work. The search unit can also evaluate the update frequency and reliability of job postings and provide the latest and most reliable information. This allows the search unit to help users efficiently search for job postings and apply for the most suitable occupation. Furthermore, the search unit can analyze the user's search history and application history and suggest more appropriate job postings based on the user's interests and tendencies. This allows the search unit to streamline the user's job search and find the most suitable occupation.

[0077] The generation unit generates a resume when a user expresses interest in applying. For example, it automatically generates a resume based on personality and skill assessment results and sends it to the company. Specifically, it uses AI to generate the optimal resume based on the user's information. The AI ​​considers the user's personality, skills, and experience to create a resume that matches the ideal candidate profile sought by the company. For example, if a user applies for a sales position, it generates a resume emphasizing sales and interpersonal skills. Similarly, if a user applies for an engineering position, it generates a resume emphasizing technical skills and project experience. The generation unit centrally manages user information and can update the resume content as needed. For example, if a user acquires a new skill, that information is reflected in the resume. Furthermore, the generation unit provides multiple resume templates, allowing users to select the most suitable template based on their preferences and the requirements of the company they are applying to. This enables the generation unit to efficiently create resumes and support applications. Additionally, the generation unit manages the user's application history and collects feedback from the companies they apply to. This allows users to understand their application status and use it as a reference for the next step. The generation unit streamlines the user's job search process and provides support to help them find the most suitable job.

[0078] The data collection unit can conduct tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, tests for quick reflexes, athletic ability, memorization ability, information processing ability, empathy ability, interpersonal skills, language skills, clerical skills, and PC skills to assess an individual's skills and personality. For example, the data collection unit can conduct personality assessment tests to determine the user's personality in detail. The data collection unit can also conduct SPI comprehensive aptitude tests to evaluate the user's aptitude. The data collection unit can conduct IQ intelligence quotient tests to measure the user's intelligence quotient. The data collection unit can measure quick reflexes and evaluate the user's quick reflexes. The data collection unit can measure athletic ability and evaluate the user's athletic ability. The data collection unit can measure memorization ability and evaluate the user's memorization ability. The data collection unit can measure information processing ability and evaluate the user's information processing ability. The data collection unit can measure empathy ability and evaluate the user's empathy ability. The data collection unit can measure interpersonal skills and evaluate the user's interpersonal skills. The data collection unit can measure language skills and evaluate the user's language skills. The data collection unit can also measure and evaluate the user's administrative skills. The data collection unit can also measure and evaluate the user's PC skills. This allows for a more detailed assessment of an individual's skills and personality, enabling more appropriate career suggestions. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the results of a personality assessment test into a generating AI, which can then analyze the personality and output the results.

[0079] The data collection unit can register experiences such as sports, music, and studying. For example, the data collection unit can register a user's sports experience. The data collection unit can also register a user's music experience. The data collection unit can also register a user's study experience. The data collection unit can register a user's sports experience in detail and reflect it in career suggestions. The data collection unit can register a user's music experience in detail and reflect it in career suggestions. The data collection unit can register a user's study experience in detail and reflect it in career suggestions. This improves the accuracy of career suggestions by registering a variety of user experiences. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's sports experience into a generating AI, which can then analyze the experience and output results.

[0080] The data collection unit can register family structure and lifestyle. For example, the data collection unit can register the user's family structure. The data collection unit can also register the user's lifestyle. The data collection unit can register the user's family structure in detail and reflect it in career suggestions. The data collection unit can also register information such as living alone, living with a partner, living with three children, or caring for grandparents. The data collection unit can also register lifestyles such as being an early riser who goes to the gym three times a week and spends evenings relaxing at home, or being a late riser but enjoying drinking parties and going out for drinks with colleagues or friends almost every day. This makes it possible to provide career suggestions that take into account the user's living situation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's family structure and lifestyle into a generating AI, which can analyze the information and output results.

[0081] The analysis department can search for ideal careers by referencing past data based on the collected information. For example, the analysis department searches for ideal careers by referencing past data based on information such as the results of personality assessment tests and SPI comprehensive aptitude tests, family structure, and lifestyle. The analysis department uses AI to perform analysis to suggest the most suitable occupation based on the collected information. For example, the analysis department searches for the most suitable occupation for the user by referring to data such as past work history and success stories. This makes it possible to make more appropriate occupation suggestions by referring to past data. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected information into a generating AI, which can analyze the information and suggest the most suitable occupation.

[0082] The suggestion department can make suggestions such as "○○ is a good fit for you!" based on the analysis results. For example, the suggestion department can suggest the most suitable occupation to the user based on the analysis results. The suggestion department can use AI to suggest the most suitable occupation to the user based on the analysis results. For example, the suggestion department can use generative AI to make suggestions such as "○○ is a good fit for you!" This makes it possible to make specific occupation suggestions based on the analysis results. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input the analysis results into generative AI, and the generative AI can suggest the most suitable occupation.

[0083] The suggestion department can provide advice such as, "You are X% ready to start right now, and you would be even more suitable if you acquire skills such as XX and YY!" The suggestion department can, for example, provide specific advice to the user. The suggestion department can use AI to provide specific advice to the user. The suggestion department can, for example, use generative AI to provide advice such as, "You are X% ready to start right now, and you would be even more suitable if you acquire skills such as XX and YY!" By providing specific advice to the user, it can serve as a reference for career selection. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input user information into a generative AI, and the generative AI can provide specific advice.

[0084] The search unit can search for job postings related to a proposed occupation in conjunction with existing job sites and present the job details in a list. For example, the search unit can search for job postings related to a proposed occupation and present them to the user. The search unit can use AI to efficiently search for job postings related to a proposed occupation. For example, the search unit can use a generation AI to search for job postings related to a proposed occupation and present the job details in a list. This allows for efficient searching and presentation of job postings related to a proposed occupation. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input job postings related to a proposed occupation into a generation AI, which can then search for the job postings and output the results.

[0085] The generation unit can automatically generate a resume based on the results of a personality and skills assessment when a user expresses interest in applying, and send it to the company. For example, the generation unit generates a resume based on the user's information. The generation unit can use AI to generate an optimal resume based on the user's information. For example, the generation unit can use a generation AI to automatically generate a resume based on the results of a personality and skills assessment and send it to the company. This streamlines the job search process by automatically generating and sending a resume to the company when a user expresses interest in applying. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user information into a generation AI, which can then generate a resume and output the results.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay information collection until the user is relaxed. If the user is relaxed, the data collection unit can start collecting information immediately. If the user is in a hurry, the data collection unit can collect information quickly. By adjusting the timing of information collection according to the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and output the result.

[0087] The data collection unit can analyze the user's past information provision history and select the optimal data collection method. For example, the data collection unit may prioritize information provision methods that the user has frequently used in the past (such as surveys and interviews). The data collection unit can adjust the data collection method based on the level of detail of the information the user has provided in the past. The data collection unit can select the most efficient data collection method from the user's past information provision history. In this way, the optimal data collection method can be selected by analyzing the user's past information provision history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past information provision history into a generating AI, which can then analyze the information and select the optimal data collection method.

[0088] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information relevant to the user's current living situation (work, family, etc.). The data collection unit can filter information based on the user's areas of interest (hobbies, interests, etc.). The data collection unit can adjust the scope of information collection according to the user's living situation and areas of interest. This allows for the collection of more relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current living situation and areas of interest into a generating AI, which can then filter the information and output the results.

[0089] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information of lower importance. If the user is relaxed, the data collection unit can prioritize collecting information of higher importance. If the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and output the results.

[0090] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can prioritize collecting information related to places the user has visited in the past. The data collection unit can prioritize collecting information related to places the user plans to visit in the future. By collecting information while considering the user's geographical location, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the information and output the results.

[0091] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. The data collection unit can collect relevant information based on accounts followed by the user on social media. The data collection unit can collect relevant information based on groups the user participates in on social media. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into a generating AI, which can then analyze the information and output results.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can provide a presentation that includes detailed information. If the user is in a hurry, the analysis unit can provide a presentation that gets straight to the point. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and output the results.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can perform a detailed analysis on information of high importance, and a simplified analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the information. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the importance of the collected information into a generating AI, which can then analyze the information and output the results.

[0094] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a psychological analysis algorithm to personality information, a technical analysis algorithm to skills information, and a sociological analysis algorithm to lifestyle information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of information into a generating AI, which can then analyze the information and output the results.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and output the results.

[0096] The analysis unit can determine the priority of analysis based on when the information was submitted. For example, the analysis unit may prioritize the analysis of recently submitted information. The analysis unit may postpone the analysis of older information. The analysis unit can adjust the priority of analysis in stages according to the submission date. This allows for more efficient analysis by determining the priority of analysis based on when the information was submitted. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the information into a generating AI, which can then analyze the information and output the results.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis step by step according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the information into a generating AI, and the generating AI can analyze the information and output the results.

[0098] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily visible presentation. If the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. If the user is in a hurry, the suggestion unit can provide a presentation that gets straight to the point. By adjusting the presentation of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotion and output the result.

[0099] The proposal unit can adjust the level of detail of its proposals based on the importance of the occupations. For example, the proposal unit can provide detailed proposals for highly important occupations and simplified proposals for less important occupations. The proposal unit can adjust the level of detail of its proposals in stages according to the importance of the occupations. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the occupations. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of occupations into a generating AI, which can then analyze the information and output results.

[0100] The proposal unit can apply different proposal algorithms depending on the occupation category when making a proposal. For example, the proposal unit can apply a technical proposal algorithm to technical occupations. For creative occupations, it can apply a creative proposal algorithm. For service occupations, it can apply a proposal algorithm that emphasizes interpersonal skills. By applying different proposal algorithms depending on the occupation category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the occupation category into a generating AI, which can analyze the information and output the result.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotions and output the results.

[0102] The proposal department can determine the priority of proposals based on when the occupations were submitted. For example, the proposal department may prioritize recently submitted occupations. It may also postpone the submission of older occupations. The proposal department can adjust the priority of proposals in stages according to the submission date. This allows for more efficient proposals by determining the priority of proposals based on the submission date of occupations. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the submission date of occupations into a generating AI, which can then analyze the information and output results.

[0103] The proposal unit can adjust the order of proposals based on the relevance of the occupations. For example, the proposal unit may prioritize proposing occupations with high relevance. The proposal unit may postpone proposing occupations with low relevance. The proposal unit can adjust the order of proposals in stages according to the relevance of the occupations. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the occupations. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the relevance of occupations into a generating AI, which can then analyze the information and output results.

[0104] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is nervous, the search unit can provide simple and easily visible search criteria. If the user is relaxed, the search unit can provide search criteria that include detailed information. If the user is in a hurry, the search unit can provide concise search criteria. By adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI, which can then estimate the emotions and output the results.

[0105] The search unit can improve the accuracy of searches by considering the interrelationships between occupations. For example, the search unit can group related occupations and display the search results. The search unit can adjust the priority of search results based on the interrelationships between occupations. The search unit can improve the accuracy of search results by considering the interrelationships between occupations. This allows for the provision of more appropriate search results by improving search accuracy by considering the interrelationships between occupations. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the interrelationships between occupations into a generating AI, which can then analyze the information and output results.

[0106] The search unit can perform searches while considering the attribute information of the job submitter. For example, the search unit can prioritize relevant occupations based on the submitter's age and gender. The search unit can prioritize relevant occupations based on the submitter's work history and skills. The search unit can prioritize relevant occupations based on the submitter's place of residence and lifestyle. By considering the attribute information of the job submitter, the search unit can provide more appropriate search results. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the attribute information of the job submitter into a generating AI, which can analyze the information and output results.

[0107] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is nervous, the search unit can display search results in a simple and highly visible order. If the user is relaxed, the search unit can display search results in an order that includes detailed information. If the user is in a hurry, the search unit can display search results in an order that gets straight to the point. In this way, by adjusting the order in which search results are displayed according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI, which can then estimate the emotions and output the results.

[0108] The search unit can perform searches while considering the geographical distribution of occupations. For example, the search unit can prioritize searching for occupations close to the user's place of residence. The search unit can prioritize searching for occupations in the user's desired region. The search unit can adjust the priority of search results based on the geographical distribution of occupations. This allows for more appropriate search results by considering the geographical distribution of occupations. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the geographical distribution of occupations into a generating AI, which can then analyze the information and output results.

[0109] The search unit can improve the accuracy of its searches by referring to relevant literature on occupations during the search process. For example, the search unit can improve the accuracy of search results by referring to literature on relevant occupations. The search unit can adjust search results based on the latest research and reports on occupations. The search unit can improve the accuracy of search results by analyzing relevant literature on occupations. This allows for the provision of more appropriate search results by improving search accuracy through the referencing of relevant literature on occupations. Some or all of the above processes in the search unit may be performed using AI or not. For example, the search unit can input relevant literature on occupations into a generating AI, which can then analyze the information and output results.

[0110] The generation unit can estimate the user's emotions and adjust the resume generation method based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a simple and highly legible resume. If the user is relaxed, the generation unit can generate a resume with detailed information. If the user is in a hurry, the generation unit can generate a concise resume. This allows for the generation of a more appropriate resume by adjusting the resume generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and output the result.

[0111] The generation unit can generate an optimal resume by analyzing the user's past work experience during resume generation. For example, the generation unit generates an optimal resume based on the user's past work experience. The generation unit can adjust the content of the resume based on the level of detail of the user's work experience. The generation unit can analyze the user's work experience and generate the most effective resume. This allows for the provision of a more appropriate resume by analyzing the user's past work experience and generating an optimal resume. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past work experience into a generation AI, which can analyze the information and output the results.

[0112] The generation unit can customize the content of a resume based on the user's current living situation when generating a resume. For example, the generation unit can customize the content of a resume based on the user's current living situation (family, work, etc.). The generation unit can adjust the content of a resume according to the user's lifestyle. The generation unit can generate an optimal resume by taking into account the user's current living situation. This allows for the provision of a more appropriate resume by customizing the content of the resume based on the user's current living situation. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's current living situation into a generation AI, which can then analyze the information and output the results.

[0113] The generation unit can estimate the user's emotions and prioritize information in the resume based on those emotions. For example, if the user is nervous, the generation unit can prioritize less important information in the resume. If the user is relaxed, the generation unit can prioritize more important information in the resume. If the user is in a hurry, the generation unit can prioritize information that can be written quickly. This allows for the provision of a more appropriate resume by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI, which can then estimate the emotions and output the result.

[0114] The generation unit can generate an optimal resume by considering the user's geographical location information when generating a resume. For example, the generation unit can include relevant information in the resume based on the user's place of residence. The generation unit can also include information about the region the user desires in the resume. The generation unit can generate an optimal resume by considering the user's geographical location information. This allows for the provision of a more appropriate resume by generating an optimal resume that considers the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into a generation AI, which can then analyze the information and output the results.

[0115] The generation unit can analyze the user's social media activity and suggest resume content when generating a resume. For example, the generation unit can suggest resume content based on information the user has shared on social media. The generation unit can suggest resume content based on accounts the user follows on social media. The generation unit can suggest resume content based on groups the user participates in on social media. This allows for the provision of a more appropriate resume by analyzing the user's social media activity and suggesting resume content. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity into a generation AI, which can then analyze the information and output the results.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion can be delayed until they are relaxed. If the user is relaxed, the suggestion can be made immediately. If the user is in a hurry, the suggestion can be made quickly. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0118] The information gathering unit can estimate the user's emotions and adjust the information gathering method based on the estimated emotions. For example, if the user is nervous, it can start with simple questions and gradually gather more detailed information. If the user is relaxed, it can ask detailed questions all at once. If the user is in a hurry, it can ask concise questions. By adjusting the information gathering method according to the user's emotions, more appropriate information can be gathered.

[0119] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it can prioritize analyzing information of lower importance. If the user is relaxed, it can prioritize analyzing information of higher importance. If the user is in a hurry, it can prioritize analyzing information that can be processed quickly. By prioritizing analysis according to the user's emotions, more appropriate analysis becomes possible.

[0120] The suggestion function can estimate the user's emotions and adjust the content of the suggestion based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand suggestion. If the user is relaxed, it can provide a suggestion that includes detailed information. If the user is in a hurry, it can provide a suggestion that gets straight to the point. By adjusting the content of the suggestion according to the user's emotions, it becomes possible to provide more appropriate suggestions.

[0121] The generation unit can estimate the user's emotions and adjust the resume content based on those emotions. For example, if the user is nervous, it can generate a simple and easy-to-read resume. If the user is relaxed, it can generate a resume with detailed information. If the user is in a hurry, it can generate a resume that gets straight to the point. In this way, by adjusting the resume content according to the user's emotions, a more appropriate resume can be generated.

[0122] The data collection unit can analyze the user's past work history and select the most suitable information collection method. For example, it can prioritize information delivery methods that the user has frequently used in the past (such as surveys and interviews). It can also adjust the collection method based on the level of detail the user has provided in the past. This allows the system to select the most suitable information collection method by analyzing the user's past work history.

[0123] The analysis department can apply different analysis algorithms depending on the category of the collected information. For example, a psychological analysis algorithm can be applied to personality information, a technical analysis algorithm to skills information, and a sociological analysis algorithm to lifestyle information. By applying different analysis algorithms according to the category of information, more appropriate analysis results can be provided.

[0124] The proposal department can prioritize proposals based on when the occupation was submitted. For example, recently submitted occupations can be given priority. Older submitted occupations can be postponed. The priority of proposals can be adjusted in stages according to the submission date. This allows for more efficient proposals by prioritizing proposals based on when the occupation was submitted.

[0125] The search function can perform searches while considering the geographical distribution of occupations. For example, it can prioritize searches for occupations close to the user's place of residence. It can also prioritize searches for occupations in the user's desired region. The priority of search results can be adjusted based on the geographical distribution of occupations. As a result, by considering the geographical distribution of occupations when performing searches, more appropriate search results can be provided.

[0126] The generation unit can analyze the user's social media activity and suggest resume content when generating a resume. For example, it can suggest resume content based on information the user has shared on social media. It can suggest resume content based on accounts the user follows on social media. It can suggest resume content based on groups the user participates in on social media. By analyzing the user's social media activity and suggesting resume content accordingly, it can provide a more appropriate resume.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects user information. The data collection unit collects information such as the user's personality, experience and skills, family structure, and lifestyle. The data collection unit can assess an individual's skills and personality by conducting tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, quick thinking tests, athletic ability tests, memorization tests, information processing tests, empathy tests, interpersonal skills tests, language skills tests, clerical skills tests, and PC skills tests. The data collection unit can also register experiences such as sports, music, and studying. The data collection unit can also register family structure and lifestyle. For example, the data collection unit can register information such as living alone, living with a partner, living with three children, or caring for grandparents. The data collection unit can also register lifestyles such as being a morning person who goes to the gym three times a week and spends evenings relaxing at home, or being a morning person but enjoying drinking parties and going out for drinks with colleagues and friends almost every day. Step 2: The Analysis Department analyzes the information collected by the Collection Department. The Analysis Department searches for an ideal career by referring to past data, based on information such as the results of personality tests and SPI comprehensive aptitude tests, family structure, and lifestyle. The Analysis Department uses AI to perform analysis based on the collected information to suggest the most suitable occupation. Step 3: The Proposal Department proposes the most suitable occupation based on the analysis results obtained by the Analysis Department. The Proposal Department can make suggestions such as, "You are well-suited for XX!" The Proposal Department can also provide advice such as, "You are X% ready to start right now, and you would be even more suitable if you develop skills such as XX and YY!" Step 4: The search unit searches for job postings for the occupation suggested by the suggestion unit. For example, the search unit searches for job postings related to the suggested occupation in conjunction with existing job sites and presents the job details in a list. The search unit can efficiently search for job postings related to the suggested occupation using AI. Step 5: The generation unit generates a resume when the user expresses interest in applying. The generation unit automatically generates a resume based on, for example, the results of personality and skills assessments, and sends it to the company. The generation unit can use AI to generate the optimal resume based on the user's information.

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, search unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as the user's personality, experience and skills, family structure, and lifestyle. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for a suitable job based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable occupation. The search unit is implemented by the control unit 46A of the smart device 14 and searches for job postings related to the proposed occupation. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a resume based on the user's information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, search unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as the user's personality, experience and skills, family structure, and lifestyle. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for a suitable job based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable occupation. The search unit is implemented by the control unit 46A of the smart glasses 214 and searches for job postings related to the proposed occupation. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a resume based on the user's information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, search unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as the user's personality, experience and skills, family structure, and lifestyle. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for a suitable job based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable occupation. The search unit is implemented by the control unit 46A of the headset terminal 314 and searches for job postings related to the proposed occupation. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a resume based on the user's information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, search unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information such as the user's personality, experience and skills, family structure, and lifestyle. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for a suitable job based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable occupation. The search unit is implemented by the control unit 46A of the robot 414 and searches for job postings related to the proposed occupation. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a resume based on the user's information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable occupation, A search unit that searches for job postings for the occupations proposed by the aforementioned proposal unit, The system includes a generation unit that generates a resume when a user indicates their intention to apply based on job information retrieved by the search unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We conduct tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, tests for quick reflexes, athletic ability, memorization ability, information processing ability, empathy ability, interpersonal skills, language skills, clerical skills, and PC skills to assess an individual's skills and personality. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Register your experiences in sports, music, studying, etc. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Register your family structure and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Based on the collected information, search for your ideal job by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the analysis results, we make suggestions such as, "XX is a good fit for you!" The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, The advice provided is along the lines of, "You're ready to start right now (X%), and if you develop skills like XX and YY, you'll be even more suitable!" The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned search unit, The system searches for job postings related to the proposed occupation in conjunction with existing job sites and presents a list of job descriptions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When a user expresses interest in applying, a resume is automatically generated based on the results of a personality and skills assessment, and sent to the company. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past information provision history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the occupation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the occupation category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the job titles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the profession. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned search unit, When searching, consider the interrelationships between occupations to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned search unit, When performing a search, the system takes into account the attribute information of the person submitting the occupation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned search unit, When searching, consider the geographical distribution of occupations. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned search unit, When searching, refer to relevant literature related to your profession to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is It estimates the user's emotions and adjusts the resume generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is When generating a resume, the system analyzes the user's past work experience to create the most suitable resume. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating a resume, the content of the resume is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is It estimates the user's emotions and prioritizes resumes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The generating unit is When generating a resume, the system takes the user's geographical location into consideration to create the most optimal resume. The system described in Appendix 1, characterized by the features described herein. (Note 39) The generating unit is When generating a resume, the system analyzes the user's social media activity and suggests resume content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable occupation, A search unit that searches for job postings for the occupations proposed by the aforementioned proposal unit, The system includes a generation unit that generates a resume when a user indicates their intention to apply based on job information retrieved by the search unit. A system characterized by the following features.

2. The aforementioned collection unit is We conduct tests such as personality assessment tests, SPI comprehensive aptitude tests, IQ intelligence quotient tests, tests for quick reflexes, athletic ability, memorization ability, information processing ability, empathy ability, interpersonal skills, language skills, clerical skills, and PC skills to assess an individual's skills and personality. The system according to feature 1.

3. The aforementioned collection unit is Register your experiences in sports, music, studying, etc. The system according to feature 1.

4. The aforementioned collection unit is Register your family structure and lifestyle. The system according to feature 1.

5. The aforementioned analysis unit is Based on the collected information, search for your ideal job by referring to past data. The system according to feature 1.

6. The aforementioned search unit, The system searches for job postings related to the proposed occupation in conjunction with existing job sites and presents a list of job descriptions. The system according to feature 1.

Images