system

Abstract

The system according to this embodiment aims to suggest the most suitable occupation based on individual skills and career goals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a referral unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes specific positions based on the analysis results obtained by the analysis unit. The reception unit receives requests from companies. The referral unit introduces highly suitable candidates based on the requests received by the reception unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

[0001] The technology of the present disclosure relates to a system. [[ID=⑦]]

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, it has not been sufficiently done to propose an optimal occupation based on individual skills and career goals, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal occupation based on individual skills and career goals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a referral unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes specific positions based on the analysis results obtained by the analysis unit. The reception unit receives requests from companies. The referral unit introduces highly suitable candidates based on the requests received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable occupation based on individual skills and career goals. [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 labeled 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 including 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 matching and coaching system according to an embodiment of the present invention is a system that proposes the optimal career path and occupation based on an individual's skills, interests, values, and career goals. This system collects information such as the user's resume, work history, performance data, and personality analysis, and the AI ​​analyzes this data to propose specific positions. Companies also input their requirements for the type of personnel they are looking for, and the AI ​​introduces highly matched candidates. Furthermore, as a coaching function, it compares the user's current skill set with the skills required for their desired career path and analyzes the skill gap. The AI ​​then suggests necessary training and qualification acquisition. As a result, users can find occupations and career paths that suit them and lead fulfilling work lives without stress. Thus, the career matching and coaching system can efficiently collect, analyze, propose, receive, and introduce user information.

[0029] The career matching and coaching system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a referral unit. The collection unit collects user information. User information includes, but is not limited to, resumes, work history, performance data, and personality analysis. The collection unit collects information, for example, through questionnaires and interviews. The collection unit can also collect information from users' online activities using data mining techniques. For example, the collection unit analyzes users' social media activities and collects relevant information. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the user's skills, experience, interests, values, and career goals, and generates data to propose specific positions. The proposal unit proposes specific positions based on the analysis results obtained by the analysis unit. The proposal unit performs, for example, skill matching and career path proposals. For example, the proposal unit proposes the optimal career path and occupation based on the user's skills, experience, interests, values, and career goals. The reception department receives requests from companies. The reception department receives requests from companies, for example, through online forms or telephone inquiries. The introduction department introduces highly suitable candidates based on the requests received by the reception department. The introduction department introduces candidates that meet the company's requirements, for example, using matching algorithms or recommendation systems. As a result, the career matching and coaching system according to this embodiment can efficiently collect, analyze, propose, receive, and introduce user information.

[0030] The data collection unit collects user information. This information includes, but is not limited to, resumes, work history, performance data, and personality analyses. The unit collects information through methods such as surveys and interviews. Specifically, it uses online survey forms to ask users detailed questions and stores the responses in a database. Interviews are conducted via video call or in person, with interviewers recording the user's responses. Furthermore, the data collection unit can also use data mining techniques to collect information from users' online activities. For example, it analyzes users' social media activity and collects relevant information. Specifically, it analyzes users' posts, comments, and like history to understand their interests and values. It also collects activity history from online communities and forums that users participate in to evaluate their expertise and skills. This allows the data collection unit to efficiently collect multifaceted information about users and provide it to the analysis unit. In addition, the data collection unit implements strict security measures in data collection and storage to protect user privacy. For example, data is encrypted and stored on secure servers. Furthermore, information is collected only with the user's consent, and the purpose of using the collected data is clearly defined. This allows the data collection unit to effectively collect necessary information while gaining the user's trust.

[0031] The analysis department analyzes the information collected by the data collection department. For example, the analysis department uses statistical analysis and machine learning algorithms to analyze the information. Specifically, it analyzes users' skills, experience, interests, values, and career goals to generate data for suggesting specific positions. For instance, it uses machine learning algorithms to analyze users' resumes and work histories to evaluate past work experience and skill sets. It also analyzes users' personality traits and behavioral patterns based on personality analysis data to identify suitable job types and work environments. Furthermore, the analysis department analyzes users' performance data to evaluate past achievements and results. This clarifies users' strengths and weaknesses and generates foundational data for suggesting optimal career paths. The analysis department integrates this data to create a comprehensive user profile. Additionally, the analysis department can predict future career possibilities and growth opportunities based on historical data and industry trends. For example, it can predict how specific skill sets will be valued in the future market and provide users with advice for skill development. The analysis department also collects user feedback to continuously improve the accuracy of its analysis algorithms. This allows the analysis unit to provide users with more accurate career recommendations.

[0032] The Proposal Department proposes specific positions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department provides skill matching and career path suggestions. Specifically, it proposes optimal career paths and occupations based on the user's skills, experience, interests, values, and career goals. For instance, it suggests job types with high demand in the current market based on the user's specific skill set. It also designs short-term and long-term career paths and proposes specific steps according to the user's career goals. Furthermore, the Proposal Department can also suggest companies with suitable work environments and corporate cultures, taking into account the user's interests and values. This allows users to work in workplaces where they can maximize their skills and experience. The Proposal Department clearly explains the suggestions to the user and provides concrete action plans. For example, it introduces training programs and educational institutions to acquire the skills and qualifications necessary for the suggested occupations. The Proposal Department also collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. This enables the Proposal Department to provide users with optimal career suggestions and support their career success.

[0033] The reception department receives requests from companies. For example, it receives requests through online forms and telephone inquiries. Specifically, it collects detailed requirements for the personnel that companies are seeking, such as skill sets, experience, qualifications, and personality traits. The online form provides a user-friendly interface to allow companies to easily enter their requirements. Telephone inquiries are handled by specialized staff who listen to company requests and record detailed information. Furthermore, the reception department stores company requests in a database, making it accessible to the analysis and placement departments. This ensures a smooth matching process based on company requests. The reception department conducts regular follow-ups and collects feedback to facilitate smooth communication with companies. For example, it checks whether suitable personnel have been found for the requests submitted by companies and accepts revisions or additional requests as needed. The reception department also strives to respond quickly to company requests and spares no effort in increasing company satisfaction. This allows the reception department to respond flexibly to company needs and achieve optimal matching for both companies and users.

[0034] The recruitment department introduces highly suitable candidates based on requests received by the reception department. For example, the recruitment department uses matching algorithms and recommendation systems to introduce candidates that meet the company's requirements. Specifically, it searches user information in its database based on the company's requirements such as skill sets, experience, and personality traits to identify the most suitable candidates. The matching algorithm evaluates the user's skills, experience, and personality traits and calculates the degree of match against the company's requirements. The recommendation system recommends the most suitable candidate based on past matching data and success stories. This allows the recruitment department to respond quickly and accurately to company requests and introduce the most suitable candidates. Furthermore, the recruitment department coordinates the interview and selection process between companies and candidates, supporting smooth communication. For example, it schedules interviews and prepares necessary documents, ensuring a smooth selection process for both companies and candidates. The recruitment department also collects feedback on the matching results and continuously improves the accuracy of its matching algorithms and recommendation systems. This allows the recruitment department to achieve optimal matching for both companies and users, maximizing the effectiveness of the career matching and coaching system.

[0035] The comparison unit can compare the user's current skill set with the skills required for their desired career path. For example, the comparison unit can store the user's skill set in a database and compare it with the skills required for their desired career path. The comparison unit can also compare the user's skill set with the skills required for their desired career path in real time. For example, when the user acquires a new skill, the comparison unit updates the skill set and compares it with the skills required for their desired career path. This allows the system to identify skill gaps by comparing the user's skill set with the skills required for their desired career path. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can identify skill gaps using an AI model that takes the user's skill set and the skills required for their desired career path as input and outputs the skill gap.

[0036] The identification unit can identify skill gaps. For example, the identification unit can identify skill gaps by comparing the user's current skill set with the skills required for their desired career path. The identification unit can also identify skill gaps by comparing the user's skill set with the skills required for their desired career path in real time. For example, when a user acquires a new skill, the identification unit updates the skill set and identifies the skill gap. This makes it possible to clarify the skills the user needs by identifying the skill gap. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can identify skill gaps using an AI model that takes the user's skill set and the skills required for their desired career path as input and outputs the skill gap.

[0037] The service provider can provide information on necessary training and certifications. For example, the service provider can identify a user's skill gaps and provide information on necessary training and certifications. The service provider can also identify a user's skill gaps in real time and provide information on necessary training and certifications. For example, when a user acquires a new skill, the service provider updates the skill gap and provides information on necessary training and certifications. This allows users to obtain concrete means to improve their skills by providing information on necessary training and certifications. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes a user's skill gaps as input and outputs information on necessary training and certifications.

[0038] The suggestion department can propose optimal career paths and occupations based on the user's skills, experience, interests, values, and career goals. For example, if the suggestion department analyzes the user's skills, experience, interests, values, and career goals, it will suggest the most suitable career paths and occupations. For instance, if the user inputs "I have marketing experience and am interested in creative work," the suggestion department will suggest positions such as "Creative Director" or "Marketing Manager." The suggestion department can also propose career paths based on the user's skills, experience, interests, values, and career goals. For example, if the user aims to become a "Creative Director," the suggestion department will identify a lack of "Project Management" skills and propose "Project Management" training. In this way, by suggesting optimal career paths and occupations based on the user's skills, experience, interests, values, and career goals, the user can find an occupation that suits them. Some or all of the above processing in the suggestion department is performed using generative AI. For example, the suggestion department can make suggestions using a generative AI model that takes the user's skills, experience, interests, values, and career goals as input and outputs the most suitable career paths and occupations.

[0039] The recruitment department can introduce highly suitable candidates based on the company's requirements. For example, if a company inputs "We want someone with marketing experience and an interest in creative work," the department will introduce candidates who match positions such as "Creative Director" or "Marketing Manager." The recruitment department can also recommend highly suitable candidates based on the company's requirements. For example, if a company inputs "We want someone with project management skills," the department will recommend candidates who match positions such as "Project Manager" or "Project Leader." This improves the accuracy of matching companies with job seekers by introducing highly suitable candidates based on the company's requirements. Some or all of the above processes in the recruitment department may be performed using AI or not. For example, the recruitment department can introduce candidates using an AI model that takes a company's requirements as input and outputs highly suitable candidates.

[0040] The data collection unit can analyze the user's past information provision history and select the optimal data collection method. For example, if the user has preferred using text input in the past, the data collection unit will prioritize suggesting text input. It can also recommend voice input if the user has frequently used voice input in the past. For example, if the data collection unit has provided information during a specific time period in the past, it will collect data during that time period. This allows the system to efficiently collect information by analyzing the user's past information provision history and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can select a data collection method using an AI model that takes the user's past information provision history as input and outputs the optimal data collection method.

[0041] The data collection unit can filter information based on the user's current occupation and areas of interest during the information gathering process. For example, if the user is interested in marketing, the data collection unit will prioritize collecting marketing-related information. It can also collect IT-related information if the user works in the IT industry. For example, if the user is interested in education, the data collection unit will collect education-related information. This allows for the efficient collection of highly relevant information by filtering it based on the user's current occupation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can filter information using an AI model that takes the user's occupation and areas of interest as input and outputs relevant information.

[0042] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user lives in a specific city, the data collection unit will prioritize collecting information related to that city. Similarly, if the user works in a specific region, the data collection unit can collect information related to that region. For example, if the user is traveling, the data collection unit will collect information related to their travel destination. This allows for the efficient collection of highly relevant information by considering the user's geographical location. 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 collect information using an AI model that takes the user's geographical location as input and outputs relevant information.

[0043] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information based on the user's interests and passions shared on social media. The data collection unit can also analyze the content of accounts that a user follows and collect relevant information. For example, the data collection unit can collect information by referring to the activities of groups and communities that a user participates in. This allows for the efficient collection of relevant information by analyzing a 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 collect information using an AI model that takes a user's social media activity as input and outputs relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit will perform a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit will perform a detailed analysis on information of high interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the information, a detailed analysis can be performed on important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes the importance of the information as input and outputs the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a marketing analysis algorithm to marketing information. For example, it can apply an educational analysis algorithm to educational information. By applying different analysis algorithms depending on the category of information, the analysis unit can perform the most optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can perform analysis using an AI model that takes the category of information as input and outputs the analysis algorithm to be applied.

[0046] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit can prioritize the analysis of the latest information. It can also postpone the analysis of older information. For example, the analysis unit can determine the priority of analysis based on the submission timing specified by the user. This allows for the prioritization of analysis of the latest information by determining the priority of analysis based on the information submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can determine the priority of analysis using an AI model that takes the information submission timing as input and outputs the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information of high interest to the user. It can also postpone the analysis of less relevant information. For example, the analysis unit can adjust the order of analysis based on the relevance specified by the user. This allows for the prioritization of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the order of analysis using an AI model that takes the relevance of information as input and outputs the order of analysis.

[0048] The proposal unit can adjust the level of detail of a proposal based on the importance of the position. For example, the proposal unit will provide a detailed proposal for important positions. It can also provide a concise proposal for general positions. For example, the proposal unit will provide a detailed proposal for positions of high user interest. By adjusting the level of detail of a proposal based on the importance of the position, it is possible to provide detailed proposals for important positions. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can adjust the level of detail of a proposal using an AI model that takes the importance of the position as input and outputs the level of detail of the proposal.

[0049] The proposal unit can apply different proposal algorithms depending on the position category when making a proposal. For example, the proposal unit can apply a technical proposal algorithm to technical positions. It can also apply a marketing proposal algorithm to marketing positions. For example, it can apply an educational proposal algorithm to educational positions. By applying different proposal algorithms depending on the position category, it is possible to make proposals that are optimal for each category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can make proposals using an AI model that takes the position category as input and outputs the proposal algorithm to be applied.

[0050] The proposal department can determine the priority of proposals based on the submission timing of the positions. For example, the proposal department will prioritize proposals for the most recent positions. It can also postpone proposals for older positions. For example, the proposal department can determine the priority of proposals based on the submission timing specified by the user. This allows for prioritizing proposals for the most recent positions by determining the priority based on the submission timing of the positions. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can determine the priority of proposals using an AI model that takes the submission timing of the positions as input and outputs the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the positions when making proposals. For example, the proposal unit will prioritize proposals for positions of high user interest. Conversely, the proposal unit can also postpone proposals for less relevant positions. For example, the proposal unit can adjust the order of proposals based on the relevance specified by the user. This allows for prioritizing the proposal of highly relevant positions by adjusting the order of proposals based on the relevance of the positions. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can adjust the order of proposals using an AI model that takes the relevance of positions as input and outputs the order of proposals.

[0052] The reception department can analyze a company's past request history and select the most suitable reception method when receiving a request from a company. For example, the reception department can automatically suggest requests based on the skill sets that the company has frequently requested in the past. The reception department can also prioritize suggesting request input methods (text, voice, etc.) that the company has used in the past. For example, the reception department can predict and suggest requests to be used during specific time periods based on the company's past request history. This allows for the selection of the most suitable reception method by analyzing the company's past request history, enabling efficient request processing. Some or all of the above processes in the reception department may be performed using AI, or they may not. For example, the reception department can select a reception method using an AI model that takes the company's past request history as input and outputs the most suitable reception method.

[0053] The reception desk can filter requests from companies based on their industry and size. For example, it might request detailed requests from large companies, while requesting more concise requests from small and medium-sized enterprises. For instance, it might prioritize suggesting requests relevant to a specific industry. This allows for efficient reception of highly relevant requests by filtering based on the company's industry and size. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk could filter requests using an AI model that takes the company's industry and size as input and outputs filtered results.

[0054] The reception desk can prioritize requests from companies by considering the company's geographical location. For example, if a company is located in a specific city, the reception desk will prioritize requests related to that city. Similarly, if a company operates in a specific region, the reception desk can prioritize requests related to that region. For example, if a company operates internationally, the reception desk will prioritize global requests. This allows for the efficient reception of highly relevant requests by considering the company's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can receive requests using an AI model that takes a company's geographical location as input and outputs relevant requests.

[0055] The reception department can analyze a company's social media activity and receive relevant requests when receiving requests from companies. For example, the reception department can receive relevant requests based on requests that companies have shared on social media. The reception department can also analyze the content of accounts that companies follow and receive relevant requests. For example, the reception department can receive requests by referring to the activities of groups and communities that companies participate in. In this way, relevant requests can be efficiently received by analyzing a company's social media activity. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can receive requests using an AI model that takes a company's social media activity as input and outputs relevant requests.

[0056] The referral function can adjust the level of detail in a referral based on the importance of the position. For example, the referral function will provide a detailed referral for important positions. It can also provide a concise referral for general positions. For example, the referral function will provide a detailed referral for positions of high user interest. By adjusting the level of detail in a referral based on the importance of the position, it is possible to provide detailed referrals for important positions. Some or all of the above processing in the referral function may be performed using AI or not. For example, the referral function can adjust the level of detail in a referral using an AI model that takes the importance of the position as input and outputs the level of detail in the referral.

[0057] The referral system can apply different referral algorithms depending on the position category during the referral process. For example, it can apply a technical referral algorithm to technical positions, a marketing referral algorithm to marketing positions, and an education referral algorithm to education positions. By applying different referral algorithms depending on the position category, the system can provide the most suitable referrals for each category. Some or all of the above-described processes in the referral system may be performed using AI, or they may not. For example, the referral system can perform referrals using an AI model that takes the position category as input and outputs the referral algorithm to apply.

[0058] The referral system can determine the priority of referrals based on the timing of position submissions. For example, the referral system will prioritize referrals for the most recent positions. It can also postpone referrals for older positions. For example, the referral system can determine the priority of referrals based on the submission timing specified by the user. This allows for priority referrals for the most recent positions by determining the priority based on the submission timing. Some or all of the above processes in the referral system may be performed using AI or not. For example, the referral system can determine the priority of referrals using an AI model that takes the position submission timing as input and outputs the priority of referrals.

[0059] The referral system can adjust the order of referrals based on the relevance of the positions. For example, the referral system will prioritize referrals to positions of high user interest. It can also postpone referrals to less relevant positions. For example, the referral system can adjust the order of referrals based on the relevance specified by the user. This allows for the preferential referral of highly relevant positions by adjusting the order of referrals based on the relevance of the positions. Some or all of the above processing in the referral system may be performed using AI or not. For example, the referral system can adjust the order of referrals using an AI model that takes the relevance of positions as input and outputs the order of referrals.

[0060] The comparison unit can select the optimal comparison method by referring to the user's past skill history when comparing skills. For example, the comparison unit can propose the optimal comparison method based on the skills the user has acquired in the past. The comparison unit can also prioritize the comparison of relevant skills from the user's past skill history. For example, the comparison unit can analyze the user's past skill history and propose the most efficient comparison method. This allows for efficient skill comparison by selecting the optimal comparison method by referring to the user's past skill history. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can select a comparison method using an AI model that takes the user's past skill history as input and outputs the optimal comparison method.

[0061] The comparison unit can select the optimal comparison method when comparing skills, taking into account the user's geographical location information. For example, if the user lives in a specific city, the comparison unit will prioritize comparing skills related to that city. It can also compare skills related to a specific region if the user works in that region. For example, if the user is traveling, the comparison unit will compare skills related to their travel destination. This allows for efficient comparison of highly relevant skills by considering the user's geographical location information. Some or all of the above processing in the comparison unit may be performed using AI, or not. For example, the comparison unit can select a comparison method using an AI model that takes the user's geographical location information as input and outputs the optimal comparison method.

[0062] The identification unit can select the optimal identification method when identifying skill gaps by referring to the user's past skill history. For example, the identification unit can propose the optimal skill gap identification method based on the skills the user has acquired in the past. The identification unit can also prioritize the identification of relevant skill gaps from the user's past skill history. For example, the identification unit can analyze the user's past skill history and propose the most efficient skill gap identification method. This allows for the selection of the optimal identification method and efficient identification of skill gaps by referring to the user's past skill history. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can select an identification method using an AI model that takes the user's past skill history as input and outputs the optimal identification method.

[0063] The identification unit can select the optimal identification method when identifying skill gaps, taking into account the user's geographical location information. For example, if the user lives in a specific city, the identification unit will prioritize identifying skill gaps related to that city. Furthermore, if the user works in a specific region, the identification unit can identify skill gaps related to that region. For example, if the user is traveling, the identification unit will identify skill gaps related to the travel destination. This allows for the efficient identification of highly relevant skill gaps by considering the user's geographical location information. Some or all of the above processing in the identification unit may be performed using AI, or not. For example, the identification unit can select an identification method using an AI model that takes the user's geographical location information as input and outputs the optimal identification method.

[0064] The service provider can select the optimal delivery method by referring to the user's past learning history when providing training and qualification acquisition information. For example, the service provider can suggest the most suitable training information based on the training the user has previously taken. The service provider can also prioritize providing relevant training information based on the user's past learning history. For example, the service provider can analyze the user's past learning history and suggest the most efficient training information. In this way, by referring to the user's past learning history, the service provider can select the optimal delivery method and provide information efficiently. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can select a delivery method using an AI model that takes the user's past learning history as input and outputs the optimal delivery method.

[0065] The service provider can select the optimal delivery method when providing training and qualification information, taking into account the user's geographical location. For example, if a user lives in a specific city, the service provider will prioritize providing training information related to that city. Similarly, if a user works in a specific region, the service provider can provide training information related to that region. For example, if a user is traveling, the service provider will provide training information related to their travel destination. This allows for the efficient provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a delivery method using an AI model that takes the user's geographical location as input and outputs the optimal delivery method.

[0066] The service provider can select the optimal delivery method when providing training or qualification information, taking into account the user's health condition. For example, if the user is tired, the service provider can provide training information that can be completed in a short time. The service provider can also provide physical training information if the user is seeking healthy exercise. For example, if the user is unwell, the service provider can provide training information that can be taken online. This allows the service provider to provide information appropriate to the user by considering their health condition. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a delivery method using an AI model that takes the user's health condition as input and outputs the optimal delivery method.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The data collection unit can analyze the user's past information provision history and select the optimal data collection method. For example, if the user has preferred using text input in the past, the data collection unit will prioritize suggesting text input. It can also recommend voice input if the user has frequently used voice input in the past. For example, if the data collection unit has provided information during a specific time period in the past, it will collect data during that time period. This allows the system to efficiently collect information by analyzing the user's past information provision history and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can select a data collection method using an AI model that takes the user's past information provision history as input and outputs the optimal data collection method.

[0069] The data collection unit can filter information based on the user's current occupation and areas of interest during the information gathering process. For example, if the user is interested in marketing, the data collection unit will prioritize collecting marketing-related information. It can also collect IT-related information if the user works in the IT industry. For example, if the user is interested in education, the data collection unit will collect education-related information. This allows for the efficient collection of highly relevant information by filtering it based on the user's current occupation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can filter information using an AI model that takes the user's occupation and areas of interest as input and outputs relevant information.

[0070] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user lives in a specific city, the data collection unit will prioritize collecting information related to that city. Similarly, if the user works in a specific region, the data collection unit can collect information related to that region. For example, if the user is traveling, the data collection unit will collect information related to their travel destination. This allows for the efficient collection of highly relevant information by considering the user's geographical location. 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 collect information using an AI model that takes the user's geographical location as input and outputs relevant information.

[0071] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information based on the user's interests and passions shared on social media. The data collection unit can also analyze the content of accounts that a user follows and collect relevant information. For example, the data collection unit can collect information by referring to the activities of groups and communities that a user participates in. This allows for the efficient collection of relevant information by analyzing a 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 collect information using an AI model that takes a user's social media activity as input and outputs relevant information.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit will perform a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit will perform a detailed analysis on information of high interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the information, a detailed analysis can be performed on important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes the importance of the information as input and outputs the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a marketing analysis algorithm to marketing information. For example, it can apply an educational analysis algorithm to educational information. By applying different analysis algorithms depending on the category of information, the analysis unit can perform the most optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can perform analysis using an AI model that takes the category of information as input and outputs the analysis algorithm to be applied.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit collects user information. This information includes resumes, work history, performance data, and personality analysis. The data collection unit can collect information through surveys and interviews, as well as using data mining techniques to gather information from users' online activities. For example, the data collection unit can analyze users' social media activity and collect relevant information. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the user's skills, experience, interests, values, career goals, etc. Step 3: The Proposal Department proposes specific positions based on the analysis results obtained by the Analysis Department. The Proposal Department performs skill matching and career path proposals, suggesting the optimal career path and occupation based on the user's skills, experience, interests, values, and career goals. Step 4: The reception department receives requests from companies. The reception department receives requests from companies through online forms or telephone inquiries. Step 5: The recruitment department introduces highly suitable candidates based on the requests received by the reception department. The recruitment department uses matching algorithms and recommendation systems to introduce candidates that meet the company's requirements.

[0076] (Example of form 2) The career matching and coaching system according to an embodiment of the present invention is a system that proposes the optimal career path and occupation based on an individual's skills, interests, values, and career goals. This system collects information such as the user's resume, work history, performance data, and personality analysis, and the AI ​​analyzes this data to propose specific positions. Companies also input their requirements for the type of personnel they are looking for, and the AI ​​introduces highly matched candidates. Furthermore, as a coaching function, it compares the user's current skill set with the skills required for their desired career path and analyzes the skill gap. The AI ​​then suggests necessary training and qualification acquisition. As a result, users can find occupations and career paths that suit them and lead fulfilling work lives without stress. Thus, the career matching and coaching system can efficiently collect, analyze, propose, receive, and introduce user information.

[0077] The career matching and coaching system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a referral unit. The collection unit collects user information. User information includes, but is not limited to, resumes, work history, performance data, and personality analysis. The collection unit collects information, for example, through questionnaires and interviews. The collection unit can also collect information from users' online activities using data mining techniques. For example, the collection unit analyzes users' social media activities and collects relevant information. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the user's skills, experience, interests, values, and career goals, and generates data to propose specific positions. The proposal unit proposes specific positions based on the analysis results obtained by the analysis unit. The proposal unit performs, for example, skill matching and career path proposals. For example, the proposal unit proposes the optimal career path and occupation based on the user's skills, experience, interests, values, and career goals. The reception department receives requests from companies. The reception department receives requests from companies, for example, through online forms or telephone inquiries. The introduction department introduces highly suitable candidates based on the requests received by the reception department. The introduction department introduces candidates that meet the company's requirements, for example, using matching algorithms or recommendation systems. As a result, the career matching and coaching system according to this embodiment can efficiently collect, analyze, propose, receive, and introduce user information.

[0078] The data collection unit collects user information. This information includes, but is not limited to, resumes, work history, performance data, and personality analyses. The unit collects information through methods such as surveys and interviews. Specifically, it uses online survey forms to ask users detailed questions and stores the responses in a database. Interviews are conducted via video call or in person, with interviewers recording the user's responses. Furthermore, the data collection unit can also use data mining techniques to collect information from users' online activities. For example, it analyzes users' social media activity and collects relevant information. Specifically, it analyzes users' posts, comments, and like history to understand their interests and values. It also collects activity history from online communities and forums that users participate in to evaluate their expertise and skills. This allows the data collection unit to efficiently collect multifaceted information about users and provide it to the analysis unit. In addition, the data collection unit implements strict security measures in data collection and storage to protect user privacy. For example, data is encrypted and stored on secure servers. Furthermore, information is collected only with the user's consent, and the purpose of using the collected data is clearly defined. This allows the data collection unit to effectively collect necessary information while gaining the user's trust.

[0079] The analysis department analyzes the information collected by the data collection department. For example, the analysis department uses statistical analysis and machine learning algorithms to analyze the information. Specifically, it analyzes users' skills, experience, interests, values, and career goals to generate data for suggesting specific positions. For instance, it uses machine learning algorithms to analyze users' resumes and work histories to evaluate past work experience and skill sets. It also analyzes users' personality traits and behavioral patterns based on personality analysis data to identify suitable job types and work environments. Furthermore, the analysis department analyzes users' performance data to evaluate past achievements and results. This clarifies users' strengths and weaknesses and generates foundational data for suggesting optimal career paths. The analysis department integrates this data to create a comprehensive user profile. Additionally, the analysis department can predict future career possibilities and growth opportunities based on historical data and industry trends. For example, it can predict how specific skill sets will be valued in the future market and provide users with advice for skill development. The analysis department also collects user feedback to continuously improve the accuracy of its analysis algorithms. This allows the analysis unit to provide users with more accurate career recommendations.

[0080] The Proposal Department proposes specific positions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department provides skill matching and career path suggestions. Specifically, it proposes optimal career paths and occupations based on the user's skills, experience, interests, values, and career goals. For instance, it suggests job types with high demand in the current market based on the user's specific skill set. It also designs short-term and long-term career paths and proposes specific steps according to the user's career goals. Furthermore, the Proposal Department can also suggest companies with suitable work environments and corporate cultures, taking into account the user's interests and values. This allows users to work in workplaces where they can maximize their skills and experience. The Proposal Department clearly explains the suggestions to the user and provides concrete action plans. For example, it introduces training programs and educational institutions to acquire the skills and qualifications necessary for the suggested occupations. The Proposal Department also collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. This enables the Proposal Department to provide users with optimal career suggestions and support their career success.

[0081] The reception department receives requests from companies. For example, it receives requests through online forms and telephone inquiries. Specifically, it collects detailed requirements for the personnel that companies are seeking, such as skill sets, experience, qualifications, and personality traits. The online form provides a user-friendly interface to allow companies to easily enter their requirements. Telephone inquiries are handled by specialized staff who listen to company requests and record detailed information. Furthermore, the reception department stores company requests in a database, making it accessible to the analysis and placement departments. This ensures a smooth matching process based on company requests. The reception department conducts regular follow-ups and collects feedback to facilitate smooth communication with companies. For example, it checks whether suitable personnel have been found for the requests submitted by companies and accepts revisions or additional requests as needed. The reception department also strives to respond quickly to company requests and spares no effort in increasing company satisfaction. This allows the reception department to respond flexibly to company needs and achieve optimal matching for both companies and users.

[0082] The recruitment department introduces highly suitable candidates based on requests received by the reception department. For example, the recruitment department uses matching algorithms and recommendation systems to introduce candidates that meet the company's requirements. Specifically, it searches user information in its database based on the company's requirements such as skill sets, experience, and personality traits to identify the most suitable candidates. The matching algorithm evaluates the user's skills, experience, and personality traits and calculates the degree of match against the company's requirements. The recommendation system recommends the most suitable candidate based on past matching data and success stories. This allows the recruitment department to respond quickly and accurately to company requests and introduce the most suitable candidates. Furthermore, the recruitment department coordinates the interview and selection process between companies and candidates, supporting smooth communication. For example, it schedules interviews and prepares necessary documents, ensuring a smooth selection process for both companies and candidates. The recruitment department also collects feedback on the matching results and continuously improves the accuracy of its matching algorithms and recommendation systems. This allows the recruitment department to achieve optimal matching for both companies and users, maximizing the effectiveness of the career matching and coaching system.

[0083] The comparison unit can compare the user's current skill set with the skills required for their desired career path. For example, the comparison unit can store the user's skill set in a database and compare it with the skills required for their desired career path. The comparison unit can also compare the user's skill set with the skills required for their desired career path in real time. For example, when the user acquires a new skill, the comparison unit updates the skill set and compares it with the skills required for their desired career path. This allows the system to identify skill gaps by comparing the user's skill set with the skills required for their desired career path. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can identify skill gaps using an AI model that takes the user's skill set and the skills required for their desired career path as input and outputs the skill gap.

[0084] The identification unit can identify skill gaps. For example, the identification unit can identify skill gaps by comparing the user's current skill set with the skills required for their desired career path. The identification unit can also identify skill gaps by comparing the user's skill set with the skills required for their desired career path in real time. For example, when a user acquires a new skill, the identification unit updates the skill set and identifies the skill gap. This makes it possible to clarify the skills the user needs by identifying the skill gap. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can identify skill gaps using an AI model that takes the user's skill set and the skills required for their desired career path as input and outputs the skill gap.

[0085] The service provider can provide information on necessary training and certifications. For example, the service provider can identify a user's skill gaps and provide information on necessary training and certifications. The service provider can also identify a user's skill gaps in real time and provide information on necessary training and certifications. For example, when a user acquires a new skill, the service provider updates the skill gap and provides information on necessary training and certifications. This allows users to obtain concrete means to improve their skills by providing information on necessary training and certifications. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes a user's skill gaps as input and outputs information on necessary training and certifications.

[0086] The suggestion department can propose optimal career paths and occupations based on the user's skills, experience, interests, values, and career goals. For example, if the suggestion department analyzes the user's skills, experience, interests, values, and career goals, it will suggest the most suitable career paths and occupations. For instance, if the user inputs "I have marketing experience and am interested in creative work," the suggestion department will suggest positions such as "Creative Director" or "Marketing Manager." The suggestion department can also propose career paths based on the user's skills, experience, interests, values, and career goals. For example, if the user aims to become a "Creative Director," the suggestion department will identify a lack of "Project Management" skills and propose "Project Management" training. In this way, by suggesting optimal career paths and occupations based on the user's skills, experience, interests, values, and career goals, the user can find an occupation that suits them. Some or all of the above processing in the suggestion department is performed using generative AI. For example, the suggestion department can make suggestions using a generative AI model that takes the user's skills, experience, interests, values, and career goals as input and outputs the most suitable career paths and occupations.

[0087] The recruitment department can introduce highly suitable candidates based on the company's requirements. For example, if a company inputs "We want someone with marketing experience and an interest in creative work," the department will introduce candidates who match positions such as "Creative Director" or "Marketing Manager." The recruitment department can also recommend highly suitable candidates based on the company's requirements. For example, if a company inputs "We want someone with project management skills," the department will recommend candidates who match positions such as "Project Manager" or "Project Leader." This improves the accuracy of matching companies with job seekers by introducing highly suitable candidates based on the company's requirements. Some or all of the above processes in the recruitment department may be performed using AI or not. For example, the recruitment department can introduce candidates using an AI model that takes a company's requirements as input and outputs highly suitable candidates.

[0088] 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 the timing of information collection so that the user can input information in a relaxed state. Conversely, if the user is focused, the data collection unit can start collecting information immediately and acquire data efficiently. For example, if the user is tired, the data collection unit can adjust the timing to collect information after a break. In this way, by adjusting the timing of information collection based on the user's emotions, information can be provided to the user in an optimal state. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can analyze the user's past information provision history and select the optimal data collection method. For example, if the user has preferred using text input in the past, the data collection unit will prioritize suggesting text input. It can also recommend voice input if the user has frequently used voice input in the past. For example, if the data collection unit has provided information during a specific time period in the past, it will collect data during that time period. This allows the system to efficiently collect information by analyzing the user's past information provision history and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can select a data collection method using an AI model that takes the user's past information provision history as input and outputs the optimal data collection method.

[0090] The data collection unit can filter information based on the user's current occupation and areas of interest during the information gathering process. For example, if the user is interested in marketing, the data collection unit will prioritize collecting marketing-related information. It can also collect IT-related information if the user works in the IT industry. For example, if the user is interested in education, the data collection unit will collect education-related information. This allows for the efficient collection of highly relevant information by filtering it based on the user's current occupation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can filter information using an AI model that takes the user's occupation and areas of interest as input and outputs relevant information.

[0091] 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 excited, the data collection unit will prioritize collecting information that is of interest to them. Conversely, if the user is relaxed, the data collection unit can also collect detailed information. For example, if the user is stressed, the data collection unit will prioritize collecting concise and important information. In this way, by prioritizing the information to be collected based on the user's emotions, information that is important to the user can be collected preferentially. 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 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 to determine the priority of information.

[0092] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user lives in a specific city, the data collection unit will prioritize collecting information related to that city. Similarly, if the user works in a specific region, the data collection unit can collect information related to that region. For example, if the user is traveling, the data collection unit will collect information related to their travel destination. This allows for the efficient collection of highly relevant information by considering the user's geographical location. 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 collect information using an AI model that takes the user's geographical location as input and outputs relevant information.

[0093] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information based on the user's interests and passions shared on social media. The data collection unit can also analyze the content of accounts that a user follows and collect relevant information. For example, the data collection unit can collect information by referring to the activities of groups and communities that a user participates in. This allows for the efficient collection of relevant information by analyzing a 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 collect information using an AI model that takes a user's social media activity as input and outputs relevant information.

[0094] 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 tense, the analysis unit can provide simple and highly visual analysis results. It can also provide detailed analysis results if the user is relaxed. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis based on the user's emotions, the system can provide analysis results that are easy for the user to understand. 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-described processes 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 and adjust the presentation of the analysis.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit will perform a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit will perform a detailed analysis on information of high interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the information, a detailed analysis can be performed on important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes the importance of the information as input and outputs the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a marketing analysis algorithm to marketing information. For example, it can apply an educational analysis algorithm to educational information. By applying different analysis algorithms depending on the category of information, the analysis unit can perform the most optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can perform analysis using an AI model that takes the category of information as input and outputs the analysis algorithm to be applied.

[0097] 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 also provide a detailed analysis. For example, if the user is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and adjust the length of the analysis.

[0098] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit can prioritize the analysis of the latest information. It can also postpone the analysis of older information. For example, the analysis unit can determine the priority of analysis based on the submission timing specified by the user. This allows for the prioritization of analysis of the latest information by determining the priority of analysis based on the information submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can determine the priority of analysis using an AI model that takes the information submission timing as input and outputs the analysis priority.

[0099] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information of high interest to the user. It can also postpone the analysis of less relevant information. For example, the analysis unit can adjust the order of analysis based on the relevance specified by the user. This allows for the prioritization of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the order of analysis using an AI model that takes the relevance of information as input and outputs the order of analysis.

[0100] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is tense, the suggestion unit can provide simple and highly visual suggestions. If the user is relaxed, it can also provide detailed suggestions. If the user is excited, for example, the suggestion unit can provide visually appealing suggestions. By adjusting the way suggestions are presented based on the user's emotions, suggestions that are easy for the user to understand 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and adjust the way suggestions are presented.

[0101] The proposal unit can adjust the level of detail of a proposal based on the importance of the position. For example, the proposal unit will provide a detailed proposal for important positions. It can also provide a concise proposal for general positions. For example, the proposal unit will provide a detailed proposal for positions of high user interest. By adjusting the level of detail of a proposal based on the importance of the position, it is possible to provide detailed proposals for important positions. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can adjust the level of detail of a proposal using an AI model that takes the importance of the position as input and outputs the level of detail of the proposal.

[0102] The proposal unit can apply different proposal algorithms depending on the position category when making a proposal. For example, the proposal unit can apply a technical proposal algorithm to technical positions. It can also apply a marketing proposal algorithm to marketing positions. For example, it can apply an educational proposal algorithm to educational positions. By applying different proposal algorithms depending on the position category, it is possible to make proposals that are optimal for each category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can make proposals using an AI model that takes the position category as input and outputs the proposal algorithm to be applied.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide more detailed suggestions. If the user is excited, for example, the suggestion unit will provide visually appealing suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. 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 processing described above 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 and adjust the length of suggestions.

[0104] The proposal department can determine the priority of proposals based on the submission timing of the positions. For example, the proposal department will prioritize proposals for the most recent positions. It can also postpone proposals for older positions. For example, the proposal department can determine the priority of proposals based on the submission timing specified by the user. This allows for prioritizing proposals for the most recent positions by determining the priority based on the submission timing of the positions. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can determine the priority of proposals using an AI model that takes the submission timing of the positions as input and outputs the priority of proposals.

[0105] The proposal unit can adjust the order of proposals based on the relevance of the positions when making proposals. For example, the proposal unit will prioritize proposals for positions of high user interest. Conversely, the proposal unit can also postpone proposals for less relevant positions. For example, the proposal unit can adjust the order of proposals based on the relevance specified by the user. This allows for prioritizing the proposal of highly relevant positions by adjusting the order of proposals based on the relevance of the positions. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can adjust the order of proposals using an AI model that takes the relevance of positions as input and outputs the order of proposals.

[0106] The reception department can analyze a company's past request history and select the most suitable reception method when receiving a request from a company. For example, the reception department can automatically suggest requests based on the skill sets that the company has frequently requested in the past. The reception department can also prioritize suggesting request input methods (text, voice, etc.) that the company has used in the past. For example, the reception department can predict and suggest requests to be used during specific time periods based on the company's past request history. This allows for the selection of the most suitable reception method by analyzing the company's past request history, enabling efficient request processing. Some or all of the above processes in the reception department may be performed using AI, or they may not. For example, the reception department can select a reception method using an AI model that takes the company's past request history as input and outputs the most suitable reception method.

[0107] The reception desk can filter requests from companies based on their industry and size. For example, it might request detailed requests from large companies, while requesting more concise requests from small and medium-sized enterprises. For instance, it might prioritize suggesting requests relevant to a specific industry. This allows for efficient reception of highly relevant requests by filtering based on the company's industry and size. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk could filter requests using an AI model that takes the company's industry and size as input and outputs filtered results.

[0108] The reception desk can prioritize requests from companies by considering the company's geographical location. For example, if a company is located in a specific city, the reception desk will prioritize requests related to that city. Similarly, if a company operates in a specific region, the reception desk can prioritize requests related to that region. For example, if a company operates internationally, the reception desk will prioritize global requests. This allows for the efficient reception of highly relevant requests by considering the company's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can receive requests using an AI model that takes a company's geographical location as input and outputs relevant requests.

[0109] The reception department can analyze a company's social media activity and receive relevant requests when receiving requests from companies. For example, the reception department can receive relevant requests based on requests that companies have shared on social media. The reception department can also analyze the content of accounts that companies follow and receive relevant requests. For example, the reception department can receive requests by referring to the activities of groups and communities that companies participate in. In this way, relevant requests can be efficiently received by analyzing a company's social media activity. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can receive requests using an AI model that takes a company's social media activity as input and outputs relevant requests.

[0110] The introduction section can estimate the user's emotions and adjust the presentation of the introduction based on those emotions. For example, if the user is nervous, the introduction section can provide a simple and visually clear introduction. If the user is relaxed, it can provide a more detailed introduction. If the user is excited, for example, the introduction section can provide a visually appealing introduction. By adjusting the presentation of the introduction based on the user's emotions, the introduction can be made easier for the user to understand. 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 introduction section may be performed using AI or not. For example, the introduction section can input user emotion data into a generative AI and adjust the presentation of the introduction.

[0111] The referral function can adjust the level of detail in a referral based on the importance of the position. For example, the referral function will provide a detailed referral for important positions. It can also provide a concise referral for general positions. For example, the referral function will provide a detailed referral for positions of high user interest. By adjusting the level of detail in a referral based on the importance of the position, it is possible to provide detailed referrals for important positions. Some or all of the above processing in the referral function may be performed using AI or not. For example, the referral function can adjust the level of detail in a referral using an AI model that takes the importance of the position as input and outputs the level of detail in the referral.

[0112] The referral system can apply different referral algorithms depending on the position category during the referral process. For example, it can apply a technical referral algorithm to technical positions, a marketing referral algorithm to marketing positions, and an education referral algorithm to education positions. By applying different referral algorithms depending on the position category, the system can provide the most suitable referrals for each category. Some or all of the above-described processes in the referral system may be performed using AI, or they may not. For example, the referral system can perform referrals using an AI model that takes the position category as input and outputs the referral algorithm to apply.

[0113] The introduction section can estimate the user's emotions and adjust the length of the introduction based on those emotions. For example, if the user is in a hurry, the introduction section can provide a short, concise introduction. If the user is relaxed, it can provide a more detailed introduction. If the user is excited, for example, the introduction section can provide a visually appealing introduction. By adjusting the length of the introduction based on the user's emotions, the introduction can be of an appropriate length for the user. 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 processing described above in the introduction section may be performed using AI or not. For example, the introduction section can input user emotion data into a generative AI and adjust the length of the introduction.

[0114] The referral system can determine the priority of referrals based on the timing of position submissions. For example, the referral system will prioritize referrals for the most recent positions. It can also postpone referrals for older positions. For example, the referral system can determine the priority of referrals based on the submission timing specified by the user. This allows for priority referrals for the most recent positions by determining the priority based on the submission timing. Some or all of the above processes in the referral system may be performed using AI or not. For example, the referral system can determine the priority of referrals using an AI model that takes the position submission timing as input and outputs the priority of referrals.

[0115] The referral system can adjust the order of referrals based on the relevance of the positions. For example, the referral system will prioritize referrals to positions of high user interest. It can also postpone referrals to less relevant positions. For example, the referral system can adjust the order of referrals based on the relevance specified by the user. This allows for the preferential referral of highly relevant positions by adjusting the order of referrals based on the relevance of the positions. Some or all of the above processing in the referral system may be performed using AI or not. For example, the referral system can adjust the order of referrals using an AI model that takes the relevance of positions as input and outputs the order of referrals.

[0116] The comparison unit can estimate the user's emotions and adjust the skill comparison method based on the estimated emotions. For example, if the user is nervous, the comparison unit can provide a simple and visually clear skill comparison. If the user is relaxed, the comparison unit can also provide a detailed skill comparison. For example, if the user is excited, the comparison unit can provide a visually appealing skill comparison. In this way, by adjusting the skill comparison method based on the user's emotions, a skill comparison that is easy for the user to understand 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 comparison unit may be performed using AI or not. For example, the comparison unit can input user emotion data into a generative AI and adjust the skill comparison method.

[0117] The comparison unit can select the optimal comparison method by referring to the user's past skill history when comparing skills. For example, the comparison unit can propose the optimal comparison method based on the skills the user has acquired in the past. The comparison unit can also prioritize the comparison of relevant skills from the user's past skill history. For example, the comparison unit can analyze the user's past skill history and propose the most efficient comparison method. This allows for efficient skill comparison by selecting the optimal comparison method by referring to the user's past skill history. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can select a comparison method using an AI model that takes the user's past skill history as input and outputs the optimal comparison method.

[0118] The comparison unit can estimate the user's emotions and determine the priority of skill comparisons based on the estimated emotions. For example, if the user is excited, the comparison unit will prioritize comparing skills that are of interest. Conversely, if the user is relaxed, the comparison unit can perform detailed skill comparisons. For example, if the user is stressed, the comparison unit will prioritize comparing concise and important skills. This allows for prioritizing skill comparisons based on the user's emotions, thereby ensuring that skills important to the user are prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input user emotion data into a generative AI to determine the priority of skill comparisons.

[0119] The comparison unit can select the optimal comparison method when comparing skills, taking into account the user's geographical location information. For example, if the user lives in a specific city, the comparison unit will prioritize comparing skills related to that city. It can also compare skills related to a specific region if the user works in that region. For example, if the user is traveling, the comparison unit will compare skills related to their travel destination. This allows for efficient comparison of highly relevant skills by considering the user's geographical location information. Some or all of the above processing in the comparison unit may be performed using AI, or not. For example, the comparison unit can select a comparison method using an AI model that takes the user's geographical location information as input and outputs the optimal comparison method.

[0120] The identification unit can estimate the user's emotions and adjust the method of identifying skill gaps based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visual identification of skill gaps. It can also provide a more detailed identification of skill gaps if the user is relaxed. For example, if the user is excited, the identification unit can provide a visually appealing identification of skill gaps. This allows for the provision of skill gap identification that is easy for the user to understand by adjusting the method of identifying skill gaps based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into a generative AI and adjust the method of identifying skill gaps.

[0121] The identification unit can select the optimal identification method when identifying skill gaps by referring to the user's past skill history. For example, the identification unit can propose the optimal skill gap identification method based on the skills the user has acquired in the past. The identification unit can also prioritize the identification of relevant skill gaps from the user's past skill history. For example, the identification unit can analyze the user's past skill history and propose the most efficient skill gap identification method. This allows for the selection of the optimal identification method and efficient identification of skill gaps by referring to the user's past skill history. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can select an identification method using an AI model that takes the user's past skill history as input and outputs the optimal identification method.

[0122] The identification unit can estimate the user's emotions and prioritize skill gaps based on the estimated emotions. For example, if the user is excited, the identification unit will prioritize identifying skill gaps that are of interest to the user. The identification unit can also perform detailed skill gap identification if the user is relaxed. For example, if the user is stressed, the identification unit will prioritize identifying concise and important skill gaps. This allows for the prioritization of skill gaps that are important to the user by determining the priority of skill gaps based on 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 identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into a generative AI to determine the priority of skill gaps.

[0123] The identification unit can select the optimal identification method when identifying skill gaps, taking into account the user's geographical location information. For example, if the user lives in a specific city, the identification unit will prioritize identifying skill gaps related to that city. Furthermore, if the user works in a specific region, the identification unit can identify skill gaps related to that region. For example, if the user is traveling, the identification unit will identify skill gaps related to the travel destination. This allows for the efficient identification of highly relevant skill gaps by considering the user's geographical location information. Some or all of the above processing in the identification unit may be performed using AI, or not. For example, the identification unit can select an identification method using an AI model that takes the user's geographical location information as input and outputs the optimal identification method.

[0124] The service provider can estimate the user's emotions and adjust the way training and qualification information is delivered based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and highly visual training information. If the user is relaxed, the service provider can also provide detailed training information. For example, if the user is excited, the service provider can provide visually appealing training information. By adjusting the way training and qualification information is delivered based on the user's emotions, information that is easy for the user to understand 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and adjust the way training and qualification information is delivered.

[0125] The service provider can select the optimal delivery method by referring to the user's past learning history when providing training and qualification acquisition information. For example, the service provider can suggest the most suitable training information based on the training the user has previously taken. The service provider can also prioritize providing relevant training information based on the user's past learning history. For example, the service provider can analyze the user's past learning history and suggest the most efficient training information. In this way, by referring to the user's past learning history, the service provider can select the optimal delivery method and provide information efficiently. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can select a delivery method using an AI model that takes the user's past learning history as input and outputs the optimal delivery method.

[0126] The service provider can estimate the user's emotions and prioritize training and qualification information based on those emotions. For example, if the user is excited, the service provider will prioritize providing interesting training information. It can also provide detailed training information if the user is relaxed. For example, if the user is stressed, the service provider will prioritize providing concise and important training information. This allows the service provider to prioritize information important to the user by prioritizing training and qualification information based on their 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI to determine the priority of training and qualification information.

[0127] The service provider can select the optimal delivery method when providing training and qualification information, taking into account the user's geographical location. For example, if a user lives in a specific city, the service provider will prioritize providing training information related to that city. Similarly, if a user works in a specific region, the service provider can provide training information related to that region. For example, if a user is traveling, the service provider will provide training information related to their travel destination. This allows for the efficient provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a delivery method using an AI model that takes the user's geographical location as input and outputs the optimal delivery method.

[0128] The service provider can select the optimal delivery method when providing training or qualification information, taking into account the user's health condition. For example, if the user is tired, the service provider can provide training information that can be completed in a short time. The service provider can also provide physical training information if the user is seeking healthy exercise. For example, if the user is unwell, the service provider can provide training information that can be taken online. This allows the service provider to provide information appropriate to the user by considering their health condition. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can select a delivery method using an AI model that takes the user's health condition as input and outputs the optimal delivery method.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] 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 the timing of information collection so that the user can input information in a relaxed state. Conversely, if the user is focused, the data collection unit can start collecting information immediately and acquire data efficiently. For example, if the user is tired, the data collection unit can adjust the timing to collect information after a break. In this way, by adjusting the timing of information collection based on the user's emotions, information can be provided to the user in an optimal state. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0131] The data collection unit can analyze the user's past information provision history and select the optimal data collection method. For example, if the user has preferred using text input in the past, the data collection unit will prioritize suggesting text input. It can also recommend voice input if the user has frequently used voice input in the past. For example, if the data collection unit has provided information during a specific time period in the past, it will collect data during that time period. This allows the system to efficiently collect information by analyzing the user's past information provision history and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can select a data collection method using an AI model that takes the user's past information provision history as input and outputs the optimal data collection method.

[0132] The data collection unit can filter information based on the user's current occupation and areas of interest during the information gathering process. For example, if the user is interested in marketing, the data collection unit will prioritize collecting marketing-related information. It can also collect IT-related information if the user works in the IT industry. For example, if the user is interested in education, the data collection unit will collect education-related information. This allows for the efficient collection of highly relevant information by filtering it based on the user's current occupation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can filter information using an AI model that takes the user's occupation and areas of interest as input and outputs relevant information.

[0133] 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 excited, the data collection unit will prioritize collecting information that is of interest to them. Conversely, if the user is relaxed, the data collection unit can also collect detailed information. For example, if the user is stressed, the data collection unit will prioritize collecting concise and important information. In this way, by prioritizing the information to be collected based on the user's emotions, information that is important to the user can be collected preferentially. 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 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 to determine the priority of information.

[0134] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user lives in a specific city, the data collection unit will prioritize collecting information related to that city. Similarly, if the user works in a specific region, the data collection unit can collect information related to that region. For example, if the user is traveling, the data collection unit will collect information related to their travel destination. This allows for the efficient collection of highly relevant information by considering the user's geographical location. 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 collect information using an AI model that takes the user's geographical location as input and outputs relevant information.

[0135] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information based on the user's interests and passions shared on social media. The data collection unit can also analyze the content of accounts that a user follows and collect relevant information. For example, the data collection unit can collect information by referring to the activities of groups and communities that a user participates in. This allows for the efficient collection of relevant information by analyzing a 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 collect information using an AI model that takes a user's social media activity as input and outputs relevant information.

[0136] 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 tense, the analysis unit can provide simple and highly visual analysis results. It can also provide detailed analysis results if the user is relaxed. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis based on the user's emotions, the system can provide analysis results that are easy for the user to understand. 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-described processes 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 and adjust the presentation of the analysis.

[0137] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit will perform a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit will perform a detailed analysis on information of high interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the information, a detailed analysis can be performed on important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes the importance of the information as input and outputs the level of detail of the analysis.

[0138] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a marketing analysis algorithm to marketing information. For example, it can apply an educational analysis algorithm to educational information. By applying different analysis algorithms depending on the category of information, the analysis unit can perform the most optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can perform analysis using an AI model that takes the category of information as input and outputs the analysis algorithm to be applied.

[0139] 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 also provide a detailed analysis. For example, if the user is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and adjust the length of the analysis.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The data collection unit collects user information. This information includes resumes, work history, performance data, and personality analysis. The data collection unit can collect information through surveys and interviews, as well as using data mining techniques to gather information from users' online activities. For example, the data collection unit can analyze users' social media activity and collect relevant information. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the user's skills, experience, interests, values, career goals, etc. Step 3: The Proposal Department proposes specific positions based on the analysis results obtained by the Analysis Department. The Proposal Department performs skill matching and career path proposals, suggesting the optimal career path and occupation based on the user's skills, experience, interests, values, and career goals. Step 4: The reception department receives requests from companies. The reception department receives requests from companies through online forms or telephone inquiries. Step 5: The recruitment department introduces highly suitable candidates based on the requests received by the reception department. The recruitment department uses matching algorithms and recommendation systems to introduce candidates that meet the company's requirements.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, introduction unit, comparison unit, identification unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a specific position based on the analysis results. The reception unit receives requests from companies, for example, by the control unit 46A of the smart device 14. The introduction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and introduces personnel that match the company's requirements. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and compares the user's skill set with the skills required for the desired career path. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the skill gap. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and provides information on necessary training and qualification acquisition. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, introduction unit, comparison unit, identification unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a specific position based on the analysis results. The reception unit receives requests from companies, for example, by the control unit 46A of the smart glasses 214. The introduction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and introduces personnel that match the company's requirements. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and compares the user's skill set with the skills required for the desired career path. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the skill gap. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and provides information on necessary training and qualification acquisition. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, introduction unit, comparison unit, identification unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes a specific position based on the analysis results. The reception unit receives requests from companies by, for example, the control unit 46A of the headset terminal 314. The introduction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and introduces personnel that match the company's requirements. The comparison unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and compares the user's skill set with the skills required for the desired career path. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the skill gap. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and provides information on necessary training and qualification acquisition. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, introduction unit, comparison unit, identification unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes a specific position based on the analysis results. The reception unit receives requests from companies by, for example, the control unit 46A of the robot 414. The introduction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and introduces personnel that match the company's requirements. The comparison unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and compares the user's skill set with the skills required for the desired career path. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the skill gap. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and provides information on necessary training and qualification acquisition. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit proposes a specific position based on the analysis results obtained by the analysis unit, A reception desk that handles requests from companies, The system includes a referral department that introduces highly suitable personnel based on requests received by the aforementioned reception department. A system characterized by the following features. (Note 2) It includes a comparison section that compares the user's current skill set with the skills required for their desired career path. The system described in Appendix 1, characterized by the features described herein. (Note 3) The comparison unit is, It includes a section for identifying skill gaps. The system described in Appendix 2, characterized by the features described herein. (Note 4) The specified part is, We have a department that provides information on necessary training and qualification acquisition. The system described in Appendix 3, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the user's skills, experience, interests, values, and career goals, we propose the most suitable career path and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned introductory section is, We introduce highly suitable candidates based on the company's requirements. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) 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 9) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current occupation and areas of interest. 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 prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) 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 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, 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 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the position. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the position category. 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 length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the position was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the position. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is When receiving requests from companies, we analyze their past request history and select the most suitable method of receiving them. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is When receiving requests from companies, we filter them based on the company's industry and size. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is When receiving requests from companies, we prioritize requests that are highly relevant, taking into account the companies' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is When receiving requests from companies, we analyze their social media activities and receive requests related to those activities. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned introductory section is, The system estimates the user's emotions and adjusts the presentation of the introduction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned introductory section is, When making a referral, adjust the level of detail based on the importance of the position. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned introductory section is, When making a referral, a different referral algorithm is applied depending on the position category. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned introductory section is, It estimates the user's emotions and adjusts the length of the introduction based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned introductory section is, When making a referral, we will prioritize referrals based on when the position was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned introductory section is, When making introductions, adjust the order of introductions based on the relevance of the positions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The comparison unit is, We estimate the user's emotions and adjust the skill comparison method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The comparison unit is, When comparing skills, the system selects the optimal comparison method by referring to the user's past skill history. The system described in Appendix 2, characterized by the features described herein. (Note 37) The comparison unit is, It estimates the user's emotions and determines the priority of skill comparisons based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The comparison unit is, When comparing skills, the optimal comparison method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 39) The specified part is, We estimate user sentiment and adjust the method for identifying skill gaps based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 40) The specified part is, When identifying skill gaps, the optimal identification method is selected by referring to the user's past skill history. The system described in Appendix 3, characterized by the features described herein. (Note 41) The specified part is, The system estimates user sentiment and prioritizes skill gaps based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 42) The specified part is, When identifying skill gaps, the optimal identification method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned supply unit is, It estimates the user's emotions and adjusts how training and qualification information are provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned supply unit is, When providing training and certification information, the system selects the optimal delivery method by referring to the user's past learning history. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned supply unit is, It estimates the user's emotions and prioritizes training and certification information based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned supply unit is, When providing training and certification information, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned supply unit is, When providing training and certification information, the optimal delivery method will be selected considering the user's health condition. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0214] 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, A proposal unit proposes a specific position based on the analysis results obtained by the analysis unit, A reception desk that handles requests from companies, The system includes a referral department that introduces highly suitable personnel based on requests received by the aforementioned reception department. A system characterized by the following features.

2. It includes a comparison section that compares the user's current skill set with the skills required for their desired career path. The system according to feature 1.

3. The comparison unit is, It includes a section for identifying skill gaps. The system according to feature 2.

4. The specified part is, We have a department that provides information on necessary training and qualification acquisition. The system according to claim 3.

5. The aforementioned proposal section is, Based on the user's skills, experience, interests, values, and career goals, we propose the most suitable career path and occupation. The system according to feature 1.

6. The aforementioned introductory section is, We introduce highly suitable candidates based on the company's requirements. The system according to feature 1.

7. 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 according to feature 1.

8. The aforementioned collection unit is Analyze the user's past information provision history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current occupation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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