system

Abstract

The system according to this embodiment aims to propose an optimal workplace tailored to the individual needs and constraints of senior citizens. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as resume data, interview results, work history, hobbies, and health status. The analysis unit comprehensively analyzes the information collected by the collection unit. The proposal unit proposes the most suitable workplace based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 is difficult to find an optimal workplace that meets the individual needs and constraints of the senior generation, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal workplace that meets the individual needs and constraints of the senior generation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as resume data, interview results, work history, hobbies, and health status. The analysis unit comprehensively analyzes the information collected by the collection unit. The proposal unit proposes an optimal workplace based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal workplace tailored to the individual needs and constraints of senior citizens. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI-Personalized Career Matching Platform according to an embodiment of the present invention is a career matching system specifically for senior citizens (OGOBs) who have retired. This system collects and comprehensively analyzes information such as resume data, interview results, work history, hobbies, and health status. Next, the system meticulously matches the recruitment needs of companies with the skill sets of OGOBs and proposes the most suitable workplace. Furthermore, the system considers the individual needs of OGOBs, such as their physical strength, desired working hours, and commuting distance, and provides flexible work options such as remote work and part-time work. The system also analyzes the skill gaps of OGOBs and proposes a personalized learning plan based on this analysis. This service will improve the employment rate of OGOBs, alleviate labor shortages in companies, and contribute to increased productivity across society. For example, the system analyzes the past work history and skill sets of OGOBs to determine what kind of work is suitable. Next, the system meticulously matches the recruitment needs of companies with the skill sets of OGOBs and proposes the most suitable workplace. For example, the system compares the skills required by companies with the skills possessed by OGOBs to find the most suitable workplace. Furthermore, the system considers the individual needs of alumni, such as their physical condition, desired working hours, and commuting distance, and provides flexible work options such as remote work and part-time work. For example, if an alumnus wishes to work from home, the system will suggest workplaces that allow remote work. The system also analyzes the alumni's skill gaps and proposes personalized learning plans based on that analysis. For example, the system provides online learning resources for alumni to acquire new skills. This service improves the employment rate of alumni and alleviates labor shortages in companies. For example, the system leverages the alumni's extensive experience and skills to provide valuable human resources to companies. The system also supports the alumni's continuous skill development, contributing to increased productivity in society as a whole. For example, the system helps alumni acquire new skills so that they can take on more advanced work.This means that the AI-powered personalized career matching platform can improve the employment rate of retired senior citizens (OGOBs), alleviate labor shortages in companies, and contribute to increased productivity across society.

[0029] The career matching system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as resume data, interview results, work history, hobbies, and health status. For example, the collection unit collects resume data in digital format. The collection unit can also store interview results in a database and use them for analysis. Furthermore, the collection unit can collect work history information as detailed information such as past employers, positions, and job duties. For example, the collection unit scans and digitizes resume data and stores it in a database. Interview results are collected as data including interviewer evaluations and questions asked. Work history information records past employers, positions, and job duties in detail. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input resume data into AI, which can analyze the data and extract necessary information. The analysis unit comprehensively analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, statistical analysis or machine learning algorithms. Furthermore, the analysis department can evaluate the skill sets and health status of alumni based on the collected information. In addition, the analysis department can conduct analyses to find suitable workplaces by considering the alumni's hobbies and interests. For example, the analysis department can use statistical analysis to evaluate the alumni's skill sets. Machine learning algorithms can predict the alumni's health status based on the collected data. Analysis based on hobbies and interests is conducted to find workplaces where alumni can enjoy working. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected data into an AI, which can analyze the data to evaluate skill sets and health status. The proposal department proposes the most suitable workplaces based on the analysis results obtained by the analysis department. The proposal department, for example, can find the most suitable workplaces by closely matching companies' recruitment needs with the alumni's skill sets. The proposal department can also offer flexible work options such as remote work or part-time work, taking into account the alumni's individual needs, such as their physical condition, desired working hours, and commuting distance.Furthermore, the proposal department can analyze the skill gaps of former employees (OGOBs) and propose personalized learning plans based on that analysis. For example, the proposal department can compare the recruitment needs of companies with the skill sets of OGOBs and propose the most suitable workplaces. Remote work and part-time work options are provided based on the OGOBs' physical capabilities and desired working hours. Skill gap analysis is performed to propose learning plans for OGOBs to acquire new skills. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the analysis results into AI, which can then propose the most suitable workplaces. This allows the career matching system according to the embodiment to collect and comprehensively analyze information such as resume data, interview results, work history, hobbies, and health status, and propose the most suitable workplaces.

[0030] The data collection department collects information such as resume data, interview results, work history, hobbies, and health status. For example, the department collects resume data in digital format. Specifically, it scans and digitizes paper resumes submitted by applicants and converts them into text data using OCR (optical character recognition) technology. This allows even handwritten resumes to be treated as digital data. The department also stores interview results in a database and uses them for analysis. Interview results include evaluation sheets entered by interviewers, as well as audio and video recordings of interviews converted into text. Furthermore, the department can collect work history information as detailed information such as past employers, positions, and job duties. For example, it scans and digitizes the work history documents provided by applicants and stores them in a database. Work history information includes detailed records of employment periods, responsibilities, and achievements. Information on hobbies and health status is also collected through forms and questionnaires filled out by applicants. For example, information on hobbies is collected in a format where applicants enter free-form text or select from a set of options. Information regarding health status is collected through self-reporting by applicants or through the submission of health check results. 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 input resume data into AI, which can then analyze the data and extract the necessary information. The AI ​​can use OCR technology to recognize handwritten characters and convert them into text data. It can also use speech recognition technology to convert audio data of interview results into text and automatically analyze the contents of evaluation sheets. This allows the data collection unit to efficiently collect data in various formats and manage it centrally as digital data.

[0031] The Analysis Department comprehensively 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 uses statistical analysis to understand the trends in applicants' skill sets and work history, and to evaluate their strengths and weaknesses. For example, it can analyze applicants' work history data to evaluate years of experience and achievements in specific industries or job types. Machine learning algorithms predict applicants' health status based on the collected data. For example, it can analyze applicants' health check results and self-reported health status to predict future health risks. Furthermore, the Analysis Department can conduct analyses to find suitable workplaces by considering applicants' hobbies and interests. For example, it can analyze data on applicants' hobbies and interests to identify companies with a work environment and corporate culture where applicants can enjoy working. Some or all of the above processes in the Analysis Department may be performed using AI, or not. For example, the Analysis Department can input the collected data into an AI, which can analyze the data to evaluate skill sets and health status. The AI ​​uses natural language processing technology to analyze text data from applicants' resumes and work histories, automatically extracting their skills and experience. It also employs machine learning algorithms to assess applicants' health status and hobbies, performing analyses to find the most suitable workplace. This allows the analytics department to efficiently and accurately analyze the collected information, providing foundational data for suggesting the best possible job to each applicant.

[0032] The Proposal Department proposes the most suitable workplaces based on the analysis results obtained by the Analysis Department. For example, the Proposal Department meticulously matches companies' recruitment needs with applicants' skill sets to find the best workplaces. Specifically, it stores job information provided by companies in a database and matches it with applicants' skill sets and work experience. Using AI, it can automatically match companies' recruitment needs with applicants' skill sets and propose the most suitable workplaces. The Proposal Department can also consider individual needs such as applicants' physical strength, desired working hours, and commuting distance, and offer flexible work options such as remote work or part-time work. For example, if an applicant wants to reduce their commute time, the Proposal Department will prioritize suggesting companies within commuting distance or companies that allow remote work. Furthermore, the Proposal Department can analyze applicants' skill gaps and propose personalized learning plans based on that. For example, it can suggest training or courses necessary for applicants to acquire specific skills, supporting them in advancing their careers. Some or all of the above processes in the Proposal Department may be performed using AI or not. For example, the Proposal Department can input the analysis results into AI, which can then propose the most suitable workplaces. The AI ​​matches company job postings with applicants' skill sets to achieve the best possible match. It also considers applicants' individual needs and skill gaps to suggest flexible work options and learning plans. This allows the proposal department to suggest the most suitable workplace for applicants and support their career advancement.

[0033] The proposal department can propose the most suitable workplace by thoroughly matching companies' recruitment needs with the skill sets of alumni. For example, the proposal department can thoroughly analyze companies' recruitment needs and match them with the skill sets of alumni. The proposal department can compare the skills required by companies with the skills possessed by alumni and propose the most suitable workplace. The proposal department can comprehensively analyze companies' recruitment needs and the skill sets of alumni and propose the most suitable workplace. This allows for a thorough matching of companies' recruitment needs and the skill sets of alumni, enabling the proposal of the most suitable workplace. Specific methods and criteria for this detailed matching include, for example, skill matching algorithms and evaluation criteria. Some or all of the above-described processes in the proposal department may be performed using AI, or not. For example, the proposal department can input companies' recruitment needs and the skill sets of alumni into an AI, which can then propose the most suitable workplace.

[0034] The proposal department can consider the individual needs of former employees (OGOBs), such as their physical fitness, desired working hours, and commuting distance, and provide flexible work options such as remote work and part-time work. For example, the proposal department can propose less strenuous work arrangements considering the physical fitness of OGOBs. The proposal department can propose the optimal work arrangement based on desired working hours. The proposal department can provide options such as remote work and part-time work considering commuting distance. This allows the proposal department to provide flexible work options such as remote work and part-time work, taking into account the individual needs of OGOBs, such as their physical fitness, desired working hours, and commuting distance. Specific evaluation criteria and collection methods for physical fitness include, for example, health check results and self-reporting. Specific collection methods and evaluation criteria for desired working hours include, for example, questionnaires and self-reporting. Specific evaluation criteria and collection methods for commuting distance include, for example, geographic information systems and self-reporting. Specific content and methods of providing flexible work options include, for example, remote work and part-time work. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data such as the physical condition, desired working hours, and commuting distance of former employees into the AI, which can then propose the most suitable work style.

[0035] The analysis department can analyze the skill gaps of alumni and propose personalized learning plans based on that analysis. For example, the analysis department can analyze the skill gaps of alumni in detail and propose an optimal learning plan. The analysis department can provide personalized learning resources based on the skill gaps. The analysis department can comprehensively analyze the skill gaps of alumni and propose an optimal learning plan. This allows the analysis department to analyze the skill gaps of alumni and propose a personalized learning plan based on that analysis. Specific evaluation criteria and analysis methods for skill gaps include, for example, skill matrices and self-assessments. Specific content and delivery methods for personalized learning plans include, for example, online courses and training programs. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input alumni skill gap data into AI, and the AI ​​can propose an optimal learning plan.

[0036] The proposal department can provide online learning resources to support the continuous skill development of alumni. For example, the proposal department can provide online learning resources to support the skill development of alumni. The proposal department can propose learning plans to support continuous skill development. The proposal department can provide online learning resources to comprehensively support the skill development of alumni. This allows for the provision of online learning resources to support the continuous skill development of alumni. Specific content and methods of providing online learning resources include, for example, e-learning platforms and teaching materials. Some or all of the above-described processes in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill development data into AI, which can then propose the most suitable online learning resources.

[0037] The data collection unit can analyze the user's past work history and health status and select the optimal information collection method. For example, the data collection unit can analyze the user's past work history and prioritize the collection of relevant information. The data collection unit can consider the user's health status and select an information collection method that is less burdensome. The data collection unit can comprehensively analyze the user's work history and health status and propose the optimal information collection method. This allows for efficient information collection by analyzing the user's past work history and health status and selecting the optimal information collection method. Specific selection criteria and methods for the optimal information collection method include, for example, questionnaires and interviews. 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 work history and health status data into AI, which can then select the optimal information collection method.

[0038] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize the collection of relevant information, taking into account the user's current living situation. The data collection unit can filter necessary information based on the user's areas of interest. The data collection unit can comprehensively analyze the user's living situation and areas of interest to collect the most relevant information. This allows for the collection of highly relevant information by filtering based on the user's current living situation and areas of interest. Specific details and collection methods for living situation include, for example, home environment and lifestyle habits. Specific details and collection methods for areas of interest include, for example, hobbies and topics of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's living situation and areas of interest data into AI, which can then filter the most relevant information.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of relevant information based on the user's current location. The data collection unit can collect the most relevant information by considering the user's geographical location information. The data collection unit can collect the most relevant information by comprehensively analyzing the user's location information and areas of interest. This allows for efficient data collection by prioritizing the collection of highly relevant information by considering the user's geographical location information. Specific methods for collecting and evaluating geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into AI, which can then collect the most relevant information.

[0040] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit analyzes the user's social media activity and collects relevant information. The data collection unit can collect necessary information based on the user's areas of interest on social media. The data collection unit comprehensively analyzes the user's social media activity and work history to collect the most relevant information. This allows for efficient data collection by analyzing the user's social media activity and collecting relevant information. Specific details of social media activity and collection methods include, for example, posts and follower counts. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into AI, which can then collect the most relevant information.

[0041] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on important information and a simplified analysis on less important information. The analysis unit can make a comprehensive judgment on the importance of the information and perform the optimal analysis. This allows for efficient analysis by adjusting the level of detail based on the importance of the information. Specific criteria and methods for evaluating the importance of information include, for example, impact and relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information importance data into AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a work history analysis algorithm to work history information. For health status information, it can apply a health analysis algorithm. For hobby information, it can apply a hobby analysis algorithm. This allows for efficient analysis by applying different analysis algorithms depending on the category of information. Specific classification criteria and methods for information categories include, for example, work information and personal information. Specific types and methods of implementation of analysis algorithms include, for example, regression analysis and clustering. 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 information category data into AI, and the AI ​​can apply the most suitable analysis algorithm.

[0043] The analysis department can determine the priority of analysis based on the submission date of the information. For example, the analysis department may prioritize the analysis of the most recent information. For older information, the analysis department may perform a simplified analysis. The analysis department can make a comprehensive judgment on the submission date and perform the optimal analysis. This allows for efficient analysis by determining the priority of analysis based on the submission date of the information. Specific evaluation criteria and methods for the submission date of the information include, for example, the submission date and submission frequency. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the information submission date data into AI, and the AI ​​can determine the priority of analysis.

[0044] 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 highly relevant information. For less relevant information, the analysis unit can perform a simplified analysis. The analysis unit can make a comprehensive judgment on the relevance of the information and perform the optimal analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Specific evaluation criteria and methods for information relevance include, for example, the degree of thematic agreement and related topics. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input information relevance data into AI, and the AI ​​can adjust the order of analysis.

[0045] The proposal department can propose the most suitable workplace by thoroughly matching the recruitment needs of companies with the skill sets of alumni. For example, the proposal department can thoroughly analyze the recruitment needs of companies and match them with the skill sets of alumni. The proposal department can compare the skills required by companies with the skills possessed by alumni and propose the most suitable workplace. The proposal department can comprehensively analyze the recruitment needs of companies and the skill sets of alumni and propose the most suitable workplace. This allows for a thorough matching of the recruitment needs of companies and the skill sets of alumni, enabling the proposal of the most suitable workplace. Specific methods and criteria for this detailed matching include, for example, skill matching algorithms and evaluation criteria. Some or all of the above-described processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the recruitment needs of companies and the skill sets of alumni into an AI, which can then propose the most suitable workplace.

[0046] The proposal department can, when making proposals, consider the individual needs of former employees (OGOBs), such as their physical condition, desired working hours, and commuting distance, and offer flexible work options such as remote work and part-time work. For example, the proposal department can propose a less strenuous work style considering the physical condition of the OGOBs. The proposal department can propose the optimal work style based on desired working hours. The proposal department can offer options such as remote work and part-time work considering commuting distance. This allows the proposal department to offer flexible work options such as remote work and part-time work, taking into account the individual needs of former employees (OGOBs), such as their physical condition, desired working hours, and commuting distance. Specific evaluation criteria and collection methods for physical condition include, for example, health check results and self-reporting. Specific collection methods and evaluation criteria for desired working hours include, for example, questionnaires and self-reporting. Specific evaluation criteria and collection methods for commuting distance include, for example, geographic information systems and self-reporting. Specific content and methods of providing flexible work options include, for example, remote work and part-time work. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data such as the physical condition, desired working hours, and commuting distance of former employees into the AI, which can then propose the most suitable work style.

[0047] The proposal department can analyze the skill gaps of alumni (OBs) and propose personalized learning plans based on that analysis. For example, the proposal department can analyze the skill gaps of alumni in detail and propose an optimal learning plan. The proposal department can provide personalized learning resources based on the skill gaps. The proposal department can comprehensively analyze the skill gaps of alumni and propose an optimal learning plan. This allows the proposal department to analyze the skill gaps of alumni and propose personalized learning plans based on that analysis. Specific evaluation criteria and analysis methods for skill gaps include, for example, skill matrices and self-assessments. Specific content and delivery methods for personalized learning plans include, for example, online courses and training programs. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill gap data into an AI, which can then propose an optimal learning plan.

[0048] The proposal department can provide online learning resources to support the continuous skill development of alumni when making a proposal. For example, the proposal department can provide online learning resources to support the skill development of alumni. The proposal department can propose learning plans to support continuous skill development. The proposal department can provide online learning resources to comprehensively support the skill development of alumni. This allows for the provision of online learning resources to support the continuous skill development of alumni. The specific content and method of provision of online learning resources include, for example, e-learning platforms and teaching materials. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill development data into AI, and the AI ​​can propose the most suitable online learning resources.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the past work history and health status of alumni and select the most suitable information collection method. For example, it can analyze the past work history of alumni and prioritize the collection of relevant information. It can also consider the health status of alumni and select a less burdensome information collection method. It can comprehensively analyze the work history and health status of alumni and propose the most suitable information collection method. This allows for efficient information collection by analyzing the past work history and health status of alumni and selecting the most suitable information collection method. Specific selection criteria and methods for the most suitable information collection method include questionnaires and interviews. 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 alumni's work history and health status data into AI, which can then select the most suitable information collection method.

[0051] The data collection unit can filter data based on the current living situation and areas of interest of alumni. For example, it can prioritize the collection of relevant information considering the alumni's current living situation. It can also filter necessary information based on the alumni's areas of interest. By comprehensively analyzing the alumni's living situation and areas of interest, it can collect the most relevant information. This allows for the collection of highly relevant information by filtering based on the alumni's current living situation and areas of interest. Specific details and collection methods for living situation include family environment and lifestyle habits. Specific details and collection methods for areas of interest include hobbies and topics of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input alumni's living situation and areas of interest data into an AI, which can then filter the most relevant information.

[0052] The proposal department can provide online learning resources to support the continuous skill development of alumni. For example, it can provide online learning resources to support the skill development of alumni. It can propose learning plans to support continuous skill development. It can provide online learning resources to comprehensively support the skill development of alumni. This allows for the provision of online learning resources to support the continuous skill development of alumni. The specific content and method of provision of online learning resources include e-learning platforms and teaching materials. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill development data into AI, and the AI ​​can propose the most suitable online learning resources.

[0053] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of OGOBs (Old Graduates and Observers) during information gathering. For example, it can prioritize the collection of relevant information based on the current location of OGOBs. It can collect the most relevant information by considering the geographical location information of OGOBs. It can collect the most relevant information by comprehensively analyzing the location information and areas of interest of OGOBs. This allows for efficient information collection by prioritizing the collection of highly relevant information by considering the geographical location information of OGOBs. Specific methods for collecting and evaluating geographical location information include GPS data and address information. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input OGOB geographical location data into AI, which can then collect the most relevant information.

[0054] The analysis department can apply different analysis algorithms depending on the category of information. For example, a work history analysis algorithm can be applied to work history information, a health analysis algorithm can be applied to health status information, and a hobby analysis algorithm can be applied to hobby information. This allows for efficient analysis by applying different analysis algorithms depending on the category of information. Specific classification criteria and methods for information categories include business information and personal information. Specific types and methods of analysis algorithms include regression analysis and clustering. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input information category data into an AI, which can then apply the most suitable analysis algorithm.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects information such as resume data, interview results, work history, hobbies, and health status. For example, it collects resume data in digital format, stores interview results in a database, and collects work history information as detailed information such as past employers, positions, and job duties. Processing in the data collection unit may also be performed using AI, which can analyze the data and extract the necessary information. Step 2: The analysis department comprehensively analyzes the information collected by the data collection department. For example, it uses statistical analysis and machine learning algorithms to analyze the information, evaluate the skill sets and health status of alumni, and conducts analyses to find suitable workplaces, taking into account their hobbies and interests. Processing in the analysis department may also be done using AI, which can analyze the data and evaluate skill sets and health status. Step 3: The proposal department proposes the most suitable workplace based on the analysis results obtained by the analysis department. For example, it meticulously matches the company's recruitment needs with the skill sets of alumni to find the best fit. It also considers the individual needs of alumni, such as their physical condition, desired working hours, and commuting distance, and offers flexible work options such as remote work or part-time work. Furthermore, it analyzes skill gaps and proposes a personalized learning plan based on that analysis. Processing in the proposal department may also be done using AI, which can then propose the most suitable workplace.

[0057] (Example of form 2) The AI-Personalized Career Matching Platform according to an embodiment of the present invention is a career matching system specifically for senior citizens (OGOBs) who have retired. This system collects and comprehensively analyzes information such as resume data, interview results, work history, hobbies, and health status. Next, the system meticulously matches the recruitment needs of companies with the skill sets of OGOBs and proposes the most suitable workplace. Furthermore, the system considers the individual needs of OGOBs, such as their physical strength, desired working hours, and commuting distance, and provides flexible work options such as remote work and part-time work. The system also analyzes the skill gaps of OGOBs and proposes a personalized learning plan based on this analysis. This service will improve the employment rate of OGOBs, alleviate labor shortages in companies, and contribute to increased productivity across society. For example, the system analyzes the past work history and skill sets of OGOBs to determine what kind of work is suitable. Next, the system meticulously matches the recruitment needs of companies with the skill sets of OGOBs and proposes the most suitable workplace. For example, the system compares the skills required by companies with the skills possessed by OGOBs to find the most suitable workplace. Furthermore, the system considers the individual needs of alumni, such as their physical condition, desired working hours, and commuting distance, and provides flexible work options such as remote work and part-time work. For example, if an alumnus wishes to work from home, the system will suggest workplaces that allow remote work. The system also analyzes the alumni's skill gaps and proposes personalized learning plans based on that analysis. For example, the system provides online learning resources for alumni to acquire new skills. This service improves the employment rate of alumni and alleviates labor shortages in companies. For example, the system leverages the alumni's extensive experience and skills to provide valuable human resources to companies. The system also supports the alumni's continuous skill development, contributing to increased productivity in society as a whole. For example, the system helps alumni acquire new skills so that they can take on more advanced work.This means that the AI-powered personalized career matching platform can improve the employment rate of retired senior citizens (OGOBs), alleviate labor shortages in companies, and contribute to increased productivity across society.

[0058] The career matching system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as resume data, interview results, work history, hobbies, and health status. For example, the collection unit collects resume data in digital format. The collection unit can also store interview results in a database and use them for analysis. Furthermore, the collection unit can collect work history information as detailed information such as past employers, positions, and job duties. For example, the collection unit scans and digitizes resume data and stores it in a database. Interview results are collected as data including interviewer evaluations and questions asked. Work history information records past employers, positions, and job duties in detail. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input resume data into AI, which can analyze the data and extract necessary information. The analysis unit comprehensively analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, statistical analysis or machine learning algorithms. Furthermore, the analysis department can evaluate the skill sets and health status of alumni based on the collected information. In addition, the analysis department can conduct analyses to find suitable workplaces by considering the alumni's hobbies and interests. For example, the analysis department can use statistical analysis to evaluate the alumni's skill sets. Machine learning algorithms can predict the alumni's health status based on the collected data. Analysis based on hobbies and interests is conducted to find workplaces where alumni can enjoy working. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected data into an AI, which can analyze the data to evaluate skill sets and health status. The proposal department proposes the most suitable workplaces based on the analysis results obtained by the analysis department. The proposal department, for example, can find the most suitable workplaces by closely matching companies' recruitment needs with the alumni's skill sets. The proposal department can also offer flexible work options such as remote work or part-time work, taking into account the alumni's individual needs, such as their physical condition, desired working hours, and commuting distance.Furthermore, the proposal department can analyze the skill gaps of former employees (OGOBs) and propose personalized learning plans based on that analysis. For example, the proposal department can compare the recruitment needs of companies with the skill sets of OGOBs and propose the most suitable workplaces. Remote work and part-time work options are provided based on the OGOBs' physical capabilities and desired working hours. Skill gap analysis is performed to propose learning plans for OGOBs to acquire new skills. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the analysis results into AI, which can then propose the most suitable workplaces. This allows the career matching system according to the embodiment to collect and comprehensively analyze information such as resume data, interview results, work history, hobbies, and health status, and propose the most suitable workplaces.

[0059] The data collection department collects information such as resume data, interview results, work history, hobbies, and health status. For example, the department collects resume data in digital format. Specifically, it scans and digitizes paper resumes submitted by applicants and converts them into text data using OCR (optical character recognition) technology. This allows even handwritten resumes to be treated as digital data. The department also stores interview results in a database and uses them for analysis. Interview results include evaluation sheets entered by interviewers, as well as audio and video recordings of interviews converted into text. Furthermore, the department can collect work history information as detailed information such as past employers, positions, and job duties. For example, it scans and digitizes the work history documents provided by applicants and stores them in a database. Work history information includes detailed records of employment periods, responsibilities, and achievements. Information on hobbies and health status is also collected through forms and questionnaires filled out by applicants. For example, information on hobbies is collected in a format where applicants enter free-form text or select from a set of options. Information regarding health status is collected through self-reporting by applicants or through the submission of health check results. 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 input resume data into AI, which can then analyze the data and extract the necessary information. The AI ​​can use OCR technology to recognize handwritten characters and convert them into text data. It can also use speech recognition technology to convert audio data of interview results into text and automatically analyze the contents of evaluation sheets. This allows the data collection unit to efficiently collect data in various formats and manage it centrally as digital data.

[0060] The Analysis Department comprehensively 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 uses statistical analysis to understand the trends in applicants' skill sets and work history, and to evaluate their strengths and weaknesses. For example, it can analyze applicants' work history data to evaluate years of experience and achievements in specific industries or job types. Machine learning algorithms predict applicants' health status based on the collected data. For example, it can analyze applicants' health check results and self-reported health status to predict future health risks. Furthermore, the Analysis Department can conduct analyses to find suitable workplaces by considering applicants' hobbies and interests. For example, it can analyze data on applicants' hobbies and interests to identify companies with a work environment and corporate culture where applicants can enjoy working. Some or all of the above processes in the Analysis Department may be performed using AI, or not. For example, the Analysis Department can input the collected data into an AI, which can analyze the data to evaluate skill sets and health status. The AI ​​uses natural language processing technology to analyze text data from applicants' resumes and work histories, automatically extracting their skills and experience. It also employs machine learning algorithms to assess applicants' health status and hobbies, performing analyses to find the most suitable workplace. This allows the analytics department to efficiently and accurately analyze the collected information, providing foundational data for suggesting the best possible job to each applicant.

[0061] The Proposal Department proposes the most suitable workplaces based on the analysis results obtained by the Analysis Department. For example, the Proposal Department meticulously matches companies' recruitment needs with applicants' skill sets to find the best workplaces. Specifically, it stores job information provided by companies in a database and matches it with applicants' skill sets and work experience. Using AI, it can automatically match companies' recruitment needs with applicants' skill sets and propose the most suitable workplaces. The Proposal Department can also consider individual needs such as applicants' physical strength, desired working hours, and commuting distance, and offer flexible work options such as remote work or part-time work. For example, if an applicant wants to reduce their commute time, the Proposal Department will prioritize suggesting companies within commuting distance or companies that allow remote work. Furthermore, the Proposal Department can analyze applicants' skill gaps and propose personalized learning plans based on that. For example, it can suggest training or courses necessary for applicants to acquire specific skills, supporting them in advancing their careers. Some or all of the above processes in the Proposal Department may be performed using AI or not. For example, the Proposal Department can input the analysis results into AI, which can then propose the most suitable workplaces. The AI ​​matches company job postings with applicants' skill sets to achieve the best possible match. It also considers applicants' individual needs and skill gaps to suggest flexible work options and learning plans. This allows the proposal department to suggest the most suitable workplace for applicants and support their career advancement.

[0062] The proposal department can propose the most suitable workplace by thoroughly matching companies' recruitment needs with the skill sets of alumni. For example, the proposal department can thoroughly analyze companies' recruitment needs and match them with the skill sets of alumni. The proposal department can compare the skills required by companies with the skills possessed by alumni and propose the most suitable workplace. The proposal department can comprehensively analyze companies' recruitment needs and the skill sets of alumni and propose the most suitable workplace. This allows for a thorough matching of companies' recruitment needs and the skill sets of alumni, enabling the proposal of the most suitable workplace. Specific methods and criteria for this detailed matching include, for example, skill matching algorithms and evaluation criteria. Some or all of the above-described processes in the proposal department may be performed using AI, or not. For example, the proposal department can input companies' recruitment needs and the skill sets of alumni into an AI, which can then propose the most suitable workplace.

[0063] The proposal department can consider the individual needs of former employees (OGOBs), such as their physical fitness, desired working hours, and commuting distance, and provide flexible work options such as remote work and part-time work. For example, the proposal department can propose less strenuous work arrangements considering the physical fitness of OGOBs. The proposal department can propose the optimal work arrangement based on desired working hours. The proposal department can provide options such as remote work and part-time work considering commuting distance. This allows the proposal department to provide flexible work options such as remote work and part-time work, taking into account the individual needs of OGOBs, such as their physical fitness, desired working hours, and commuting distance. Specific evaluation criteria and collection methods for physical fitness include, for example, health check results and self-reporting. Specific collection methods and evaluation criteria for desired working hours include, for example, questionnaires and self-reporting. Specific evaluation criteria and collection methods for commuting distance include, for example, geographic information systems and self-reporting. Specific content and methods of providing flexible work options include, for example, remote work and part-time work. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data such as the physical condition, desired working hours, and commuting distance of former employees into the AI, which can then propose the most suitable work style.

[0064] The analysis department can analyze the skill gaps of alumni and propose personalized learning plans based on that analysis. For example, the analysis department can analyze the skill gaps of alumni in detail and propose an optimal learning plan. The analysis department can provide personalized learning resources based on the skill gaps. The analysis department can comprehensively analyze the skill gaps of alumni and propose an optimal learning plan. This allows the analysis department to analyze the skill gaps of alumni and propose a personalized learning plan based on that analysis. Specific evaluation criteria and analysis methods for skill gaps include, for example, skill matrices and self-assessments. Specific content and delivery methods for personalized learning plans include, for example, online courses and training programs. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input alumni skill gap data into AI, and the AI ​​can propose an optimal learning plan.

[0065] The proposal department can provide online learning resources to support the continuous skill development of alumni. For example, the proposal department can provide online learning resources to support the skill development of alumni. The proposal department can propose learning plans to support continuous skill development. The proposal department can provide online learning resources to comprehensively support the skill development of alumni. This allows for the provision of online learning resources to support the continuous skill development of alumni. Specific content and methods of providing online learning resources include, for example, e-learning platforms and teaching materials. Some or all of the above-described processes in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill development data into AI, which can then propose the most suitable online learning resources.

[0066] 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 will collect information during times when the user is relaxed. If the user is relaxed, the data collection unit can collect detailed information. If the user is in a hurry, the data collection unit can quickly collect the necessary information. By adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using 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 an AI, which can then adjust the timing of information collection.

[0067] The data collection unit can analyze the user's past work history and health status and select the optimal information collection method. For example, the data collection unit can analyze the user's past work history and prioritize the collection of relevant information. The data collection unit can consider the user's health status and select an information collection method that is less burdensome. The data collection unit can comprehensively analyze the user's work history and health status and propose the optimal information collection method. This allows for efficient information collection by analyzing the user's past work history and health status and selecting the optimal information collection method. Specific selection criteria and methods for the optimal information collection method include, for example, questionnaires and interviews. 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 work history and health status data into AI, which can then select the optimal information collection method.

[0068] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize the collection of relevant information, taking into account the user's current living situation. The data collection unit can filter necessary information based on the user's areas of interest. The data collection unit can comprehensively analyze the user's living situation and areas of interest to collect the most relevant information. This allows for the collection of highly relevant information by filtering based on the user's current living situation and areas of interest. Specific details and collection methods for living situation include, for example, home environment and lifestyle habits. Specific details and collection methods for areas of interest include, for example, hobbies and topics of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's living situation and areas of interest data into AI, which can then filter the most relevant information.

[0069] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information. If the user is relaxed, the data collection unit can collect detailed information. If the user is in a hurry, the data collection unit can quickly collect necessary information. This allows for the priority collection of important information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 an AI, which can then determine the priority of information.

[0070] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of relevant information based on the user's current location. The data collection unit can collect the most relevant information by considering the user's geographical location information. The data collection unit can collect the most relevant information by comprehensively analyzing the user's location information and areas of interest. This allows for efficient data collection by prioritizing the collection of highly relevant information by considering the user's geographical location information. Specific methods for collecting and evaluating geographical location information include, for example, GPS data and address information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into AI, which can then collect the most relevant information.

[0071] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit analyzes the user's social media activity and collects relevant information. The data collection unit can collect necessary information based on the user's areas of interest on social media. The data collection unit comprehensively analyzes the user's social media activity and work history to collect the most relevant information. This allows for efficient data collection by analyzing the user's social media activity and collecting relevant information. Specific details of social media activity and collection methods include, for example, posts and follower counts. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into AI, which can then collect the most relevant information.

[0072] 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 easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on important information and a simplified analysis on less important information. The analysis unit can make a comprehensive judgment on the importance of the information and perform the optimal analysis. This allows for efficient analysis by adjusting the level of detail based on the importance of the information. Specific criteria and methods for evaluating the importance of information include, for example, impact and relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information importance data into AI, which can then adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a work history analysis algorithm to work history information. For health status information, it can apply a health analysis algorithm. For hobby information, it can apply a hobby analysis algorithm. This allows for efficient analysis by applying different analysis algorithms depending on the category of information. Specific classification criteria and methods for information categories include, for example, work information and personal information. Specific types and methods of implementation of analysis algorithms include, for example, regression analysis and clustering. 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 information category data into AI, and the AI ​​can apply the most suitable analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can then adjust the length of the analysis.

[0076] The analysis department can determine the priority of analysis based on the submission date of the information. For example, the analysis department may prioritize the analysis of the most recent information. For older information, the analysis department may perform a simplified analysis. The analysis department can make a comprehensive judgment on the submission date and perform the optimal analysis. This allows for efficient analysis by determining the priority of analysis based on the submission date of the information. Specific evaluation criteria and methods for the submission date of the information include, for example, the submission date and submission frequency. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the information submission date data into AI, and the AI ​​can determine the priority of analysis.

[0077] 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 highly relevant information. For less relevant information, the analysis unit can perform a simplified analysis. The analysis unit can make a comprehensive judgment on the relevance of the information and perform the optimal analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Specific evaluation criteria and methods for information relevance include, for example, the degree of thematic agreement and related topics. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input information relevance data into AI, and the AI ​​can adjust the order of analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can present detailed suggestions. If the user is in a hurry, the suggestion unit can present concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the way suggestions are presented.

[0079] The proposal department can propose the most suitable workplace by thoroughly matching the recruitment needs of companies with the skill sets of alumni. For example, the proposal department can thoroughly analyze the recruitment needs of companies and match them with the skill sets of alumni. The proposal department can compare the skills required by companies with the skills possessed by alumni and propose the most suitable workplace. The proposal department can comprehensively analyze the recruitment needs of companies and the skill sets of alumni and propose the most suitable workplace. This allows for a thorough matching of the recruitment needs of companies and the skill sets of alumni, enabling the proposal of the most suitable workplace. Specific methods and criteria for this detailed matching include, for example, skill matching algorithms and evaluation criteria. Some or all of the above-described processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the recruitment needs of companies and the skill sets of alumni into an AI, which can then propose the most suitable workplace.

[0080] The proposal department can, when making proposals, consider the individual needs of former employees (OGOBs), such as their physical condition, desired working hours, and commuting distance, and offer flexible work options such as remote work and part-time work. For example, the proposal department can propose a less strenuous work style considering the physical condition of the OGOBs. The proposal department can propose the optimal work style based on desired working hours. The proposal department can offer options such as remote work and part-time work considering commuting distance. This allows the proposal department to offer flexible work options such as remote work and part-time work, taking into account the individual needs of former employees (OGOBs), such as their physical condition, desired working hours, and commuting distance. Specific evaluation criteria and collection methods for physical condition include, for example, health check results and self-reporting. Specific collection methods and evaluation criteria for desired working hours include, for example, questionnaires and self-reporting. Specific evaluation criteria and collection methods for commuting distance include, for example, geographic information systems and self-reporting. Specific content and methods of providing flexible work options include, for example, remote work and part-time work. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data such as the physical condition, desired working hours, and commuting distance of former employees into the AI, which can then propose the most suitable work style.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions 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 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 an AI, which can then adjust the length of the suggestions.

[0082] The proposal department can analyze the skill gaps of alumni (OBs) and propose personalized learning plans based on that analysis. For example, the proposal department can analyze the skill gaps of alumni in detail and propose an optimal learning plan. The proposal department can provide personalized learning resources based on the skill gaps. The proposal department can comprehensively analyze the skill gaps of alumni and propose an optimal learning plan. This allows the proposal department to analyze the skill gaps of alumni and propose personalized learning plans based on that analysis. Specific evaluation criteria and analysis methods for skill gaps include, for example, skill matrices and self-assessments. Specific content and delivery methods for personalized learning plans include, for example, online courses and training programs. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill gap data into an AI, which can then propose an optimal learning plan.

[0083] The proposal department can provide online learning resources to support the continuous skill development of alumni when making a proposal. For example, the proposal department can provide online learning resources to support the skill development of alumni. The proposal department can propose learning plans to support continuous skill development. The proposal department can provide online learning resources to comprehensively support the skill development of alumni. This allows for the provision of online learning resources to support the continuous skill development of alumni. The specific content and method of provision of online learning resources include, for example, e-learning platforms and teaching materials. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill development data into AI, and the AI ​​can propose the most suitable online learning resources.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The proposal unit can estimate the emotions of the OGOB (Owner, Manager, and Client) and adjust the timing of proposals based on the estimated emotions. For example, if the OGOB is stressed, the proposal can be made during a time when they can relax. If the OGOB is relaxed, a detailed proposal can be made. If the OGOB is in a hurry, a quick, concise proposal can be made. In this way, by adjusting the timing of proposals based on the OGOB's emotions, proposals can be made at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the OGOB's emotion data into AI, and the AI ​​can adjust the timing of proposals.

[0086] The data collection unit can analyze the past work history and health status of alumni and select the most suitable information collection method. For example, it can analyze the past work history of alumni and prioritize the collection of relevant information. It can also consider the health status of alumni and select a less burdensome information collection method. It can comprehensively analyze the work history and health status of alumni and propose the most suitable information collection method. This allows for efficient information collection by analyzing the past work history and health status of alumni and selecting the most suitable information collection method. Specific selection criteria and methods for the most suitable information collection method include questionnaires and interviews. 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 alumni's work history and health status data into AI, which can then select the most suitable information collection method.

[0087] The analysis unit can estimate the emotions of the OGOB (Owner, Manager, and Client) and determine the priority of analysis based on the estimated emotions. For example, if the OGOB is stressed, important information will be prioritized for analysis. If the OGOB is relaxed, detailed information can be analyzed. If the OGOB is in a hurry, necessary information can be quickly analyzed. This allows for prioritizing the analysis of important information by determining the priority of analysis based on the OGOB's emotions. Emotion estimation is achieved using an emotion engine or generative 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 OGOB emotion data into an AI, which can then determine the priority of analysis.

[0088] The proposal unit can estimate the emotions of the OGOB (Occupational, General, and Biological) and adjust the way the proposal is presented based on the estimated emotions. For example, if the OGOB is nervous, a simple and easily understandable proposal can be presented. If the OGOB is relaxed, a detailed proposal can be presented. If the OGOB is in a hurry, a concise proposal can be presented. In this way, by adjusting the way the proposal is presented based on the OGOB's emotions, more appropriate proposals can be made. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the OGOB's emotion data into AI, and the AI ​​can adjust the way the proposal is presented.

[0089] The data collection unit can filter data based on the current living situation and areas of interest of alumni. For example, it can prioritize the collection of relevant information considering the alumni's current living situation. It can also filter necessary information based on the alumni's areas of interest. By comprehensively analyzing the alumni's living situation and areas of interest, it can collect the most relevant information. This allows for the collection of highly relevant information by filtering based on the alumni's current living situation and areas of interest. Specific details and collection methods for living situation include family environment and lifestyle habits. Specific details and collection methods for areas of interest include hobbies and topics of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input alumni's living situation and areas of interest data into an AI, which can then filter the most relevant information.

[0090] The analysis unit can estimate the emotions of the OGOB (Occupational, General, and Biological) and adjust the presentation of the analysis based on the estimated emotions. For example, if the OGOB is tense, it can provide a simple and easy-to-understand analysis result. If the OGOB is relaxed, it can provide a detailed analysis result. If the OGOB is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis based on the OGOB's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion engine or generative 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 the OGOB's emotional data into the AI, which can then adjust the presentation of the analysis.

[0091] The proposal department can provide online learning resources to support the continuous skill development of alumni. For example, it can provide online learning resources to support the skill development of alumni. It can propose learning plans to support continuous skill development. It can provide online learning resources to comprehensively support the skill development of alumni. This allows for the provision of online learning resources to support the continuous skill development of alumni. The specific content and method of provision of online learning resources include e-learning platforms and teaching materials. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input alumni skill development data into AI, and the AI ​​can propose the most suitable online learning resources.

[0092] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of OGOBs (Old Graduates and Observers) during information gathering. For example, it can prioritize the collection of relevant information based on the current location of OGOBs. It can collect the most relevant information by considering the geographical location information of OGOBs. It can collect the most relevant information by comprehensively analyzing the location information and areas of interest of OGOBs. This allows for efficient information collection by prioritizing the collection of highly relevant information by considering the geographical location information of OGOBs. Specific methods for collecting and evaluating geographical location information include GPS data and address information. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input OGOB geographical location data into AI, which can then collect the most relevant information.

[0093] The analysis department can apply different analysis algorithms depending on the category of information. For example, a work history analysis algorithm can be applied to work history information, a health analysis algorithm can be applied to health status information, and a hobby analysis algorithm can be applied to hobby information. This allows for efficient analysis by applying different analysis algorithms depending on the category of information. Specific classification criteria and methods for information categories include business information and personal information. Specific types and methods of analysis algorithms include regression analysis and clustering. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input information category data into an AI, which can then apply the most suitable analysis algorithm.

[0094] The suggestion unit can estimate the emotions of the OGOB (Owner, Manager, and Client) and adjust the length of the suggestion based on the estimated emotion. For example, if the OGOB is in a hurry, a short, to-the-point suggestion can be made. If the OGOB is relaxed, a detailed suggestion can be made. If the OGOB is excited, a visually stimulating suggestion can be made. In this way, by adjusting the length of the suggestion based on the OGOB's emotion, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion engine or generative AI, etc. 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 the OGOB's emotion data into the AI, and the AI ​​can adjust the length of the suggestion.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit collects information such as resume data, interview results, work history, hobbies, and health status. For example, it collects resume data in digital format, stores interview results in a database, and collects work history information as detailed information such as past employers, positions, and job duties. Processing in the data collection unit may also be performed using AI, which can analyze the data and extract the necessary information. Step 2: The analysis department comprehensively analyzes the information collected by the data collection department. For example, it uses statistical analysis and machine learning algorithms to analyze the information, evaluate the skill sets and health status of alumni, and conducts analyses to find suitable workplaces, taking into account their hobbies and interests. Processing in the analysis department may also be done using AI, which can analyze the data and evaluate skill sets and health status. Step 3: The proposal department proposes the most suitable workplace based on the analysis results obtained by the analysis department. For example, it meticulously matches the company's recruitment needs with the skill sets of alumni to find the best fit. It also considers the individual needs of alumni, such as their physical condition, desired working hours, and commuting distance, and offers flexible work options such as remote work or part-time work. Furthermore, it analyzes skill gaps and proposes a personalized learning plan based on that analysis. Processing in the proposal department may also be done using AI, which can then propose the most suitable workplace.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects resume data and interview results using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal workplace based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal 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 resume data and interview results using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and comprehensively analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal workplace based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects resume data and interview results using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A transmits the data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal workplace based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

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

[0136] The 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.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects resume data and interview results using the camera 42 and microphone 238 of the robot 414, and transmits the data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and comprehensively analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal workplace based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A collection department that collects information such as resume data, interview results, work history, hobbies, and health status, An analysis unit comprehensively analyzes the information collected by the aforementioned collection unit, The system includes a proposal unit that proposes the optimal workplace based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We meticulously match companies' recruitment needs with the skill sets of alumni to propose the most suitable workplaces. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We offer flexible work options such as remote work and part-time work, taking into account the individual needs of our alumni, including their physical condition, desired working hours, and commuting distance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We analyze the skill gaps among alumni and propose personalized learning plans based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide online learning resources to support the continuous skill development of alumni. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Analyze the user's past work history and health status to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) 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 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is 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 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, We meticulously match companies' recruitment needs with the skill sets of alumni to propose the most suitable workplaces. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We offer flexible work options such as remote work and part-time work, taking into account the individual needs of our alumni, including their physical condition, desired working hours, and commuting distance. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned proposal section is, When making a proposal, we analyze the skills gap among alumni and propose a personalized learning plan based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, provide online learning resources to support the ongoing skill development of alumni. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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 department that collects information such as resume data, interview results, work history, hobbies, and health status, An analysis unit comprehensively analyzes the information collected by the aforementioned collection unit, The system includes a proposal unit that proposes the optimal workplace based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, We meticulously match companies' recruitment needs with the skill sets of alumni to propose the most suitable workplaces. The system according to feature 1.

3. The aforementioned proposal section is, We offer flexible work options such as remote work and part-time work, taking into account the individual needs of our alumni, including their physical condition, desired working hours, and commuting distance. The system according to feature 1.

4. The aforementioned analysis unit is We analyze the skill gaps among alumni and propose personalized learning plans based on that analysis. The system according to feature 1.

5. The aforementioned proposal section is, We provide online learning resources to support the continuous skill development of alumni. The system according to feature 1.

6. 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.

7. The aforementioned collection unit is Analyze the user's past work history and health status to select the most suitable information gathering method. The system according to feature 1.

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

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