system

Abstract

The system according to this embodiment aims to provide citizens with the most suitable job opportunities and career advice by performing optimal matching based on job information and citizen skill data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a matching unit, a provision unit, and a proposal unit. The collection unit collects job information and citizen skill data. The analysis unit analyzes the data collected by the collection unit. The matching unit performs optimal matching based on the data analyzed by the analysis unit. The provision unit provides citizens with the most suitable job and career advice based on the results obtained by the matching unit. The proposal unit proposes suitable positions within companies based on the advice provided by the provision 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a lack of an effective matching system for the shortage of human resources in local enterprises and the employment needs of citizens, and there is room for improvement.

[0005] The system according to the embodiment aims to perform optimal matching based on job offers and citizens' skill data, and provide citizens with optimal job opportunities and career advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, a provision unit, and a proposal unit. The collection unit collects job information and citizen skill data. The analysis unit analyzes the data collected by the collection unit. The matching unit performs optimal matching based on the data analyzed by the analysis unit. The provision unit provides citizens with the most suitable job opportunities and career advice based on the results obtained by the matching unit. The proposal unit proposes suitable positions within companies based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform optimal matching based on job information and citizen skill data, and provide citizens with the most suitable job opportunities and career advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The employment matching system according to an embodiment of the present invention is a system using generative AI to address the shortage of personnel in local companies and the growing employment needs of citizens. The employment matching system collects job information held by local governments and skill data of citizens, and the generative AI analyzes this data to perform optimal matching. The generative AI compares citizens' skills with job information and provides optimal job placements and career advice. Furthermore, the employment matching system can also be applied to placing the right people in the right positions within companies. By collecting employee skill data within companies and analyzing it, the generative AI proposes the most suitable department and job for each employee. This can reduce employee turnover and improve employee satisfaction. For example, the employment matching system collects job information held by local governments and skill data of citizens. Next, the generative AI analyzes the collected data to perform optimal matching. The generative AI compares citizens' skills with job information and provides optimal job placements and career advice. Furthermore, the employment matching system can also be applied to placing the right people in the right positions within companies. By collecting employee skill data within companies and analyzing it, the generative AI proposes the most suitable department and job for each employee. This can reduce employee turnover and improve employee satisfaction. Thus, a job matching system using generated AI can help alleviate labor shortages in local companies and contribute to job creation in the region by supporting citizens' employment. Furthermore, it can be applied to placing the right people in the right positions within companies, leading to reduced turnover and improved employee satisfaction. In short, a job matching system can contribute to job creation in the region by addressing labor shortages in local companies and supporting citizens' employment.

[0029] The employment matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, a provision unit, and a proposal unit. The collection unit collects job information and citizen skill data. The collection unit can, for example, collect job information and citizen skill data held by local governments. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes the collected data and performs optimal matching. The matching unit performs optimal matching based on the data analyzed by the analysis unit. The matching unit, for example, compares the citizen's skills with job information and provides optimal job placement and career advice. The provision unit provides optimal job placement and career advice to citizens based on the results obtained by the matching unit. The provision unit can, for example, provide optimal job placement and career advice. The proposal unit proposes suitable positions within companies based on the advice provided by the provision unit. The proposal unit can, for example, propose the most suitable department and job for each employee. As a result, the employment matching system according to this embodiment can efficiently collect, analyze, match, provide, and propose job information and citizen skill data.

[0030] The data collection unit collects job postings and citizen skill data. Specifically, it can collect job postings and citizen skill data held by local governments. Job postings include detailed information such as company name, job title, work location, salary, and required skills and qualifications. This information is collected when companies register on the local government's job portal. On the other hand, citizen skill data is collected when individuals register on the local government's skill database. Skill data includes educational background, work history, acquired qualifications, professional skills, and language proficiency. The data collection unit centrally manages this data and stores it in a database. Furthermore, the data collection unit can update and add data in real time. For example, if new job postings are registered or if a citizen acquires new skills, the data collection unit immediately updates the database. In addition, the data collection unit regularly verifies and cleans the data to ensure its accuracy. This allows the data collection unit to always maintain the latest and most accurate data, providing a foundation for the analysis and matching units to function efficiently.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes the collected job postings and citizen skill data to generate foundational data for optimal matching. The analysis unit uses AI to analyze the data and identify the most suitable citizen for each job posting. For example, it uses natural language processing technology to analyze the text of the job postings and extract the necessary skills and qualifications. It also analyzes citizen skill data and evaluates the level and relevance of each skill. Furthermore, the analysis unit learns from past matching results and success stories and optimizes algorithms to improve matching accuracy. As a result, the analysis unit can efficiently and accurately analyze job postings and citizen skill data and provide foundational data for optimal matching.

[0032] The matching department performs optimal matching based on data analyzed by the analysis department. Specifically, it compares citizens' skills with job postings and provides optimal employment and career advice. The matching department uses AI to identify the most suitable job postings for each citizen and improve the accuracy of matching. For example, it uses machine learning algorithms to compare citizens' skill sets with the requirements of job postings and calculates a matching score. Based on this score, it recommends the most suitable employment. The matching department can also consider citizens' career paths and future goals and provide long-term career advice. In this way, the matching department can provide citizens with optimal employment and career advice, maximizing their employment opportunities.

[0033] The service provider department will provide citizens with the most suitable job opportunities and career advice based on the results obtained by the matching department. Specifically, it will provide an interface for providing citizens with the most suitable job opportunities and career advice. The service provider department will notify citizens of the matching results, for example, through a web portal or mobile app. Citizens can view recommended job postings and career advice and proceed with the application process through the service provider department's interface. The service provider department will also collect feedback from citizens and use it to improve matching accuracy and service quality. In this way, the service provider department can provide citizens with timely and appropriate information and support the maximization of employment opportunities.

[0034] The Proposal Department proposes the right people for the right jobs within a company, based on advice provided by the Service Department. Specifically, it provides data to suggest the most suitable department and job for each employee. The Proposal Department analyzes the company's organizational structure and operations, and proposes optimal placements based on each employee's skills and aptitudes. For example, it uses AI to compare employees' skill sets with the company's operational requirements to identify the most suitable department and job. The Proposal Department can also consider employees' career paths and growth goals and propose long-term career plans. This allows the Proposal Department to achieve the right people for the right jobs within the company and improve employee motivation and productivity. Furthermore, the Proposal Department can collect feedback from companies and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to achieve the right people for the right jobs within the company and improve overall organizational performance.

[0035] The data collection unit can collect job postings and citizen skill data held by local governments. For example, the data collection unit can collect publicly advertised job postings, private job postings, and contract employee job postings held by local governments. The data collection unit can also collect citizen skill data such as qualification information, work history, and skill sets. This allows for the efficient collection of job postings and citizen skill data held by local governments. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input job postings and citizen skill data held by local governments into a generation AI, and the generation AI can collect the data.

[0036] The analysis unit can analyze the collected data and perform optimal matching. For example, the analysis unit analyzes the collected data and performs optimal matching based on criteria such as skill matching, proximity of work location, and salary conditions. By analyzing the collected data and performing optimal matching, highly accurate matching becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the collected data into a generation AI, and the generation AI can analyze the data.

[0037] The matching unit can compare citizens' skills with job openings and provide optimal job placements and career advice. For example, the matching unit compares citizens' skills with job openings and provides optimal job placements and career advice based on criteria such as job suitability and career path suggestions. This allows the matching unit to provide optimal job placements and career advice by comparing citizens' skills with job openings. Some or all of the above processing in the matching unit may be performed using or without a generating AI. For example, the matching unit can input citizens' skills and job openings into a generating AI, which can then provide optimal job placements and career advice.

[0038] The service provider can collect and analyze employee skill data within a company. For example, the service provider can collect and analyze employee skill data such as internal evaluations, work history, and skill sets. By collecting and analyzing employee skill data, it becomes possible to propose the right person for the right job. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input employee skill data into a generative AI, which can then analyze the data.

[0039] The proposal department can suggest the most suitable department or job for each employee. The proposal department suggests the most suitable department or job for each employee based on criteria such as suitability of job content and skill matching. This allows for the placement of the right person in the right place within the company. Some or all of the above-described processes in the proposal department may be performed using or without a generating AI. For example, the proposal department can input each employee's skill data into a generating AI, which can then suggest the most suitable department or job.

[0040] The data collection unit can analyze citizens' past employment history and select the optimal data collection method. For example, if a citizen has previously worked in a specific industry, the data collection unit will prioritize collecting job postings related to that industry. The data collection unit can also identify citizens with specific skills from their past employment history and collect data related to those skills. Furthermore, by analyzing citizens' past employment history, the data collection unit can identify citizens who are interested in specific companies or job types and collect data related to those companies or job types. In this way, the optimal data collection method can be selected by analyzing citizens' past employment history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input citizens' past employment history into a generative AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the citizen's current living situation and areas of interest during the collection process. For example, if a citizen is able to work during a specific time period in their current living situation, the data collection unit will prioritize collecting job postings corresponding to that time period. The data collection unit can also filter and collect relevant job postings based on the citizen's areas of interest. Furthermore, the data collection unit can filter and collect appropriate skills data based on the citizen's living situation and areas of interest. This ensures that appropriate data is collected by filtering based on the citizen's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input data on the citizen's living situation and areas of interest into a generation AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of citizens during the collection process. For example, if a citizen lives in a specific area, the data collection unit can prioritize the collection of job postings related to that area. Furthermore, based on the citizen's geographical location information, the data collection unit can prioritize the collection of job postings within a commutable distance. In addition, the data collection unit can prioritize the collection of region-specific skills data by considering the citizen's geographical location information. This allows for the priority collection of highly relevant data by considering the citizen's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the citizen's geographical location information into a generation AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze citizens' social media activity and collect relevant data during the collection process. For example, the data collection unit can identify industries and occupations of interest from citizens' social media activity and collect relevant job information. The data collection unit can also analyze citizens' social media activity and collect data related to specific skills. Furthermore, based on citizens' social media activity, the data collection unit can identify citizens who are interested in specific companies or occupations and collect data related to those companies or occupations. This allows for the efficient collection of relevant data by analyzing citizens' social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input citizens' social media activity data into a generative AI, which can then collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a skill matching algorithm to skill data. It can also apply a job matching algorithm to job postings. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the data category. This allows for highly accurate analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into a generative AI, which can then apply the most suitable analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the data collection timing into the generation AI, and the generation AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0048] The matching unit can improve the accuracy of matching by considering the interrelationships between data during the matching process. For example, the matching unit can analyze the interrelationship between citizens' skills and job postings to perform optimal matching. Furthermore, the matching unit can perform matching by considering the interrelationship between citizens' past work history and job postings. In addition, the matching unit can analyze the interrelationship between citizens' areas of interest and job postings to perform optimal matching. This improves the accuracy of matching by considering the interrelationships between data. Some or all of the above-described processes in the matching unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the matching unit can input the interrelationship between citizens' skills and job postings into a generative AI, which can then perform optimal matching.

[0049] The matching unit can perform matching while considering the attribute information of citizens. For example, the matching unit can match the most suitable job information by considering the age and gender of citizens. It can also match the most suitable job information by considering the educational background and work history of citizens. Furthermore, the matching unit can match the most suitable job information by considering the place of residence and commuting distance of citizens. In this way, the most suitable job information can be matched by considering the attribute information of citizens. Some or all of the above processing in the matching unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the matching unit can input the attribute information of citizens into a generation AI, and the generation AI can match the most suitable job information.

[0050] The matching unit can perform matching while considering the geographical distribution of the data. For example, the matching unit can prioritize matching job postings that are close to the citizen's place of residence. It can also prioritize matching job postings that are within the citizen's commuting range. Furthermore, the matching unit can match the most suitable job postings based on the citizen's desired work location. In this way, the most suitable job postings can be matched by considering the geographical distribution of the data. Some or all of the above processing in the matching unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the matching unit can input the geographical distribution data of citizens into a generation AI, and the generation AI can match the most suitable job postings.

[0051] The matching unit can improve the accuracy of matching by referring to relevant literature during the matching process. For example, the matching unit can apply the latest matching algorithm based on relevant literature. The matching unit can also improve the accuracy of matching for specific industries or occupations by referring to relevant literature. Furthermore, the matching unit can adjust the matching criteria based on relevant literature to improve accuracy. In this way, the accuracy of matching is improved by referring to relevant literature. Some or all of the above processes in the matching unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the matching unit can input relevant literature into a generative AI, which can then improve the accuracy of matching.

[0052] The advice delivery unit can adjust the level of detail based on the importance of the advice at the time of delivery. For example, the delivery unit can provide a detailed explanation for highly important advice, and a concise explanation for less important advice. Furthermore, the delivery unit can adjust the level of detail in stages according to the importance of the advice. This allows for efficient advice delivery by adjusting the level of detail based on the importance of the advice. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input the importance of the advice into the generation AI, and the generation AI can adjust the level of detail.

[0053] The service provider can apply different service provision algorithms depending on the category of advice provided. For example, for career advice, the service provider can apply a career matching algorithm. For job placement suggestions, the service provider can apply a job matching algorithm. Furthermore, the service provider can select and apply the most suitable service provision algorithm depending on the category of advice. This allows for highly accurate advice by applying the most suitable service provision algorithm according to the category of advice. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the category of advice into a generative AI, and the generative AI can apply the most suitable service provision algorithm.

[0054] The advice delivery unit can prioritize advice based on when it is submitted. For example, it may prioritize urgent advice. It can also adjust the priority of advice in stages according to the submission date. Furthermore, it can prioritize the most recent advice, delaying older advice. This enables efficient advice delivery by prioritizing advice based on its submission date. Some or all of the above processes in the advice delivery unit may be performed using a generative AI, or not. For example, the advice delivery unit can input the submission dates of the advice into a generative AI, which can then determine the priority.

[0055] The advice delivery unit can adjust the order of advice based on its relevance when providing it. For example, the delivery unit may prioritize providing highly relevant advice. It can also postpone providing less relevant advice. Furthermore, the delivery unit can adjust the order of delivery in stages according to the relevance of the advice. This allows for efficient advice delivery by adjusting the order based on the relevance of the advice. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the relevance of the advice into a generative AI, and the generative AI can adjust the order.

[0056] The proposal department can analyze an employee's past work history to select the most suitable proposal method. For example, the proposal department can propose the most suitable department or job based on the employee's past work history. Furthermore, the proposal department can analyze an employee's past work history and make proposals tailored to their skills. In addition, the proposal department can propose a career path based on the employee's past work history. This allows for the selection of the most suitable proposal method by analyzing the employee's past work history. Some or all of the above processes in the proposal department may be performed using a generation AI, or they may not. For example, the proposal department can input an employee's past work history into a generation AI, which can then select the most suitable proposal method.

[0057] The proposal department can customize the proposal method based on the employee's current job situation when making a proposal. For example, the proposal department can make the most appropriate proposal by considering the employee's current job situation. Furthermore, the proposal department can customize the proposal method based on the employee's current job situation. In addition, the proposal department can analyze the employee's current job situation and make appropriate proposals. This makes it possible to make appropriate proposals by customizing the proposal method based on the employee's current job situation. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input the employee's current job situation into a generation AI, and the generation AI can customize the proposal method.

[0058] The proposal department can select the most suitable proposal method when making a proposal, taking into account the employee's geographical location. For example, the proposal department can propose departments or jobs close to the employee's place of residence. It can also propose departments or jobs within the employee's commuting distance. Furthermore, the proposal department can make optimal proposals based on the employee's desired work location. In this way, the most suitable proposal method can be selected by considering the employee's geographical location. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input the employee's geographical location information into a generation AI, which can then select the most suitable proposal method.

[0059] The proposal department can analyze employees' social media activity and propose methods for making proposals. For example, the proposal department can identify industries and job types of interest from employees' social media activity and make relevant proposals. The proposal department can also analyze employees' social media activity and make proposals related to specific skills. Furthermore, based on employees' social media activity, the proposal department can make relevant proposals to employees who are interested in specific companies or job types. In this way, relevant proposals become possible by analyzing employees' social media activity. Some or all of the above processing in the proposal department may be performed using generative AI, or not. For example, the proposal department can input employee social media activity data into generative AI, and the generative AI can make relevant proposals.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The employment matching system can also include a feedback unit. The feedback unit collects feedback from citizens and companies regarding the matching results and provides it to the analysis unit. For example, the feedback unit can collect citizens' satisfaction levels with the jobs they are offered. It can also collect feedback from companies regarding the performance of their employees. Furthermore, the feedback unit provides the collected feedback to the analysis unit, which can then use that feedback to improve the matching algorithm. This allows for improved matching accuracy through the use of feedback.

[0062] The job matching system can also include a training department. This department provides training programs to support the skill development of citizens. For example, based on citizens' skill data, the training department can suggest online courses to improve necessary skills. It can also provide customized training tailored to the needs of companies. Furthermore, the training department can monitor training progress and provide additional support as needed. This helps improve citizens' skills and increases the success rate of job matching.

[0063] The job matching system can also include a networking department. This department plans and manages networking events between citizens and companies. For example, it can host online meetups focused on specific industries or occupations. It can also provide webinars where citizens can directly interact with company representatives. Furthermore, it can operate community forums where citizens can exchange information with other job seekers. This promotes networking between citizens and companies and increases job matching opportunities.

[0064] The job matching system can also include an incentive section. This section encourages the use of the system by providing incentives to citizens and businesses. For example, the incentive section could offer a bonus to citizens who find employment within a certain period. It could also offer discount coupons to businesses if employees hired through the system perform well. Furthermore, the incentive section could award points to citizens who complete skills training, which can then be exchanged for rewards. This leverages incentives to promote system usage and increase the success rate of job matching.

[0065] The employment matching system can also include a monitoring department. This department continuously monitors the work performance of citizens after they have found employment and provides support as needed. For example, the monitoring department can regularly check how well citizens are adapting to their new workplace. It can also provide counseling and coaching if citizens are experiencing work-related problems. Furthermore, based on citizens' work performance, the monitoring department can revise their career paths or suggest new job opportunities. This strengthens post-employment support and improves citizens' job satisfaction.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects job postings and citizen skills data. For example, it can collect job postings and citizen skills data held by local governments. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data and performs optimal matching. Step 3: The matching unit performs optimal matching based on the data analyzed by the analysis unit. For example, it compares citizens' skills with job postings and provides optimal job placements and career advice. Step 4: The provision department provides citizens with the most suitable job opportunities and career advice based on the results obtained by the matching department. For example, it can provide the most suitable job opportunities and career advice. Step 5: The proposal department proposes the right person for the right job within the company based on the advice provided by the supply department. For example, they can suggest the most suitable department or job for each employee.

[0068] (Example of form 2) The employment matching system according to an embodiment of the present invention is a system using generative AI to address the shortage of personnel in local companies and the growing employment needs of citizens. The employment matching system collects job information held by local governments and skill data of citizens, and the generative AI analyzes this data to perform optimal matching. The generative AI compares citizens' skills with job information and provides optimal job placements and career advice. Furthermore, the employment matching system can also be applied to placing the right people in the right positions within companies. By collecting employee skill data within companies and analyzing it, the generative AI proposes the most suitable department and job for each employee. This can reduce employee turnover and improve employee satisfaction. For example, the employment matching system collects job information held by local governments and skill data of citizens. Next, the generative AI analyzes the collected data to perform optimal matching. The generative AI compares citizens' skills with job information and provides optimal job placements and career advice. Furthermore, the employment matching system can also be applied to placing the right people in the right positions within companies. By collecting employee skill data within companies and analyzing it, the generative AI proposes the most suitable department and job for each employee. This can reduce employee turnover and improve employee satisfaction. Thus, a job matching system using generated AI can help alleviate labor shortages in local companies and contribute to job creation in the region by supporting citizens' employment. Furthermore, it can be applied to placing the right people in the right positions within companies, leading to reduced turnover and improved employee satisfaction. In short, a job matching system can contribute to job creation in the region by addressing labor shortages in local companies and supporting citizens' employment.

[0069] The employment matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, a provision unit, and a proposal unit. The collection unit collects job information and citizen skill data. The collection unit can, for example, collect job information and citizen skill data held by local governments. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes the collected data and performs optimal matching. The matching unit performs optimal matching based on the data analyzed by the analysis unit. The matching unit, for example, compares the citizen's skills with job information and provides optimal job placement and career advice. The provision unit provides optimal job placement and career advice to citizens based on the results obtained by the matching unit. The provision unit can, for example, provide optimal job placement and career advice. The proposal unit proposes suitable positions within companies based on the advice provided by the provision unit. The proposal unit can, for example, propose the most suitable department and job for each employee. As a result, the employment matching system according to this embodiment can efficiently collect, analyze, match, provide, and propose job information and citizen skill data.

[0070] The data collection unit collects job postings and citizen skill data. Specifically, it can collect job postings and citizen skill data held by local governments. Job postings include detailed information such as company name, job title, work location, salary, and required skills and qualifications. This information is collected when companies register on the local government's job portal. On the other hand, citizen skill data is collected when individuals register on the local government's skill database. Skill data includes educational background, work history, acquired qualifications, professional skills, and language proficiency. The data collection unit centrally manages this data and stores it in a database. Furthermore, the data collection unit can update and add data in real time. For example, if new job postings are registered or if a citizen acquires new skills, the data collection unit immediately updates the database. In addition, the data collection unit regularly verifies and cleans the data to ensure its accuracy. This allows the data collection unit to always maintain the latest and most accurate data, providing a foundation for the analysis and matching units to function efficiently.

[0071] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes the collected job postings and citizen skill data to generate foundational data for optimal matching. The analysis unit uses AI to analyze the data and identify the most suitable citizen for each job posting. For example, it uses natural language processing technology to analyze the text of the job postings and extract the necessary skills and qualifications. It also analyzes citizen skill data and evaluates the level and relevance of each skill. Furthermore, the analysis unit learns from past matching results and success stories and optimizes algorithms to improve matching accuracy. As a result, the analysis unit can efficiently and accurately analyze job postings and citizen skill data and provide foundational data for optimal matching.

[0072] The matching department performs optimal matching based on data analyzed by the analysis department. Specifically, it compares citizens' skills with job postings and provides optimal employment and career advice. The matching department uses AI to identify the most suitable job postings for each citizen and improve the accuracy of matching. For example, it uses machine learning algorithms to compare citizens' skill sets with the requirements of job postings and calculates a matching score. Based on this score, it recommends the most suitable employment. The matching department can also consider citizens' career paths and future goals and provide long-term career advice. In this way, the matching department can provide citizens with optimal employment and career advice, maximizing their employment opportunities.

[0073] The service provider department will provide citizens with the most suitable job opportunities and career advice based on the results obtained by the matching department. Specifically, it will provide an interface for providing citizens with the most suitable job opportunities and career advice. The service provider department will notify citizens of the matching results, for example, through a web portal or mobile app. Citizens can view recommended job postings and career advice and proceed with the application process through the service provider department's interface. The service provider department will also collect feedback from citizens and use it to improve matching accuracy and service quality. In this way, the service provider department can provide citizens with timely and appropriate information and support the maximization of employment opportunities.

[0074] The Proposal Department proposes the right people for the right jobs within a company, based on advice provided by the Service Department. Specifically, it provides data to suggest the most suitable department and job for each employee. The Proposal Department analyzes the company's organizational structure and operations, and proposes optimal placements based on each employee's skills and aptitudes. For example, it uses AI to compare employees' skill sets with the company's operational requirements to identify the most suitable department and job. The Proposal Department can also consider employees' career paths and growth goals and propose long-term career plans. This allows the Proposal Department to achieve the right people for the right jobs within the company and improve employee motivation and productivity. Furthermore, the Proposal Department can collect feedback from companies and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to achieve the right people for the right jobs within the company and improve overall organizational performance.

[0075] The data collection unit can collect job postings and citizen skill data held by local governments. For example, the data collection unit can collect publicly advertised job postings, private job postings, and contract employee job postings held by local governments. The data collection unit can also collect citizen skill data such as qualification information, work history, and skill sets. This allows for the efficient collection of job postings and citizen skill data held by local governments. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input job postings and citizen skill data held by local governments into a generation AI, and the generation AI can collect the data.

[0076] The analysis unit can analyze the collected data and perform optimal matching. For example, the analysis unit analyzes the collected data and performs optimal matching based on criteria such as skill matching, proximity of work location, and salary conditions. By analyzing the collected data and performing optimal matching, highly accurate matching becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the collected data into a generation AI, and the generation AI can analyze the data.

[0077] The matching unit can compare citizens' skills with job openings and provide optimal job placements and career advice. For example, the matching unit compares citizens' skills with job openings and provides optimal job placements and career advice based on criteria such as job suitability and career path suggestions. This allows the matching unit to provide optimal job placements and career advice by comparing citizens' skills with job openings. Some or all of the above processing in the matching unit may be performed using or without a generating AI. For example, the matching unit can input citizens' skills and job openings into a generating AI, which can then provide optimal job placements and career advice.

[0078] The service provider can collect and analyze employee skill data within a company. For example, the service provider can collect and analyze employee skill data such as internal evaluations, work history, and skill sets. By collecting and analyzing employee skill data, it becomes possible to propose the right person for the right job. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input employee skill data into a generative AI, which can then analyze the data.

[0079] The proposal department can suggest the most suitable department or job for each employee. The proposal department suggests the most suitable department or job for each employee based on criteria such as suitability of job content and skill matching. This allows for the placement of the right person in the right place within the company. Some or all of the above-described processes in the proposal department may be performed using or without a generating AI. For example, the proposal department can input each employee's skill data into a generating AI, which can then suggest the most suitable department or job.

[0080] The data collection unit can estimate citizens' emotions and adjust the timing of collecting job postings and skills data based on the estimated emotions. For example, if a citizen is stressed, the data collection unit can delay the collection timing to reduce the burden on the citizen. Conversely, if a citizen is relaxed, the data collection unit can accelerate the collection timing to collect data efficiently. Furthermore, if a citizen is in a hurry, the data collection unit can immediately set the collection timing to collect data quickly. This allows for efficient data collection by adjusting the collection timing based on citizens' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input citizens' emotion data into a generative AI, which can estimate the emotion and adjust the collection timing.

[0081] The data collection unit can analyze citizens' past employment history and select the optimal data collection method. For example, if a citizen has previously worked in a specific industry, the data collection unit will prioritize collecting job postings related to that industry. The data collection unit can also identify citizens with specific skills from their past employment history and collect data related to those skills. Furthermore, by analyzing citizens' past employment history, the data collection unit can identify citizens who are interested in specific companies or job types and collect data related to those companies or job types. In this way, the optimal data collection method can be selected by analyzing citizens' past employment history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input citizens' past employment history into a generative AI, which can then select the optimal data collection method.

[0082] The data collection unit can filter data based on the citizen's current living situation and areas of interest during the collection process. For example, if a citizen is able to work during a specific time period in their current living situation, the data collection unit will prioritize collecting job postings corresponding to that time period. The data collection unit can also filter and collect relevant job postings based on the citizen's areas of interest. Furthermore, the data collection unit can filter and collect appropriate skills data based on the citizen's living situation and areas of interest. This ensures that appropriate data is collected by filtering based on the citizen's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input data on the citizen's living situation and areas of interest into a generation AI, which can then perform the filtering.

[0083] The data collection unit can estimate citizens' emotions and prioritize the data to be collected based on those estimated emotions. For example, if a citizen is stressed, the data collection unit will postpone the collection of less important data. Conversely, if a citizen is relaxed, the data collection unit can prioritize the collection of more important data. Furthermore, if a citizen is in a hurry, the data collection unit can collect the most important data quickly. This enables efficient data collection by prioritizing data based on citizens' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input citizens' emotion data into a generative AI, which can estimate emotions and determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of citizens during the collection process. For example, if a citizen lives in a specific area, the data collection unit can prioritize the collection of job postings related to that area. Furthermore, based on the citizen's geographical location information, the data collection unit can prioritize the collection of job postings within a commutable distance. In addition, the data collection unit can prioritize the collection of region-specific skills data by considering the citizen's geographical location information. This allows for the priority collection of highly relevant data by considering the citizen's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the citizen's geographical location information into a generation AI, which can then prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze citizens' social media activity and collect relevant data during the collection process. For example, the data collection unit can identify industries and occupations of interest from citizens' social media activity and collect relevant job information. The data collection unit can also analyze citizens' social media activity and collect data related to specific skills. Furthermore, based on citizens' social media activity, the data collection unit can identify citizens who are interested in specific companies or occupations and collect data related to those companies or occupations. This allows for the efficient collection of relevant data by analyzing citizens' social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input citizens' social media activity data into a generative AI, which can then collect relevant data.

[0086] The analysis unit can estimate citizens' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a citizen is relaxed, the analysis unit can provide detailed analysis results. If a citizen is stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if a citizen is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis based on citizens' emotions, easy-to-understand analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input citizens' emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a skill matching algorithm to skill data. It can also apply a job matching algorithm to job postings. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the data category. This allows for highly accurate analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into a generative AI, which can then apply the most suitable analysis algorithm.

[0089] The analysis unit can estimate citizens' emotions and adjust the length of the analysis based on the estimated emotions. For example, if a citizen is relaxed, the analysis unit can perform a detailed analysis and provide a longer report. If a citizen is stressed, the analysis unit can perform a concise analysis and provide a shorter report. Furthermore, if a citizen is in a hurry, the analysis unit can perform a short, to-the-point analysis. By adjusting the length of the analysis based on the citizen's emotions, the analysis unit can provide results of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input citizens' emotion data into the generative AI, which can estimate the emotions and adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the data collection timing into the generation AI, and the generation AI can determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0092] The matching unit can estimate the emotions of citizens and adjust the matching criteria based on the estimated emotions. For example, if a citizen is relaxed, the matching unit can apply detailed matching criteria. If a citizen is stressed, the matching unit can apply concise matching criteria. Furthermore, if a citizen is in a hurry, the matching unit can apply criteria for rapid matching. This allows for appropriate matching by adjusting the matching criteria based on the emotions of citizens. 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 matching unit may be performed using or without a generative AI. For example, the matching unit can input citizen emotion data into a generative AI, which can estimate the emotions and adjust the matching criteria.

[0093] The matching unit can improve the accuracy of matching by considering the interrelationships between data during the matching process. For example, the matching unit can analyze the interrelationship between citizens' skills and job postings to perform optimal matching. Furthermore, the matching unit can perform matching by considering the interrelationship between citizens' past work history and job postings. In addition, the matching unit can analyze the interrelationship between citizens' areas of interest and job postings to perform optimal matching. This improves the accuracy of matching by considering the interrelationships between data. Some or all of the above-described processes in the matching unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the matching unit can input the interrelationship between citizens' skills and job postings into a generative AI, which can then perform optimal matching.

[0094] The matching unit can perform matching while considering the attribute information of citizens. For example, the matching unit can match the most suitable job information by considering the age and gender of citizens. It can also match the most suitable job information by considering the educational background and work history of citizens. Furthermore, the matching unit can match the most suitable job information by considering the place of residence and commuting distance of citizens. In this way, the most suitable job information can be matched by considering the attribute information of citizens. Some or all of the above processing in the matching unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the matching unit can input the attribute information of citizens into a generation AI, and the generation AI can match the most suitable job information.

[0095] The matching unit can estimate the emotions of citizens and adjust the order in which matching results are displayed based on the estimated emotions. For example, if a citizen is relaxed, the matching unit can display detailed matching results in a sequential manner. If a citizen is stressed, the matching unit can prioritize displaying concise matching results. Furthermore, if a citizen is in a hurry, the matching unit can display the most important matching results first. This allows for easy-to-understand results by adjusting the display order of matching results based on the emotions of citizens. 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 matching unit may be performed using or without a generative AI. For example, the matching unit can input citizen emotion data into a generative AI, which can estimate emotions and adjust the display order of matching results.

[0096] The matching unit can perform matching while considering the geographical distribution of the data. For example, the matching unit can prioritize matching job postings that are close to the citizen's place of residence. It can also prioritize matching job postings that are within the citizen's commuting range. Furthermore, the matching unit can match the most suitable job postings based on the citizen's desired work location. In this way, the most suitable job postings can be matched by considering the geographical distribution of the data. Some or all of the above processing in the matching unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the matching unit can input the geographical distribution data of citizens into a generation AI, and the generation AI can match the most suitable job postings.

[0097] The matching unit can improve the accuracy of matching by referring to relevant literature during the matching process. For example, the matching unit can apply the latest matching algorithm based on relevant literature. The matching unit can also improve the accuracy of matching for specific industries or occupations by referring to relevant literature. Furthermore, the matching unit can adjust the matching criteria based on relevant literature to improve accuracy. In this way, the accuracy of matching is improved by referring to relevant literature. Some or all of the above processes in the matching unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the matching unit can input relevant literature into a generative AI, which can then improve the accuracy of matching.

[0098] The service provider can estimate a citizen's emotions and adjust the way it presents advice based on the estimated emotions. For example, if a citizen is relaxed, the service provider can provide detailed advice. If a citizen is stressed, the service provider can provide concise and to-the-point advice. Furthermore, if a citizen is in a hurry, the service provider can provide advice in a format that can be quickly understood. In this way, by adjusting the way advice is presented based on the citizen's emotions, easy-to-understand advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input citizen emotion data into a generative AI, which can estimate emotions and adjust the way advice is presented.

[0099] The advice delivery unit can adjust the level of detail based on the importance of the advice at the time of delivery. For example, the delivery unit can provide a detailed explanation for highly important advice, and a concise explanation for less important advice. Furthermore, the delivery unit can adjust the level of detail in stages according to the importance of the advice. This allows for efficient advice delivery by adjusting the level of detail based on the importance of the advice. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input the importance of the advice into the generation AI, and the generation AI can adjust the level of detail.

[0100] The service provider can apply different service provision algorithms depending on the category of advice provided. For example, for career advice, the service provider can apply a career matching algorithm. For job placement suggestions, the service provider can apply a job matching algorithm. Furthermore, the service provider can select and apply the most suitable service provision algorithm depending on the category of advice. This allows for highly accurate advice by applying the most suitable service provision algorithm according to the category of advice. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the category of advice into a generative AI, and the generative AI can apply the most suitable service provision algorithm.

[0101] The service provider can estimate a citizen's emotions and adjust the length of the advice provided based on the estimated emotions. For example, if a citizen is relaxed, the service provider can provide detailed advice. If a citizen is stressed, the service provider can provide concise advice. Furthermore, if a citizen is in a hurry, the service provider can provide short, to-the-point advice. By adjusting the length of the advice based on the citizen's emotions, the service provider can provide advice of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input citizen emotion data into a generative AI, which can estimate the emotion and adjust the length of the advice.

[0102] The advice delivery unit can prioritize advice based on when it is submitted. For example, it may prioritize urgent advice. It can also adjust the priority of advice in stages according to the submission date. Furthermore, it can prioritize the most recent advice, delaying older advice. This enables efficient advice delivery by prioritizing advice based on its submission date. Some or all of the above processes in the advice delivery unit may be performed using a generative AI, or not. For example, the advice delivery unit can input the submission dates of the advice into a generative AI, which can then determine the priority.

[0103] The advice delivery unit can adjust the order of advice based on its relevance when providing it. For example, the delivery unit may prioritize providing highly relevant advice. It can also postpone providing less relevant advice. Furthermore, the delivery unit can adjust the order of delivery in stages according to the relevance of the advice. This allows for efficient advice delivery by adjusting the order based on the relevance of the advice. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the relevance of the advice into a generative AI, and the generative AI can adjust the order.

[0104] The proposal unit can estimate citizens' emotions and adjust the method of proposals based on the estimated emotions. For example, if a citizen is relaxed, the proposal unit can provide detailed proposals. If a citizen is stressed, the proposal unit can provide concise and to-the-point proposals. Furthermore, if a citizen is in a hurry, the proposal unit can provide proposals in a format that can be quickly understood. In this way, by adjusting the method of proposals based on citizens' emotions, easy-to-understand proposals can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using generative AI or not. For example, the proposal unit can input citizens' emotion data into a generative AI, which can estimate emotions and adjust the method of proposals.

[0105] The proposal department can analyze an employee's past work history to select the most suitable proposal method. For example, the proposal department can propose the most suitable department or job based on the employee's past work history. Furthermore, the proposal department can analyze an employee's past work history and make proposals tailored to their skills. In addition, the proposal department can propose a career path based on the employee's past work history. This allows for the selection of the most suitable proposal method by analyzing the employee's past work history. Some or all of the above processes in the proposal department may be performed using a generation AI, or they may not. For example, the proposal department can input an employee's past work history into a generation AI, which can then select the most suitable proposal method.

[0106] The proposal department can customize the proposal method based on the employee's current job situation when making a proposal. For example, the proposal department can make the most appropriate proposal by considering the employee's current job situation. Furthermore, the proposal department can customize the proposal method based on the employee's current job situation. In addition, the proposal department can analyze the employee's current job situation and make appropriate proposals. This makes it possible to make appropriate proposals by customizing the proposal method based on the employee's current job situation. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input the employee's current job situation into a generation AI, and the generation AI can customize the proposal method.

[0107] The proposal department can estimate citizens' emotions and determine the priority of proposals based on those estimated emotions. For example, if a citizen is relaxed, the proposal department will prioritize detailed proposals. If a citizen is stressed, the proposal department will prioritize concise proposals. Furthermore, if a citizen is in a hurry, the proposal department will prioritize the most important proposals. This allows for efficient proposals by prioritizing proposals based on citizens' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input citizens' emotion data into a generative AI, which can estimate emotions and determine the priority of proposals.

[0108] The proposal department can select the most suitable proposal method when making a proposal, taking into account the employee's geographical location. For example, the proposal department can propose departments or jobs close to the employee's place of residence. It can also propose departments or jobs within the employee's commuting distance. Furthermore, the proposal department can make optimal proposals based on the employee's desired work location. In this way, the most suitable proposal method can be selected by considering the employee's geographical location. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input the employee's geographical location information into a generation AI, which can then select the most suitable proposal method.

[0109] The proposal department can analyze employees' social media activity and propose methods for making proposals. For example, the proposal department can identify industries and job types of interest from employees' social media activity and make relevant proposals. The proposal department can also analyze employees' social media activity and make proposals related to specific skills. Furthermore, based on employees' social media activity, the proposal department can make relevant proposals to employees who are interested in specific companies or job types. In this way, relevant proposals become possible by analyzing employees' social media activity. Some or all of the above processing in the proposal department may be performed using generative AI, or not. For example, the proposal department can input employee social media activity data into generative AI, and the generative AI can make relevant proposals.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The employment matching system can also include a feedback unit. The feedback unit collects feedback from citizens and companies regarding the matching results and provides it to the analysis unit. For example, the feedback unit can collect citizens' satisfaction levels with the jobs they are offered. It can also collect feedback from companies regarding the performance of their employees. Furthermore, the feedback unit provides the collected feedback to the analysis unit, which can then use that feedback to improve the matching algorithm. This allows for improved matching accuracy through the use of feedback.

[0112] The job matching system can also include a training department. This department provides training programs to support the skill development of citizens. For example, based on citizens' skill data, the training department can suggest online courses to improve necessary skills. It can also provide customized training tailored to the needs of companies. Furthermore, the training department can monitor training progress and provide additional support as needed. This helps improve citizens' skills and increases the success rate of job matching.

[0113] The job matching system can also include a networking department. This department plans and manages networking events between citizens and companies. For example, it can host online meetups focused on specific industries or occupations. It can also provide webinars where citizens can directly interact with company representatives. Furthermore, it can operate community forums where citizens can exchange information with other job seekers. This promotes networking between citizens and companies and increases job matching opportunities.

[0114] The job matching system can also include an incentive section. This section encourages the use of the system by providing incentives to citizens and businesses. For example, the incentive section could offer a bonus to citizens who find employment within a certain period. It could also offer discount coupons to businesses if employees hired through the system perform well. Furthermore, the incentive section could award points to citizens who complete skills training, which can then be exchanged for rewards. This leverages incentives to promote system usage and increase the success rate of job matching.

[0115] The employment matching system can also include a monitoring department. This department continuously monitors the work performance of citizens after they have found employment and provides support as needed. For example, the monitoring department can regularly check how well citizens are adapting to their new workplace. It can also provide counseling and coaching if citizens are experiencing work-related problems. Furthermore, based on citizens' work performance, the monitoring department can revise their career paths or suggest new job opportunities. This strengthens post-employment support and improves citizens' job satisfaction.

[0116] The employment matching system can also be equipped with an emotional feedback unit. This unit monitors citizens' emotions in real time and provides feedback to the analysis unit. For example, if a citizen is stressed, the emotional feedback unit provides this information to the analysis unit, which can then adjust the matching algorithm. Similarly, if a citizen is relaxed, the emotional feedback unit can use this information to enable the analysis unit to provide detailed advice. Furthermore, if a citizen is in a hurry, the emotional feedback unit can use this information to enable the matching unit to perform a quicker match. This allows the entire system to operate based on citizens' emotions, providing more appropriate services.

[0117] The employment matching system can also be equipped with an emotion analysis unit. The emotion analysis unit analyzes citizens' emotional data and provides the results to other elements. For example, if the emotion analysis unit is stressed, it can provide this information to the service unit, which can then provide concise advice. If the emotion analysis unit is relaxed, it can provide this information to the suggestion unit, which can then provide detailed suggestions. Furthermore, if the emotion analysis unit is in a hurry, it can provide this information to the information collection unit, which can then quickly collect the data. This allows each element of the system to operate appropriately based on citizens' emotions, enabling the provision of better services.

[0118] The employment matching system can also be equipped with an emotion prediction unit. This unit predicts future emotions based on citizens' past emotional data and provides the results to other elements. For example, if the emotion prediction unit predicts that a citizen is prone to stress at a particular time, it can provide this information to the analysis unit, which can then perform matching tailored to that period. The emotion prediction unit can also predict when citizens are likely to be relaxed and provide this information to the service provider, which can then provide detailed advice during that time. Furthermore, the emotion prediction unit can predict when citizens are in a hurry and provide this information to the data collection unit, which can then quickly collect the data during that period. This allows the system to optimize its operation by predicting citizens' emotions and provide better services.

[0119] The employment matching system can also be equipped with an emotion tracking unit. The emotion tracking unit continuously tracks changes in citizens' emotions and provides the results to other elements. For example, if a citizen's emotions change rapidly, the emotion tracking unit provides this information to the analysis unit, which can then perform matching in response to the change. If a citizen's emotions are stable, the emotion tracking unit provides this information to the service provider, which can then provide detailed advice. Furthermore, if a citizen's emotions are unstable, the emotion tracking unit provides this information to the data collection unit, which can then adjust the timing of data collection. This allows the system to adjust its operation in response to changes in citizens' emotions, enabling it to provide more appropriate services.

[0120] The employment matching system can also be equipped with an emotion reporting unit. The emotion reporting unit creates reports based on citizens' emotional data and provides these reports to other elements. For example, the emotion reporting unit can analyze the emotional trends of citizens and provide the results to the service provider, which can then provide advice based on those trends. The emotion reporting unit can also report changes in citizens' emotions and provide that information to the suggestion unit, which can then make suggestions in response to those changes. Furthermore, the emotion reporting unit can report the history of citizens' emotions and provide that information to the data collection unit, which can then collect data based on that history. This allows the system's operation to be optimized by utilizing citizens' emotional data, enabling the provision of better services.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects job postings and citizen skills data. For example, it can collect job postings and citizen skills data held by local governments. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data and performs optimal matching. Step 3: The matching unit performs optimal matching based on the data analyzed by the analysis unit. For example, it compares citizens' skills with job postings and provides optimal job placements and career advice. Step 4: The provision department provides citizens with the most suitable job opportunities and career advice based on the results obtained by the matching department. For example, it can provide the most suitable job opportunities and career advice. Step 5: The proposal department proposes the right person for the right job within the company based on the advice provided by the supply department. For example, they can suggest the most suitable department or job for each employee.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, provision unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects job information held by the local government and citizen skill data by the control unit 46A of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The matching unit performs optimal matching based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit provides optimal job placement and career advice by the control unit 46A of the smart device 14. The proposal unit proposes suitable positions within a company by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, provision unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects job information held by the local government and citizen skill data by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The matching unit performs optimal matching based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit provides optimal job placement and career advice by the control unit 46A of the smart glasses 214. The proposal unit proposes suitable positions within a company by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, provision unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects job information held by the local government and citizen skill data by the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12. The matching unit performs optimal matching based on the data analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides optimal job placement and career advice by, for example, the control unit 46A of the headset terminal 314. The proposal unit proposes suitable positions within companies by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, provision unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects job information held by the local government and citizen skill data by the control unit 46A of the robot 414. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12. The matching unit performs optimal matching based on the data analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides optimal job placement and career advice by, for example, the control unit 46A of the robot 414. The proposal unit proposes suitable positions within companies by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection department that collects job postings and citizen skills data, An analysis unit analyzes the data collected by the aforementioned collection unit, A matching unit that performs optimal matching based on the data analyzed by the aforementioned analysis unit, Based on the results obtained by the aforementioned matching department, the provision department provides citizens with the most suitable job opportunities and career advice. The system comprises a proposal unit that proposes the right person for the right job within a company based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect job postings and citizen skills data held by local governments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to perform optimal matching. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is We compare citizens' skills with job openings to provide optimal employment and career advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Collect and analyze employee skill data within a company. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the most suitable department and job for each employee. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate citizens' sentiments and adjust the timing of job postings and skills data collection based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze citizens' past employment history and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During collection, filtering is performed based on citizens' current living situations and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate public sentiment and prioritize the data to collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the geographical location of citizens. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During collection, we analyze citizens' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the sentiments of citizens and adjust the representation of the analysis based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the sentiment of the citizens and adjusts the length of the analysis based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is The system estimates citizens' sentiments and adjusts matching criteria based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During matching, the accuracy of the matching process is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, the matchmaking process takes into account the attributes of the citizens. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is It estimates citizens' sentiments and adjusts the order in which matching results are displayed based on the estimated citizens' sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During matching, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, we refer to relevant literature to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the sentiments of citizens and adjust the way advice is presented based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, different delivery algorithms are applied depending on the category of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the sentiment of citizens and adjusts the length of the advice provided based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Prioritize the advice based on when it is provided. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, the order will be adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, We estimate the sentiments of the citizens and adjust the proposal method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, we analyze the employee's past work history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, customize the proposal method based on the employee's current job situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, Estimate public sentiment and determine the priority of proposals based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, the most suitable proposal method will be selected, taking into account the geographical location information of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, we analyze employees' social media activity and suggest methods for making the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 job postings and citizen skills data, An analysis unit analyzes the data collected by the aforementioned collection unit, A matching unit that performs optimal matching based on the data analyzed by the aforementioned analysis unit, Based on the results obtained by the aforementioned matching department, the provision department provides citizens with the most suitable job opportunities and career advice. The system comprises a proposal unit that proposes the right person for the right job within a company based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect job postings and citizen skills data held by local governments. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to perform optimal matching. The system according to feature 1.

4. The matching unit is We compare citizens' skills with job openings to provide optimal employment and career advice. The system according to feature 1.

5. The aforementioned supply unit is, Collect and analyze employee skill data within a company. The system according to feature 1.

6. The aforementioned proposal section is, We propose the most suitable department and job for each employee. The system according to feature 1.

7. The aforementioned collection unit is We estimate citizens' sentiments and adjust the timing of job postings and skills data collection based on those estimated sentiments. The system according to feature 1.

8. The aforementioned collection unit is Analyze citizens' past employment history and select the most suitable data collection method. The system according to feature 1.

9. The aforementioned collection unit is During collection, filtering is performed based on citizens' current living situations and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is We estimate public sentiment and prioritize the data to collect based on that estimated sentiment. The system according to feature 1.

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