system

Abstract

The system according to this embodiment aims to propose an optimal career path based on an individual's skills, experience, and interests, and to provide promising job information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects data on an individual's skills, experience, and interests. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes career suggestions based on the analysis results obtained by the analysis unit. The provision unit provides promising job information based on the careers proposed by the proposal unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 problem that an optimal career path has not been sufficiently proposed based on an individual's skills, experience, and interests, and appropriate job information has not been provided.

[0005] The system according to the embodiment aims to propose an optimal career path based on an individual's skills, experience, and interests, and provide promising job information.

Means for Solving the Problems

[0007] The system according to this embodiment can suggest an optimal career path based on an individual's skills, experience, and interests, and provide promising job information. [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, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The career suggestion system according to an embodiment of the present invention is a system that suggests the optimal career path based on an individual's skills, experience, and interests. This career suggestion system improves the success rate of job hunting and career changes by matching with current market trends and providing promising job information. For example, the career suggestion system collects data on an individual's skills, experience, and interests. This includes information such as resumes, work histories, and self-assessment sheets. Next, the career suggestion system uses AI to analyze the collected data and make career suggestions tailored to individual needs. For example, it suggests job types and industries where an individual with a specific skill set can utilize those skills. Furthermore, the career suggestion system updates market data in real time and provides promising job information based on current market trends. This allows job seekers to make career choices based on the latest information. For example, if job openings are increasing in a particular industry, the system suggests that job seekers change jobs to that industry based on that information. This career suggestion system brings about concrete numerical effects such as an increase in the success rate of career changes, an increase in job seeker satisfaction, and an improvement in the efficiency of matching with market needs. For example, the success rate of career changes increases by an average of 30%, and job seeker satisfaction reaches an average rating of 4.5 / 5. Furthermore, the efficiency of matching with market needs will improve by 50%. This system will support individual career growth and reduce anxiety when changing careers by suggesting occupational choices based on market demand. In addition, it will meet user needs through precise data analysis using the latest AI technology, the design of an intuitive user interface, and the development of a sustainable career support system. The target audience is men and women in their 20s to 50s who are aiming for career advancement, and companies from small and medium-sized enterprises to large corporations that invest in the career development of their employees. This will solve challenges such as the uncertainty of career paths and the difficulty of responding to market fluctuations. By generating optimal career paths from individual profiles and market data using generative AI and providing personalized suggestions, the aim is to realize a society where individuals can understand their own abilities and market demands and build fulfilling careers in today's world where changes in the labor market and advancements in AI technology are creating demand for new services.This allows the career suggestion system to improve the success rate of job hunting and career changes by suggesting the optimal career path based on an individual's skills, experience, and interests, and by providing promising job information.

[0029] The career suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects data on an individual's skills, experience, and interests. The collection unit collects information such as resumes, work histories, and self-assessment sheets. The collection unit can also use AI to convert the collected data into a format that is easy to analyze. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and makes career suggestions tailored to individual needs. The analysis unit can also use AI to analyze data patterns and suggest the optimal career path. The suggestion unit makes career suggestions based on the analysis results obtained by the analysis unit. The suggestion unit, for example, suggests job types and industries where an individual with a specific skill set can utilize those skills. The suggestion unit can also use AI to make career suggestions that are optimal for individual needs. The provision unit provides promising job information based on the careers suggested by the suggestion unit. The provision unit, for example, updates market data in real time and provides promising job information based on current market trends. The service provider can also use AI to provide job information based on the latest market data. As a result, the career suggestion system according to the embodiment can improve the success rate of job hunting and career changes by suggesting the optimal career path based on an individual's skills, experience, and interests, and by providing promising job information.

[0030] The data collection unit collects data on individuals' skills, experience, and interests. Specifically, it collects information such as resumes, CVs, and self-assessment sheets. This information details the types of jobs an individual has held, the skills they possess, and the career paths they are interested in. To efficiently collect this information, the data collection unit can utilize online forms and digital platforms. For example, it can provide a dedicated portal site for individuals to upload their resumes and CVs, and make self-assessment sheets available for online completion. Furthermore, the data collection unit can use AI to convert the collected data into a format that is easy to analyze. Specifically, it can use natural language processing technology to convert text data into structured data and store it in a database. This makes the collected data easier to analyze by the subsequent analysis unit. For example, it can analyze the contents of a resume to extract work experience and skill sets and store them in a standardized format. It can also analyze responses to self-assessment sheets to quantify an individual's interests and career aspirations. In this way, the data collection unit can efficiently collect detailed information on individuals and provide a foundation for advanced analysis by the analysis unit.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the collected data and provide career suggestions tailored to individual needs. First, the analysis department preprocesses the collected data to remove noise and missing values. Next, it analyzes data patterns to identify the optimal career path based on individual skills, experience, and interests. For example, it uses machine learning algorithms to learn successful career path patterns from past data and uses this to provide new career suggestions. Specifically, it uses clustering techniques to group individuals with similar skill sets and experience and proposes the optimal career path for each group. It also uses natural language processing technology to analyze the contents of resumes and work histories to understand individual skills and experience in detail. Furthermore, it analyzes responses to self-assessment sheets to quantify individual interests and career aspirations. This allows the analysis department to provide a foundation for providing career suggestions that are optimal for individual needs. In addition, the analysis department can utilize past data and statistical information to predict long-term career paths and perform trend analysis. For example, it can analyze the changes in career paths in specific industries and occupations to predict future career directions. This allows the analysis department to provide sophisticated career proposals tailored to individual needs.

[0032] The Proposal Department provides career suggestions based on the analysis results obtained by the Analysis Department. Specifically, it proposes job types and industries where individuals with specific skill sets can utilize those skills. The Proposal Department can use AI to provide career suggestions that are best suited to individual needs. For example, based on the skill sets and experience identified by the Analysis Department, it can list the most suitable job types and industries for an individual and provide detailed career path information for each job type and industry. Furthermore, the Proposal Department can customize career suggestions considering individual career aspirations and interests. For example, based on responses to a self-assessment sheet, it can identify areas and job types that an individual is interested in and provide career suggestions accordingly. The Proposal Department can also utilize historical data and statistical information to evaluate the success rate and future prospects of careers in specific job types and industries and provide career suggestions based on that. This allows the Proposal Department to provide a foundation for providing career suggestions that are best suited to individual needs. In addition, the Proposal Department can collect user feedback and update its suggestion algorithm to continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to always provide highly accurate career suggestions based on the latest information and support individual career success.

[0033] The provision department provides promising job information based on the careers proposed by the proposal department. Specifically, it updates market data in real time and provides promising job information based on current market trends. The provision department can use AI to provide job information based on the latest market data. For example, it collects the latest job information from online job sites and company recruitment pages and stores it in a database. Next, it analyzes the collected job information and identifies the job information best suited to each individual career proposal. Specifically, it uses machine learning algorithms to analyze the content of job information and extract job information that matches specific skill sets and experience. It also uses natural language processing technology to analyze the text of job information and understand the job content, required skills, and experience in detail. This allows the provision department to quickly provide job information best suited to each individual career proposal. Furthermore, the provision department can diversify the methods of providing job information and deliver information in the most optimal way for users. For example, it can provide job information in a way that suits user needs, such as email notifications, push notifications, and display on a dedicated portal site. In addition, the provision department can collect feedback from users and continuously improve the delivery methods and the accuracy of job information. This allows the service provider to consistently offer highly accurate job information based on the latest market trends, supporting the career success of individuals.

[0034] The data collection unit can collect information such as resumes, work histories, and self-assessment sheets. For example, the data collection unit can collect resumes to understand an individual's skills and experience. The data collection unit can also collect work histories to understand an individual's work experience. The data collection unit can also collect self-assessment sheets to understand an individual's interests and self-assessment. In this way, by collecting information such as resumes, work histories, and self-assessment sheets, the data collection unit can accurately understand data about an individual's skills, experience, and interests. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from resumes, work histories, and self-assessment sheets into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the collected data and provide career suggestions tailored to individual needs. For example, the analysis unit can use AI to analyze the collected data and provide the most suitable career suggestions for each individual. The analysis unit can also use AI to analyze data patterns and suggest the optimal career path. For example, the analysis unit can suggest job types and industries where individuals with a specific skill set can utilize those skills. The analysis unit can also use AI to provide the most suitable career suggestions for each individual. In this way, the analysis unit can analyze the collected data and provide career suggestions tailored to each individual, thereby suggesting the most suitable career path for each individual. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The service provider can update market data in real time and provide promising job information based on current market trends. For example, the service provider can update market data in real time and provide the latest job information. The service provider can also use AI to provide job information based on the latest market data. For example, if job openings are increasing in a particular industry, the service provider can use that information to suggest a career change to that industry to job seekers. The service provider can also use AI to suggest a career change to job seekers based on information that job openings are increasing in a particular industry. In this way, by updating market data in real time and providing promising job information based on current market trends, the service provider enables career choices based on the latest information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the latest market data into a generating AI and have the generating AI perform the provision of job information.

[0037] The proposal department can suggest job types and industries where individuals with specific skill sets can utilize those skills. For example, the proposal department can suggest job types and industries where individuals with specific skill sets can utilize those skills. The proposal department can also use AI to suggest job types and industries where individuals with specific skill sets can utilize those skills. For example, the proposal department can suggest job types in the IT industry to individuals with programming skills. The proposal department can also use AI to suggest job types in the IT industry to individuals with programming skills. In this way, the proposal department supports the career growth of individuals by suggesting job types and industries where individuals with specific skill sets can utilize those skills. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data of individuals with specific skill sets into a generating AI and have the generating AI generate job type and industry suggestions.

[0038] The service provider can, based on information that job openings in a particular industry are increasing, suggest a career change to that industry to job seekers. For example, if job openings in a particular industry are increasing, the service provider can suggest a career change to that industry to job seekers based on that information. The service provider can also use AI to suggest career changes to job seekers based on information that job openings in a particular industry are increasing. For example, if job openings in the medical industry are increasing, the service provider can suggest a career change to the medical industry to job seekers based on that information. The service provider can also use AI to suggest career changes to job seekers based on information that job openings in the medical industry are increasing. In this way, the service provider can improve the success rate of career changes by suggesting a career change to a particular industry to job seekers based on information that job openings in that industry are increasing. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input information that job openings in a particular industry are increasing into a generating AI and have the generating AI execute career change suggestions.

[0039] The data collection unit can analyze the user's past career history and select the optimal data collection method. For example, the data collection unit can select the optimal question format based on data previously entered by the user. The data collection unit can also automatically select necessary data items from the user's past career history. The data collection unit can also analyze the user's past career history and determine the priority of data collection. This allows the data collection unit to efficiently collect data by analyzing the user's past career history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past career history data into a generating AI and have the generating AI select the data collection method.

[0040] The data collection unit can filter data based on the user's current occupation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current occupation. The data collection unit can also filter relevant data based on the user's areas of interest. The data collection unit can also exclude unnecessary data based on the user's occupation and areas of interest. This allows the data collection unit to collect highly relevant data by filtering based on the user's current occupation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's current occupation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of job postings related to the user's current location. The data collection unit can also filter relevant data based on the user's geographical location information. The data collection unit can also prioritize the collection of region-specific data based on the user's location information. In this way, the data collection unit can collect region-specific data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform data collection.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant data. The data collection unit can also collect relevant data based on the user's social media followers. The data collection unit can also analyze a user's social media activity history and collect relevant data. This allows the data collection unit to collect a wider variety of data by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0043] 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 important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific analysis algorithm to skill data. The analysis unit can also apply a different analysis algorithm to experience data. The analysis unit can also apply yet another analysis algorithm to interest data. By applying different analysis algorithms depending on the data category, the analysis unit can obtain more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can also adjust the order of analysis based on the submission date. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal unit can adjust the level of detail of a proposal based on the importance of the career. For example, the proposal unit can provide detailed proposals for important careers. It can also provide simpler proposals for less important careers. The proposal unit can also determine the priority of proposals according to the importance of the career. This allows the proposal unit to provide detailed proposals for important careers by adjusting the level of detail based on the importance of the career. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of the careers into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the career category when making a proposal. For example, the proposal unit may apply a specific proposal algorithm to technical positions. The proposal unit may also apply a different proposal algorithm to management positions. The proposal unit may also apply yet another proposal algorithm to creative positions. This allows the proposal unit to make more accurate proposals by applying different proposal algorithms depending on the career category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input career categories into a generating AI and have the generating AI perform the application of proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the submission date of the careers when submitting them. For example, the proposal department will prioritize the most recent career information. The proposal department can also postpone older career information. The proposal department can also adjust the order of proposals based on the submission date. This allows the proposal department to prioritize the most recent career information by determining the priority of proposals based on the submission date of the careers. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the career submission dates into a generating AI and have the generating AI perform the determination of proposal priorities.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the careers when making proposals. For example, the proposal unit will prioritize proposing highly relevant career information. The proposal unit can also postpone proposing less relevant career information. The proposal unit can also adjust the order of proposals based on the relevance of the careers. In this way, the proposal unit can prioritize proposing highly relevant career information by adjusting the order of proposals based on the relevance of the careers. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input career relevance into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0051] The provisioning unit can improve the accuracy of its provision by considering the interrelationships of job postings at the time of provision. For example, the provisioning unit can analyze the relationships between job postings and provide related job postings together. The provisioning unit can also provide the most suitable job postings by considering the interrelationships of job postings. The provisioning unit can also adjust the order of provision based on the relationships between job postings. In this way, the provisioning unit can provide related job postings together by improving the accuracy of provision by considering the interrelationships of job postings. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the interrelationships of job postings into a generating AI and have the generating AI perform the improvement of provision accuracy.

[0052] The provisioning unit can provide job postings while considering the attribute information of the job posting submitter. For example, the provisioning unit can provide the most suitable job postings based on the industry and occupation of the job posting submitter. The provisioning unit can also analyze the attribute information of the job posting submitter and provide relevant job postings. The provisioning unit can also adjust the order of provision based on the attribute information of the job posting submitter. In this way, the provisioning unit can provide the most suitable job postings by considering the attribute information of the job posting submitter. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the attribute information of the job posting submitter into a generating AI and have the generating AI perform the adjustment of the provision.

[0053] The service provider can provide job postings while considering their geographical distribution. For example, the service provider can prioritize providing job postings that are close to the user's current location. The service provider can also analyze the geographical distribution of job postings and provide the most suitable job postings. The service provider can also adjust the order of provision based on the geographical distribution. This allows the service provider to prioritize providing job postings that are close to the user's current location by considering the geographical distribution of job postings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the geographical distribution of job postings into a generating AI and have the generating AI perform the adjustment of the provision order.

[0054] The provisioning unit can improve the accuracy of its provision by referring to relevant literature for job postings at the time of provision. For example, the provisioning unit can refer to literature related to job postings and provide detailed information. The provisioning unit can also analyze relevant literature for job postings and provide the most suitable job postings. The provisioning unit can also adjust the order of provision based on the relevant literature. In this way, the provisioning unit can provide detailed information by improving the accuracy of provision by referring to relevant literature for job postings. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input relevant literature for job postings into a generating AI and have the generating AI perform adjustments to the provision.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze the user's past career history and select the optimal data collection method. For example, it can select the most appropriate question format based on data the user has previously entered. It can also automatically select necessary data items from the user's past career history. It can also analyze the user's past career history and determine the priority of data collection. As a result, the data collection unit can efficiently collect data by analyzing the user's past career history and selecting the optimal data collection method.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. It can also determine the priority of the analysis according to the importance of the data. As a result, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail based on the importance of the data.

[0058] The proposal department can apply different proposal algorithms depending on the career category when submitting proposals. For example, a specific proposal algorithm can be applied to technical positions. A different proposal algorithm can be applied to management positions. Yet another proposal algorithm can be applied to creative positions. This allows the proposal department to make more accurate proposals by applying different proposal algorithms depending on the career category.

[0059] The delivery unit can improve the accuracy of its delivery by considering the interrelationships of job postings. For example, it can analyze the relationships between job postings and provide related job postings together. It can also provide the most suitable job postings by considering the interrelationships of job postings. It can also adjust the order of delivery based on the relationships between job postings. In this way, the delivery unit can provide related job postings together by improving the accuracy of its delivery by considering the interrelationships of job postings.

[0060] The service provider can provide job postings while considering their geographical distribution. For example, it can prioritize providing job postings that are close to the user's current location. It can also analyze the geographical distribution of job postings and provide the most suitable ones. It can also adjust the order of provision based on the geographical distribution. As a result, by considering the geographical distribution of job postings when providing them, the service provider can prioritize providing job postings that are close to the user's current location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit gathers data on an individual's skills, experience, and interests. For example, it collects information such as resumes, work histories, and self-assessment sheets. The data collection unit can also use AI to convert the collected data into a format that is easy to analyze. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze the collected data and provide career suggestions tailored to individual needs. The analysis unit can also use AI to analyze data patterns and suggest the optimal career path. Step 3: The Proposal Department makes career suggestions based on the analysis results obtained by the Analysis Department. For example, it suggests job types and industries where individuals with a specific skill set can utilize those skills. The Proposal Department can also use AI to provide career suggestions that are best suited to individual needs. Step 4: The provisioning department provides promising job opportunities based on the careers proposed by the suggestion department. For example, it updates market data in real time and provides promising job opportunities based on current market trends. The provisioning department can also use AI to provide job opportunities based on the latest market data.

[0063] (Example of form 2) The career suggestion system according to an embodiment of the present invention is a system that suggests the optimal career path based on an individual's skills, experience, and interests. This career suggestion system improves the success rate of job hunting and career changes by matching with current market trends and providing promising job information. For example, the career suggestion system collects data on an individual's skills, experience, and interests. This includes information such as resumes, work histories, and self-assessment sheets. Next, the career suggestion system uses AI to analyze the collected data and make career suggestions tailored to individual needs. For example, it suggests job types and industries where an individual with a specific skill set can utilize those skills. Furthermore, the career suggestion system updates market data in real time and provides promising job information based on current market trends. This allows job seekers to make career choices based on the latest information. For example, if job openings are increasing in a particular industry, the system suggests that job seekers change jobs to that industry based on that information. This career suggestion system brings about concrete numerical effects such as an increase in the success rate of career changes, an increase in job seeker satisfaction, and an improvement in the efficiency of matching with market needs. For example, the success rate of career changes increases by an average of 30%, and job seeker satisfaction reaches an average rating of 4.5 / 5. Furthermore, the efficiency of matching with market needs will improve by 50%. This system will support individual career growth and reduce anxiety when changing careers by suggesting occupational choices based on market demand. In addition, it will meet user needs through precise data analysis using the latest AI technology, the design of an intuitive user interface, and the development of a sustainable career support system. The target audience is men and women in their 20s to 50s who are aiming for career advancement, and companies from small and medium-sized enterprises to large corporations that invest in the career development of their employees. This will solve challenges such as the uncertainty of career paths and the difficulty of responding to market fluctuations. By generating optimal career paths from individual profiles and market data using generative AI and providing personalized suggestions, the aim is to realize a society where individuals can understand their own abilities and market demands and build fulfilling careers in today's world where changes in the labor market and advancements in AI technology are creating demand for new services.This allows the career suggestion system to improve the success rate of job hunting and career changes by suggesting the optimal career path based on an individual's skills, experience, and interests, and by providing promising job information.

[0064] The career suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects data on an individual's skills, experience, and interests. The collection unit collects information such as resumes, work histories, and self-assessment sheets. The collection unit can also use AI to convert the collected data into a format that is easy to analyze. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and makes career suggestions tailored to individual needs. The analysis unit can also use AI to analyze data patterns and suggest the optimal career path. The suggestion unit makes career suggestions based on the analysis results obtained by the analysis unit. The suggestion unit, for example, suggests job types and industries where an individual with a specific skill set can utilize those skills. The suggestion unit can also use AI to make career suggestions that are optimal for individual needs. The provision unit provides promising job information based on the careers suggested by the suggestion unit. The provision unit, for example, updates market data in real time and provides promising job information based on current market trends. The service provider can also use AI to provide job information based on the latest market data. As a result, the career suggestion system according to the embodiment can improve the success rate of job hunting and career changes by suggesting the optimal career path based on an individual's skills, experience, and interests, and by providing promising job information.

[0065] The data collection unit collects data on individuals' skills, experience, and interests. Specifically, it collects information such as resumes, CVs, and self-assessment sheets. This information details the types of jobs an individual has held, the skills they possess, and the career paths they are interested in. To efficiently collect this information, the data collection unit can utilize online forms and digital platforms. For example, it can provide a dedicated portal site for individuals to upload their resumes and CVs, and make self-assessment sheets available for online completion. Furthermore, the data collection unit can use AI to convert the collected data into a format that is easy to analyze. Specifically, it can use natural language processing technology to convert text data into structured data and store it in a database. This makes the collected data easier to analyze by the subsequent analysis unit. For example, it can analyze the contents of a resume to extract work experience and skill sets and store them in a standardized format. It can also analyze responses to self-assessment sheets to quantify an individual's interests and career aspirations. In this way, the data collection unit can efficiently collect detailed information on individuals and provide a foundation for advanced analysis by the analysis unit.

[0066] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the collected data and provide career suggestions tailored to individual needs. First, the analysis department preprocesses the collected data to remove noise and missing values. Next, it analyzes data patterns to identify the optimal career path based on individual skills, experience, and interests. For example, it uses machine learning algorithms to learn successful career path patterns from past data and uses this to provide new career suggestions. Specifically, it uses clustering techniques to group individuals with similar skill sets and experience and proposes the optimal career path for each group. It also uses natural language processing technology to analyze the contents of resumes and work histories to understand individual skills and experience in detail. Furthermore, it analyzes responses to self-assessment sheets to quantify individual interests and career aspirations. This allows the analysis department to provide a foundation for providing career suggestions that are optimal for individual needs. In addition, the analysis department can utilize past data and statistical information to predict long-term career paths and perform trend analysis. For example, it can analyze the changes in career paths in specific industries and occupations to predict future career directions. This allows the analysis department to provide sophisticated career proposals tailored to individual needs.

[0067] The Proposal Department provides career suggestions based on the analysis results obtained by the Analysis Department. Specifically, it proposes job types and industries where individuals with specific skill sets can utilize those skills. The Proposal Department can use AI to provide career suggestions that are best suited to individual needs. For example, based on the skill sets and experience identified by the Analysis Department, it can list the most suitable job types and industries for an individual and provide detailed career path information for each job type and industry. Furthermore, the Proposal Department can customize career suggestions considering individual career aspirations and interests. For example, based on responses to a self-assessment sheet, it can identify areas and job types that an individual is interested in and provide career suggestions accordingly. The Proposal Department can also utilize historical data and statistical information to evaluate the success rate and future prospects of careers in specific job types and industries and provide career suggestions based on that. This allows the Proposal Department to provide a foundation for providing career suggestions that are best suited to individual needs. In addition, the Proposal Department can collect user feedback and update its suggestion algorithm to continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to always provide highly accurate career suggestions based on the latest information and support individual career success.

[0068] The provision department provides promising job information based on the careers proposed by the proposal department. Specifically, it updates market data in real time and provides promising job information based on current market trends. The provision department can use AI to provide job information based on the latest market data. For example, it collects the latest job information from online job sites and company recruitment pages and stores it in a database. Next, it analyzes the collected job information and identifies the job information best suited to each individual career proposal. Specifically, it uses machine learning algorithms to analyze the content of job information and extract job information that matches specific skill sets and experience. It also uses natural language processing technology to analyze the text of job information and understand the job content, required skills, and experience in detail. This allows the provision department to quickly provide job information best suited to each individual career proposal. Furthermore, the provision department can diversify the methods of providing job information and deliver information in the most optimal way for users. For example, it can provide job information in a way that suits user needs, such as email notifications, push notifications, and display on a dedicated portal site. In addition, the provision department can collect feedback from users and continuously improve the delivery methods and the accuracy of job information. This allows the service provider to consistently offer highly accurate job information based on the latest market trends, supporting the career success of individuals.

[0069] The data collection unit can collect information such as resumes, work histories, and self-assessment sheets. For example, the data collection unit can collect resumes to understand an individual's skills and experience. The data collection unit can also collect work histories to understand an individual's work experience. The data collection unit can also collect self-assessment sheets to understand an individual's interests and self-assessment. In this way, by collecting information such as resumes, work histories, and self-assessment sheets, the data collection unit can accurately understand data about an individual's skills, experience, and interests. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from resumes, work histories, and self-assessment sheets into a generating AI and have the generating AI perform data analysis.

[0070] The analysis unit can analyze the collected data and provide career suggestions tailored to individual needs. For example, the analysis unit can use AI to analyze the collected data and provide the most suitable career suggestions for each individual. The analysis unit can also use AI to analyze data patterns and suggest the optimal career path. For example, the analysis unit can suggest job types and industries where individuals with a specific skill set can utilize those skills. The analysis unit can also use AI to provide the most suitable career suggestions for each individual. In this way, the analysis unit can analyze the collected data and provide career suggestions tailored to each individual, thereby suggesting the most suitable career path for each individual. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0071] The service provider can update market data in real time and provide promising job information based on current market trends. For example, the service provider can update market data in real time and provide the latest job information. The service provider can also use AI to provide job information based on the latest market data. For example, if job openings are increasing in a particular industry, the service provider can use that information to suggest a career change to that industry to job seekers. The service provider can also use AI to suggest a career change to job seekers based on information that job openings are increasing in a particular industry. In this way, by updating market data in real time and providing promising job information based on current market trends, the service provider enables career choices based on the latest information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the latest market data into a generating AI and have the generating AI perform the provision of job information.

[0072] The proposal department can suggest job types and industries where individuals with specific skill sets can utilize those skills. For example, the proposal department can suggest job types and industries where individuals with specific skill sets can utilize those skills. The proposal department can also use AI to suggest job types and industries where individuals with specific skill sets can utilize those skills. For example, the proposal department can suggest job types in the IT industry to individuals with programming skills. The proposal department can also use AI to suggest job types in the IT industry to individuals with programming skills. In this way, the proposal department supports the career growth of individuals by suggesting job types and industries where individuals with specific skill sets can utilize those skills. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data of individuals with specific skill sets into a generating AI and have the generating AI generate job type and industry suggestions.

[0073] The service provider can, based on information that job openings in a particular industry are increasing, suggest a career change to that industry to job seekers. For example, if job openings in a particular industry are increasing, the service provider can suggest a career change to that industry to job seekers based on that information. The service provider can also use AI to suggest career changes to job seekers based on information that job openings in a particular industry are increasing. For example, if job openings in the medical industry are increasing, the service provider can suggest a career change to the medical industry to job seekers based on that information. The service provider can also use AI to suggest career changes to job seekers based on information that job openings in the medical industry are increasing. In this way, the service provider can improve the success rate of career changes by suggesting a career change to a particular industry to job seekers based on information that job openings in that industry are increasing. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input information that job openings in a particular industry are increasing into a generating AI and have the generating AI execute career change suggestions.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. If the user is focused, the data collection unit can also collect detailed data at that time. If the user is tired, the data collection unit can collect simple data and then collect detailed data at a later date. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past career history and select the optimal data collection method. For example, the data collection unit can select the optimal question format based on data previously entered by the user. The data collection unit can also automatically select necessary data items from the user's past career history. The data collection unit can also analyze the user's past career history and determine the priority of data collection. This allows the data collection unit to efficiently collect data by analyzing the user's past career history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past career history data into a generating AI and have the generating AI select the data collection method.

[0076] The data collection unit can filter data based on the user's current occupation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current occupation. The data collection unit can also filter relevant data based on the user's areas of interest. The data collection unit can also exclude unnecessary data based on the user's occupation and areas of interest. This allows the data collection unit to collect highly relevant data by filtering based on the user's current occupation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's current occupation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can also collect detailed data. If the user is in a hurry, the data collection unit can also prioritize collecting simple data. In this way, the data collection unit can prioritize collecting important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of job postings related to the user's current location. The data collection unit can also filter relevant data based on the user's geographical location information. The data collection unit can also prioritize the collection of region-specific data based on the user's location information. In this way, the data collection unit can collect region-specific data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform data collection.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant data. The data collection unit can also collect relevant data based on the user's social media followers. The data collection unit can also analyze a user's social media activity history and collect relevant data. This allows the data collection unit to collect a wider variety of data by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] 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 important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific analysis algorithm to skill data. The analysis unit can also apply a different analysis algorithm to experience data. The analysis unit can also apply yet another analysis algorithm to interest data. By applying different analysis algorithms depending on the data category, the analysis unit can obtain more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually stimulating analysis. In this way, the analysis unit can provide analysis results tailored to the user's situation by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can also adjust the order of analysis based on the submission date. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can present detailed suggestions. If the user is in a hurry, the suggestion unit can present concise suggestions. In this way, by adjusting the way it presents suggestions based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0087] The proposal unit can adjust the level of detail of a proposal based on the importance of the career. For example, the proposal unit can provide detailed proposals for important careers. It can also provide simpler proposals for less important careers. The proposal unit can also determine the priority of proposals according to the importance of the career. This allows the proposal unit to provide detailed proposals for important careers by adjusting the level of detail based on the importance of the career. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of the careers into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0088] The proposal unit can apply different proposal algorithms depending on the career category when making a proposal. For example, the proposal unit may apply a specific proposal algorithm to technical positions. The proposal unit may also apply a different proposal algorithm to management positions. The proposal unit may also apply yet another proposal algorithm to creative positions. This allows the proposal unit to make more accurate proposals by applying different proposal algorithms depending on the career category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input career categories into a generating AI and have the generating AI perform the application of proposal algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. In this way, the suggestion unit can provide suggestions tailored to the user's situation by adjusting the length of the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0090] The proposal department can determine the priority of proposals based on the submission date of the careers when submitting them. For example, the proposal department will prioritize the most recent career information. The proposal department can also postpone older career information. The proposal department can also adjust the order of proposals based on the submission date. This allows the proposal department to prioritize the most recent career information by determining the priority of proposals based on the submission date of the careers. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the career submission dates into a generating AI and have the generating AI perform the determination of proposal priorities.

[0091] The proposal unit can adjust the order of proposals based on the relevance of the careers when making proposals. For example, the proposal unit will prioritize proposing highly relevant career information. The proposal unit can also postpone proposing less relevant career information. The proposal unit can also adjust the order of proposals based on the relevance of the careers. In this way, the proposal unit can prioritize proposing highly relevant career information by adjusting the order of proposals based on the relevance of the careers. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input career relevance into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0092] The service provider can estimate the user's emotions and determine the priority of job postings to offer based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize important job postings. If the user is relaxed, the service provider may also offer more detailed job postings. If the user is in a hurry, the service provider may also prioritize simpler job postings. In this way, the service provider can prioritize important job postings by determining the priority of job postings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of job postings.

[0093] The provisioning unit can improve the accuracy of its provision by considering the interrelationships of job postings at the time of provision. For example, the provisioning unit can analyze the relationships between job postings and provide related job postings together. The provisioning unit can also provide the most suitable job postings by considering the interrelationships of job postings. The provisioning unit can also adjust the order of provision based on the relationships between job postings. In this way, the provisioning unit can provide related job postings together by improving the accuracy of provision by considering the interrelationships of job postings. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the interrelationships of job postings into a generating AI and have the generating AI perform the improvement of provision accuracy.

[0094] The provisioning unit can provide job postings while considering the attribute information of the job posting submitter. For example, the provisioning unit can provide the most suitable job postings based on the industry and occupation of the job posting submitter. The provisioning unit can also analyze the attribute information of the job posting submitter and provide relevant job postings. The provisioning unit can also adjust the order of provision based on the attribute information of the job posting submitter. In this way, the provisioning unit can provide the most suitable job postings by considering the attribute information of the job posting submitter. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the attribute information of the job posting submitter into a generating AI and have the generating AI perform the adjustment of the provision.

[0095] The service provider can estimate the user's emotions and adjust how job postings are displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. If the user is in a hurry, the service provider can provide a display that gets straight to the point. In this way, by adjusting how job postings are displayed based on the user's emotions, the service provider can provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0096] The service provider can provide job postings while considering their geographical distribution. For example, the service provider can prioritize providing job postings that are close to the user's current location. The service provider can also analyze the geographical distribution of job postings and provide the most suitable job postings. The service provider can also adjust the order of provision based on the geographical distribution. This allows the service provider to prioritize providing job postings that are close to the user's current location by considering the geographical distribution of job postings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the geographical distribution of job postings into a generating AI and have the generating AI perform the adjustment of the provision order.

[0097] The provisioning unit can improve the accuracy of its provision by referring to relevant literature for job postings at the time of provision. For example, the provisioning unit can refer to literature related to job postings and provide detailed information. The provisioning unit can also analyze relevant literature for job postings and provide the most suitable job postings. The provisioning unit can also adjust the order of provision based on the relevant literature. In this way, the provisioning unit can provide detailed information by improving the accuracy of provision by referring to relevant literature for job postings. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input relevant literature for job postings into a generating AI and have the generating AI perform adjustments to the provision.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of important data. If the user is relaxed, it can also analyze detailed data. If the user is in a hurry, it can also prioritize the analysis of simple data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0100] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, suggestions can be made during a time when they can relax. If the user is focused, detailed suggestions can be made at that time. If the user is tired, simple suggestions can be made, with more detailed suggestions provided later. In this way, the suggestion function can make suggestions at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0101] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on those emotions. For example, if the user is stressed, it can provide simple, easy-to-read information. If the user is relaxed, it can provide more detailed information. If the user is in a hurry, it can provide concise information. In this way, the service provider can provide information that is easy for the user to understand by adjusting the level of detail based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0102] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, it can select a simple data collection method. If the user is relaxed, it can select a more detailed data collection method. If the user is in a hurry, it can select a rapid data collection method. In this way, the data collection unit can collect data in a more appropriate way by adjusting the data collection method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0103] The analysis unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is nervous, it can provide simple, easy-to-understand feedback. If the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise feedback. In this way, the analysis unit can provide feedback that is easy for the user to understand by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0104] The data collection unit can analyze the user's past career history and select the optimal data collection method. For example, it can select the most appropriate question format based on data the user has previously entered. It can also automatically select necessary data items from the user's past career history. It can also analyze the user's past career history and determine the priority of data collection. As a result, the data collection unit can efficiently collect data by analyzing the user's past career history and selecting the optimal data collection method.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. It can also determine the priority of the analysis according to the importance of the data. As a result, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail based on the importance of the data.

[0106] The proposal department can apply different proposal algorithms depending on the career category when submitting proposals. For example, a specific proposal algorithm can be applied to technical positions. A different proposal algorithm can be applied to management positions. Yet another proposal algorithm can be applied to creative positions. This allows the proposal department to make more accurate proposals by applying different proposal algorithms depending on the career category.

[0107] The delivery unit can improve the accuracy of its delivery by considering the interrelationships of job postings. For example, it can analyze the relationships between job postings and provide related job postings together. It can also provide the most suitable job postings by considering the interrelationships of job postings. It can also adjust the order of delivery based on the relationships between job postings. In this way, the delivery unit can provide related job postings together by improving the accuracy of its delivery by considering the interrelationships of job postings.

[0108] The service provider can provide job postings while considering their geographical distribution. For example, it can prioritize providing job postings that are close to the user's current location. It can also analyze the geographical distribution of job postings and provide the most suitable ones. It can also adjust the order of provision based on the geographical distribution. As a result, by considering the geographical distribution of job postings when providing them, the service provider can prioritize providing job postings that are close to the user's current location.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit gathers data on an individual's skills, experience, and interests. For example, it collects information such as resumes, work histories, and self-assessment sheets. The data collection unit can also use AI to convert the collected data into a format that is easy to analyze. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze the collected data and provide career suggestions tailored to individual needs. The analysis unit can also use AI to analyze data patterns and suggest the optimal career path. Step 3: The Proposal Department makes career suggestions based on the analysis results obtained by the Analysis Department. For example, it suggests job types and industries where individuals with a specific skill set can utilize those skills. The Proposal Department can also use AI to provide career suggestions that are best suited to individual needs. Step 4: The provisioning department provides promising job opportunities based on the careers proposed by the suggestion department. For example, it updates market data in real time and provides promising job opportunities based on current market trends. The provisioning department can also use AI to provide job opportunities based on the latest market data.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as resumes, work histories, and self-assessment sheets. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes career suggestions based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and updates market data in real time and provides promising job information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as resumes, work histories, and self-assessment sheets. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes career suggestions based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and updates market data in real time and provides promising job information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as resumes, work histories, and self-assessment sheets. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes career suggestions based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and updates market data in real time and provides promising job information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information such as resumes, work histories, and self-assessment sheets. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes career suggestions based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and updates market data in real time and provides promising job information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection department that collects data on individuals' skills, experience, and interests, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that makes career proposals based on the analysis results obtained by the aforementioned analysis unit, The system comprises: a provisioning unit that provides promising job information based on the career proposed by the proposal unit; and A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as resumes, work history documents, and self-assessment sheets. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data and provide career suggestions tailored to individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We update market data in real time and provide promising job opportunities based on current market trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose job types and industries where individuals with specific skill sets can utilize those skills. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, If there is an increase in job openings in a particular industry, we can use that information to suggest job seekers to switch to that industry. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past career history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the 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, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. 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 aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the career. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the career category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their career relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes the job postings it provides based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing job postings, we improve the accuracy of the information by considering the interrelationships between the job postings. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing job postings, the attribute information of the applicant will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We estimate the user's emotions and adjust how job postings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing job postings, we will take into account the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing job postings, we refer to relevant literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 data collection department that collects data on individuals' skills, experience, and interests, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that makes career proposals based on the analysis results obtained by the aforementioned analysis unit, The system comprises: a provisioning unit that provides promising job information based on the career proposed by the proposal unit; and A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as resumes, work history documents, and self-assessment sheets. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data and provide career suggestions tailored to individual needs. The system according to feature 1.

4. The aforementioned supply unit is, We update market data in real time and provide promising job opportunities based on current market trends. The system according to feature 1.

5. The aforementioned proposal section is, We propose job types and industries where individuals with specific skill sets can utilize those skills. The system according to feature 1.

6. The aforementioned supply unit is, If there is an increase in job openings in a particular industry, we can use that information to suggest job seekers to switch to that industry. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

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