system

Abstract

The system according to the embodiment aims to provide career advice and customize learning plans for unemployed people based on their skills and aptitudes. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects information on the skills and aptitudes of unemployed people. The analysis unit analyzes the information collected by the collection unit. The provision unit provides career advice based on the analysis results obtained by the analysis unit. The customization unit customizes a study plan based on the advice provided by the provision unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to efficiently provide unemployed people with career advice based on their skills and aptitudes.

[0005] The system according to the embodiment aims to provide career advice and customize learning plans for unemployed people based on their skills and aptitudes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects information on the skills and aptitudes of unemployed people. The analysis unit analyzes the information collected by the collection unit. The provision unit provides career advice based on the analysis results obtained by the analysis unit. The customization unit customizes a study plan based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide career advice and customize learning plans based on the skills and aptitudes of unemployed individuals. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A career advice system according to an embodiment of the present invention provides unemployed individuals with career advice based on their skills and aptitudes. This system allows unemployed individuals to input information about their skills and aptitudes, analyzes that information, and provides appropriate job-changing support and new job suggestions. Furthermore, the system customizes optimal learning plans for each individual unemployed individual, helping them find employment opportunities. The development and maintenance of AI concierges also creates new jobs. For example, unemployed individuals input information about their skills and aptitudes, such as past work experience, acquired qualifications, and areas of interest. This information is then entered into the career advice system, which then analyzes the input information. The career advice system evaluates the unemployed individual's skills and aptitudes and provides appropriate job-changing support and new job suggestions. For example, if the unemployed individual is interested in the IT field, the career advice system suggests IT-related jobs. Furthermore, the career advice system customizes optimal learning plans for each individual unemployed individual. For example, it suggests online courses and training programs for the unemployed individual to acquire new skills. This allows the unemployed individual to improve their skills and receive assistance in finding employment opportunities. The development and maintenance of AI concierges also creates new jobs. Human resources will be needed to develop and maintain the career advice system to improve its functions. This will provide employment opportunities not only for users of the career advice system, but also for those involved in the development and maintenance of the AI ​​concierge. In this way, a career advice system that provides career advice to the unemployed based on their skills and aptitudes has a wide range of functions, such as supporting job changes, suggesting new occupations, customizing learning plans, and providing employment opportunities, thereby supporting the reemployment of the unemployed. As a result, the career advice system can support the reemployment of the unemployed by providing career advice based on the unemployed's skills and aptitudes and customizing learning plans.

[0029] A career advice system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects information regarding the skills and aptitudes of unemployed individuals. Information regarding the skills and aptitudes of unemployed individuals includes, but is not limited to, technical skills, soft skills, past work experience, and qualifications. The collection unit allows unemployed individuals to input information such as their past work experience, acquired qualifications, and areas of interest. The collection unit also allows unemployed individuals to provide information through questionnaire surveys or interviews. The analysis unit analyzes the information collected by the collection unit. The analysis unit evaluates the skills and aptitudes of unemployed individuals using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit evaluates the skills of unemployed individuals using, for example, a skill matrix. The analysis unit can also evaluate the aptitudes of unemployed individuals using aptitude tests. The analysis unit can also evaluate the skills and aptitudes of unemployed individuals using performance evaluations. The provision unit provides career advice based on the analysis results obtained by the analysis unit. The career advice includes, but is not limited to, job suggestions, advice for improving skills, and support for job hunting. For example, if the unemployed person is interested in the IT field, the provision unit may suggest IT-related jobs. The provision unit may also suggest online courses or training programs for the unemployed person to acquire new skills. Furthermore, the provision unit may also provide interview preparation and resume editing for the unemployed person when job hunting. The customization unit customizes a study plan based on the advice provided by the provision unit. The study plan may include, but is not limited to, selecting online courses, creating a study schedule, and providing individualized instruction. For example, the customization unit may select online courses for the unemployed person to acquire new skills. The customization unit may also create a study schedule for the unemployed person. Furthermore, the customization unit may provide individualized instruction for the unemployed person.As a result, the career advice system according to this embodiment can support unemployed individuals in finding new jobs by providing career advice based on their skills and aptitudes and customizing their learning plans.

[0030] The data collection unit can collect information on unemployed individuals' past work experience, acquired qualifications, and areas of interest. For example, the unit allows unemployed individuals to input information such as their past work experience, acquired qualifications, and areas of interest. Past work experience includes, but is not limited to, job duties, employment period, and assigned tasks. The unit can, for example, collect detailed information on the work performed by unemployed individuals in the past. The unit can also collect specific details and acquisition dates of acquired qualifications. Furthermore, the unit can collect information on training and seminars attended by unemployed individuals in the past. Acquired qualifications include, but are not limited to, technical qualifications, professional qualifications, and certifications. For example, the unit can collect detailed information on technical qualifications acquired by unemployed individuals. The unit can also collect specific details on professional qualifications acquired by unemployed individuals. Furthermore, the unit can collect information on certifications acquired by unemployed individuals. Areas of interest include, but are not limited to, IT, healthcare, and education. The unit can, for example, collect information on areas of interest for unemployed individuals. Furthermore, the data collection unit can also collect information on the types of jobs and industries that unemployed individuals are interested in. In addition, the data collection unit can collect information on areas that unemployed individuals have been interested in in the past. This allows for the collection of more detailed information on unemployed individuals, enabling the provision of more accurate career advice. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input information entered by unemployed individuals into an AI, and have the AI ​​perform the information collection.

[0031] The analysis unit can analyze the collected information and evaluate the skills and aptitudes of the unemployed. The analysis unit analyzes the collected information using techniques such as data mining, statistical analysis, and machine learning algorithms. Skill and aptitude evaluation methods include, but are not limited to, skill matrices, aptitude tests, and performance evaluations. The analysis unit can evaluate the skills of the unemployed using, for example, a skill matrix. A skill matrix is ​​a tool that visually displays the skills of the unemployed and can evaluate the level and importance of each skill. The analysis unit can also evaluate the aptitude of the unemployed using aptitude tests. Aptitude tests are tests for evaluating the personality and behavioral characteristics of the unemployed and can identify occupations and industries for which the unemployed are highly suitable. The analysis unit can also evaluate the skills and aptitudes of the unemployed using performance evaluations. Performance evaluations are methods for evaluating the skills and aptitudes of the unemployed based on their past performance and achievements, and can reflect their actual performance in the job. By evaluating the skills and aptitudes of the unemployed, appropriate career advice can be provided. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected information into the AI ​​and have the AI ​​perform skill and aptitude evaluations.

[0032] The service provider can provide appropriate job placement support and suggest new job types based on the analysis results. For example, the service provider can suggest job types to unemployed individuals based on the analysis results. Job type suggestions include, but are not limited to, IT-related jobs, medical-related jobs, and education-related jobs, based on the unemployed person's skills and aptitudes. For example, if an unemployed person is interested in the IT field, the service provider can suggest IT-related jobs. Also, if an unemployed person is interested in the medical field, the service provider can suggest medical-related jobs. Furthermore, if an unemployed person is interested in the education field, the service provider can suggest education-related jobs. For example, the service provider can suggest online courses or training programs for unemployed individuals to acquire new skills. Online courses include, but are not limited to, courses in programming, data science, and marketing. For example, if an unemployed person wants to learn programming, the service provider can suggest an online programming course. Also, if an unemployed person wants to learn data science, the service provider can suggest an online data science course. Furthermore, if an unemployed person wants to learn marketing, the service provider can suggest an online marketing course. The service provider offers services such as interview preparation and resume editing for unemployed individuals seeking new jobs. Interview preparation includes, but is not limited to, mock interviews, interview tips, and methods for answering questions. For example, the service provider might offer mock interviews for unemployed individuals to practice. It could also provide advice to help unemployed individuals learn interview techniques. Furthermore, it could provide guidance to help unemployed individuals learn how to answer questions. This allows the service provider to support unemployed individuals in finding new jobs by providing appropriate job placement support and suggesting new occupations based on the analysis results. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider could input analysis results into AI and have the AI ​​perform job suggestions and job placement support.

[0033] The customization department can customize learning plans to suit the needs of unemployed individuals. For example, the customization department can select online courses for unemployed individuals to acquire new skills. These online courses may include, but are not limited to, courses in programming, data science, and marketing. For instance, if an unemployed individual wants to learn programming, the customization department can select an online programming course. Similarly, if an unemployed individual wants to learn data science, the customization department can select an online data science course. Furthermore, if an unemployed individual wants to learn marketing, the customization department can select an online marketing course. The customization department can also create learning schedules for unemployed individuals. These schedules may include, but are not limited to, a start date, an end date, and study hours. For example, if an unemployed individual can study full-time, the customization department can create a full-time learning schedule. If an unemployed individual can study part-time, the customization department can also create a part-time learning schedule. Furthermore, if an unemployed individual can study online, the customization department can create an online learning schedule. The customization department can also provide, for example, individualized instruction to unemployed individuals. Individualized instruction includes, but is not limited to, online individualized instruction, in-person individualized instruction, and group individualized instruction. For example, if an unemployed person wants to receive online individualized instruction, the customization unit will provide online individualized instruction. The customization unit can also provide in-person individualized instruction if the unemployed person wants to receive in-person individualized instruction. Furthermore, the customization unit can provide group individualized instruction if the unemployed person wants to receive it as part of a group. This allows for the provision of an optimal learning plan for the unemployed person, thereby supporting their skill development. Some or all of the above-described processes in the customization unit may be performed using, for example, AI, or not using AI. For example, the customization unit can input the unemployed person's information into AI and have the AI ​​customize the learning plan.

[0034] The career advice system includes a development department that develops an AI concierge. The development department develops the AI ​​concierge. The AI ​​concierge is developed using technologies such as natural language processing, machine learning algorithms, and user interfaces. Natural language processing is a technology that enables the AI ​​concierge to interact with users and understand user input and generate appropriate responses. Machine learning algorithms are a technology that enables the AI ​​concierge to learn user behavior and patterns and provide more appropriate advice. The user interface is an interface through which the AI ​​concierge interacts with users and is designed to improve usability and visibility. The development department, for example, uses natural language processing technology to develop a system in which the AI ​​concierge understands user input and generates appropriate responses. The development department can also use machine learning algorithms to develop a system in which the AI ​​concierge learns user behavior and patterns and provides more appropriate advice. Furthermore, the development department can design a user interface and develop an interface through which the AI ​​concierge interacts with users. In this way, the development of the AI ​​concierge can improve the functionality of the career advice system. Some or all of the processes described above in the development department may be performed using AI, for example, or without AI. For example, in the development of the AI ​​concierge, the development department may have the AI ​​execute natural language processing techniques and machine learning algorithms.

[0035] The career advice system includes a maintenance department that performs maintenance on the AI ​​concierge. The maintenance department performs maintenance on the AI ​​concierge. Maintenance includes, but is not limited to, periodic system checks, bug fixes, and feature updates. Periodic system checks are a process to verify that the AI ​​concierge is functioning correctly and are performed to maintain system performance and stability. Bug fixes are a process to correct problems that occur when the AI ​​concierge is not functioning correctly. Feature updates are a process to improve the AI ​​concierge's functionality by adding new features or improving existing ones. The maintenance department, for example, performs periodic system checks to verify that the AI ​​concierge is functioning correctly. The maintenance department can also perform bug fixes to correct problems that occur when the AI ​​concierge is not functioning correctly. Furthermore, the maintenance department can perform feature updates to improve the AI ​​concierge's functionality by adding new features or improving existing ones. In this way, the stability of the career advice system can be maintained by performing maintenance on the AI ​​concierge. Some or all of the above processes in the maintenance department may be performed using, for example, AI, or not using AI. For example, the maintenance department can have the AI ​​perform system checks and bug fixes during the maintenance of the AI ​​concierge.

[0036] The collection unit can improve the accuracy of the information by collecting a detailed history of the unemployed person's past work experience and acquired qualifications. The collection unit, for example, collects detailed work details of the jobs the unemployed person has previously performed. Past work experience includes, for example, job content, working period, and responsibilities, but is not limited to these examples. The collection unit, for example, collects detailed work details of the jobs the unemployed person has previously performed. The collection unit can also collect specific details of qualifications the unemployed person has obtained and the time of acquisition. Acquired qualifications include, for example, technical qualifications, professional qualifications, and certifications, but is not limited to these examples. The collection unit, for example, collects detailed details of technical qualifications the unemployed person has obtained. The collection unit can also collect specific details of professional qualifications the unemployed person has obtained. Furthermore, the collection unit can collect information on certifications the unemployed person has obtained. The collection unit, for example, collects information on training and seminars the unemployed person has previously attended. Training and seminars include, for example, technical training, management training, industry seminars, etc., but are not limited to these examples. The collection unit, for example, collects detailed details of technical training the unemployed person has attended. The collection unit can also collect specific details of management training sessions attended by the unemployed. Furthermore, the collection unit can also collect information on industry seminars attended by the unemployed. By collecting detailed histories in this way, the accuracy of the information can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input detailed histories of the unemployed into AI and have the AI ​​collect the information.

[0037] The collection unit can customize the scope of information collection based on the unemployed person's current living situation and areas of interest. For example, the collection unit customizes the scope of information collection based on the unemployed person's current living situation. Examples of current living situations include, but are not limited to, family environment, financial situation, and health condition. For example, if the unemployed person cannot work full-time, the collection unit can provide a part-time or online learning plan. If the unemployed person can only study at certain times due to family circumstances, the collection unit can also provide a learning plan tailored to those time slots. Furthermore, if the unemployed person is in a financially difficult situation, the collection unit can also provide free or low-cost learning plans. For example, the collection unit customizes the scope of information collection based on the unemployed person's areas of interest. Examples of areas of interest include, but are not limited to, IT, healthcare, education, etc. For example, if the unemployed person is interested in IT, the collection unit can collect information on IT-related occupations and industries. If the unemployed person is interested in healthcare, the collection unit can collect information on healthcare-related occupations and industries. Furthermore, if the unemployed person is interested in the education field, the collection unit can collect information on education-related occupations and industries. This allows for customizing the scope of information collection based on the living situation and interests of the unemployed person, thereby collecting more relevant information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can have AI customize the scope of information collection based on the living situation and areas of interest of the unemployed person.

[0038] The data collection unit can collect region-specific work experience and qualification information based on the geographical location information of unemployed individuals. For example, the data collection unit can collect specific work experience in the region where the unemployed person lives. Geographical location information includes, but is not limited to, GPS data, region-specific work information, and local job postings. The data collection unit can also collect qualification information available in the region where the unemployed person lives. Furthermore, the data collection unit can collect employment market trends in the region where the unemployed person lives. This allows for the collection of region-specific information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the geographical location information of unemployed individuals into AI and have the AI ​​collect region-specific information.

[0039] The data collection unit can analyze the social media activity of unemployed individuals and collect relevant information. For example, the data collection unit can collect information on the occupations and industries that unemployed individuals are interested in on social media. Social media activity includes, but is not limited to, analysis of post content, follower analysis, and activity frequency. The data collection unit can also collect the latest information on companies and organizations that unemployed individuals follow. Furthermore, the data collection unit can collect information on the activities of online communities that unemployed individuals participate in. This allows relevant information to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the social media activity of unemployed individuals into AI and have the AI ​​collect relevant information.

[0040] The analysis unit can perform a detailed analysis of the collected information and evaluate the unemployed person's skills and aptitude from multiple perspectives. The analysis unit performs a detailed analysis of the collected information using techniques such as data mining, statistical analysis, and machine learning algorithms. Examples of detailed analysis include, but are not limited to, data granularity, depth of analysis, and multiple perspectives. For example, the analysis unit can perform a detailed evaluation of the unemployed person's skill set based on their past work experience. The analysis unit can also perform a multifaceted evaluation of the unemployed person's aptitude based on their qualifications. Furthermore, the analysis unit can evaluate the unemployed person's aptitude based on their areas of interest. This allows for a multifaceted evaluation of skills and aptitude through detailed analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform a detailed analysis.

[0041] During the analysis, the analysis unit can compare the unemployed person's past work experience with current market trends to evaluate them. For example, the analysis unit compares the unemployed person's past work experience with current market trends to evaluate in-demand skills. Market trends include, but are not limited to, industry reports, job-seeking trends, and economic indicator analysis. For example, the analysis unit compares the unemployed person's past work experience with current market trends to evaluate in-demand skills. The analysis unit can also compare the unemployed person's acquired qualifications with current market trends to evaluate their value. Furthermore, the analysis unit can compare the unemployed person's areas of interest with current market trends to evaluate their aptitude. This allows for a more appropriate evaluation by comparing the unemployed person's past work experience with market trends. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the unemployed person's past work experience and market trends into AI and have the AI ​​perform the comparative evaluation.

[0042] The analysis unit can perform its analysis while considering the geographical factors of the unemployed. For example, the analysis unit can perform its analysis while considering the trends in the employment market in the area where the unemployed person lives. Geographical factors include, but are not limited to, local economic conditions, local job information, and local job market trends. The analysis unit can also perform its analysis while considering the skills in demand in the area where the unemployed person lives. Furthermore, the analysis unit can perform its analysis while considering information on specific occupations or industries in the area where the unemployed person lives. This allows for a more appropriate analysis by considering geographical factors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical factors of the unemployed person into AI and have AI perform the analysis.

[0043] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and industry reports on unemployed individuals during the analysis process. For example, the analysis unit may refer to relevant literature when evaluating the skills and aptitudes of unemployed individuals. Relevant literature and industry reports include, but are not limited to, references to academic papers, analysis of industry reports, and use of databases. The analysis unit may also refer to industry reports when evaluating the areas of interest of unemployed individuals. Furthermore, the analysis unit may refer to relevant research when evaluating the past work experience of unemployed individuals. This improves the accuracy of the analysis by referring to relevant literature and industry reports. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may have AI refer to relevant literature and industry reports to evaluate the skills and aptitudes of unemployed individuals.

[0044] The service provider can adjust the level of detail of the advice based on the unemployed person's skills and aptitudes when providing it. For example, if the unemployed person has high skills, the service provider will provide detailed advice. Skill and aptitude assessments include, but are not limited to, skill matrices, aptitude tests, and performance assessments. The service provider can also provide concise advice if the unemployed person has low skills. Furthermore, the service provider can provide appropriate advice based on the unemployed person's aptitudes. This allows for more appropriate advice to be provided by adjusting the level of detail based on skills and aptitudes. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the unemployed person's skills and aptitudes into AI and have AI adjust the level of detail of the advice.

[0045] The service provider can apply different advice algorithms depending on the unemployed person's area of ​​interest at the time of delivery. For example, if the unemployed person is interested in the IT field, the service provider will apply an IT-related advice algorithm. The advice algorithms include, but are not limited to, rule-based algorithms, machine learning algorithms, and hybrid algorithms. The service provider can also apply a medical-related advice algorithm if the unemployed person is interested in the medical field. Furthermore, if the unemployed person is interested in the education field, the service provider can apply an education-related advice algorithm. This allows for more appropriate career advice by providing advice tailored to the area of ​​interest. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have AI apply an advice algorithm based on the unemployed person's area of ​​interest.

[0046] The providing unit can determine the priority of advice based on the unemployed person's past work experience when providing the advice. The providing unit, for example, prioritizes providing the most relevant advice based on the unemployed person's past work experience. Past work experience includes, but is not limited to, job content, length of employment, and responsibilities. The providing unit, for example, prioritizes providing the most relevant advice based on the unemployed person's past work experience. The providing unit can also prioritize providing advice regarding in-demand skills based on the unemployed person's past work experience. Furthermore, the providing unit can prioritize providing advice regarding career changes based on the unemployed person's past work experience. In this way, by prioritizing advice based on the unemployed person's past work experience, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the unemployed person's past work experience into AI and have the AI ​​determine the priority of advice.

[0047] The providing unit can adjust the order of advice based on the relevance of the unemployed person when providing the advice. For example, the providing unit first provides advice related to the skills of the unemployed person. Relevance includes, but is not limited to, for example, skills, aptitudes, areas of interest, etc. For example, the providing unit first provides advice related to the skills of the unemployed person. The providing unit can also provide advice related to the aptitude of the unemployed person next. Furthermore, the providing unit can also provide advice related to the areas of interest of the unemployed person last. In this way, by adjusting the order of advice based on relevance, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to adjust the order of advice based on the relevance of the unemployed person.

[0048] The customization unit can select the optimal learning plan by referring to the unemployed person's past learning history during the customization process. For example, the customization unit provides the optimal learning plan based on the unemployed person's past learning history. Past learning history includes, but is not limited to, records of learning outcomes, reference to past learning plans, and analysis of learning history. The customization unit can also provide the optimal learning plan based on the unemployed person's past learning history to improve their skills. Furthermore, the customization unit can also provide the optimal learning plan based on the unemployed person's past learning history to change careers. In this way, the optimal learning plan can be provided by referring to past learning history. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the unemployed person's past learning history into AI and have the AI ​​select the optimal learning plan.

[0049] During customization, the customization unit can customize the study plan based on the unemployed person's current living situation. The customization unit customizes the study plan based on, for example, the unemployed person's current living situation. Current living situations include, but are not limited to, the unemployed person's family environment, financial situation, and health condition. For example, if the unemployed person cannot work full-time, the customization unit can provide a study plan that allows part-time or online learning. Furthermore, if the unemployed person can only study at specific times due to family circumstances, the customization unit can provide a study plan tailored to those times. Furthermore, if the unemployed person is in a difficult financial situation, the customization unit can provide a free or low-cost study plan. This allows the unemployed person to be provided with a more appropriate study plan by customizing the study plan based on their current living situation. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit may input the unemployed person's current living situation into AI and have the AI ​​customize the study plan.

[0050] The customization unit can select the optimal learning plan by considering the geographical location of the unemployed during the customization process. For example, the customization unit can incorporate training and seminars held in the area where the unemployed person lives into the learning plan. Geographical location information includes, but is not limited to, GPS data, region-specific learning plans, and information on local educational institutions. The customization unit can also incorporate information on educational institutions and training centers in the area where the unemployed person lives into the learning plan. Furthermore, the customization unit can incorporate online learning resources in the area where the unemployed person lives into the learning plan. This allows for the provision of an optimal learning plan by considering geographical location information. Some or all of the above processing in the customization unit may be performed using, for example, AI, or not using AI. For example, the customization unit can input the geographical location information of the unemployed person into AI and have the AI ​​select the optimal learning plan.

[0051] During customization, the customization unit can analyze the unemployed person's social media activity to propose a study plan. The customization unit, for example, proposes a study plan based on the areas of interest the unemployed person has on social media. Examples of social media activity include, but are not limited to, analysis of post content, analysis of followers, and activity frequency. The customization unit, for example, proposes a study plan based on the areas of interest the unemployed person has on social media. The customization unit can also incorporate information about educational institutions and training programs followed by the unemployed person into the study plan. Furthermore, the customization unit can incorporate activity content from online communities in which the unemployed person participates into the study plan. This allows for the provision of a more appropriate study plan by analyzing social media activity. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the unemployed person's social media activity into AI and have the AI ​​execute the study plan proposal.

[0052] During development, the development department can select the optimal development method by referring to past development history. The development department, for example, selects the most effective development method based on the past development history. Past development history includes, for example, records of development projects, reference to past development methods, and analysis of development history, but is not limited to these examples. The development department, for example, selects the most effective development method based on the past development history. The development department can also select the most efficient development method based on the past development history. Furthermore, the development department can select the development method with the highest success rate based on the past development history. In this way, the optimal development method can be selected by referring to the past development history. Some or all of the above-mentioned processing in the development department may be performed, for example, using AI, or may be performed without using AI. For example, the development department can input the past development history into AI and have it select the optimal development method.

[0053] During development, the development department can improve the accuracy of development by referring to related technical literature. The development department, for example, refers to related technical literature and incorporates the latest technology. Relevant technical literature includes, but is not limited to, referring to academic papers, analyzing technical reports, and using databases. The development department, for example, refers to related technical literature and incorporates the latest technology. The development department can also improve the accuracy of development by referring to related technical literature. Furthermore, the development department can improve the efficiency of development by referring to related technical literature. As a result, the accuracy of development is improved by referring to related technical literature. Some or all of the above-mentioned processing in the development department may be performed, for example, using AI, or may be performed without using AI. For example, the development department can input related technical literature into AI and have the AI ​​improve the accuracy of development.

[0054] During maintenance, the maintenance unit can select the optimal maintenance method by referring to past maintenance history. The maintenance unit, for example, selects the most effective maintenance method based on the past maintenance history. Past maintenance history includes, for example, records of maintenance work, reference to past maintenance methods, and analysis of maintenance history, but is not limited to these examples. The maintenance unit, for example, selects the most effective maintenance method based on the past maintenance history. The maintenance unit can also select the most efficient maintenance method based on the past maintenance history. Furthermore, the maintenance unit can select the maintenance method with the highest success rate based on the past maintenance history. In this way, the optimal maintenance method can be selected by referring to the past maintenance history. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input past maintenance history into AI and have the AI ​​select the optimal maintenance method.

[0055] The maintenance department can improve the accuracy of maintenance by referring to relevant technical literature during maintenance. For example, the maintenance department can incorporate the latest technologies by referring to relevant technical literature. Relevant technical literature includes, but is not limited to, referencing academic papers, analyzing technical reports, and using databases. The maintenance department can also improve the accuracy of maintenance by referring to relevant technical literature. Furthermore, the maintenance department can improve the efficiency of maintenance by referring to relevant technical literature. As a result, the accuracy of maintenance is improved by referring to relevant technical literature. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input relevant technical literature into AI and have the AI ​​perform the maintenance accuracy improvement.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can consider not only the unemployed person's past work experience but also their non-work experience, such as hobbies and volunteer activities, when evaluating their skills and aptitudes. For example, if an unemployed person programs as a hobby, that skill can be included in the evaluation. Similarly, if an unemployed person demonstrates leadership in volunteer activities, that aptitude can be reflected in the evaluation. Furthermore, knowledge and skills acquired through hobbies can be evaluated on par with work experience. This allows for a more accurate assessment of the unemployed person's overall skills and aptitudes.

[0058] The provider can not only suggest jobs based on the unemployed person's skills and aptitudes, but also jobs that suit their lifestyle and values. For example, if the unemployed person values ​​work-life balance, the provider can suggest jobs that allow remote work. If the unemployed person is interested in contributing to society, the provider can suggest jobs with NPOs or social enterprises. Furthermore, if the unemployed person wants a creative job, the provider can suggest jobs related to design or art. In this way, by suggesting jobs that suit the unemployed person's values ​​and lifestyle, the provider can support them in making more satisfying career choices.

[0059] When customizing the learning plan for the unemployed, the customization unit can provide a plan that suits the learning style and learning pace of the unemployed. For example, if the unemployed prefers visual learning, a learning plan centered on video materials can be provided. Also, if the unemployed wants to study intensively in a short period of time, an intensive course-style learning plan can be provided. Furthermore, if the unemployed wants to study slowly, a long-term learning plan can be provided. In this way, by providing a learning plan that suits the learning style and pace of the unemployed, the learning effect can be maximized.

[0060] When collecting information on the skills and aptitudes of unemployed persons, the collection department can collect not only the unemployed person's self-assessment but also third-party evaluations. For example, it can collect recommendation letters and evaluations from the unemployed person's past superiors and colleagues. It can also collect the results and feedback of projects in which the unemployed person has participated. It can also collect evaluations of training and education that the unemployed person has received. This allows for a more objective evaluation of the unemployed person's skills and aptitudes.

[0061] When assessing the skills and aptitudes of unemployed individuals, the analysis unit can also take into account their future career goals and desired career paths. For example, if an unemployed individual hopes to pursue a management position in the future, the skills and experience required for that can be included in the assessment. Also, if an unemployed individual wishes to pursue a career in a specific industry, the aptitude for that industry can be assessed. Furthermore, if an unemployed individual wishes to pursue an international career, language ability and intercultural understanding skills can be included in the assessment. This makes it possible to provide more appropriate career advice by conducting an assessment tailored to the unemployed individual's future career goals.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects information about the skills and aptitudes of unemployed people. Information about the skills and aptitudes of unemployed people includes technical skills, soft skills, past work experience, qualifications, etc. The collection unit allows unemployed people to enter information such as their past work experience, qualifications obtained, and areas of interest. The collection unit also allows unemployed people to provide information through questionnaires and interviews. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning algorithms to evaluate the skills and aptitudes of the unemployed. For example, the analysis unit uses skill matrices, aptitude tests, and performance evaluations to evaluate the skills and aptitudes of the unemployed. Step 3: The service provider provides career advice based on the analysis results obtained by the analysis provider. Career advice includes job suggestions, advice for skill development, and support for job hunting. For example, if an unemployed person is interested in the IT field, the service provider will suggest IT-related jobs and online courses and training programs for acquiring new skills. The service will also provide interview preparation and resume editing services for job hunting. Step 4: The customization team customizes the learning plan based on the advice provided by the service provider. The learning plan includes selecting online courses, creating a study schedule, and providing individualized instruction. For example, they might select online courses for an unemployed person to acquire new skills, create a study schedule, and provide individualized instruction.

[0064] (Example 2) A career advice system according to an embodiment of the present invention provides unemployed individuals with career advice based on their skills and aptitudes. This system allows unemployed individuals to input information about their skills and aptitudes, analyzes that information, and provides appropriate job-changing support and new job suggestions. Furthermore, the system customizes optimal learning plans for each individual unemployed individual, helping them find employment opportunities. The development and maintenance of AI concierges also creates new jobs. For example, unemployed individuals input information about their skills and aptitudes, such as past work experience, acquired qualifications, and areas of interest. This information is then entered into the career advice system, which then analyzes the input information. The career advice system evaluates the unemployed individual's skills and aptitudes and provides appropriate job-changing support and new job suggestions. For example, if the unemployed individual is interested in the IT field, the career advice system suggests IT-related jobs. Furthermore, the career advice system customizes optimal learning plans for each individual unemployed individual. For example, it suggests online courses and training programs for the unemployed individual to acquire new skills. This allows the unemployed individual to improve their skills and receive assistance in finding employment opportunities. The development and maintenance of AI concierges also creates new jobs. Human resources will be needed to develop and maintain the career advice system to improve its functions. This will provide employment opportunities not only for users of the career advice system, but also for those involved in the development and maintenance of the AI ​​concierge. In this way, a career advice system that provides career advice to the unemployed based on their skills and aptitudes has a wide range of functions, such as supporting job changes, suggesting new occupations, customizing learning plans, and providing employment opportunities, thereby supporting the reemployment of the unemployed. As a result, the career advice system can support the reemployment of the unemployed by providing career advice based on the unemployed's skills and aptitudes and customizing learning plans.

[0065] A career advice system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects information regarding the skills and aptitudes of unemployed individuals. Information regarding the skills and aptitudes of unemployed individuals includes, but is not limited to, technical skills, soft skills, past work experience, and qualifications. The collection unit allows unemployed individuals to input information such as their past work experience, acquired qualifications, and areas of interest. The collection unit also allows unemployed individuals to provide information through questionnaire surveys or interviews. The analysis unit analyzes the information collected by the collection unit. The analysis unit evaluates the skills and aptitudes of unemployed individuals using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit evaluates the skills of unemployed individuals using, for example, a skill matrix. The analysis unit can also evaluate the aptitudes of unemployed individuals using aptitude tests. The analysis unit can also evaluate the skills and aptitudes of unemployed individuals using performance evaluations. The provision unit provides career advice based on the analysis results obtained by the analysis unit. The career advice includes, but is not limited to, job suggestions, advice for improving skills, and support for job hunting. For example, if the unemployed person is interested in the IT field, the provision unit may suggest IT-related jobs. The provision unit may also suggest online courses or training programs for the unemployed person to acquire new skills. Furthermore, the provision unit may also provide interview preparation and resume editing for the unemployed person when job hunting. The customization unit customizes a study plan based on the advice provided by the provision unit. The study plan may include, but is not limited to, selecting online courses, creating a study schedule, and providing individualized instruction. For example, the customization unit may select online courses for the unemployed person to acquire new skills. The customization unit may also create a study schedule for the unemployed person. Furthermore, the customization unit may provide individualized instruction for the unemployed person.As a result, the career advice system according to this embodiment can support unemployed individuals in finding new jobs by providing career advice based on their skills and aptitudes and customizing their learning plans.

[0066] The data collection unit can collect information on unemployed individuals' past work experience, acquired qualifications, and areas of interest. For example, the unit allows unemployed individuals to input information such as their past work experience, acquired qualifications, and areas of interest. Past work experience includes, but is not limited to, job duties, employment period, and assigned tasks. The unit can, for example, collect detailed information on the work performed by unemployed individuals in the past. The unit can also collect specific details and acquisition dates of acquired qualifications. Furthermore, the unit can collect information on training and seminars attended by unemployed individuals in the past. Acquired qualifications include, but are not limited to, technical qualifications, professional qualifications, and certifications. For example, the unit can collect detailed information on technical qualifications acquired by unemployed individuals. The unit can also collect specific details on professional qualifications acquired by unemployed individuals. Furthermore, the unit can collect information on certifications acquired by unemployed individuals. Areas of interest include, but are not limited to, IT, healthcare, and education. The unit can, for example, collect information on areas of interest for unemployed individuals. Furthermore, the data collection unit can also collect information on the types of jobs and industries that unemployed individuals are interested in. In addition, the data collection unit can collect information on areas that unemployed individuals have been interested in in the past. This allows for the collection of more detailed information on unemployed individuals, enabling the provision of more accurate career advice. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input information entered by unemployed individuals into an AI, and have the AI ​​perform the information collection.

[0067] The analysis unit can analyze the collected information and evaluate the skills and aptitudes of the unemployed. The analysis unit analyzes the collected information using techniques such as data mining, statistical analysis, and machine learning algorithms. Skill and aptitude evaluation methods include, but are not limited to, skill matrices, aptitude tests, and performance evaluations. The analysis unit can evaluate the skills of the unemployed using, for example, a skill matrix. A skill matrix is ​​a tool that visually displays the skills of the unemployed and can evaluate the level and importance of each skill. The analysis unit can also evaluate the aptitude of the unemployed using aptitude tests. Aptitude tests are tests for evaluating the personality and behavioral characteristics of the unemployed and can identify occupations and industries for which the unemployed are highly suitable. The analysis unit can also evaluate the skills and aptitudes of the unemployed using performance evaluations. Performance evaluations are methods for evaluating the skills and aptitudes of the unemployed based on their past performance and achievements, and can reflect their actual performance in the job. By evaluating the skills and aptitudes of the unemployed, appropriate career advice can be provided. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected information into the AI ​​and have the AI ​​perform skill and aptitude evaluations.

[0068] The service provider can provide appropriate job placement support and suggest new job types based on the analysis results. For example, the service provider can suggest job types to unemployed individuals based on the analysis results. Job type suggestions include, but are not limited to, IT-related jobs, medical-related jobs, and education-related jobs, based on the unemployed person's skills and aptitudes. For example, if an unemployed person is interested in the IT field, the service provider can suggest IT-related jobs. Also, if an unemployed person is interested in the medical field, the service provider can suggest medical-related jobs. Furthermore, if an unemployed person is interested in the education field, the service provider can suggest education-related jobs. For example, the service provider can suggest online courses or training programs for unemployed individuals to acquire new skills. Online courses include, but are not limited to, courses in programming, data science, and marketing. For example, if an unemployed person wants to learn programming, the service provider can suggest an online programming course. Also, if an unemployed person wants to learn data science, the service provider can suggest an online data science course. Furthermore, if an unemployed person wants to learn marketing, the service provider can suggest an online marketing course. The service provider offers services such as interview preparation and resume editing for unemployed individuals seeking new jobs. Interview preparation includes, but is not limited to, mock interviews, interview tips, and methods for answering questions. For example, the service provider might offer mock interviews for unemployed individuals to practice. It could also provide advice to help unemployed individuals learn interview techniques. Furthermore, it could provide guidance to help unemployed individuals learn how to answer questions. This allows the service provider to support unemployed individuals in finding new jobs by providing appropriate job placement support and suggesting new occupations based on the analysis results. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider could input analysis results into AI and have the AI ​​perform job suggestions and job placement support.

[0069] The customization department can customize learning plans to suit the needs of unemployed individuals. For example, the customization department can select online courses for unemployed individuals to acquire new skills. These online courses may include, but are not limited to, courses in programming, data science, and marketing. For instance, if an unemployed individual wants to learn programming, the customization department can select an online programming course. Similarly, if an unemployed individual wants to learn data science, the customization department can select an online data science course. Furthermore, if an unemployed individual wants to learn marketing, the customization department can select an online marketing course. The customization department can also create learning schedules for unemployed individuals. These schedules may include, but are not limited to, a start date, an end date, and study hours. For example, if an unemployed individual can study full-time, the customization department can create a full-time learning schedule. If an unemployed individual can study part-time, the customization department can also create a part-time learning schedule. Furthermore, if an unemployed individual can study online, the customization department can create an online learning schedule. The customization department can also provide, for example, individualized instruction to unemployed individuals. Individualized instruction includes, but is not limited to, online individualized instruction, in-person individualized instruction, and group individualized instruction. For example, if an unemployed person wants to receive online individualized instruction, the customization unit will provide online individualized instruction. The customization unit can also provide in-person individualized instruction if the unemployed person wants to receive in-person individualized instruction. Furthermore, the customization unit can provide group individualized instruction if the unemployed person wants to receive it as part of a group. This allows for the provision of an optimal learning plan for the unemployed person, thereby supporting their skill development. Some or all of the above-described processes in the customization unit may be performed using, for example, AI, or not using AI. For example, the customization unit can input the unemployed person's information into AI and have the AI ​​customize the learning plan.

[0070] The career advice system includes a development department that develops an AI concierge. The development department develops the AI ​​concierge. The AI ​​concierge is developed using technologies such as natural language processing, machine learning algorithms, and user interfaces. Natural language processing is a technology that enables the AI ​​concierge to interact with users and understand user input and generate appropriate responses. Machine learning algorithms are a technology that enables the AI ​​concierge to learn user behavior and patterns and provide more appropriate advice. The user interface is an interface through which the AI ​​concierge interacts with users and is designed to improve usability and visibility. The development department, for example, uses natural language processing technology to develop a system in which the AI ​​concierge understands user input and generates appropriate responses. The development department can also use machine learning algorithms to develop a system in which the AI ​​concierge learns user behavior and patterns and provides more appropriate advice. Furthermore, the development department can design a user interface and develop an interface through which the AI ​​concierge interacts with users. In this way, the development of the AI ​​concierge can improve the functionality of the career advice system. Some or all of the processes described above in the development department may be performed using AI, for example, or without AI. For example, in the development of the AI ​​concierge, the development department may have the AI ​​execute natural language processing techniques and machine learning algorithms.

[0071] The career advice system includes a maintenance department that performs maintenance on the AI ​​concierge. The maintenance department performs maintenance on the AI ​​concierge. Maintenance includes, but is not limited to, periodic system checks, bug fixes, and feature updates. Periodic system checks are a process to verify that the AI ​​concierge is functioning correctly and are performed to maintain system performance and stability. Bug fixes are a process to correct problems that occur when the AI ​​concierge is not functioning correctly. Feature updates are a process to improve the AI ​​concierge's functionality by adding new features or improving existing ones. The maintenance department, for example, performs periodic system checks to verify that the AI ​​concierge is functioning correctly. The maintenance department can also perform bug fixes to correct problems that occur when the AI ​​concierge is not functioning correctly. Furthermore, the maintenance department can perform feature updates to improve the AI ​​concierge's functionality by adding new features or improving existing ones. In this way, the stability of the career advice system can be maintained by performing maintenance on the AI ​​concierge. Some or all of the above processes in the maintenance department may be performed using, for example, AI, or not using AI. For example, the maintenance department can have the AI ​​perform system checks and bug fixes during the maintenance of the AI ​​concierge.

[0072] The data collection unit can estimate the emotions of unemployed individuals and adjust the timing of information collection based on these estimated emotions. For example, the data collection unit can estimate the emotions of unemployed individuals and adjust the timing of information collection based on these estimated emotions. Emotion estimation can utilize, but is not limited to, technologies such as facial recognition, speech analysis, and text analysis. Facial recognition is a technology that captures the facial expressions of unemployed individuals with a camera and estimates their emotions from those expressions. Speech analysis is a technology that records the voices of unemployed individuals and estimates their emotions from those voices. Text analysis is a technology that estimates emotions from text entered by unemployed individuals. For example, if an unemployed person is feeling stressed, the data collection unit will collect information during relaxed periods. Furthermore, if the unemployed person is relaxed, the data collection unit can collect detailed information. Additionally, if the unemployed person is in a hurry, the data collection unit can collect information quickly. This allows for the collection of more appropriate information by adjusting the timing of information collection according to the emotions of the unemployed person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0073] The collection unit can improve the accuracy of the information by collecting a detailed history of the unemployed person's past work experience and acquired qualifications. The collection unit, for example, collects detailed work details of the jobs the unemployed person has previously performed. Past work experience includes, for example, job content, working period, and responsibilities, but is not limited to these examples. The collection unit, for example, collects detailed work details of the jobs the unemployed person has previously performed. The collection unit can also collect specific details of qualifications the unemployed person has obtained and the time of acquisition. Acquired qualifications include, for example, technical qualifications, professional qualifications, and certifications, but is not limited to these examples. The collection unit, for example, collects detailed details of technical qualifications the unemployed person has obtained. The collection unit can also collect specific details of professional qualifications the unemployed person has obtained. Furthermore, the collection unit can collect information on certifications the unemployed person has obtained. The collection unit, for example, collects information on training and seminars the unemployed person has previously attended. Training and seminars include, for example, technical training, management training, industry seminars, etc., but are not limited to these examples. The collection unit, for example, collects detailed details of technical training the unemployed person has attended. The collection unit can also collect specific details of management training sessions attended by the unemployed. Furthermore, the collection unit can also collect information on industry seminars attended by the unemployed. By collecting detailed histories in this way, the accuracy of the information can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input detailed histories of the unemployed into AI and have the AI ​​collect the information.

[0074] The collection unit can customize the scope of information collection based on the unemployed person's current living situation and areas of interest. For example, the collection unit customizes the scope of information collection based on the unemployed person's current living situation. Examples of current living situations include, but are not limited to, family environment, financial situation, and health condition. For example, if the unemployed person cannot work full-time, the collection unit can provide a part-time or online learning plan. If the unemployed person can only study at certain times due to family circumstances, the collection unit can also provide a learning plan tailored to those time slots. Furthermore, if the unemployed person is in a financially difficult situation, the collection unit can also provide free or low-cost learning plans. For example, the collection unit customizes the scope of information collection based on the unemployed person's areas of interest. Examples of areas of interest include, but are not limited to, IT, healthcare, education, etc. For example, if the unemployed person is interested in IT, the collection unit can collect information on IT-related occupations and industries. If the unemployed person is interested in healthcare, the collection unit can collect information on healthcare-related occupations and industries. Furthermore, if the unemployed person is interested in the education field, the collection unit can collect information on education-related occupations and industries. This allows for customizing the scope of information collection based on the living situation and interests of the unemployed person, thereby collecting more relevant information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can have AI customize the scope of information collection based on the living situation and areas of interest of the unemployed person.

[0075] The data collection unit can estimate the emotions of unemployed individuals and prioritize the information to be collected based on those estimated emotions. For example, the data collection unit can estimate the emotions of unemployed individuals and prioritize the information to be collected based on those estimated emotions. Emotion estimation can utilize, but is not limited to, technologies such as facial recognition, speech analysis, and text analysis. Facial recognition is a technology that captures the facial expressions of unemployed individuals with a camera and estimates their emotions from those expressions. Speech analysis is a technology that records the voices of unemployed individuals and estimates their emotions from those voices. Text analysis is a technology that estimates emotions from text entered by unemployed individuals. For example, if an unemployed individual is feeling anxious, the data collection unit will prioritize collecting information that provides a sense of security. Furthermore, if an unemployed individual is agitated, the data collection unit can prioritize collecting information about challenging jobs. Additionally, if an unemployed individual is calm, the data collection unit can prioritize collecting detailed job information. This allows for the collection of more appropriate information by prioritizing information based on emotions. Emotion estimation can be achieved using emotion estimation functions, such as emotion engines or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0076] The data collection unit can collect region-specific work experience and qualification information based on the geographical location information of unemployed individuals. For example, the data collection unit can collect specific work experience in the region where the unemployed person lives. Geographical location information includes, but is not limited to, GPS data, region-specific work information, and local job postings. The data collection unit can also collect qualification information available in the region where the unemployed person lives. Furthermore, the data collection unit can collect employment market trends in the region where the unemployed person lives. This allows for the collection of region-specific information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the geographical location information of unemployed individuals into AI and have the AI ​​collect region-specific information.

[0077] The data collection unit can analyze the social media activity of unemployed individuals and collect relevant information. For example, the data collection unit can collect information on the occupations and industries that unemployed individuals are interested in on social media. Social media activity includes, but is not limited to, analysis of post content, follower analysis, and activity frequency. The data collection unit can also collect the latest information on companies and organizations that unemployed individuals follow. Furthermore, the data collection unit can collect information on the activities of online communities that unemployed individuals participate in. This allows relevant information to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the social media activity of unemployed individuals into AI and have the AI ​​collect relevant information.

[0078] The analysis unit can estimate the emotions of unemployed individuals and adjust the analysis method based on the estimated emotions. For example, the analysis unit estimates the emotions of unemployed individuals and adjusts the analysis method based on the estimated emotions. Emotion estimation may use, but is not limited to, techniques such as facial recognition, speech analysis, and text analysis. Facial recognition is a technique that captures the facial expressions of unemployed individuals with a camera and estimates their emotions from those expressions. Speech analysis is a technique that records the voices of unemployed individuals and estimates their emotions from those voices. Text analysis is a technique that estimates emotions from text entered by unemployed individuals. For example, if an unemployed individual is feeling stressed, the analysis unit provides concise and easy-to-understand analysis results. Furthermore, if an unemployed individual is relaxed, the analysis unit can provide detailed analysis results. Additionally, if an unemployed individual is in a hurry, the analysis unit can provide rapid analysis results. By adjusting the analysis method based on emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generating AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0079] The analysis unit can perform a detailed analysis of the collected information and evaluate the unemployed person's skills and aptitude from multiple perspectives. The analysis unit performs a detailed analysis of the collected information using techniques such as data mining, statistical analysis, and machine learning algorithms. Examples of detailed analysis include, but are not limited to, data granularity, depth of analysis, and multiple perspectives. For example, the analysis unit can perform a detailed evaluation of the unemployed person's skill set based on their past work experience. The analysis unit can also perform a multifaceted evaluation of the unemployed person's aptitude based on their qualifications. Furthermore, the analysis unit can evaluate the unemployed person's aptitude based on their areas of interest. This allows for a multifaceted evaluation of skills and aptitude through detailed analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform a detailed analysis.

[0080] During the analysis, the analysis unit can compare the unemployed person's past work experience with current market trends to evaluate them. For example, the analysis unit compares the unemployed person's past work experience with current market trends to evaluate in-demand skills. Market trends include, but are not limited to, industry reports, job-seeking trends, and economic indicator analysis. For example, the analysis unit compares the unemployed person's past work experience with current market trends to evaluate in-demand skills. The analysis unit can also compare the unemployed person's acquired qualifications with current market trends to evaluate their value. Furthermore, the analysis unit can compare the unemployed person's areas of interest with current market trends to evaluate their aptitude. This allows for a more appropriate evaluation by comparing the unemployed person's past work experience with market trends. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the unemployed person's past work experience and market trends into AI and have the AI ​​perform the comparative evaluation.

[0081] The analysis unit can estimate the emotions of the unemployed person and adjust the display method of the analysis results based on the estimated emotions of the unemployed person. The analysis unit, for example, estimates the emotions of the unemployed person and adjusts the display method of the analysis results based on the estimated emotions. Emotion estimation can use technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed person with a camera and estimates the emotion from the facial expression. Voice analysis is a technology that records the voice of the unemployed person and estimates the emotion from the voice. Text analysis is a technology that estimates the emotion from text entered by the unemployed person. For example, if the unemployed person is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the unemployed person is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the unemployed person is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the display method based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generating AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0082] The analysis unit can perform its analysis while considering the geographical factors of the unemployed. For example, the analysis unit can perform its analysis while considering the trends in the employment market in the area where the unemployed person lives. Geographical factors include, but are not limited to, local economic conditions, local job information, and local job market trends. The analysis unit can also perform its analysis while considering the skills in demand in the area where the unemployed person lives. Furthermore, the analysis unit can perform its analysis while considering information on specific occupations or industries in the area where the unemployed person lives. This allows for a more appropriate analysis by considering geographical factors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical factors of the unemployed person into AI and have AI perform the analysis.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and industry reports on unemployed individuals during the analysis process. For example, the analysis unit may refer to relevant literature when evaluating the skills and aptitudes of unemployed individuals. Relevant literature and industry reports include, but are not limited to, references to academic papers, analysis of industry reports, and use of databases. The analysis unit may also refer to industry reports when evaluating the areas of interest of unemployed individuals. Furthermore, the analysis unit may refer to relevant research when evaluating the past work experience of unemployed individuals. This improves the accuracy of the analysis by referring to relevant literature and industry reports. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may have AI refer to relevant literature and industry reports to evaluate the skills and aptitudes of unemployed individuals.

[0084] The providing unit can estimate the emotions of the unemployed person and adjust the way the advice is presented based on the estimated emotions of the unemployed person. The providing unit, for example, estimates the emotions of the unemployed person and adjusts the way the advice is presented based on the estimated emotions. Emotion estimation can use technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed person with a camera and estimates the emotion from the facial expression. Voice analysis is a technology that records the voice of the unemployed person and estimates the emotion from the voice. Text analysis is a technology that estimates the emotion from text entered by the unemployed person. For example, if the unemployed person is nervous, the providing unit provides a simple, highly visible display method. Furthermore, if the unemployed person is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the unemployed person is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows for more appropriate advice to be provided by adjusting the way the advice is presented based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may cause AI to execute facial expression recognition technology to estimate the emotions of unemployed people.

[0085] The service provider can adjust the level of detail of the advice based on the unemployed person's skills and aptitudes when providing it. For example, if the unemployed person has high skills, the service provider will provide detailed advice. Skill and aptitude assessments include, but are not limited to, skill matrices, aptitude tests, and performance assessments. The service provider can also provide concise advice if the unemployed person has low skills. Furthermore, the service provider can provide appropriate advice based on the unemployed person's aptitudes. This allows for more appropriate advice to be provided by adjusting the level of detail based on skills and aptitudes. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the unemployed person's skills and aptitudes into AI and have AI adjust the level of detail of the advice.

[0086] The service provider can apply different advice algorithms depending on the unemployed person's area of ​​interest at the time of delivery. For example, if the unemployed person is interested in the IT field, the service provider will apply an IT-related advice algorithm. The advice algorithms include, but are not limited to, rule-based algorithms, machine learning algorithms, and hybrid algorithms. The service provider can also apply a medical-related advice algorithm if the unemployed person is interested in the medical field. Furthermore, if the unemployed person is interested in the education field, the service provider can apply an education-related advice algorithm. This allows for more appropriate career advice by providing advice tailored to the area of ​​interest. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have AI apply an advice algorithm based on the unemployed person's area of ​​interest.

[0087] The providing unit can estimate the emotions of the unemployed person and adjust the length of the advice based on the estimated emotions of the unemployed person. The providing unit, for example, estimates the emotions of the unemployed person and adjusts the length of the advice based on the estimated emotions. Emotion estimation can be achieved using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed person with a camera and estimates the emotion from the facial expression. Voice analysis is a technology that records the voice of the unemployed person and estimates the emotion from the voice. Text analysis is a technology that estimates the emotion from text entered by the unemployed person. For example, if the unemployed person is in a hurry, the providing unit can provide short, concise advice. Furthermore, if the unemployed person is relaxed, the providing unit can also provide detailed advice. Furthermore, if the unemployed person is excited, the providing unit can provide visually stimulating advice. This allows for more appropriate advice to be provided by adjusting the length of the advice based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may cause AI to execute facial expression recognition technology to estimate the emotions of unemployed people.

[0088] The providing unit can determine the priority of advice based on the unemployed person's past work experience when providing the advice. The providing unit, for example, prioritizes providing the most relevant advice based on the unemployed person's past work experience. Past work experience includes, but is not limited to, job content, length of employment, and responsibilities. The providing unit, for example, prioritizes providing the most relevant advice based on the unemployed person's past work experience. The providing unit can also prioritize providing advice regarding in-demand skills based on the unemployed person's past work experience. Furthermore, the providing unit can prioritize providing advice regarding career changes based on the unemployed person's past work experience. In this way, by prioritizing advice based on the unemployed person's past work experience, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the unemployed person's past work experience into AI and have the AI ​​determine the priority of advice.

[0089] The providing unit can adjust the order of advice based on the relevance of the unemployed person when providing the advice. For example, the providing unit first provides advice related to the skills of the unemployed person. Relevance includes, but is not limited to, for example, skills, aptitudes, areas of interest, etc. For example, the providing unit first provides advice related to the skills of the unemployed person. The providing unit can also provide advice related to the aptitude of the unemployed person next. Furthermore, the providing unit can also provide advice related to the areas of interest of the unemployed person last. In this way, by adjusting the order of advice based on relevance, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to adjust the order of advice based on the relevance of the unemployed person.

[0090] The customization unit can estimate the emotions of the unemployed person and adjust the content of the study plan based on the estimated emotions of the unemployed person. The customization unit, for example, estimates the emotions of the unemployed person and adjusts the content of the study plan based on the estimated emotions. Emotion estimation can be achieved using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed person with a camera and estimates their emotions from the facial expression. Voice analysis is a technology that records the voice of the unemployed person and estimates their emotions from the voice. Text analysis is a technology that estimates emotions from text entered by the unemployed person. For example, if the unemployed person is feeling stressed, the customization unit can provide a relaxing study plan. Furthermore, if the unemployed person is relaxed, the customization unit can also provide a detailed study plan. Furthermore, if the unemployed person is in a hurry, the customization unit can quickly provide a study plan. This allows the content of the study plan to be adjusted based on the emotions, thereby providing a more appropriate study plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit may cause the AI ​​to perform facial expression recognition technology to estimate the emotions of unemployed people.

[0091] The customization unit can select the optimal learning plan by referring to the unemployed person's past learning history during the customization process. For example, the customization unit provides the optimal learning plan based on the unemployed person's past learning history. Past learning history includes, but is not limited to, records of learning outcomes, reference to past learning plans, and analysis of learning history. The customization unit can also provide the optimal learning plan based on the unemployed person's past learning history to improve their skills. Furthermore, the customization unit can also provide the optimal learning plan based on the unemployed person's past learning history to change careers. In this way, the optimal learning plan can be provided by referring to past learning history. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the unemployed person's past learning history into AI and have the AI ​​select the optimal learning plan.

[0092] During customization, the customization unit can customize the study plan based on the unemployed person's current living situation. The customization unit customizes the study plan based on, for example, the unemployed person's current living situation. Current living situations include, but are not limited to, the unemployed person's family environment, financial situation, and health condition. For example, if the unemployed person cannot work full-time, the customization unit can provide a study plan that allows part-time or online learning. Furthermore, if the unemployed person can only study at specific times due to family circumstances, the customization unit can provide a study plan tailored to those times. Furthermore, if the unemployed person is in a difficult financial situation, the customization unit can provide a free or low-cost study plan. This allows the unemployed person to be provided with a more appropriate study plan by customizing the study plan based on their current living situation. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit may input the unemployed person's current living situation into AI and have the AI ​​customize the study plan.

[0093] The customization unit can estimate the emotions of the unemployed person and prioritize the study plans based on the estimated emotions of the unemployed person. The customization unit, for example, estimates the emotions of the unemployed person and prioritizes the study plans based on the estimated emotions. Emotion estimation can be achieved using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed person with a camera and estimates the emotion from the facial expression. Voice analysis is a technology that records the voice of the unemployed person and estimates the emotion from the voice. Text analysis is a technology that estimates the emotion from text entered by the unemployed person. For example, if the unemployed person is feeling anxious, the customization unit can prioritize providing a study plan that gives a sense of security. Furthermore, if the unemployed person is excited, the customization unit can prioritize providing a challenging study plan. Furthermore, if the unemployed person is calm, the customization unit can prioritize providing a detailed study plan. By prioritizing the study plans based on emotions, more appropriate study plans can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit may cause the AI ​​to perform facial expression recognition technology to estimate the emotions of unemployed people.

[0094] The customization unit can select the optimal learning plan by considering the geographical location of the unemployed during the customization process. For example, the customization unit can incorporate training and seminars held in the area where the unemployed person lives into the learning plan. Geographical location information includes, but is not limited to, GPS data, region-specific learning plans, and information on local educational institutions. The customization unit can also incorporate information on educational institutions and training centers in the area where the unemployed person lives into the learning plan. Furthermore, the customization unit can incorporate online learning resources in the area where the unemployed person lives into the learning plan. This allows for the provision of an optimal learning plan by considering geographical location information. Some or all of the above processing in the customization unit may be performed using, for example, AI, or not using AI. For example, the customization unit can input the geographical location information of the unemployed person into AI and have the AI ​​select the optimal learning plan.

[0095] During customization, the customization unit can analyze the unemployed person's social media activity to propose a study plan. The customization unit, for example, proposes a study plan based on the areas of interest the unemployed person has on social media. Examples of social media activity include, but are not limited to, analysis of post content, analysis of followers, and activity frequency. The customization unit, for example, proposes a study plan based on the areas of interest the unemployed person has on social media. The customization unit can also incorporate information about educational institutions and training programs followed by the unemployed person into the study plan. Furthermore, the customization unit can incorporate activity content from online communities in which the unemployed person participates into the study plan. This allows for the provision of a more appropriate study plan by analyzing social media activity. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the unemployed person's social media activity into AI and have the AI ​​execute the study plan proposal.

[0096] The development department can estimate the emotions of unemployed individuals and determine development priorities based on those estimated emotions. For example, the development department can estimate the emotions of unemployed individuals and determine development priorities based on those estimated emotions. Emotion estimation can be performed using technologies such as facial recognition, speech analysis, and text analysis, but is not limited to these examples. Facial recognition is a technology that captures the facial expressions of unemployed individuals with a camera and estimates their emotions from those expressions. Speech analysis is a technology that records the voices of unemployed individuals and estimates their emotions from those voices. Text analysis is a technology that estimates emotions from text entered by unemployed individuals. For example, if an unemployed individual is feeling anxious, the development department can prioritize the development of features that provide a sense of security. The development department can also prioritize the development of challenging features if the unemployed individual is excited. Furthermore, if the unemployed individual is calm, the development department can prioritize the development of detailed features. This allows for the development of more appropriate features by determining development priorities based on emotions. Emotion estimation can be achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processes described above in the development department may be performed using AI, for example, or not using AI. For example, the development department may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0097] During development, the development department can select the optimal development method by referring to past development history. The development department, for example, selects the most effective development method based on the past development history. Past development history includes, for example, records of development projects, reference to past development methods, and analysis of development history, but is not limited to these examples. The development department, for example, selects the most effective development method based on the past development history. The development department can also select the most efficient development method based on the past development history. Furthermore, the development department can select the development method with the highest success rate based on the past development history. In this way, the optimal development method can be selected by referring to the past development history. Some or all of the above-mentioned processing in the development department may be performed, for example, using AI, or may be performed without using AI. For example, the development department can input the past development history into AI and have it select the optimal development method.

[0098] The development department can estimate the emotions of the unemployed and adjust the progress of development based on the estimated emotions of the unemployed. The development department, for example, estimates the emotions of the unemployed and adjusts the progress of development based on the estimated emotions. Emotion estimation can be achieved using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed with a camera and estimates the emotion from the facial expression. Voice analysis is a technology that records the voice of the unemployed and estimates the emotion from the voice. Text analysis is a technology that estimates the emotion from the text entered by the unemployed. For example, if the unemployed is feeling anxious, the development department can frequently report the progress and reassure the unemployed. Furthermore, if the unemployed is excited, the development department can quickly advance the progress and set challenging goals. Furthermore, if the unemployed is calm, the development department can report the progress in detail and proceed in a planned manner. This allows for more appropriate development by adjusting the progress of development based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processes described above in the development department may be performed using AI, for example, or not using AI. For example, the development department may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0099] During development, the development department can improve the accuracy of development by referring to related technical literature. The development department, for example, refers to related technical literature and incorporates the latest technology. Relevant technical literature includes, but is not limited to, referring to academic papers, analyzing technical reports, and using databases. The development department, for example, refers to related technical literature and incorporates the latest technology. The development department can also improve the accuracy of development by referring to related technical literature. Furthermore, the development department can improve the efficiency of development by referring to related technical literature. As a result, the accuracy of development is improved by referring to related technical literature. Some or all of the above-mentioned processing in the development department may be performed, for example, using AI, or may be performed without using AI. For example, the development department can input related technical literature into AI and have the AI ​​improve the accuracy of development.

[0100] The maintenance department can estimate the emotions of unemployed individuals and determine maintenance priorities based on those estimated emotions. For example, the maintenance department can estimate the emotions of unemployed individuals and determine maintenance priorities based on those estimated emotions. Emotion estimation may utilize, but is not limited to, technologies such as facial recognition, speech analysis, and text analysis. Facial recognition is a technology that captures the facial expressions of unemployed individuals with a camera and estimates their emotions from those expressions. Speech analysis is a technology that records the voices of unemployed individuals and estimates their emotions from those voices. Text analysis is a technology that estimates emotions from text entered by unemployed individuals. For example, if an unemployed individual is feeling anxious, the maintenance department will prioritize maintenance that provides a sense of security. Furthermore, if an unemployed individual is agitated, the maintenance department may prioritize challenging maintenance. Additionally, if an unemployed individual is calm, the maintenance department may prioritize detailed maintenance. This allows for more appropriate maintenance by determining maintenance priorities based on emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the maintenance unit may be performed using AI, or not using AI. For example, the maintenance unit may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0101] During maintenance, the maintenance unit can select the optimal maintenance method by referring to past maintenance history. The maintenance unit, for example, selects the most effective maintenance method based on the past maintenance history. Past maintenance history includes, for example, records of maintenance work, reference to past maintenance methods, and analysis of maintenance history, but is not limited to these examples. The maintenance unit, for example, selects the most effective maintenance method based on the past maintenance history. The maintenance unit can also select the most efficient maintenance method based on the past maintenance history. Furthermore, the maintenance unit can select the maintenance method with the highest success rate based on the past maintenance history. In this way, the optimal maintenance method can be selected by referring to the past maintenance history. Some or all of the above-described processing in the maintenance unit may be performed using, for example, AI, or may be performed without using AI. For example, the maintenance unit can input past maintenance history into AI and have the AI ​​select the optimal maintenance method.

[0102] The maintenance unit can estimate the emotions of the unemployed person and adjust the progress of maintenance based on the estimated emotions of the unemployed person. For example, the maintenance unit estimates the emotions of the unemployed person and adjusts the progress of maintenance based on the estimated emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that captures the facial expression of the unemployed person with a camera and estimates the emotion from the facial expression. Voice analysis is a technology that records the voice of the unemployed person and estimates the emotion from the voice. Text analysis is a technology that estimates the emotion from the text entered by the unemployed person. For example, if the unemployed person is feeling anxious, the maintenance unit can frequently report the progress and provide reassurance. Furthermore, if the unemployed person is excited, the maintenance unit can quickly advance the progress and set challenging goals. Furthermore, if the unemployed person is calm, the maintenance unit can report the progress in detail and proceed in a planned manner. This allows for more appropriate maintenance by adjusting the progress of maintenance based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the maintenance unit may be performed using AI, or not using AI. For example, the maintenance unit may have an AI perform facial recognition technology to estimate the emotions of unemployed people.

[0103] The maintenance department can improve the accuracy of maintenance by referring to relevant technical literature during maintenance. For example, the maintenance department can incorporate the latest technologies by referring to relevant technical literature. Relevant technical literature includes, but is not limited to, referencing academic papers, analyzing technical reports, and using databases. The maintenance department can also improve the accuracy of maintenance by referring to relevant technical literature. Furthermore, the maintenance department can improve the efficiency of maintenance by referring to relevant technical literature. As a result, the accuracy of maintenance is improved by referring to relevant technical literature. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input relevant technical literature into AI and have the AI ​​perform the maintenance accuracy improvement. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, customization unit, development unit, maintenance unit, and emotion estimation function, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on unemployed persons using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides career advice based on the analysis results. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes an optimal study plan for the unemployed. The development unit is realized by the specific processing unit 290 of the data processing device 12 and develops an AI concierge. The maintenance unit is realized by the specific processing unit 290 of the data processing device 12 and maintains the AI ​​concierge. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the emotions of the unemployed. === Hard Collateral 1-2 === Each of the above-mentioned components, including the data collection unit, analysis unit, provision unit, customization unit, development unit, maintenance unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information on unemployed individuals and transmits this information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides career advice based on the analysis results. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and customizes the optimal learning plan for unemployed individuals. The development unit is implemented by the specific processing unit 290 of the data processing unit 12 and develops the AI ​​concierge. The maintenance unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs maintenance on the AI ​​concierge. The emotion estimation function is implemented by the identification processing unit 290 of the data processing device 12, which estimates the emotions of unemployed individuals. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, customization unit, development unit, maintenance unit, and emotion estimation function, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information on unemployed persons using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides career advice based on the analysis results. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes an optimal study plan for the unemployed. The development unit is realized by the specific processing unit 290 of the data processing device 12 and develops an AI concierge. The maintenance unit is realized by the specific processing unit 290 of the data processing device 12 and maintains the AI ​​concierge. The emotion estimation function is implemented by the identification processing unit 290 of the data processing device 12, which estimates the emotions of unemployed individuals. === Hard Collateral 1-4 === Each of the above-mentioned components, including the data collection unit, analysis unit, provision unit, customization unit, development unit, maintenance unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information on unemployed individuals and transmits this information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides career advice based on the analysis results. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and customizes the optimal learning plan for unemployed individuals. The development unit is implemented by the specific processing unit 290 of the data processing unit 12 and develops the AI ​​concierge. The maintenance unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs maintenance on the AI ​​concierge. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the emotions of unemployed individuals.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can consider not only the unemployed person's past work experience but also their non-work experience, such as hobbies and volunteer activities, when evaluating their skills and aptitudes. For example, if an unemployed person programs as a hobby, that skill can be included in the evaluation. Similarly, if an unemployed person demonstrates leadership in volunteer activities, that aptitude can be reflected in the evaluation. Furthermore, knowledge and skills acquired through hobbies can be evaluated on par with work experience. This allows for a more accurate assessment of the unemployed person's overall skills and aptitudes.

[0106] The provider can not only suggest jobs based on the unemployed person's skills and aptitudes, but also jobs that suit their lifestyle and values. For example, if the unemployed person values ​​work-life balance, the provider can suggest jobs that allow remote work. If the unemployed person is interested in contributing to society, the provider can suggest jobs with NPOs or social enterprises. Furthermore, if the unemployed person wants a creative job, the provider can suggest jobs related to design or art. In this way, by suggesting jobs that suit the unemployed person's values ​​and lifestyle, the provider can support them in making more satisfying career choices.

[0107] When customizing the learning plan for the unemployed, the customization unit can provide a plan that suits the learning style and learning pace of the unemployed. For example, if the unemployed prefers visual learning, a learning plan centered on video materials can be provided. Also, if the unemployed wants to study intensively in a short period of time, an intensive course-style learning plan can be provided. Furthermore, if the unemployed wants to study slowly, a long-term learning plan can be provided. In this way, by providing a learning plan that suits the learning style and pace of the unemployed, the learning effect can be maximized.

[0108] When collecting information on the skills and aptitudes of unemployed persons, the collection department can collect not only the unemployed person's self-assessment but also third-party evaluations. For example, it can collect recommendation letters and evaluations from the unemployed person's past superiors and colleagues. It can also collect the results and feedback of projects in which the unemployed person has participated. It can also collect evaluations of training and education that the unemployed person has received. This allows for a more objective evaluation of the unemployed person's skills and aptitudes.

[0109] When assessing the skills and aptitudes of unemployed individuals, the analysis unit can also take into account their future career goals and desired career paths. For example, if an unemployed individual hopes to pursue a management position in the future, the skills and experience required for that can be included in the assessment. Also, if an unemployed individual wishes to pursue a career in a specific industry, the aptitude for that industry can be assessed. Furthermore, if an unemployed individual wishes to pursue an international career, language ability and intercultural understanding skills can be included in the assessment. This makes it possible to provide more appropriate career advice by conducting an assessment tailored to the unemployed individual's future career goals.

[0110] The providing unit can estimate the emotions of the unemployed person and adjust the timing of advice based on the estimated emotions. For example, if the unemployed person is feeling stressed, advice can be provided at a time when the unemployed person is relaxed. Also, if the unemployed person is relaxed, detailed advice can be provided. Furthermore, if the unemployed person is in a hurry, advice can be provided quickly. In this way, more effective advice can be provided by adjusting the timing of advice according to the emotions of the unemployed person.

[0111] The customization unit can estimate the emotions of the unemployed person and adjust the progress of the study plan based on the estimated emotions. For example, if the unemployed person is feeling stressed, the progress of the study plan can be slowed down. Also, if the unemployed person is feeling relaxed, the progress of the study plan can be accelerated. Furthermore, if the unemployed person is in a hurry, a plan that allows for intensive study in a short period of time can be provided. In this way, by adjusting the progress of the study plan according to the emotions of the unemployed person, more effective learning can be supported.

[0112] The analysis unit can estimate the emotions of unemployed individuals and adjust the feedback method of the analysis results based on those estimated emotions. For example, if an unemployed person is stressed, positive feedback can be prioritized. If the unemployed person is relaxed, detailed feedback can be provided. Furthermore, if the unemployed person is in a hurry, concise feedback can be provided. In this way, by adjusting the feedback method according to the emotions of the unemployed person, more effective feedback can be provided.

[0113] The system can estimate the emotions of unemployed individuals and adjust the content of advice based on those estimated emotions. For example, if an unemployed person is feeling anxious, it can provide reassuring advice. If an unemployed person is agitated, it can provide challenging advice. Furthermore, if an unemployed person is calm, it can provide detailed advice. By adjusting the content of advice according to the emotions of the unemployed person, it is possible to provide more appropriate advice.

[0114] The customization function can estimate the emotions of unemployed individuals and adjust the feedback method of their learning plan based on those estimated emotions. For example, if an unemployed person is stressed, positive feedback can be prioritized. If the unemployed person is relaxed, detailed feedback can be provided. Furthermore, if the unemployed person is in a hurry, concise feedback can be provided. In this way, by adjusting the feedback method according to the emotions of the unemployed person, it is possible to support more effective learning.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects information about the skills and aptitudes of unemployed people. Information about the skills and aptitudes of unemployed people includes technical skills, soft skills, past work experience, qualifications, etc. The collection unit allows unemployed people to enter information such as their past work experience, qualifications obtained, and areas of interest. The collection unit also allows unemployed people to provide information through questionnaires and interviews. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning algorithms to evaluate the skills and aptitudes of the unemployed. For example, the analysis unit uses skill matrices, aptitude tests, and performance evaluations to evaluate the skills and aptitudes of the unemployed. Step 3: The service provider provides career advice based on the analysis results obtained by the analysis provider. Career advice includes job suggestions, advice for skill development, and support for job hunting. For example, if an unemployed person is interested in the IT field, the service provider will suggest IT-related jobs and online courses and training programs for acquiring new skills. The service will also provide interview preparation and resume editing services for job hunting. Step 4: The customization team customizes the learning plan based on the advice provided by the service provider. The learning plan includes selecting online courses, creating a study schedule, and providing individualized instruction. For example, they might select online courses for an unemployed person to acquire new skills, create a study schedule, and provide individualized instruction.

[0117] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects information on the skills and aptitudes of unemployed people; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides career advice based on the analysis results obtained by the analyzing unit; a customization unit that customizes a study plan based on the advice provided by the provision unit. A system characterized by:

2. The collecting unit Collect information on the unemployed person's past work experience, qualifications, and areas of interest The system of claim 1 .

3. The analysis unit Analyze the collected information and evaluate the skills and aptitudes of unemployed people The system of claim 1 .

4. The providing unit Based on the analysis results, we provide appropriate career change support and suggest new job types. The system of claim 1 .

5. The customization unit Customize learning plans for the unemployed The system of claim 1 .

6. Equipped with a development department to develop AI concierges The system of claim 1 .

7. Equipped with a maintenance department that handles maintenance of the AI ​​concierge The system of claim 1 .

8. The collecting unit Estimate the emotions of the unemployed and adjust the timing of information gathering based on the estimated emotions of the unemployed. The system of claim 1 .

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