system

Abstract

The system according to this embodiment aims to efficiently carry out the processes of reskilling and job placement support. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a reskilling unit, a monitoring unit, and a proposal unit. The reception unit receives user information. The generation unit analyzes the information received by the reception unit and generates a reskilling plan. The reskilling unit performs reskilling based on the plan generated by the generation unit. The monitoring unit monitors the progress of the reskilling unit. The proposal unit proposes the most suitable job based on the progress monitored by the monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the processes of reskilling and job transfer support are not performed efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform the processes of reskilling and job transfer support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a reskilling unit, a monitoring unit, and a proposal unit. The reception unit receives user information. The generation unit analyzes the information received by the reception unit and generates a reskilling plan. The reskilling unit performs reskilling based on the plan generated by the generation unit. The monitoring unit monitors the progress of the reskilling unit. The proposal unit proposes the most suitable job based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently carry out the processes of reskilling and job placement support. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The reskilling and job placement support system according to an embodiment of the present invention is a system that optimizes reskilling using a generative AI and provides a matching service for job placement support for middle-aged and older individuals. This system begins with the user inputting information such as their knowledge, experience, and desired job type. Next, the generative AI analyzes this information and automatically generates an optimal reskilling plan for the user. This reskilling plan takes into account the user's aptitudes up to their previous jobs and includes educational materials and training to supplement any lack of knowledge or experience. Furthermore, the generative AI monitors the user's reskilling progress and adjusts the plan according to the progress. After reskilling is complete, the generative AI proposes the most suitable job opportunities based on the user's new skill set. This proposal considers the user's desired job type, work location, salary conditions, etc., and matches them with companies that are short on personnel and want to hire immediately effective talent. For example, the user inputs information such as their knowledge, experience, and desired job type. The generative AI analyzes this information and automatically generates an optimal reskilling plan for the user. This reskilling plan takes into account the user's aptitudes up to their previous jobs and includes educational materials and training to supplement any lack of knowledge or experience. The generating AI monitors the user's reskilling progress and adjusts the plan accordingly. After reskilling is complete, the generating AI suggests the most suitable job opportunities based on the user's new skill set. These suggestions consider the user's desired job type, work location, and salary conditions, matching them with companies in need of immediately productive talent. This allows middle-aged and older job seekers to leverage their knowledge and experience while reskilling to fill in their shortcomings and find the optimal job. Companies can also efficiently recruit immediately productive talent, reducing mismatches. In this way, the reskilling and job placement support system efficiently analyzes user information, generates and monitors reskilling plans, and suggests job opportunities.

[0029] The reskilling and job placement support system according to this embodiment comprises a reception unit, a generation unit, a reskilling unit, a monitoring unit, and a proposal unit. The reception unit inputs user information. User information includes, but is not limited to, personal information, work history, and skill sets. The reception unit stores the information entered by the user in a database, for example. The reception unit can also analyze the user information in real time and extract necessary information. The generation unit uses a generation AI to analyze the information entered by the reception unit and generate a reskilling plan. The generation unit generates a reskilling plan considering, for example, the user's aptitude up to their previous job. The generation unit can also propose an optimal reskilling plan based on the user's skill set and desired job type. The generation unit uses a generation AI to analyze user information and automatically generate a reskilling plan. The reskilling unit performs reskilling based on the plan generated by the generation unit. The reskilling unit provides, for example, educational materials and training to supplement the user's lack of knowledge and experience. Furthermore, the reskilling unit can adjust the reskilling plan according to the user's progress. The reskilling unit uses generative AI to monitor the user's reskilling status and adjusts the plan according to the progress. The monitoring unit monitors the progress of the reskilling unit. For example, the monitoring unit monitors the user's reskilling status in real time and evaluates the progress. The monitoring unit can also provide feedback to adjust the user's reskilling plan. The monitoring unit uses generative AI to monitor the user's reskilling status and adjusts the plan according to the progress. The proposal unit proposes the best job opportunities based on the progress monitored by the monitoring unit. For example, the proposal unit proposes the best job opportunities considering the user's desired job type, work location, salary conditions, etc. The proposal unit can also match users with companies that are short on personnel and want to hire immediately effective talent. The proposal unit uses generative AI to propose the best job opportunities based on the user's new skill set.As a result, the reskilling and job placement support system according to this embodiment can efficiently analyze user information, generate and monitor reskilling plans, and propose job opportunities.

[0030] The reception desk inputs user information. This information includes, but is not limited to, personal information, work history, and skill sets. The reception desk stores the user-entered information in a database. It can also analyze user information in real time and extract necessary information. Specifically, when receiving user-entered information and storing it in the database, the reception desk has a function to check the integrity and consistency of the data. For example, it verifies that the user-entered work history is chronologically consistent and that the skill sets are appropriately categorized. Furthermore, the reception desk can use natural language processing technology to analyze user information in real time and extract necessary information. This allows it to extract specific keywords and phrases from information entered by users in free form and store them appropriately in the database. In addition, the reception desk can provide APIs to link user information with other systems and services. For example, it can automatically retrieve user work history from a professional network and cross-reference skill sets with industry-standard skill mapping databases to collect more accurate information. This allows the reception desk to efficiently and accurately collect user information and improve the overall system performance.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate a reskilling plan. For example, the generation unit generates a reskilling plan considering the user's aptitudes from previous jobs. The generation unit can also propose an optimal reskilling plan based on the user's skill set and desired job type. The generation unit uses a generation AI to analyze user information and automatically generate a reskilling plan. Specifically, the generation AI analyzes the user's work history and skill set, and generates an optimal reskilling plan based on past data and industry trends. For example, if the user has experience in the IT industry, the generation AI will consider current IT industry trends and demands and propose a plan to strengthen skills such as programming languages ​​and cloud technologies. The generation AI can also customize the reskilling plan considering the user's desired job type, work location, salary conditions, etc. For example, if the user aims to become a data scientist, the generation AI will propose specific learning materials and training programs to strengthen skills in data analysis and machine learning. Furthermore, the generation unit can dynamically adjust the reskilling plan based on the user's progress and feedback. This allows the generation unit to provide flexible reskilling plans tailored to user needs, thereby supporting users' career advancement.

[0032] The reskilling unit performs reskilling based on the plan generated by the generation unit. For example, the reskilling unit provides learning materials and training to supplement the user's lack of knowledge and experience. The reskilling unit can also adjust the reskilling plan according to the user's progress. Using generational AI, the reskilling unit monitors the user's reskilling progress and adjusts the plan accordingly. Specifically, the reskilling unit provides diverse learning resources, such as online courses, workshops, and practical projects. For example, a user can take an online course in a specific programming language to improve their programming skills. The reskilling unit also monitors the user's learning progress in real time and provides additional learning materials and training as needed. For example, if a user is struggling with a particular task, the reskilling unit can provide supplementary materials or individual tutoring. Furthermore, the reskilling unit can dynamically adjust the reskilling plan based on user feedback. For example, if a user develops an interest in a particular skill, the reskilling unit can add learning materials and training related to that skill. This allows the reskilling unit to provide flexible reskilling tailored to user needs and effectively support user skill development.

[0033] The monitoring unit monitors the progress of the reskilling unit. For example, the monitoring unit monitors the user's reskilling status in real time and evaluates their progress. The monitoring unit can also provide feedback to adjust the user's reskilling plan. The monitoring unit uses generative AI to monitor the user's reskilling status and adjust the plan according to their progress. Specifically, the monitoring unit collects and analyzes the user's learning data and training results in real time. For example, if a user is taking an online course, the monitoring unit monitors their attendance, test scores, assignment submission status, etc., and evaluates their progress. The monitoring unit also evaluates the effectiveness of the reskilling plan based on the user's learning data and adjusts the plan as needed. For example, if a user is falling behind in progress with a particular skill, the monitoring unit can suggest additional materials or training. Furthermore, the monitoring unit can collect user feedback and use it to improve the reskilling plan. For example, if a user is dissatisfied with a particular material, the monitoring unit can review that material and provide more appropriate materials. This allows the monitoring unit to effectively monitor the user's reskilling status and respond flexibly according to the progress.

[0034] The Proposal Department suggests the most suitable job opportunities based on progress monitored by the Monitoring Department. For example, the Proposal Department considers the user's desired job type, work location, and salary conditions when suggesting the best job opportunities. The Proposal Department can also match users with companies facing talent shortages and seeking immediately productive employees. Using generative AI, the Proposal Department suggests optimal job opportunities based on the user's new skill set. Specifically, the Proposal Department lists potential employers based on the user's reskilling plan progress and newly acquired skills. For example, if a user acquires data scientist skills, the Proposal Department generates a list of companies recruiting data scientists and suggests them to the user. Furthermore, the Proposal Department can customize and suggest the most suitable job opportunities, considering the user's desired job type, work location, and salary conditions. For example, if a user desires remote work, the Proposal Department prioritizes suggesting companies that allow remote work. In addition, the Proposal Department considers the needs of companies and can match users with companies facing talent shortages and seeking immediately productive employees. For example, by proposing users who have completed reskilling to companies that are short of personnel with specific skills, the needs of both the company and the user can be met. This allows the proposal department to suggest the most suitable job opportunities based on the user's new skill set, thereby supporting the user's career advancement.

[0035] The generation unit can generate a reskilling plan considering the user's aptitudes from previous jobs. For example, the generation unit can evaluate the user's aptitudes from previous jobs and customize the reskilling plan based on that. The generation unit can also optimize the reskilling plan considering the user's skill set from previous jobs. The generation unit uses a generation AI to analyze the user's aptitudes from previous jobs and generates a reskilling plan based on that. This allows for the generation of a more appropriate reskilling plan by considering the user's aptitudes from previous jobs. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's aptitude data from previous jobs into the generation AI and have the generation AI generate the reskilling plan.

[0036] The monitoring unit can adjust the plan according to the progress of reskilling. For example, the monitoring unit can monitor the user's reskilling status in real time and change the plan according to the progress. The monitoring unit can also update the content of the reskilling plan based on the user's progress. The monitoring unit uses a generation AI to monitor the user's reskilling status and adjust the plan according to the progress. In this way, by adjusting the plan according to the progress of reskilling, the optimal reskilling can be provided to the user. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's reskilling progress data into the generation AI and have the generation AI perform the plan adjustment.

[0037] The suggestion unit can propose the most suitable job opportunities by considering the user's desired job type, work location, salary conditions, etc. For example, the suggestion unit can list the most suitable job opportunities based on the user's desired conditions. The suggestion unit can also narrow down the list of job opportunities based on the user's desired job type, work location, salary conditions. The suggestion unit uses a generative AI to analyze the user's desired conditions and propose the most suitable job opportunities based on that analysis. This allows for the proposal of more appropriate job opportunities by considering the user's desired conditions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's desired conditions data into a generative AI and have the generative AI generate job suggestions.

[0038] The reskilling unit can provide learning materials and training to supplement the user's lack of knowledge and experience. For example, the reskilling unit can analyze the user's skill gaps and provide learning materials and training based on that analysis. The reskilling unit can also adjust the content of the learning materials and training according to the user's learning progress. The reskilling unit uses generative AI to provide learning materials and training to supplement the user's lack of knowledge and experience. This enhances the effectiveness of reskilling by supplementing the user's lack of knowledge and experience. Some or all of the above-described processes in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input the user's skill gap data into a generative AI and have the generative AI provide learning materials and training.

[0039] The proposal department can match companies facing a shortage of immediately employable personnel with users. For example, the proposal department can analyze companies' job postings and extract the requirements for personnel that companies want to hire immediately. The proposal department can also compare users' skill sets with the company's requirements to perform the optimal match. The proposal department uses a generation AI to analyze companies' job postings and users' skill sets and perform matching based on that. This reduces mismatches by matching companies that want to hire immediately employable personnel with users. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input company job posting data and user skill set data into a generation AI and have the generation AI perform the matching.

[0040] The reception desk can analyze the user's past work history and select the optimal method of information input. For example, the reception desk can automatically customize the input form based on the user's past work history. The reception desk can also automatically display relevant skills and experience as input suggestions based on the user's work history. Furthermore, the reception desk can analyze the user's work history and optimize the order and items of input. In this way, by analyzing the user's past work history, it can provide the optimal method of information input. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's work history data into a generating AI and have the generating AI select the information input method.

[0041] The reception unit can filter information based on the user's current job status and areas of interest during input. For example, the reception unit can display only relevant information based on the user's current job status. The reception unit can also customize input fields based on the user's areas of interest. Furthermore, the reception unit can hide unnecessary input fields based on the user's job status and areas of interest. This allows for the provision of more relevant information by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's job status and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, the reception desk can prioritize displaying relevant job information based on the user's current location. The reception desk can also prompt the user to input region-specific information based on their geographical location. Furthermore, the reception desk can prioritize inputting nearby job information by considering the user's location. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI determine the priority of the information.

[0043] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can automatically input the user's areas of interest, such as job types and industries, based on the user's social media activity history. The reception desk can also input relevant skills and experience based on the user's social media activity history. Furthermore, the reception desk can analyze the user's social media network and input relevant information. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the information input.

[0044] The generation unit can adjust the level of detail of a reskilling plan based on the user's aptitudes from previous jobs when generating the plan. For example, the generation unit can generate a detailed reskilling plan based on the user's aptitudes from previous jobs. It can also generate a concise reskilling plan considering the user's experience from previous jobs. Furthermore, the generation unit can generate a customized reskilling plan based on the user's skill set from previous jobs. This allows for the provision of a more appropriate reskilling plan by adjusting the level of detail based on the user's aptitudes from previous jobs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's aptitude data from previous jobs into a generation AI and have the generation AI perform the adjustment of the plan's level of detail.

[0045] The generation unit can apply different generation algorithms depending on the user's job category when generating a reskilling plan. For example, if the user is in the IT industry, the generation unit can apply a generation algorithm specialized in IT skills. Similarly, if the user is in the medical industry, the generation unit can apply a generation algorithm specialized in medical skills. Furthermore, if the user is in the education industry, the generation unit can apply a generation algorithm specialized in education skills. This allows for the provision of more appropriate reskilling plans by applying different generation algorithms according to the user's job category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's job category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0046] The generation unit can determine the priority of reskilling plans based on the user's work history when generating them. For example, the generation unit can prioritize incorporating important skills from the user's work history into the reskilling plan. The generation unit can also analyze the user's work history and prioritize incorporating necessary skills into the reskilling plan. Furthermore, the generation unit can determine the priority of reskilling plans based on the user's work history. This allows for the provision of more appropriate reskilling plans by prioritizing plans based on the user's work history. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the determination of plan priorities.

[0047] The generation unit can adjust the order of reskilling plans based on user relevance when generating them. For example, the generation unit can prioritize incorporating highly relevant skills into the reskilling plan based on the user's work history. The generation unit can also analyze the user's work history and prioritize incorporating highly relevant skills into the reskilling plan. Furthermore, the generation unit can adjust the order of the reskilling plans based on the user's work history. This allows for the provision of more appropriate reskilling plans by adjusting the order of plans based on user relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0048] The reskilling unit can customize the means of reskilling based on the user's current living situation during reskilling. For example, the reskilling unit can customize the means of reskilling based on the user's living situation. The reskilling unit can also propose means of reskilling considering the user's living situation. Furthermore, the reskilling unit can customize the means of reskilling based on the user's living situation. This allows for more appropriate reskilling by customizing the means of reskilling based on the user's current living situation. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input user living situation data into a generating AI and have the generating AI perform the customization of the reskilling means.

[0049] The reskilling unit can select the optimal reskilling method by considering the user's geographical location information during reskilling. For example, the reskilling unit can select the optimal reskilling method based on the user's geographical location information. The reskilling unit can also propose reskilling methods by considering the user's geographical location information. Furthermore, the reskilling unit can customize the reskilling methods based on the user's geographical location information. This allows for the provision of a more appropriate reskilling method by considering the user's geographical location information. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of a reskilling method.

[0050] The reskilling unit can analyze the user's social media activity during reskilling and propose reskilling methods. For example, the reskilling unit can propose reskilling methods of interest based on the user's social media activity. The reskilling unit can also analyze the user's social media activity and propose the optimal reskilling method. Furthermore, the reskilling unit can customize the reskilling methods based on the user's social media activity. This allows for the provision of more appropriate reskilling methods by analyzing the user's social media activity. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of reskilling methods.

[0051] The monitoring unit can select the optimal monitoring method by referring to the user's past reskilling history during monitoring. For example, the monitoring unit can select the optimal monitoring method based on the user's past reskilling history. The monitoring unit can also propose an effective monitoring method based on the user's reskilling history. Furthermore, the monitoring unit can analyze the user's reskilling history and select the optimal monitoring method. This allows for the provision of a more appropriate monitoring method by referring to the user's past reskilling history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's reskilling history data into a generating AI and have the generating AI perform the selection of a monitoring method.

[0052] The monitoring unit can customize the monitoring methods based on the user's current reskilling status during monitoring. For example, the monitoring unit can customize the monitoring methods based on the user's reskilling status. The monitoring unit can also propose monitoring methods considering the user's reskilling status. Furthermore, the monitoring unit can customize the monitoring methods based on the user's reskilling status. This makes it possible to perform more appropriate monitoring by customizing the monitoring methods based on the user's current reskilling status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's reskilling status data into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0053] The monitoring unit can select the optimal monitoring method during monitoring, taking into account the user's geographical location information. For example, the monitoring unit selects the optimal monitoring method based on the user's geographical location information. The monitoring unit can also propose monitoring methods, taking into account the user's geographical location information. Furthermore, the monitoring unit can customize monitoring methods based on the user's geographical location information. This allows for the provision of more appropriate monitoring methods by considering the user's geographical location information. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of a monitoring method.

[0054] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can propose monitoring methods of interest based on the user's social media activity. The monitoring unit can also analyze the user's social media activity and propose the optimal monitoring method. Furthermore, the monitoring unit can customize the monitoring methods based on the user's social media activity. This allows for the provision of more appropriate monitoring methods by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of monitoring methods.

[0055] The suggestion department can analyze a user's past work history to select the most suitable job when suggesting new positions. For example, the suggestion department can select the most suitable job based on the user's past work history. The suggestion department can also prioritize suggesting relevant job types based on the user's work history. Furthermore, the suggestion department can analyze the user's work history to select the most suitable job. This allows for the provision of more appropriate job opportunities by analyzing the user's past work history. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input the user's work history data into a generating AI and have the generating AI perform the job selection.

[0056] The suggestion unit can customize the means of finding a new job based on the user's current living situation when suggesting job opportunities. For example, the suggestion unit can customize the means of finding a new job based on the user's living situation. The suggestion unit can also suggest job opportunities considering the user's living situation. Furthermore, the suggestion unit can customize the means of finding a new job based on the user's living situation. This allows for more appropriate suggestions by customizing the means of finding a new job based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of finding a new job.

[0057] The suggestion unit can select the most suitable job when suggesting job opportunities, taking into account the user's geographical location information. For example, the suggestion unit can select the most suitable job based on the user's geographical location information. The suggestion unit can also suggest methods for obtaining a job, taking into account the user's geographical location information. Furthermore, the suggestion unit can customize methods for obtaining a job based on the user's geographical location information. This allows for the provision of more appropriate job opportunities by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information data into a generating AI and have the generating AI perform the job selection.

[0058] The suggestion unit can analyze a user's social media activity and propose job search options when suggesting new jobs. For example, the suggestion unit can suggest job opportunities that the user might be interested in based on their social media activity. It can also analyze a user's social media activity and suggest the most suitable job opportunities. Furthermore, the suggestion unit can customize job search options based on the user's social media activity. This allows the suggestion unit to provide more appropriate job search options by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate job search options.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze a user's past work history and select the optimal method of information input. For example, it can automatically customize the input form based on the user's past work history. It can also automatically display relevant skills and experience as input suggestions based on the user's work history. Furthermore, it can analyze the user's work history and optimize the order and items of input. In this way, by analyzing the user's past work history, the reception desk can provide the optimal method of information input. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's work history data into a generating AI and have the generating AI select the information input method.

[0061] The reception desk can filter information input based on the user's current job status and areas of interest. For example, it can display only relevant information based on the user's current job status. It can also customize input fields based on the user's areas of interest. Furthermore, it can hide unnecessary input fields based on the user's job status and areas of interest. This allows for the provision of more relevant information by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's job status and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0062] The generation unit can adjust the level of detail of a reskilling plan based on the user's aptitudes from previous jobs when generating the plan. For example, it can generate a detailed reskilling plan based on the user's aptitudes from previous jobs. It can also generate a concise reskilling plan considering the user's experience from previous jobs. Furthermore, it can generate a customized reskilling plan based on the user's skill set from previous jobs. This allows for the provision of a more appropriate reskilling plan by adjusting the level of detail based on the user's aptitudes from previous jobs. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's aptitude data from previous jobs into a generation AI and have the generation AI perform the adjustment of the plan's level of detail.

[0063] The generation unit can apply different generation algorithms depending on the user's job category when generating a reskilling plan. For example, if the user is in the IT industry, a generation algorithm specialized in IT skills can be applied. If the user is in the medical industry, a generation algorithm specialized in medical skills can be applied. Furthermore, if the user is in the education industry, a generation algorithm specialized in education skills can be applied. This allows for the provision of more appropriate reskilling plans by applying different generation algorithms according to the user's job category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's job category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0064] The reskilling unit can customize the reskilling method based on the user's current living situation during reskilling. For example, it can customize the reskilling method based on the user's living situation. It can also suggest a reskilling method considering the user's living situation. Furthermore, it can customize the reskilling method based on the user's living situation. This allows for more appropriate reskilling by customizing the reskilling method based on the user's current living situation. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input user living situation data into a generating AI and have the generating AI perform the customization of the reskilling method.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk enters the user's information. This information includes, for example, personal information, work history, and skill set. The reception desk can also save the information entered by the user to a database and analyze it in real time to extract necessary information. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate a reskilling plan. The generation unit proposes the optimal reskilling plan based on the user's aptitude and skill set from previous jobs and their desired job type. Step 3: The reskilling unit performs reskilling based on the plan generated by the generation unit. The reskilling unit provides learning materials and training to supplement the user's lack of knowledge and experience, and adjusts the reskilling plan according to the progress. Step 4: The monitoring unit monitors the progress of the reskilling unit. The monitoring unit monitors the user's reskilling status in real time, evaluates the progress, and provides feedback. Step 5: The Proposal Department proposes the most suitable job opportunities based on the progress monitored by the Monitoring Department. The Proposal Department considers the user's desired job type, work location, salary conditions, etc., and matches the user with companies that are short on personnel and want to hire someone who can contribute immediately.

[0067] (Example of form 2) The reskilling and job placement support system according to an embodiment of the present invention is a system that optimizes reskilling using a generative AI and provides a matching service for job placement support for middle-aged and older individuals. This system begins with the user inputting information such as their knowledge, experience, and desired job type. Next, the generative AI analyzes this information and automatically generates an optimal reskilling plan for the user. This reskilling plan takes into account the user's aptitudes up to their previous jobs and includes educational materials and training to supplement any lack of knowledge or experience. Furthermore, the generative AI monitors the user's reskilling progress and adjusts the plan according to the progress. After reskilling is complete, the generative AI proposes the most suitable job opportunities based on the user's new skill set. This proposal considers the user's desired job type, work location, salary conditions, etc., and matches them with companies that are short on personnel and want to hire immediately effective talent. For example, the user inputs information such as their knowledge, experience, and desired job type. The generative AI analyzes this information and automatically generates an optimal reskilling plan for the user. This reskilling plan takes into account the user's aptitudes up to their previous jobs and includes educational materials and training to supplement any lack of knowledge or experience. The generating AI monitors the user's reskilling progress and adjusts the plan accordingly. After reskilling is complete, the generating AI suggests the most suitable job opportunities based on the user's new skill set. These suggestions consider the user's desired job type, work location, and salary conditions, matching them with companies in need of immediately productive talent. This allows middle-aged and older job seekers to leverage their knowledge and experience while reskilling to fill in their shortcomings and find the optimal job. Companies can also efficiently recruit immediately productive talent, reducing mismatches. In this way, the reskilling and job placement support system efficiently analyzes user information, generates and monitors reskilling plans, and suggests job opportunities.

[0068] The reskilling and job placement support system according to this embodiment comprises a reception unit, a generation unit, a reskilling unit, a monitoring unit, and a proposal unit. The reception unit inputs user information. User information includes, but is not limited to, personal information, work history, and skill sets. The reception unit stores the information entered by the user in a database, for example. The reception unit can also analyze the user information in real time and extract necessary information. The generation unit uses a generation AI to analyze the information entered by the reception unit and generate a reskilling plan. The generation unit generates a reskilling plan considering, for example, the user's aptitude up to their previous job. The generation unit can also propose an optimal reskilling plan based on the user's skill set and desired job type. The generation unit uses a generation AI to analyze user information and automatically generate a reskilling plan. The reskilling unit performs reskilling based on the plan generated by the generation unit. The reskilling unit provides, for example, educational materials and training to supplement the user's lack of knowledge and experience. Furthermore, the reskilling unit can adjust the reskilling plan according to the user's progress. The reskilling unit uses generative AI to monitor the user's reskilling status and adjusts the plan according to the progress. The monitoring unit monitors the progress of the reskilling unit. For example, the monitoring unit monitors the user's reskilling status in real time and evaluates the progress. The monitoring unit can also provide feedback to adjust the user's reskilling plan. The monitoring unit uses generative AI to monitor the user's reskilling status and adjusts the plan according to the progress. The proposal unit proposes the best job opportunities based on the progress monitored by the monitoring unit. For example, the proposal unit proposes the best job opportunities considering the user's desired job type, work location, salary conditions, etc. The proposal unit can also match users with companies that are short on personnel and want to hire immediately effective talent. The proposal unit uses generative AI to propose the best job opportunities based on the user's new skill set.As a result, the reskilling and job placement support system according to this embodiment can efficiently analyze user information, generate and monitor reskilling plans, and propose job opportunities.

[0069] The reception desk inputs user information. This information includes, but is not limited to, personal information, work history, and skill sets. The reception desk stores the user-entered information in a database. It can also analyze user information in real time and extract necessary information. Specifically, when receiving user-entered information and storing it in the database, the reception desk has a function to check the integrity and consistency of the data. For example, it verifies that the user-entered work history is chronologically consistent and that the skill sets are appropriately categorized. Furthermore, the reception desk can use natural language processing technology to analyze user information in real time and extract necessary information. This allows it to extract specific keywords and phrases from information entered by users in free form and store them appropriately in the database. In addition, the reception desk can provide APIs to link user information with other systems and services. For example, it can automatically retrieve user work history from a professional network and cross-reference skill sets with industry-standard skill mapping databases to collect more accurate information. This allows the reception desk to efficiently and accurately collect user information and improve the overall system performance.

[0070] The generation unit uses a generation AI to analyze information entered by the reception unit and generate a reskilling plan. For example, the generation unit generates a reskilling plan considering the user's aptitudes from previous jobs. The generation unit can also propose an optimal reskilling plan based on the user's skill set and desired job type. The generation unit uses a generation AI to analyze user information and automatically generate a reskilling plan. Specifically, the generation AI analyzes the user's work history and skill set, and generates an optimal reskilling plan based on past data and industry trends. For example, if the user has experience in the IT industry, the generation AI will consider current IT industry trends and demands and propose a plan to strengthen skills such as programming languages ​​and cloud technologies. The generation AI can also customize the reskilling plan considering the user's desired job type, work location, salary conditions, etc. For example, if the user aims to become a data scientist, the generation AI will propose specific learning materials and training programs to strengthen skills in data analysis and machine learning. Furthermore, the generation unit can dynamically adjust the reskilling plan based on the user's progress and feedback. This allows the generation unit to provide flexible reskilling plans tailored to user needs, thereby supporting users' career advancement.

[0071] The reskilling unit performs reskilling based on the plan generated by the generation unit. For example, the reskilling unit provides learning materials and training to supplement the user's lack of knowledge and experience. The reskilling unit can also adjust the reskilling plan according to the user's progress. Using generational AI, the reskilling unit monitors the user's reskilling progress and adjusts the plan accordingly. Specifically, the reskilling unit provides diverse learning resources, such as online courses, workshops, and practical projects. For example, a user can take an online course in a specific programming language to improve their programming skills. The reskilling unit also monitors the user's learning progress in real time and provides additional learning materials and training as needed. For example, if a user is struggling with a particular task, the reskilling unit can provide supplementary materials or individual tutoring. Furthermore, the reskilling unit can dynamically adjust the reskilling plan based on user feedback. For example, if a user develops an interest in a particular skill, the reskilling unit can add learning materials and training related to that skill. This allows the reskilling unit to provide flexible reskilling tailored to user needs and effectively support user skill development.

[0072] The monitoring unit monitors the progress of the reskilling unit. For example, the monitoring unit monitors the user's reskilling status in real time and evaluates their progress. The monitoring unit can also provide feedback to adjust the user's reskilling plan. The monitoring unit uses generative AI to monitor the user's reskilling status and adjust the plan according to their progress. Specifically, the monitoring unit collects and analyzes the user's learning data and training results in real time. For example, if a user is taking an online course, the monitoring unit monitors their attendance, test scores, assignment submission status, etc., and evaluates their progress. The monitoring unit also evaluates the effectiveness of the reskilling plan based on the user's learning data and adjusts the plan as needed. For example, if a user is falling behind in progress with a particular skill, the monitoring unit can suggest additional materials or training. Furthermore, the monitoring unit can collect user feedback and use it to improve the reskilling plan. For example, if a user is dissatisfied with a particular material, the monitoring unit can review that material and provide more appropriate materials. This allows the monitoring unit to effectively monitor the user's reskilling status and respond flexibly according to the progress.

[0073] The Proposal Department suggests the most suitable job opportunities based on progress monitored by the Monitoring Department. For example, the Proposal Department considers the user's desired job type, work location, and salary conditions when suggesting the best job opportunities. The Proposal Department can also match users with companies facing talent shortages and seeking immediately productive employees. Using generative AI, the Proposal Department suggests optimal job opportunities based on the user's new skill set. Specifically, the Proposal Department lists potential employers based on the user's reskilling plan progress and newly acquired skills. For example, if a user acquires data scientist skills, the Proposal Department generates a list of companies recruiting data scientists and suggests them to the user. Furthermore, the Proposal Department can customize and suggest the most suitable job opportunities, considering the user's desired job type, work location, and salary conditions. For example, if a user desires remote work, the Proposal Department prioritizes suggesting companies that allow remote work. In addition, the Proposal Department considers the needs of companies and can match users with companies facing talent shortages and seeking immediately productive employees. For example, by proposing users who have completed reskilling to companies that are short of personnel with specific skills, the needs of both the company and the user can be met. This allows the proposal department to suggest the most suitable job opportunities based on the user's new skill set, thereby supporting the user's career advancement.

[0074] The generation unit can generate a reskilling plan considering the user's aptitudes from previous jobs. For example, the generation unit can evaluate the user's aptitudes from previous jobs and customize the reskilling plan based on that. The generation unit can also optimize the reskilling plan considering the user's skill set from previous jobs. The generation unit uses a generation AI to analyze the user's aptitudes from previous jobs and generates a reskilling plan based on that. This allows for the generation of a more appropriate reskilling plan by considering the user's aptitudes from previous jobs. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's aptitude data from previous jobs into the generation AI and have the generation AI generate the reskilling plan.

[0075] The monitoring unit can adjust the plan according to the progress of reskilling. For example, the monitoring unit can monitor the user's reskilling status in real time and change the plan according to the progress. The monitoring unit can also update the content of the reskilling plan based on the user's progress. The monitoring unit uses a generation AI to monitor the user's reskilling status and adjust the plan according to the progress. In this way, by adjusting the plan according to the progress of reskilling, the optimal reskilling can be provided to the user. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's reskilling progress data into the generation AI and have the generation AI perform the plan adjustment.

[0076] The suggestion unit can propose the most suitable job opportunities by considering the user's desired job type, work location, salary conditions, etc. For example, the suggestion unit can list the most suitable job opportunities based on the user's desired conditions. The suggestion unit can also narrow down the list of job opportunities based on the user's desired job type, work location, salary conditions. The suggestion unit uses a generative AI to analyze the user's desired conditions and propose the most suitable job opportunities based on that analysis. This allows for the proposal of more appropriate job opportunities by considering the user's desired conditions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's desired conditions data into a generative AI and have the generative AI generate job suggestions.

[0077] The reskilling unit can provide learning materials and training to supplement the user's lack of knowledge and experience. For example, the reskilling unit can analyze the user's skill gaps and provide learning materials and training based on that analysis. The reskilling unit can also adjust the content of the learning materials and training according to the user's learning progress. The reskilling unit uses generative AI to provide learning materials and training to supplement the user's lack of knowledge and experience. This enhances the effectiveness of reskilling by supplementing the user's lack of knowledge and experience. Some or all of the above-described processes in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input the user's skill gap data into a generative AI and have the generative AI provide learning materials and training.

[0078] The proposal department can match companies facing a shortage of immediately employable personnel with users. For example, the proposal department can analyze companies' job postings and extract the requirements for personnel that companies want to hire immediately. The proposal department can also compare users' skill sets with the company's requirements to perform the optimal match. The proposal department uses a generation AI to analyze companies' job postings and users' skill sets and perform matching based on that. This reduces mismatches by matching companies that want to hire immediately employable personnel with users. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input company job posting data and user skill set data into a generation AI and have the generation AI perform the matching.

[0079] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception unit can pause input and display a relaxing interface. The reception unit can also adjust the interface to allow continuous information input if the user is focused. Furthermore, if the user is tired, the reception unit can split the input and display a message prompting a break. This allows for more appropriate information input by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information input.

[0080] The reception desk can analyze the user's past work history and select the optimal method of information input. For example, the reception desk can automatically customize the input form based on the user's past work history. The reception desk can also automatically display relevant skills and experience as input suggestions based on the user's work history. Furthermore, the reception desk can analyze the user's work history and optimize the order and items of input. In this way, by analyzing the user's past work history, it can provide the optimal method of information input. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's work history data into a generating AI and have the generating AI select the information input method.

[0081] The reception unit can filter information based on the user's current job status and areas of interest during input. For example, the reception unit can display only relevant information based on the user's current job status. The reception unit can also customize input fields based on the user's areas of interest. Furthermore, the reception unit can hide unnecessary input fields based on the user's job status and areas of interest. This allows for the provision of more relevant information by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's job status and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0082] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is in a hurry, the reception desk may prioritize the input of important information. If the user is relaxed, the reception desk may also prompt the user to enter detailed information. Furthermore, if the user is stressed, the reception desk may prompt the user to enter simple information first. This allows for more efficient information entry by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of the information.

[0083] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, the reception desk can prioritize displaying relevant job information based on the user's current location. The reception desk can also prompt the user to input region-specific information based on their geographical location. Furthermore, the reception desk can prioritize inputting nearby job information by considering the user's location. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI determine the priority of the information.

[0084] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can automatically input the user's areas of interest, such as job types and industries, based on the user's social media activity history. The reception desk can also input relevant skills and experience based on the user's social media activity history. Furthermore, the reception desk can analyze the user's social media network and input relevant information. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the information input.

[0085] The generation unit can estimate the user's emotions and adjust the presentation of the reskilling plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a reskilling plan that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a concise reskilling plan that gets straight to the point. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand reskilling plan. This allows for the provision of a more appropriate plan by adjusting the presentation of the reskilling plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the reskilling plan.

[0086] The generation unit can adjust the level of detail of a reskilling plan based on the user's aptitudes from previous jobs when generating the plan. For example, the generation unit can generate a detailed reskilling plan based on the user's aptitudes from previous jobs. It can also generate a concise reskilling plan considering the user's experience from previous jobs. Furthermore, the generation unit can generate a customized reskilling plan based on the user's skill set from previous jobs. This allows for the provision of a more appropriate reskilling plan by adjusting the level of detail based on the user's aptitudes from previous jobs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's aptitude data from previous jobs into a generation AI and have the generation AI perform the adjustment of the plan's level of detail.

[0087] The generation unit can apply different generation algorithms depending on the user's job category when generating a reskilling plan. For example, if the user is in the IT industry, the generation unit can apply a generation algorithm specialized in IT skills. Similarly, if the user is in the medical industry, the generation unit can apply a generation algorithm specialized in medical skills. Furthermore, if the user is in the education industry, the generation unit can apply a generation algorithm specialized in education skills. This allows for the provision of more appropriate reskilling plans by applying different generation algorithms according to the user's job category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's job category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0088] The generation unit can estimate the user's emotions and adjust the length of the reskilling plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a reskilling plan that can be completed in a short time. It can also generate a longer reskilling plan if the user is relaxed. Furthermore, if the user is stressed, the generation unit can generate a reskilling plan that progresses in stages. This allows for the provision of a more appropriate plan by adjusting the length of the reskilling plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the reskilling plan.

[0089] The generation unit can determine the priority of reskilling plans based on the user's work history when generating them. For example, the generation unit can prioritize incorporating important skills from the user's work history into the reskilling plan. The generation unit can also analyze the user's work history and prioritize incorporating necessary skills into the reskilling plan. Furthermore, the generation unit can determine the priority of reskilling plans based on the user's work history. This allows for the provision of more appropriate reskilling plans by prioritizing plans based on the user's work history. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the determination of plan priorities.

[0090] The generation unit can adjust the order of reskilling plans based on user relevance when generating them. For example, the generation unit can prioritize incorporating highly relevant skills into the reskilling plan based on the user's work history. The generation unit can also analyze the user's work history and prioritize incorporating highly relevant skills into the reskilling plan. Furthermore, the generation unit can adjust the order of the reskilling plans based on the user's work history. This allows for the provision of more appropriate reskilling plans by adjusting the order of plans based on user relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0091] The risk-skilling unit can estimate the user's emotions and adjust the risk-skilling method based on the estimated emotions. For example, if the user is relaxed, the risk-skilling unit can provide a risk-skilling method that includes detailed explanations. If the user is in a hurry, the risk-skilling unit can also provide a concise risk-skilling method that gets straight to the point. Furthermore, if the user is stressed, the risk-skilling unit can provide a visually easy-to-understand risk-skilling method. This allows for more appropriate risk-skilling by adjusting the risk-skilling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk-skilling unit may be performed using AI or not. For example, the risk-skilling unit can input user emotion data into the generative AI and have the generative AI adjust the risk-skilling method.

[0092] The reskilling unit can customize the means of reskilling based on the user's current living situation during reskilling. For example, the reskilling unit can customize the means of reskilling based on the user's living situation. The reskilling unit can also propose means of reskilling considering the user's living situation. Furthermore, the reskilling unit can customize the means of reskilling based on the user's living situation. This allows for more appropriate reskilling by customizing the means of reskilling based on the user's current living situation. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input user living situation data into a generating AI and have the generating AI perform the customization of the reskilling means.

[0093] The reskilling unit can estimate the user's emotions and determine the priority of reskilling based on the estimated emotions. For example, if the user is in a hurry, the reskilling unit will prioritize reskilling important skills. If the user is relaxed, the reskilling unit can also provide a detailed reskilling plan. Furthermore, if the user is stressed, the reskilling unit can start reskilling with easier skills. This allows for more efficient reskilling by determining the priority of reskilling according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reskilling unit may be performed using AI or not. For example, the reskilling unit can input user emotion data into a generative AI and have the generative AI determine the priority of reskilling.

[0094] The reskilling unit can select the optimal reskilling method by considering the user's geographical location information during reskilling. For example, the reskilling unit can select the optimal reskilling method based on the user's geographical location information. The reskilling unit can also propose reskilling methods by considering the user's geographical location information. Furthermore, the reskilling unit can customize the reskilling methods based on the user's geographical location information. This allows for the provision of a more appropriate reskilling method by considering the user's geographical location information. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of a reskilling method.

[0095] The reskilling unit can analyze the user's social media activity during reskilling and propose reskilling methods. For example, the reskilling unit can propose reskilling methods of interest based on the user's social media activity. The reskilling unit can also analyze the user's social media activity and propose the optimal reskilling method. Furthermore, the reskilling unit can customize the reskilling methods based on the user's social media activity. This allows for the provision of more appropriate reskilling methods by analyzing the user's social media activity. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of reskilling methods.

[0096] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is relaxed, the monitoring unit can provide a detailed monitoring report. If the user is in a hurry, the monitoring unit can also provide a concise monitoring report that gets straight to the point. Furthermore, if the user is stressed, the monitoring unit can provide a visually easy-to-understand monitoring report. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the monitoring method.

[0097] The monitoring unit can select the optimal monitoring method by referring to the user's past reskilling history during monitoring. For example, the monitoring unit can select the optimal monitoring method based on the user's past reskilling history. The monitoring unit can also propose an effective monitoring method based on the user's reskilling history. Furthermore, the monitoring unit can analyze the user's reskilling history and select the optimal monitoring method. This allows for the provision of a more appropriate monitoring method by referring to the user's past reskilling history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's reskilling history data into a generating AI and have the generating AI perform the selection of a monitoring method.

[0098] The monitoring unit can customize the monitoring methods based on the user's current reskilling status during monitoring. For example, the monitoring unit can customize the monitoring methods based on the user's reskilling status. The monitoring unit can also propose monitoring methods considering the user's reskilling status. Furthermore, the monitoring unit can customize the monitoring methods based on the user's reskilling status. This makes it possible to perform more appropriate monitoring by customizing the monitoring methods based on the user's current reskilling status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's reskilling status data into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0099] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is in a hurry, the monitoring unit will prioritize monitoring important items. It can also perform detailed monitoring if the user is relaxed. Furthermore, if the user is stressed, the monitoring unit can start monitoring with simpler items. This allows for more efficient monitoring by prioritizing monitoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI determine monitoring priorities.

[0100] The monitoring unit can select the optimal monitoring method during monitoring, taking into account the user's geographical location information. For example, the monitoring unit selects the optimal monitoring method based on the user's geographical location information. The monitoring unit can also propose monitoring methods, taking into account the user's geographical location information. Furthermore, the monitoring unit can customize monitoring methods based on the user's geographical location information. This allows for the provision of more appropriate monitoring methods by considering the user's geographical location information. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of a monitoring method.

[0101] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can propose monitoring methods of interest based on the user's social media activity. The monitoring unit can also analyze the user's social media activity and propose the optimal monitoring method. Furthermore, the monitoring unit can customize the monitoring methods based on the user's social media activity. This allows for the provision of more appropriate monitoring methods by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of monitoring methods.

[0102] The suggestion unit can estimate the user's emotions and adjust its job search suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed job search suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand job search suggestions. By adjusting the job search suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the job search suggestions.

[0103] The suggestion department can analyze a user's past work history to select the most suitable job when suggesting new positions. For example, the suggestion department can select the most suitable job based on the user's past work history. The suggestion department can also prioritize suggesting relevant job types based on the user's work history. Furthermore, the suggestion department can analyze the user's work history to select the most suitable job. This allows for the provision of more appropriate job opportunities by analyzing the user's past work history. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input the user's work history data into a generating AI and have the generating AI perform the job selection.

[0104] The suggestion unit can customize the means of finding a new job based on the user's current living situation when suggesting job opportunities. For example, the suggestion unit can customize the means of finding a new job based on the user's living situation. The suggestion unit can also suggest job opportunities considering the user's living situation. Furthermore, the suggestion unit can customize the means of finding a new job based on the user's living situation. This allows for more appropriate suggestions by customizing the means of finding a new job based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of finding a new job.

[0105] The suggestion unit can estimate the user's emotions and determine the priority of job opportunities based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting important job opportunities. If the user is relaxed, the suggestion unit can also suggest more detailed job opportunities. Furthermore, if the user is stressed, the suggestion unit can start suggesting simpler job opportunities. This allows for more efficient suggestions by prioritizing job opportunities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of job opportunities.

[0106] The suggestion unit can select the most suitable job when suggesting job opportunities, taking into account the user's geographical location information. For example, the suggestion unit can select the most suitable job based on the user's geographical location information. The suggestion unit can also suggest methods for obtaining a job, taking into account the user's geographical location information. Furthermore, the suggestion unit can customize methods for obtaining a job based on the user's geographical location information. This allows for the provision of more appropriate job opportunities by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information data into a generating AI and have the generating AI perform the job selection.

[0107] The suggestion unit can analyze a user's social media activity and propose job search options when suggesting new jobs. For example, the suggestion unit can suggest job opportunities that the user might be interested in based on their social media activity. It can also analyze a user's social media activity and suggest the most suitable job opportunities. Furthermore, the suggestion unit can customize job search options based on the user's social media activity. This allows the suggestion unit to provide more appropriate job search options by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate job search options.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, input can be paused and a relaxing interface can be displayed. If the user is focused, the interface can be adjusted to allow continuous information input. Furthermore, if the user is tired, input can be divided and a message prompting a break can be displayed. This allows for more appropriate information input by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information input.

[0110] The generation unit can estimate the user's emotions and adjust the presentation of the reskilling plan based on the estimated emotions. For example, if the user is relaxed, it can generate a reskilling plan that includes detailed explanations. If the user is in a hurry, it can generate a concise reskilling plan that gets straight to the point. Furthermore, if the user is stressed, it can generate a visually easy-to-understand reskilling plan. This allows for the provision of a more appropriate plan by adjusting the presentation of the reskilling plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the reskilling plan.

[0111] The risk-skilling unit can estimate the user's emotions and adjust the risk-skilling method based on the estimated emotions. For example, if the user is relaxed, it can provide a risk-skilling method that includes detailed explanations. If the user is in a hurry, it can provide a concise risk-skilling method that gets straight to the point. Furthermore, if the user is stressed, it can provide a visually easy-to-understand risk-skilling method. By adjusting the risk-skilling method according to the user's emotions, more appropriate risk-skilling can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk-skilling unit may be performed using AI, for example, or not using AI. For example, the risk-skilling unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the risk-skilling method.

[0112] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed monitoring report. If the user is in a hurry, it can provide a concise monitoring report that gets straight to the point. Furthermore, if the user is stressed, it can provide a visually easy-to-understand monitoring report. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the monitoring method.

[0113] The suggestion unit can estimate the user's emotions and adjust the way it suggests job opportunities based on those emotions. For example, if the user is relaxed, it can suggest job opportunities with detailed explanations. If the user is in a hurry, it can suggest concise job opportunities that get straight to the point. Furthermore, if the user is stressed, it can suggest job opportunities that are visually easy to understand. By adjusting the way it suggests job opportunities according to the user's emotions, it becomes possible to make more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it suggests job opportunities.

[0114] The reception desk can analyze a user's past work history and select the optimal method of information input. For example, it can automatically customize the input form based on the user's past work history. It can also automatically display relevant skills and experience as input suggestions based on the user's work history. Furthermore, it can analyze the user's work history and optimize the order and items of input. In this way, by analyzing the user's past work history, the reception desk can provide the optimal method of information input. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's work history data into a generating AI and have the generating AI select the information input method.

[0115] The reception desk can filter information input based on the user's current job status and areas of interest. For example, it can display only relevant information based on the user's current job status. It can also customize input fields based on the user's areas of interest. Furthermore, it can hide unnecessary input fields based on the user's job status and areas of interest. This allows for the provision of more relevant information by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's job status and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0116] The generation unit can adjust the level of detail of a reskilling plan based on the user's aptitudes from previous jobs when generating the plan. For example, it can generate a detailed reskilling plan based on the user's aptitudes from previous jobs. It can also generate a concise reskilling plan considering the user's experience from previous jobs. Furthermore, it can generate a customized reskilling plan based on the user's skill set from previous jobs. This allows for the provision of a more appropriate reskilling plan by adjusting the level of detail based on the user's aptitudes from previous jobs. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's aptitude data from previous jobs into a generation AI and have the generation AI perform the adjustment of the plan's level of detail.

[0117] The generation unit can apply different generation algorithms depending on the user's job category when generating a reskilling plan. For example, if the user is in the IT industry, a generation algorithm specialized in IT skills can be applied. If the user is in the medical industry, a generation algorithm specialized in medical skills can be applied. Furthermore, if the user is in the education industry, a generation algorithm specialized in education skills can be applied. This allows for the provision of more appropriate reskilling plans by applying different generation algorithms according to the user's job category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's job category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0118] The reskilling unit can customize the reskilling method based on the user's current living situation during reskilling. For example, it can customize the reskilling method based on the user's living situation. It can also suggest a reskilling method considering the user's living situation. Furthermore, it can customize the reskilling method based on the user's living situation. This allows for more appropriate reskilling by customizing the reskilling method based on the user's current living situation. Some or all of the above processing in the reskilling unit may be performed using AI, for example, or without AI. For example, the reskilling unit can input user living situation data into a generating AI and have the generating AI perform the customization of the reskilling method.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk enters the user's information. This information includes, for example, personal information, work history, and skill set. The reception desk can also save the information entered by the user to a database and analyze it in real time to extract necessary information. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate a reskilling plan. The generation unit proposes the optimal reskilling plan based on the user's aptitude and skill set from previous jobs and their desired job type. Step 3: The reskilling unit performs reskilling based on the plan generated by the generation unit. The reskilling unit provides learning materials and training to supplement the user's lack of knowledge and experience, and adjusts the reskilling plan according to the progress. Step 4: The monitoring unit monitors the progress of the reskilling unit. The monitoring unit monitors the user's reskilling status in real time, evaluates the progress, and provides feedback. Step 5: The Proposal Department proposes the most suitable job opportunities based on the progress monitored by the Monitoring Department. The Proposal Department considers the user's desired job type, work location, salary conditions, etc., and matches the user with companies that are short on personnel and want to hire someone who can contribute immediately.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, generation unit, reskilling unit, monitoring unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs user information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a reskilling plan using generation AI. The reskilling unit is implemented by the output device 40 of the smart device 14, which provides teaching materials and training. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the user's reskilling status and adjusts the plan according to the progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal job change based on the user's new skill set. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, generation unit, reskilling unit, monitoring unit, and proposal unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs user information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a reskilling plan using generation AI. The reskilling unit is implemented by the speaker 240 of the smart glasses 214, which provides teaching materials and training. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the user's reskilling status and adjusts the plan according to the progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal job change based on the user's new skill set. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, generation unit, reskilling unit, monitoring unit, and proposal unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which inputs user information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a reskilling plan using generation AI. The reskilling unit is implemented by the speaker 240 of the headset terminal 314, which provides teaching materials and training. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the user's reskilling status and adjusts the plan according to the progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal job change based on the user's new skill set. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, generation unit, reskilling unit, monitoring unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs user information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a reskilling plan using generation AI. The reskilling unit is implemented by the speaker 240 of the robot 414, which provides teaching materials and training. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the user's reskilling status and adjusts the plan according to the progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal job change based on the user's new skill set. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A reception area where user information is entered, A generation unit analyzes the information input by the reception unit and generates a reskilling plan, A reskilling unit that performs reskilling based on the plan generated by the generation unit, A monitoring unit for monitoring the progress of the reskilling unit, The system includes a proposal unit that suggests the most suitable job change based on the progress monitored by the monitoring unit. A system characterized by the following features. (Note 2) The generating unit is A reskilling plan is generated considering the user's aptitude from their previous jobs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, Adjust the plan as risk-killing progresses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the most suitable job opportunities based on the user's desired job type, work location, salary conditions, and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned risk-killing section is We provide educational materials and training to supplement users' lacking knowledge and experience. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, This service matches companies facing a shortage of skilled personnel who can be hired immediately with users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past work history and select the optimal method for information input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, filtering is performed based on the user's current job status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust how the reskilling plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a reskilling plan, adjust the level of detail in the plan based on the user's aptitude from their previous jobs. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a risk-skilling plan, different generation algorithms are applied depending on the user's job category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and adjusts the length of the reskilling plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a risk-killing plan, the plan's priority is determined based on the user's work history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a risk-killing plan, the order of the plan is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned risk-killing section is We estimate the user's emotions and adjust the reskilling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned risk-killing section is During the risk-killing process, the risk-killing method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned risk-killing section is The system estimates the user's emotions and determines reskilling priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned risk-killing section is During reskilling, the optimal reskilling method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned risk-killing section is During the risk-killing process, we analyze users' social media activity and propose risk-killing strategies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the user's past reskilling history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the monitoring method is customized based on the user's current reskilling status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the job suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When suggesting job opportunities, the system analyzes the user's past work history to select the most suitable position. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When suggesting job opportunities, the system customizes the methods of finding a new job based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of potential employers based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When suggesting job opportunities, the system selects the most suitable job by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When suggesting job opportunities, we analyze the user's social media activity to propose suitable job options. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where user information is entered, A generation unit analyzes the information input by the reception unit and generates a reskilling plan, A reskilling unit that performs reskilling based on the plan generated by the generation unit, A monitoring unit for monitoring the progress of the reskilling unit, The system includes a proposal unit that suggests the most suitable job change based on the progress monitored by the monitoring unit. A system characterized by the following features.

2. The generating unit is A reskilling plan is generated considering the user's aptitude from their previous jobs. The system according to feature 1.

3. The monitoring unit, Adjust the plan as risk-killing progresses. The system according to feature 1.

4. The aforementioned proposal section is, We propose the most suitable job opportunities based on the user's desired job type, work location, salary conditions, and other factors. The system according to feature 1.

5. The aforementioned risk-killing section is We provide educational materials and training to supplement users' lacking knowledge and experience. The system according to feature 1.

6. The aforementioned proposal section is, This service matches companies facing a shortage of skilled personnel who can be hired immediately with users. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past work history and select the optimal method for information input. The system according to feature 1.

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