system

Abstract

The system according to this embodiment aims to propose an optimal career path and learning plan based on the user's skills and experience. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects data on the user's skills, education level, and work history. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal career path and learning plan based on the analysis results obtained by the analysis unit. The support unit assists the user in improving their skills and career based on the career path and learning plan proposed by the proposal 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 prior art, it has not been fully achieved to propose an optimal career path and learning plan based on the user's skills and experience, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal career path and learning plan based on the user's skills and experience.

Means for Solving the Problems

[0006] ]The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects data on the user's skills, education level, and work history. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal career path and learning plan based on the analysis results obtained by the analysis unit. The support unit assists the user in improving their skills and career based on the career path and learning plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal career path and learning plan based on the user's skills and experience. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The career support system according to an embodiment of the present invention is a system that analyzes a user's current skills, education level, and work history, and proposes an optimal career path and learning plan based on market trends and job market trends. The career support system collects data on the user's skills, education level, and work history, and the AI ​​analyzes the collected data to compare the user's current skill set with market trends. Subsequently, the AI ​​proposes an optimal career path and learning plan to the user based on market trends and job market trends. For example, the career support system collects detailed data such as information entered by the user, past work history, and educational history. For example, it collects information such as the user's qualifications and skills, and the content of education and training received in the past. Next, the AI ​​analyzes the collected data in the career support system. The AI ​​compares the user's skill set with market trends and determines how the user's current skills are valued in the market. For example, it identifies skills that are in high demand in the current market and skills that are expected to see increased demand in the future. Subsequently, the AI ​​proposes an optimal career path and learning plan to the user based on market trends and job market trends. For example, if a user is looking to change jobs using their current skill set, the system suggests suitable job roles or what new skills and qualifications they should acquire. If a user aims for career advancement, the system suggests which skills to strengthen and what training or educational programs they should participate in. This provides clear guidelines for the user's career goals and supports continuous skill development and career advancement. For instance, if a user wants to switch to a specific job role, the system clearly outlines the necessary skills and qualifications and provides a learning plan. Similarly, if a user aims for career advancement, the system clearly outlines the necessary skills and qualifications and provides a learning plan. This allows the career support system to understand the user's capabilities and market needs, enabling them to select an appropriate career path. This, in turn, improves the user's career success rate, optimizes educational efficiency, and facilitates a smoother adaptation to the job market.This allows the career support system to suggest optimal career paths and learning plans based on the user's skills, education level, and work history, thereby supporting skill development and career advancement.

[0029] The career support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects data on the user's skills, education level, and work history. The data collection unit collects detailed data such as information entered by the user, past work history, and educational history. For example, the data collection unit can collect information such as the qualifications and skills the user possesses, and the content of education and training received in the past. Some or all of the above processing in the data collection unit may be performed using AI or not. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit compares the user's skill set with market trends to determine how the user's current skills are valued in the market. For example, the analysis unit can identify skills that are in high demand in the current market and skills that are expected to be in high demand in the future. Some or all of the above processing in the analysis unit may be performed using AI or not. The proposal unit proposes an optimal career path and learning plan based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, suggest suitable job types or new skills and qualifications that a user should acquire if they are changing jobs and leveraging their current skill set. The suggestion unit can also suggest skills to strengthen and training or educational programs to participate in if the user aims for career advancement. Some or all of the above processing in the suggestion unit may be performed using AI or not. The support unit assists the user in skill development and career advancement based on the career path and learning plan proposed by the suggestion unit. For example, the support unit can provide clear guidelines for the user's career goals and support continuous skill development and career advancement. Some or all of the above processing in the support unit may be performed using AI or not. Thus, the career support system according to this embodiment can propose an optimal career path and learning plan based on the user's skills, education level, and experience, and support skill development and career advancement.

[0030] The data collection unit collects data on users' skills, education levels, and careers. Specifically, it collects detailed data such as information entered by users, past work history, and educational history. For example, it can collect information on qualifications and skills users possess, as well as the content of education and training they have received in the past. The data collection unit can utilize AI to automatically analyze the information entered by users and extract the necessary data. Using natural language processing technology, the AI ​​analyzes the text data entered by users and accurately extracts information such as skills, qualifications, and educational history. For example, if a user enters the content of training they have received in the past, the AI ​​can analyze that content and identify relevant skills and qualifications. The data collection unit can also analyze the user's work history and identify the skills and experience gained in past jobs. This allows the data collection unit to efficiently collect detailed data on users' skill sets, education levels, and careers. Furthermore, the data collection unit can centrally manage user data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. The data collection unit can also adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it compares the user's skill set with market trends to determine how the user's current skills are valued in the market. For example, it can identify skills that are currently in high demand in the market and skills that are expected to see increased demand in the future. The analysis unit uses AI to analyze this data and determine how the user's skill set is valued in the market. The AI ​​uses machine learning algorithms to analyze the collected data and compare the user's skill set with market trends. For example, the AI ​​can analyze past job posting data and market trend data to identify skills that are currently in high demand in the market and skills that are expected to see increased demand in the future. The AI ​​also analyzes the user's skill set and determines how it is valued in the current market. This allows the analysis unit to accurately determine how the user's skill set is valued in the market. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical market data, it can predict fluctuations in demand for specific skills and job types and propose future career paths. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes optimal career paths and learning plans based on the analysis results obtained by the Analysis Department. Specifically, if a user is changing jobs while leveraging their current skill set, the AI ​​can suggest suitable job types or what new skills and qualifications they should acquire. For example, if a user is changing jobs while leveraging their current skill set, the AI ​​can analyze past job posting data and market trend data to suggest the most suitable job types for the user. Furthermore, if a user aims for career advancement, the AI ​​can suggest what skills they should strengthen and what training or educational programs they should participate in. The Proposal Department uses AI to analyze this data and proposes optimal career paths and learning plans for the user. The AI ​​uses machine learning algorithms to analyze the collected data and propose optimal career paths and learning plans for the user. For example, the AI ​​can analyze past job posting data and market trend data to suggest the most suitable job types and skills for the user. The AI ​​can also analyze the user's skill set and identify the skills and qualifications necessary for career advancement. This allows the Proposal Department to propose optimal career paths and learning plans for users and support their career advancement. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, the proposals are reviewed and improved based on user feedback. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For instance, important information is delivered not only via smartphone notifications but also through voice calls, SMS, and email. This allows the proposal department to provide users with information quickly and reliably, supporting their career advancement.

[0033] The Support Department assists users in skill development and career advancement based on career paths and learning plans proposed by the Proposal Department. Specifically, it can provide clear guidelines for users' career goals and support continuous skill development and career advancement. The Support Department uses AI to monitor users' progress and provide feedback and advice as needed. For example, if a user is receiving training according to a proposed learning plan, the AI ​​can monitor their progress in real time and provide advice and feedback as needed. The Support Department can also evaluate the user's achievement of career goals and revise or improve the learning plan as necessary. This allows the Support Department to continuously support users in skill development and career advancement. Furthermore, the Support Department can collect user feedback and continuously improve the accuracy and effectiveness of its support. For example, it can revise and improve support based on user feedback. The Support Department can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the Support Department to provide information to users quickly and reliably, supporting their skill development and career advancement.

[0034] The data collection unit can collect detailed data such as information entered by the user, past work history, and educational history. For example, the data collection unit can collect detailed data such as information entered by the user, past work history, and educational history. For example, the data collection unit can collect information such as the qualifications and skills the user possesses, and the content of the education and training they have received in the past. By collecting detailed user data, more accurate analysis and suggestions become possible. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without using AI.

[0035] The analysis unit can compare the user's skill set with market trends to determine how the user's current skills are valued in the market. For example, the analysis unit can identify skills that are currently in high demand in the market or skills that are expected to see increased demand in the future. By determining how the user's skills are valued in the market, it can propose appropriate career paths and learning plans. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0036] The suggestion department can suggest what types of jobs would be suitable for a user if they were to change jobs while utilizing their current skill set, or what new skills or qualifications they should acquire. For example, the suggestion department can suggest what types of jobs would be suitable for a user if they were to change jobs while utilizing their current skill set, or what new skills or qualifications they should acquire. For example, the suggestion department can suggest what types of jobs would be suitable for a user if they were to change jobs while utilizing their current skill set. The suggestion department can also suggest what new skills or qualifications the user should acquire. This supports the user's job search by suggesting suitable job types and skills they should acquire when changing jobs. Some or all of the above processing in the suggestion department may be performed using AI, or not.

[0037] The suggestion department can suggest what skills a user should strengthen and what training or educational programs they should take if they aim to advance their career. For example, the suggestion department can suggest what skills a user should strengthen and what training or educational programs they should take if they aim to advance their career. For example, the suggestion department can suggest what skills a user should strengthen if they aim to advance their career. The suggestion department can also suggest training or educational programs that a user should take. In this way, it supports career advancement by suggesting the skills and training necessary for a user to advance their career. Some or all of the above processing in the suggestion department may be performed using AI or not.

[0038] The support department can provide clear guidelines for users' career goals and support their continuous skill development and career advancement. For example, the support department can provide clear guidelines for users' career goals and support their continuous skill development and career advancement. For example, the support department can provide clear guidelines for users' career goals. Furthermore, the support department can also support users' continuous skill development and career advancement. This allows the support department to support continuous skill development and career advancement by providing guidelines for users' career goals. Some or all of the above-described processes in the support department may be performed using AI or not.

[0039] The data collection unit can analyze the user's past work history and educational history to select the optimal data collection method. For example, the data collection unit can collect data in a similar format based on data previously entered by the user. For example, the data collection unit can prioritize questions related to relevant skills and qualifications based on the user's work history. The data collection unit can also ask questions tailored to the user's educational background, taking into account the user's educational history. In this way, the optimal data collection method can be selected by analyzing the user's past history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0040] The data collection unit can filter data based on the user's current career goals and areas of interest during data collection. For example, if a user is interested in a particular job, the data collection unit will prioritize collecting data related to that job. For example, the data collection unit can collect data on necessary skills and qualifications based on the user's career goals. The data collection unit can also collect the latest market trends related to the user's areas of interest. This allows for the collection of highly relevant data by filtering the data based on the user's career goals and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of local job postings based on the user's current location. For example, the data collection unit can collect market trends related to the user's geographical location. In addition, the data collection unit can collect information on local educational institutions and training programs based on the user's location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on relevant skills and qualifications based on a user's interests on social media. For example, the data collection unit can collect data related to career goals by referring to a user's social media activity history. The data collection unit can also analyze a user's social media network and collect relevant job information. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's skill set. For example, if the user's skill set is highly valued in the market, the analysis unit can provide detailed analysis results. For example, if the user's skill set is moderately valued in the market, the analysis unit can provide concise analysis results. Furthermore, if the user's skill set is poorly valued in the market, the analysis unit can provide detailed analysis results including areas for improvement. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis based on the importance of the user's skill set. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0044] The analysis unit can apply different analysis algorithms during analysis according to the user's career goals. For example, if the user wishes to change jobs, the analysis unit can apply an analysis algorithm suitable for job changes. For example, if the user wishes to advance their career, the analysis unit can apply an analysis algorithm suitable for career advancement. Furthermore, if the user wishes to acquire new skills, the analysis unit can apply an analysis algorithm suitable for those skills. In this way, by applying different analysis algorithms according to the user's career goals, appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0045] The analysis unit can determine the priority of analysis based on when the user's work history was submitted. For example, the analysis unit may prioritize the analysis of the user's most recently submitted work history. For example, the analysis unit may perform analysis by referring to the user's past submitted work history. Furthermore, the analysis unit can adjust the order of analysis based on when the user's work history was submitted. This allows for the provision of appropriate analysis results by determining the priority of analysis based on when the user's work history was submitted. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0046] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit may prioritize analyzing data related to the user's current career goals. For example, the analysis unit may prioritize analyzing data related to the user's skill set. The analysis unit may also prioritize analyzing data related to the user's educational history. By adjusting the order of analysis based on user relevance, appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0047] The proposal department can adjust the level of detail in its proposals based on the importance of the career path. For example, if the user's career path is highly valued in the market, the proposal department will provide a detailed proposal. If the user's career path is moderately valued in the market, the proposal department can provide a concise proposal. Furthermore, if the user's career path is poorly valued in the market, the proposal department can provide a detailed proposal that includes areas for improvement. By adjusting the level of detail in proposals based on the importance of the career path, appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI or not.

[0048] The suggestion function can apply different suggestion algorithms depending on the career path category when making suggestions. For example, if a user wishes to change jobs, the suggestion function can apply a suggestion algorithm suitable for job changes. For example, if a user wishes to advance their career, the suggestion function can apply a suggestion algorithm suitable for career advancement. Furthermore, if a user wishes to acquire new skills, the suggestion function can apply a suggestion algorithm suitable for those skills. By applying different suggestion algorithms depending on the career path category, appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI or not.

[0049] The proposal department can determine the priority of proposals based on when the career path was submitted. For example, the proposal department may prioritize career paths recently submitted by the user. For example, the proposal department may make proposals by referring to career paths previously submitted by the user. Furthermore, the proposal department can adjust the order of proposals based on when the user's career path was submitted. This allows for more appropriate proposals by prioritizing proposals based on when the career path was submitted. Some or all of the above processing in the proposal department may be performed using AI or not.

[0050] The suggestion unit can adjust the order of suggestions based on their relevance to the user's career path. For example, the suggestion unit may prioritize suggestions related to the user's current career goals. For example, it may prioritize suggestions related to the user's skill set. It may also prioritize suggestions related to the user's educational history. By adjusting the order of suggestions based on their relevance to the user's career path, appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0051] The support department can analyze the user's past career behavior during support to select the most suitable support method. For example, the support department can propose relevant support methods based on training programs the user has previously received. For example, the support department can analyze the user's past career behavior and prioritize suggesting successful methods. Furthermore, the support department can propose support methods that include areas for improvement, taking into account the user's past career behavior. In this way, the optimal support method can be selected by analyzing the user's past career behavior. Some or all of the above processes in the support department may be performed using AI or not.

[0052] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy, the support unit can suggest a short and effective support method. For example, if the user has time, the support unit can suggest a detailed support method. Furthermore, the support unit can suggest a combination of online and offline support methods depending on the user's living situation. This allows for appropriate support by customizing the means of support based on the user's current living situation. Some or all of the above processing in the support unit may be performed using AI or not.

[0053] The support department can select the optimal support method by considering the user's geographical location information during support. For example, the support department can suggest local training programs based on the user's current location. For example, the support department can provide job information related to the user's geographical location. Furthermore, the support department can suggest local career support services based on the user's location information. In this way, the optimal support method can be selected by considering the user's geographical location information. Some or all of the above processing in the support department may be performed using AI or not.

[0054] The support department can analyze the user's social media activity and propose support methods when providing assistance. For example, the support department can propose relevant support methods based on the user's interests on social media. For example, the support department can provide support related to career goals by referring to the user's social media activity history. Furthermore, the support department can analyze the user's social media network and propose relevant career support services. In this way, by analyzing the user's social media activity, appropriate support methods can be proposed. Some or all of the above processing in the support department may be performed using AI or not.

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

[0056] The career support system can propose career paths tailored to the characteristics of a region, taking into account the user's geographical location. For example, if a user lives in an urban area, it can propose career paths based on job postings and trends in urban areas. If a user lives in a rural area, it can propose career paths based on job postings and trends in that area. Furthermore, if a user lives overseas, it can propose career paths tailored to the characteristics of that country. This makes it possible to propose career paths based on the user's geographical location, providing career support that is appropriate for the region.

[0057] The career support system can analyze a user's social media activity and suggest career paths based on their interests and network. For example, if a user shows interest in a particular industry on social media, it can suggest career paths related to that industry. It can also analyze a user's social media network and suggest career paths based on trends and job postings within that network. Furthermore, it can suggest career paths based on the user's social media activity history, taking into account their past interests and activities. This enables the suggestion of career paths based on the user's social media activity, providing career support tailored to the user's interests and network.

[0058] A career support system can analyze a user's past career actions and propose successful career paths. For example, it can suggest similar career paths based on a user's past successful job changes. It can also analyze the effectiveness of training programs a user has previously participated in and suggest effective training programs. Furthermore, it can propose career paths that include areas for improvement, based on the user's past career actions. This enables the proposal of career paths based on the user's past career actions, providing career support with a high success rate.

[0059] The career support system can propose flexible learning plans based on the user's current lifestyle. For example, if the user is busy, it can propose a short and effective learning plan. If the user has more time, it can propose a more detailed learning plan. Furthermore, it can propose a learning plan that combines online and offline learning methods, depending on the user's lifestyle. This enables the proposal of flexible learning plans tailored to the user's lifestyle, providing effective learning support.

[0060] The career support system can propose learning plans tailored to the characteristics of a region, taking into account the user's geographical location. For example, if a user lives in an urban area, it can propose a learning plan based on training programs and educational institutions in urban areas. If a user lives in a rural area, it can propose a learning plan based on training programs and educational institutions in that area. Furthermore, if a user lives overseas, it can propose a learning plan tailored to the characteristics of that country. This makes it possible to propose learning plans based on the user's geographical location, providing learning support that is appropriate for the region.

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

[0062] Step 1: The data collection unit collects data on the user's skills, education level, and background. For example, it collects detailed data such as information entered by the user, past work history, and educational history. The data collection unit can collect information on the user's qualifications and skills, as well as the content of education and training received in the past. Some or all of the above processing in the data collection unit may be performed using AI or not. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it compares the user's skill set with market trends to determine how the user's current skills are valued in the market. The analysis unit can identify skills that are in high demand in the current market and skills that are expected to see increased demand in the future. Some or all of the above processing in the analysis unit may be performed using AI or not. Step 3: The proposal unit proposes the optimal career path and learning plan based on the analysis results obtained by the analysis unit. For example, if the user is looking to change jobs using their current skill set, the proposal unit can suggest what types of jobs are suitable, or what new skills or qualifications they should acquire. If the user is aiming for career advancement, the proposal unit can also suggest what skills they should strengthen and what training or educational programs they should participate in. Some or all of the above processing in the proposal unit may be performed using AI or not. Step 4: The support department assists users in skill development and career advancement based on the career path and learning plan proposed by the proposal department. For example, it can provide clear guidelines for the user's career goals and support continuous skill development and career advancement. Some or all of the above processes in the support department may be performed using AI or not.

[0063] (Example of form 2) The career support system according to an embodiment of the present invention is a system that analyzes a user's current skills, education level, and work history, and proposes an optimal career path and learning plan based on market trends and job market trends. The career support system collects data on the user's skills, education level, and work history, and the AI ​​analyzes the collected data to compare the user's current skill set with market trends. Subsequently, the AI ​​proposes an optimal career path and learning plan to the user based on market trends and job market trends. For example, the career support system collects detailed data such as information entered by the user, past work history, and educational history. For example, it collects information such as the user's qualifications and skills, and the content of education and training received in the past. Next, the AI ​​analyzes the collected data in the career support system. The AI ​​compares the user's skill set with market trends and determines how the user's current skills are valued in the market. For example, it identifies skills that are in high demand in the current market and skills that are expected to see increased demand in the future. Subsequently, the AI ​​proposes an optimal career path and learning plan to the user based on market trends and job market trends. For example, if a user is looking to change jobs using their current skill set, the system suggests suitable job roles or what new skills and qualifications they should acquire. If a user aims for career advancement, the system suggests which skills to strengthen and what training or educational programs they should participate in. This provides clear guidelines for the user's career goals and supports continuous skill development and career advancement. For instance, if a user wants to switch to a specific job role, the system clearly outlines the necessary skills and qualifications and provides a learning plan. Similarly, if a user aims for career advancement, the system clearly outlines the necessary skills and qualifications and provides a learning plan. This allows the career support system to understand the user's capabilities and market needs, enabling them to select an appropriate career path. This, in turn, improves the user's career success rate, optimizes educational efficiency, and facilitates a smoother adaptation to the job market.This allows the career support system to suggest optimal career paths and learning plans based on the user's skills, education level, and work history, thereby supporting skill development and career advancement.

[0064] The career support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects data on the user's skills, education level, and work history. The data collection unit collects detailed data such as information entered by the user, past work history, and educational history. For example, the data collection unit can collect information such as the qualifications and skills the user possesses, and the content of education and training received in the past. Some or all of the above processing in the data collection unit may be performed using AI or not. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit compares the user's skill set with market trends to determine how the user's current skills are valued in the market. For example, the analysis unit can identify skills that are in high demand in the current market and skills that are expected to be in high demand in the future. Some or all of the above processing in the analysis unit may be performed using AI or not. The proposal unit proposes an optimal career path and learning plan based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, suggest suitable job types or new skills and qualifications that a user should acquire if they are changing jobs and leveraging their current skill set. The suggestion unit can also suggest skills to strengthen and training or educational programs to participate in if the user aims for career advancement. Some or all of the above processing in the suggestion unit may be performed using AI or not. The support unit assists the user in skill development and career advancement based on the career path and learning plan proposed by the suggestion unit. For example, the support unit can provide clear guidelines for the user's career goals and support continuous skill development and career advancement. Some or all of the above processing in the support unit may be performed using AI or not. Thus, the career support system according to this embodiment can propose an optimal career path and learning plan based on the user's skills, education level, and experience, and support skill development and career advancement.

[0065] The data collection unit collects data on users' skills, education levels, and careers. Specifically, it collects detailed data such as information entered by users, past work history, and educational history. For example, it can collect information on qualifications and skills users possess, as well as the content of education and training they have received in the past. The data collection unit can utilize AI to automatically analyze the information entered by users and extract the necessary data. Using natural language processing technology, the AI ​​analyzes the text data entered by users and accurately extracts information such as skills, qualifications, and educational history. For example, if a user enters the content of training they have received in the past, the AI ​​can analyze that content and identify relevant skills and qualifications. The data collection unit can also analyze the user's work history and identify the skills and experience gained in past jobs. This allows the data collection unit to efficiently collect detailed data on users' skill sets, education levels, and careers. Furthermore, the data collection unit can centrally manage user data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. The data collection unit can also adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the data collected by the data collection unit. Specifically, it compares the user's skill set with market trends to determine how the user's current skills are valued in the market. For example, it can identify skills that are currently in high demand in the market and skills that are expected to see increased demand in the future. The analysis unit uses AI to analyze this data and determine how the user's skill set is valued in the market. The AI ​​uses machine learning algorithms to analyze the collected data and compare the user's skill set with market trends. For example, the AI ​​can analyze past job posting data and market trend data to identify skills that are currently in high demand in the market and skills that are expected to see increased demand in the future. The AI ​​also analyzes the user's skill set and determines how it is valued in the current market. This allows the analysis unit to accurately determine how the user's skill set is valued in the market. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical market data, it can predict fluctuations in demand for specific skills and job types and propose future career paths. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The Proposal Department proposes optimal career paths and learning plans based on the analysis results obtained by the Analysis Department. Specifically, if a user is changing jobs while leveraging their current skill set, the AI ​​can suggest suitable job types or what new skills and qualifications they should acquire. For example, if a user is changing jobs while leveraging their current skill set, the AI ​​can analyze past job posting data and market trend data to suggest the most suitable job types for the user. Furthermore, if a user aims for career advancement, the AI ​​can suggest what skills they should strengthen and what training or educational programs they should participate in. The Proposal Department uses AI to analyze this data and proposes optimal career paths and learning plans for the user. The AI ​​uses machine learning algorithms to analyze the collected data and propose optimal career paths and learning plans for the user. For example, the AI ​​can analyze past job posting data and market trend data to suggest the most suitable job types and skills for the user. The AI ​​can also analyze the user's skill set and identify the skills and qualifications necessary for career advancement. This allows the Proposal Department to propose optimal career paths and learning plans for users and support their career advancement. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, the proposals are reviewed and improved based on user feedback. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For instance, important information is delivered not only via smartphone notifications but also through voice calls, SMS, and email. This allows the proposal department to provide users with information quickly and reliably, supporting their career advancement.

[0068] The Support Department assists users in skill development and career advancement based on career paths and learning plans proposed by the Proposal Department. Specifically, it can provide clear guidelines for users' career goals and support continuous skill development and career advancement. The Support Department uses AI to monitor users' progress and provide feedback and advice as needed. For example, if a user is receiving training according to a proposed learning plan, the AI ​​can monitor their progress in real time and provide advice and feedback as needed. The Support Department can also evaluate the user's achievement of career goals and revise or improve the learning plan as necessary. This allows the Support Department to continuously support users in skill development and career advancement. Furthermore, the Support Department can collect user feedback and continuously improve the accuracy and effectiveness of its support. For example, it can revise and improve support based on user feedback. The Support Department can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the Support Department to provide information to users quickly and reliably, supporting their skill development and career advancement.

[0069] The data collection unit can collect detailed data such as information entered by the user, past work history, and educational history. For example, the data collection unit can collect detailed data such as information entered by the user, past work history, and educational history. For example, the data collection unit can collect information such as the qualifications and skills the user possesses, and the content of the education and training they have received in the past. By collecting detailed user data, more accurate analysis and suggestions become possible. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without using AI.

[0070] The analysis unit can compare the user's skill set with market trends to determine how the user's current skills are valued in the market. For example, the analysis unit can identify skills that are currently in high demand in the market or skills that are expected to see increased demand in the future. By determining how the user's skills are valued in the market, it can propose appropriate career paths and learning plans. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0071] The suggestion department can suggest what types of jobs would be suitable for a user if they were to change jobs while utilizing their current skill set, or what new skills or qualifications they should acquire. For example, the suggestion department can suggest what types of jobs would be suitable for a user if they were to change jobs while utilizing their current skill set, or what new skills or qualifications they should acquire. For example, the suggestion department can suggest what types of jobs would be suitable for a user if they were to change jobs while utilizing their current skill set. The suggestion department can also suggest what new skills or qualifications the user should acquire. This supports the user's job search by suggesting suitable job types and skills they should acquire when changing jobs. Some or all of the above processing in the suggestion department may be performed using AI, or not.

[0072] The suggestion department can suggest what skills a user should strengthen and what training or educational programs they should take if they aim to advance their career. For example, the suggestion department can suggest what skills a user should strengthen and what training or educational programs they should take if they aim to advance their career. For example, the suggestion department can suggest what skills a user should strengthen if they aim to advance their career. The suggestion department can also suggest training or educational programs that a user should take. In this way, it supports career advancement by suggesting the skills and training necessary for a user to advance their career. Some or all of the above processing in the suggestion department may be performed using AI or not.

[0073] The support department can provide clear guidelines for users' career goals and support their continuous skill development and career advancement. For example, the support department can provide clear guidelines for users' career goals and support their continuous skill development and career advancement. For example, the support department can provide clear guidelines for users' career goals. Furthermore, the support department can also support users' continuous skill development and career advancement. This allows the support department to support continuous skill development and career advancement by providing guidelines for users' career goals. Some or all of the above-described processes in the support department may be performed using AI or not.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay data collection until the user is relaxed. For example, if the user is focused, the data collection unit can collect detailed data at that time. Also, if the user is tired, the data collection unit can start with simple questions and gradually collect more detailed data. This allows for more effective data collection by adjusting the timing of data collection 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 data collection unit may be performed using AI or not.

[0075] The data collection unit can analyze the user's past work history and educational history to select the optimal data collection method. For example, the data collection unit can collect data in a similar format based on data previously entered by the user. For example, the data collection unit can prioritize questions related to relevant skills and qualifications based on the user's work history. The data collection unit can also ask questions tailored to the user's educational background, taking into account the user's educational history. In this way, the optimal data collection method can be selected by analyzing the user's past history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0076] The data collection unit can filter data based on the user's current career goals and areas of interest during data collection. For example, if a user is interested in a particular job, the data collection unit will prioritize collecting data related to that job. For example, the data collection unit can collect data on necessary skills and qualifications based on the user's career goals. The data collection unit can also collect the latest market trends related to the user's areas of interest. This allows for the collection of highly relevant data by filtering the data based on the user's career goals and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may prioritize collecting important data. If the user is excited, the data collection unit may prioritize collecting data that is of interest. This enables effective data collection by prioritizing data 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 processing described above in the data collection unit may be performed using AI or not.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of local job postings based on the user's current location. For example, the data collection unit can collect market trends related to the user's geographical location. In addition, the data collection unit can collect information on local educational institutions and training programs based on the user's location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on relevant skills and qualifications based on a user's interests on social media. For example, the data collection unit can collect data related to career goals by referring to a user's social media activity history. The data collection unit can also analyze a user's social media network and collect relevant job information. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's skill set. For example, if the user's skill set is highly valued in the market, the analysis unit can provide detailed analysis results. For example, if the user's skill set is moderately valued in the market, the analysis unit can provide concise analysis results. Furthermore, if the user's skill set is poorly valued in the market, the analysis unit can provide detailed analysis results including areas for improvement. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis based on the importance of the user's skill set. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0082] The analysis unit can apply different analysis algorithms during analysis according to the user's career goals. For example, if the user wishes to change jobs, the analysis unit can apply an analysis algorithm suitable for job changes. For example, if the user wishes to advance their career, the analysis unit can apply an analysis algorithm suitable for career advancement. Furthermore, if the user wishes to acquire new skills, the analysis unit can apply an analysis algorithm suitable for those skills. In this way, by applying different analysis algorithms according to the user's career goals, appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, appropriate analysis results can be provided by adjusting the length of the analysis 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 analysis unit may be performed using AI or not using AI.

[0084] The analysis unit can determine the priority of analysis based on when the user's work history was submitted. For example, the analysis unit may prioritize the analysis of the user's most recently submitted work history. For example, the analysis unit may perform analysis by referring to the user's past submitted work history. Furthermore, the analysis unit can adjust the order of analysis based on when the user's work history was submitted. This allows for the provision of appropriate analysis results by determining the priority of analysis based on when the user's work history was submitted. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0085] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit may prioritize analyzing data related to the user's current career goals. For example, the analysis unit may prioritize analyzing data related to the user's skill set. The analysis unit may also prioritize analyzing data related to the user's educational history. By adjusting the order of analysis based on user relevance, appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, more effective suggestions become possible. 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 processing described above in the suggestion unit may be performed using AI or not.

[0087] The proposal department can adjust the level of detail in its proposals based on the importance of the career path. For example, if the user's career path is highly valued in the market, the proposal department will provide a detailed proposal. If the user's career path is moderately valued in the market, the proposal department can provide a concise proposal. Furthermore, if the user's career path is poorly valued in the market, the proposal department can provide a detailed proposal that includes areas for improvement. By adjusting the level of detail in proposals based on the importance of the career path, appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI or not.

[0088] The suggestion function can apply different suggestion algorithms depending on the career path category when making suggestions. For example, if a user wishes to change jobs, the suggestion function can apply a suggestion algorithm suitable for job changes. For example, if a user wishes to advance their career, the suggestion function can apply a suggestion algorithm suitable for career advancement. Furthermore, if a user wishes to acquire new skills, the suggestion function can apply a suggestion algorithm suitable for those skills. By applying different suggestion algorithms depending on the career path category, appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI or not.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the length of suggestions according to the user's emotions, appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not.

[0090] The proposal department can determine the priority of proposals based on when the career path was submitted. For example, the proposal department may prioritize career paths recently submitted by the user. For example, the proposal department may make proposals by referring to career paths previously submitted by the user. Furthermore, the proposal department can adjust the order of proposals based on when the user's career path was submitted. This allows for more appropriate proposals by prioritizing proposals based on when the career path was submitted. Some or all of the above processing in the proposal department may be performed using AI or not.

[0091] The suggestion unit can adjust the order of suggestions based on their relevance to the user's career path. For example, the suggestion unit may prioritize suggestions related to the user's current career goals. For example, it may prioritize suggestions related to the user's skill set. It may also prioritize suggestions related to the user's educational history. By adjusting the order of suggestions based on their relevance to the user's career path, appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0092] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is relaxed, the support unit can provide detailed support. If the user is in a hurry, the support unit can provide concise support that gets straight to the point. If the user is excited, the support unit can provide visually appealing support. This allows for appropriate support by adjusting the support methods 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 support unit may be performed using AI or not.

[0093] The support department can analyze the user's past career behavior during support to select the most suitable support method. For example, the support department can propose relevant support methods based on training programs the user has previously received. For example, the support department can analyze the user's past career behavior and prioritize suggesting successful methods. Furthermore, the support department can propose support methods that include areas for improvement, taking into account the user's past career behavior. In this way, the optimal support method can be selected by analyzing the user's past career behavior. Some or all of the above processes in the support department may be performed using AI or not.

[0094] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy, the support unit can suggest a short and effective support method. For example, if the user has time, the support unit can suggest a detailed support method. Furthermore, the support unit can suggest a combination of online and offline support methods depending on the user's living situation. This allows for appropriate support by customizing the means of support based on the user's current living situation. Some or all of the above processing in the support unit may be performed using AI or not.

[0095] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is relaxed, the support unit can prioritize detailed support. If the user is in a hurry, the support unit can prioritize important support. Also, if the user is excited, the support unit can prioritize engaging support. This allows for appropriate support by prioritizing support 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 support unit may be performed using AI or not.

[0096] The support department can select the optimal support method by considering the user's geographical location information during support. For example, the support department can suggest local training programs based on the user's current location. For example, the support department can provide job information related to the user's geographical location. Furthermore, the support department can suggest local career support services based on the user's location information. In this way, the optimal support method can be selected by considering the user's geographical location information. Some or all of the above processing in the support department may be performed using AI or not.

[0097] The support department can analyze the user's social media activity and propose support methods when providing assistance. For example, the support department can propose relevant support methods based on the user's interests on social media. For example, the support department can provide support related to career goals by referring to the user's social media activity history. Furthermore, the support department can analyze the user's social media network and propose relevant career support services. In this way, by analyzing the user's social media activity, appropriate support methods can be proposed. Some or all of the above processing in the support department may be performed using AI or not.

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

[0099] The career support system can estimate the user's emotions and customize career path suggestions based on those emotions. For example, if the user is feeling anxious, the system can suggest a career path that provides reassurance. If the user is confident, it can suggest a challenging career path. Furthermore, if the user is excited, it can suggest an interesting career path. This makes it possible to suggest career paths that match the user's emotions, thereby increasing their motivation.

[0100] The career support system can propose career paths tailored to the characteristics of a region, taking into account the user's geographical location. For example, if a user lives in an urban area, it can propose career paths based on job postings and trends in urban areas. If a user lives in a rural area, it can propose career paths based on job postings and trends in that area. Furthermore, if a user lives overseas, it can propose career paths tailored to the characteristics of that country. This makes it possible to propose career paths based on the user's geographical location, providing career support that is appropriate for the region.

[0101] The career support system can analyze a user's social media activity and suggest career paths based on their interests and network. For example, if a user shows interest in a particular industry on social media, it can suggest career paths related to that industry. It can also analyze a user's social media network and suggest career paths based on trends and job postings within that network. Furthermore, it can suggest career paths based on the user's social media activity history, taking into account their past interests and activities. This enables the suggestion of career paths based on the user's social media activity, providing career support tailored to the user's interests and network.

[0102] The career support system can estimate the user's emotions and adjust the progress of the learning plan based on those emotions. For example, if the user is feeling stressed, the progress of the learning plan can be slowed down. Conversely, if the user is highly motivated, the progress of the learning plan can be accelerated. Furthermore, if the user is tired, the learning plan can be adjusted to include breaks. This enables the learning plan to progress in accordance with the user's emotions, providing effective learning support.

[0103] A career support system can analyze a user's past career actions and propose successful career paths. For example, it can suggest similar career paths based on a user's past successful job changes. It can also analyze the effectiveness of training programs a user has previously participated in and suggest effective training programs. Furthermore, it can propose career paths that include areas for improvement, based on the user's past career actions. This enables the proposal of career paths based on the user's past career actions, providing career support with a high success rate.

[0104] A career support system can estimate a user's emotions and provide career path feedback based on those emotions. For example, if a user is feeling anxious, it can provide reassuring feedback. If a user is confident, it can provide feedback that encourages further challenges. Furthermore, if a user is excited, it can provide feedback that piques their interest. This enables career path feedback tailored to the user's emotions, thereby increasing their motivation.

[0105] The career support system can propose flexible learning plans based on the user's current lifestyle. For example, if the user is busy, it can propose a short and effective learning plan. If the user has more time, it can propose a more detailed learning plan. Furthermore, it can propose a learning plan that combines online and offline learning methods, depending on the user's lifestyle. This enables the proposal of flexible learning plans tailored to the user's lifestyle, providing effective learning support.

[0106] The career support system can estimate the user's emotions and monitor the progress of their career path based on those emotions. For example, if the user is feeling stressed, the progress can be slowed down. Conversely, if the user is highly motivated, the progress can be accelerated. Furthermore, if the user is tired, the progress can be adjusted to include breaks. This allows for monitoring of career path progress in accordance with the user's emotions, enabling the provision of effective career support.

[0107] The career support system can propose learning plans tailored to the characteristics of a region, taking into account the user's geographical location. For example, if a user lives in an urban area, it can propose a learning plan based on training programs and educational institutions in urban areas. If a user lives in a rural area, it can propose a learning plan based on training programs and educational institutions in that area. Furthermore, if a user lives overseas, it can propose a learning plan tailored to the characteristics of that country. This makes it possible to propose learning plans based on the user's geographical location, providing learning support that is appropriate for the region.

[0108] A career support system can estimate a user's emotions and evaluate their career path progress based on those emotions. For example, if a user is feeling anxious, their progress can be reviewed. If a user is confident, their progress can be maintained. Furthermore, if a user is excited, their progress can be accelerated. This allows for evaluation of career path progress in accordance with the user's emotions, enabling the provision of effective career support.

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

[0110] Step 1: The data collection unit collects data on the user's skills, education level, and background. For example, it collects detailed data such as information entered by the user, past work history, and educational history. The data collection unit can collect information on the user's qualifications and skills, as well as the content of education and training received in the past. Some or all of the above processing in the data collection unit may be performed using AI or not. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it compares the user's skill set with market trends to determine how the user's current skills are valued in the market. The analysis unit can identify skills that are in high demand in the current market and skills that are expected to see increased demand in the future. Some or all of the above processing in the analysis unit may be performed using AI or not. Step 3: The proposal unit proposes the optimal career path and learning plan based on the analysis results obtained by the analysis unit. For example, if the user is looking to change jobs using their current skill set, the proposal unit can suggest what types of jobs are suitable, or what new skills or qualifications they should acquire. If the user is aiming for career advancement, the proposal unit can also suggest what skills they should strengthen and what training or educational programs they should participate in. Some or all of the above processing in the proposal unit may be performed using AI or not. Step 4: The support department assists users in skill development and career advancement based on the career path and learning plan proposed by the proposal department. For example, it can provide clear guidelines for the user's career goals and support continuous skill development and career advancement. Some or all of the above processes in the support department may be performed using AI or not.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on the user's skills, education level, and career history using the control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal career path and learning plan based on the analysis results using the specific processing unit 290 of the data processing unit 12. The support unit assists the user in improving their skills and career based on the career path and learning plan proposed by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on the user's skills, education level, and career history using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal career path and learning plan based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The support unit supports the user's skill development and career advancement based on the career path and learning plan proposed by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data on the user's skills, education level, and career history using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal career path and learning plan based on the analysis results using the specific processing unit 290 of the data processing unit 12. The support unit assists the user in improving their skills and career based on the career path and learning plan proposed by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data on the user's skills, education level, and career history using the control unit 46A of the robot 414. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal career path and learning plan based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The support unit assists the user in improving their skills and career based on the career path and learning plan proposed by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects data on users' skills, education levels, and work experience, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal career path and learning plan. The system includes a support unit that assists users in improving their skills and careers based on the career path and learning plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects detailed data such as information entered by the user, past work history, and educational history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, This tool compares the user's skill set with market trends to determine how the user's current skills are valued in the market. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, When a user is looking to change jobs and leverages their current skill set, we suggest suitable job types or what new skills and qualifications they should acquire. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, When users aim for career advancement, we suggest what skills they should strengthen and what training and educational programs they should take. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, We provide clear guidelines for users' career goals and support their continuous skill development and career advancement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past work history and educational history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current career goals and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the user's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the user's work history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the career path. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the career path category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the career path is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the career path. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, During support, we analyze the user's past career behavior to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, During support, the means of assistance are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When providing support, the optimal support method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, When providing support, we analyze the user's social media activity and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on users' skills, education levels, and work experience, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal career path and learning plan. The system includes a support unit that assists users in improving their skills and careers based on the career path and learning plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects detailed data such as information entered by the user, past work history, and educational history. The system according to feature 1.

3. The aforementioned analysis unit, This tool compares the user's skill set with market trends to determine how the user's current skills are valued in the market. The system according to feature 1.

4. The aforementioned proposal section is, When a user is looking to change jobs and leverages their current skill set, we suggest suitable job types or what new skills and qualifications they should acquire. The system according to feature 1.

5. The aforementioned proposal section is, When users aim for career advancement, we suggest what skills they should strengthen and what training and educational programs they should take. The system according to feature 1.

6. The aforementioned support unit, We provide clear guidelines for users' career goals and support their continuous skill development and career advancement. The system according to feature 1.

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

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

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