System

Abstract

An object of the system according to the embodiment is to clarify a step to be performed next by a new employee or a person who tries career change, and to promote skill improvement according to an individual career path.SOLUTION: A system includes a collection unit, an analysis unit, a presentation unit, a proposal unit, and an update unit. The collection unit collects skills, knowledge, and career targets of users. The analysis unit analyzes the information collected by the collection unit and grasps the current skill and knowledge of the user. The presentation unit presents necessary skills and knowledge based on the analysis result obtained by the analysis unit. The proposal unit proposes an operation flow based on the skill and knowledge presented by the presentation unit. The update unit updates the skill based on the business flow proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to clarify the next steps that new employees and people seeking a career change should take, and to promote skill development that is tailored to each individual's career path.

[0005] The system according to the embodiment aims to clarify the next steps that new employees and people seeking a career change should take, and to promote skill development in line with individual career paths. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, a proposal unit, and an update unit. The collection unit collects the user's skills, knowledge, and career goals. The analysis unit analyzes the information collected by the collection unit and grasps the user's current skills and knowledge. The presentation unit presents the necessary skills and knowledge based on the analysis results obtained by the analysis unit. The proposal unit proposes a workflow based on the skills and knowledge presented by the presentation unit. The update unit updates the skills based on the workflow proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment clarifies the next steps that new employees and people seeking a career change should take, and can promote skill development in line with individual career paths. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A career support system according to an embodiment of the present invention collects and analyzes a user's skills, knowledge, and career goals, presents necessary skills and knowledge, proposes workflows, and updates skills. The career support system collects and analyzes a user's current skills, knowledge, and career goals, and presents necessary skills and knowledge. Furthermore, the career support system updates skills based on the proposed workflows. For example, a user inputs their current skills, knowledge, and career goals. For example, the user inputs their work experience, educational background, and areas of interest. This information is then entered into the career support system. The career support system then analyzes the input information and identifies the user's current skills and knowledge. The career support system identifies necessary skills and knowledge based on the user's work experience, educational background, and areas of interest. For example, a new employee seeking a sales position may be informed that communication skills and knowledge of sales strategies are necessary. Furthermore, the career support system presents a proposed workflow in a simple and easy-to-understand manner, clearly indicating what new employees and transferees should do. For example, a new employee hoping to work in sales can first learn basic sales skills and then be suggested to take the step of participating in actual sales activities. This allows the user to have a concrete action plan. The career support system also updates skills in response to changes in work and the direction of the user's career path. For example, if a user is to participate in a new project, the career support system presents the skills and knowledge required for that project and supports the user so that the user can proceed smoothly with the work. This allows the career support system to efficiently enable new employees and people looking to change careers to improve their skills in line with their career path. This allows the career support system to efficiently promote skill improvement based on the user's skills, knowledge, and career goals. For example, a new employee hoping to work in sales can acquire the necessary skills with the support of the career support system and smoothly transition to actual work.In addition, employees who are transferred can quickly acquire the skills necessary for their new jobs and adapt to changes in work.

[0029] A career support system according to an embodiment includes a collection unit, an analysis unit, a presentation unit, a suggestion unit, and an update unit. The collection unit collects a user's skills, knowledge, and career goals. The collection unit can collect, for example, the user's work experience, educational background, and areas of interest. The collection unit, for example, stores information entered by the user in a database and provides the information to the analysis unit. The analysis unit analyzes the information collected by the collection unit to understand the user's current skills and knowledge. The analysis unit, for example, uses data analysis technology to analyze the user's work experience, educational background, and areas of interest. The analysis unit can also evaluate the user's skills and knowledge using, for example, a machine learning algorithm. The presentation unit presents the required skills and knowledge based on the analysis results obtained by the analysis unit. The presentation unit, for example, visually displays a skill map to show the user the skills and knowledge required. The presentation unit can also provide detailed text-based explanations, for example. The suggestion unit proposes a workflow based on the skills and knowledge presented by the presentation unit. The suggestion unit, for example, provides a step-by-step guide to help the user create a specific action plan. The suggestion unit can also present, for example, business procedures and process flows. The update unit updates skills based on the business flows suggested by the suggestion unit. The update unit, for example, provides skill training programs to help the user acquire new skills. The update unit can also provide, for example, knowledge updates and career development plans. As a result, the career support system according to the embodiment can efficiently promote skill development based on the user's skills, knowledge, and career goals.

[0030] The collection unit can collect the user's work experience or educational background, and areas of interest. The collection unit, for example, collects the user's work experience. For example, the collection unit can collect the job content, position, and scope of work that the user has experienced in the past. The collection unit can also collect the user's educational background. For example, the collection unit can collect the user's highest educational background, major, and degree obtained. Furthermore, the collection unit can collect the user's areas of interest. For example, the collection unit can collect the user's technical fields, business fields, hobbies, etc. in which the user is interested. This allows the collection unit to collect appropriate information based on the user's work experience, educational background, and areas of interest.

[0031] The analysis unit can grasp the user's current skills and knowledge based on the collected information. The analysis unit, for example, analyzes the collected information using data analysis technology. For example, the analysis unit analyzes the user's work experience, educational background, and areas of interest to grasp the user's current skills and knowledge. The analysis unit can also evaluate the user's skills and knowledge using a machine learning algorithm. For example, the analysis unit can identify the user's required skills and knowledge based on the user's work experience and educational background. Furthermore, the analysis unit can present related skills and knowledge based on the user's areas of interest. This allows the analysis unit to accurately grasp the user's current skills and knowledge.

[0032] The presentation unit can present the required skills and knowledge based on the analysis results. The presentation unit, for example, visually presents the required skills and knowledge to the user based on the analysis results. For example, the presentation unit displays a skill map to show the required skills and knowledge to the user. The presentation unit can also provide detailed text-based explanations. For example, the presentation unit explains the specific content and scope of the required skills and knowledge to the user. Furthermore, the presentation unit can present the required skills and knowledge to the user using audio presentations. For example, the presentation unit provides audio guidance to explain the required skills and knowledge to the user. This allows the presentation unit to appropriately present the required skills and knowledge to the user.

[0033] The suggestion unit can suggest a workflow based on the presented skills and knowledge. The suggestion unit, for example, suggests a specific workflow to the user based on the presented skills and knowledge. For example, the suggestion unit provides a step-by-step guide to assist the user in creating a specific action plan. The suggestion unit can also show work procedures and process flows. For example, the suggestion unit visually shows the work procedures and processes to the user and suggests a specific action plan. Furthermore, the suggestion unit can suggest an optimal workflow based on the user's career goals. For example, the suggestion unit suggests a workflow for acquiring necessary skills and knowledge in accordance with the user's career goals. In this way, the suggestion unit can suggest a specific workflow to the user.

[0034] The update unit can update skills in accordance with changes in work and the direction of a career path. The update unit updates a user's skills in accordance with changes in work and the direction of a career path, for example. For example, when a user is to participate in a new project, the update unit presents the skills and knowledge required for the project and supports the user so that the user can proceed with the work smoothly. The update unit can also provide a skill training program to help the user acquire new skills. For example, the update unit provides a skill training program to the user and supports the user in acquiring the required skills. Furthermore, the update unit can also provide knowledge updates and career development plans. For example, the update unit provides the user with the latest knowledge and suggests a career development plan. In this way, the update unit can keep the user's skills up to date.

[0035] The collection unit can analyze the user's past work experience and educational background and select an appropriate collection method. The collection unit, for example, analyzes the user's past work experience and educational background and selects the optimal collection method. For example, if the user has worked in sales in the past, the collection unit can prioritize collecting skills and knowledge related to sales. Also, if the user has an academic background in IT, the collection unit can prioritize collecting IT-related skills and knowledge. Furthermore, if the user has worked in a managerial position in the past, the collection unit can prioritize collecting information on leadership and management. This allows the collection unit to collect optimal information based on the user's past work experience and educational background.

[0036] The collection unit can perform filtering based on the user's current living situation and areas of interest at the time of collection. For example, the collection unit performs filtering based on the user's current living situation and areas of interest at the time of collection. For example, if the user is currently raising a child, the collection unit can prioritize collecting skills and knowledge that can be combined with childcare. Furthermore, if the user is currently interested in data science, the collection unit can also prioritize collecting information related to data science. Furthermore, if the user is currently working remotely, the collection unit can also prioritize collecting skills and knowledge suitable for remote work. This allows the collection unit to collect appropriate information according to the user's current living situation and areas of interest.

[0037] The collection unit can select an appropriate collection means depending on the user's input method at the time of collection. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) at the time of collection. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. This allows the collection unit to collect optimal information depending on the user's input method.

[0038] The collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant information by taking the user's geographical location information into consideration when collecting data. For example, if the user lives in a specific area, the collection unit can prioritize collecting skills and knowledge related to that area. Also, if the user works in a specific area, the collection unit can prioritize collecting information related to industry trends in that area. Furthermore, if the user is considering moving to a specific area, the collection unit can prioritize collecting skills and knowledge required in that area. This allows the collection unit to collect highly relevant information based on the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit analyzes the user's social media activities and collects related information at the time of collection. For example, the collection unit can collect related skills and knowledge based on information about accounts the user follows on social media. The collection unit can also analyze articles and posts the user shared on social media and collect related information. Furthermore, the collection unit can collect information that the user may be interested in based on the user's social media activity history. This allows the collection unit to collect related information based on the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit adjusts the type and amount of information to be collected based on feedback provided by the user in the past. The collection unit can also preferentially collect related information based on information rated by the user in the past. Furthermore, the collection unit can analyze the user's past feedback and optimize the collection method. This allows the collection unit to optimize the collection method based on the user's past feedback.

[0041] The analysis unit can improve the accuracy of the analysis based on the interrelationships of the collected information during the analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of the collected information during the analysis. For example, the analysis unit performs the analysis by taking into account the interrelationships between the user's work experience and educational background. The analysis unit can also perform the analysis by taking into account the interrelationships between the user's fields of interest and past work experience. Furthermore, the analysis unit can perform the analysis by taking into account the interrelationships between the user's current living situation and past work experience. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information.

[0042] The analysis unit can perform the analysis based on the user's work experience and educational background during the analysis. For example, the analysis unit performs the analysis taking into consideration the user's work experience and educational background during the analysis. For example, the analysis unit analyzes related skills and knowledge based on the user's work experience. The analysis unit can also analyze related skills and knowledge based on the user's educational background. Furthermore, the analysis unit can perform the analysis by comprehensively considering the user's work experience and educational background. This allows the analysis unit to perform an appropriate analysis based on the user's work experience and educational background.

[0043] The analysis unit can weight the analysis based on the frequency of user submissions during analysis. For example, the analysis unit weights the analysis based on the frequency of user submissions during analysis. For example, the analysis unit prioritizes information frequently submitted by the user during analysis. The analysis unit can also weight the analysis based on the frequency of information previously submitted by the user. Furthermore, the analysis unit can prioritize analysis of information with high importance according to the frequency of user submissions. This allows the analysis unit to adjust the weighting of the analysis according to the frequency of user submissions.

[0044] The analysis unit can perform analysis based on the geographical distribution of the collected information during analysis. For example, the analysis unit performs analysis taking into account the geographical distribution of the collected information during analysis. For example, if a user lives in a specific region, the analysis unit can prioritize analyzing information related to that region. Also, if a user works in a specific region, the analysis unit can prioritize analyzing information related to industry trends in that region. Furthermore, if a user is considering relocating to a specific region, the analysis unit can prioritize analyzing skills and knowledge required in that region. This allows the analysis unit to perform appropriate analysis based on the geographical distribution of the collected information.

[0045] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis, for example. For example, the analysis unit performs the analysis by referring to literature related to the user's work experience. The analysis unit can also perform the analysis by referring to literature related to the user's educational background. Furthermore, the analysis unit can perform the analysis by referring to literature related to the user's field of interest. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0046] The analysis unit can perform the analysis based on the market value of the information during the analysis. For example, the analysis unit performs the analysis taking into account the market value of the information during the analysis. For example, the analysis unit performs the analysis taking into account the market value of skills and knowledge in which the user is interested. The analysis unit can also perform the analysis taking into account the market value of skills and knowledge related to the user's work experience. Furthermore, the analysis unit can perform the analysis taking into account the market value of skills and knowledge related to the user's educational background. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the market value of the information.

[0047] The presentation unit can adjust the level of detail of the presentation based on the priority of the analysis result when presenting the analysis result. The presentation unit adjusts the level of detail of the presentation based on, for example, the importance of the analysis result when presenting the analysis result. For example, the presentation unit presents information about skills and knowledge with high importance in detail. The presentation unit can also present information about skills and knowledge with low importance in a concise manner. Furthermore, the presentation unit can adjust the level of detail of the information according to the importance of the analysis result. This allows the presentation unit to adjust the level of detail of the presentation according to the importance of the analysis result.

[0048] The presentation unit can apply different presentation algorithms depending on the type of information when presenting the information. For example, the presentation unit applies different presentation algorithms depending on the type of information when presenting the information. For example, the presentation unit applies a visual presentation method using a skill map when presenting information about skills. The presentation unit can also apply detailed text-based explanations when presenting information about knowledge. Furthermore, the presentation unit can apply a step-by-step guide when presenting information about career goals. This allows the presentation unit to select the optimal presentation method depending on the type of information.

[0049] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting information. For example, the presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting information. For example, the presentation unit preferentially presents related information based on information that the user has previously rated highly. The presentation unit can also exclude and present related information based on information that the user has previously rated poorly. Furthermore, the presentation unit can analyze the user's past presentation results and present optimal information. This allows the presentation unit to improve the accuracy of presentation based on the user's past presentation results.

[0050] The presentation unit can determine the order of presentation based on the time of submission of the information at the time of presentation. For example, the presentation unit determines the priority of presentation based on the time of submission of the information at the time of presentation. For example, the presentation unit preferentially presents the most recent information. The presentation unit can also present information that has been submitted earlier as needed. Furthermore, the presentation unit can adjust the priority of information based on the time of submission. This allows the presentation unit to adjust the priority of presentation based on the time of submission of the information.

[0051] The presentation unit can adjust the order of presentation based on the relationship between information when presenting the information. For example, the presentation unit adjusts the order of presentation based on the relationship between information when presenting the information. For example, the presentation unit prioritizes presenting information that is most relevant to the user's career goals. The presentation unit can also prioritize presenting information that is relevant to the user's current skills and knowledge. Furthermore, the presentation unit can adjust the order of presentation based on the relationship between information. This allows the presentation unit to adjust the order of presentation based on the relationship between information.

[0052] The presentation unit can adjust the use of terminology when presenting information according to the user's level of expertise. For example, the presentation unit adjusts the use of technical terminology when presenting information according to the user's level of expertise. For example, if the user is a beginner, the presentation unit can present information in easy-to-understand language, avoiding technical terminology. Furthermore, if the user is an intermediate user, the presentation unit can present information using technical terminology appropriately. Furthermore, if the user is an advanced user, the presentation unit can present detailed information using a lot of technical terminology. This allows the presentation unit to use appropriate technical terminology according to the user's level of expertise.

[0053] The suggestion unit can adjust the level of detail of the proposal based on the priority of the skills and knowledge when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the skills and knowledge when making the proposal. For example, the suggestion unit makes detailed suggestions regarding skills and knowledge with high importance. The suggestion unit can also make concise suggestions regarding skills and knowledge with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance of the skills and knowledge. This allows the suggestion unit to adjust the level of detail of the proposal according to the importance of the skills and knowledge.

[0054] The suggestion unit can apply different suggestion algorithms depending on the type of skill or knowledge when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the type of skill or knowledge when making a suggestion. For example, the suggestion unit applies a visual suggestion method using a skill map when making a suggestion regarding skills. Furthermore, the suggestion unit can apply detailed text-based explanations when making a suggestion regarding knowledge. Furthermore, the suggestion unit can apply a step-by-step guide when making a suggestion regarding career goals. This allows the suggestion unit to select the optimal suggestion method depending on the type of skill or knowledge.

[0055] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit prioritizes related suggestions based on suggestions that the user has previously given a high rating. The suggestion unit can also exclude related suggestions based on suggestions that the user has previously given a low rating. Furthermore, the suggestion unit can analyze the user's past suggestion results and make optimal suggestions. This allows the suggestion unit to improve the accuracy of the suggestion based on the user's past suggestion results.

[0056] The suggestion unit can determine the order of proposals based on the time of submission of skills and knowledge when making a proposal. For example, the suggestion unit determines the priority of proposals based on the time of submission of skills and knowledge when making a proposal. For example, the suggestion unit gives priority to proposals related to the latest skills and knowledge. The suggestion unit can also make proposals related to skills and knowledge that were submitted earlier as needed. Furthermore, the suggestion unit can adjust the priority of proposals based on the time of submission. This allows the suggestion unit to adjust the priority of proposals based on the time of submission of skills and knowledge.

[0057] The suggestion unit can adjust the order of the proposals based on the relationships between skills and knowledge when making a proposal. For example, the suggestion unit adjusts the order of the proposals based on the relationships between skills and knowledge when making a proposal. For example, the suggestion unit prioritizes proposals related to skills and knowledge that are most relevant to the user's career goals. The suggestion unit can also prioritize proposals related to the user's current skills and knowledge. Furthermore, the suggestion unit can adjust the order of the proposals based on the relationships between skills and knowledge. This allows the suggestion unit to adjust the order of the proposals based on the relationships between skills and knowledge.

[0058] The suggestion unit can adjust the use of terminology in the suggestion according to the user's level of expertise when making a suggestion. For example, the suggestion unit adjusts the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. For example, if the user is a beginner, the suggestion unit can make a suggestion using easy-to-understand language while avoiding technical terminology. Furthermore, if the user is an intermediate user, the suggestion unit can make a suggestion using technical terminology appropriately. Furthermore, if the user is an advanced user, the suggestion unit can make a detailed suggestion using a lot of technical terminology. This allows the suggestion unit to use appropriate technical terminology according to the user's level of expertise.

[0059] The update unit can select the optimal update method by analyzing past changes in the user's skills and knowledge during an update. For example, the update unit selects the optimal update method by analyzing past changes in the user's skills and knowledge during an update. For example, the update unit selects the optimal update method based on past changes in the user's skills and knowledge. The update unit can also analyze past changes in the user's skills and knowledge and prioritize updating necessary skills and knowledge. Furthermore, the update unit can adjust the frequency of updates by taking into account past changes in the user's skills and knowledge. This allows the update unit to select the optimal update method based on past changes in the user's skills and knowledge.

[0060] The update unit can select an appropriate update means based on the user's current living situation during an update. For example, the update unit customizes the update means based on the user's current living situation during an update. For example, if the user is currently raising a child, the update unit provides an update method that can be combined with childcare. Furthermore, if the user's current living situation is remote work, the update unit can also provide an update method that is suitable for remote work. Furthermore, the update unit can select the optimal update means based on the user's current living situation. This allows the update unit to select the optimal update means according to the user's current living situation.

[0061] The update unit can improve the update method by reflecting user feedback during an update. For example, the update unit improves the update method by reflecting user feedback during an update. For example, the update unit improves the update method based on feedback provided by the user. The update unit can also analyze user feedback and select an optimal update method. Furthermore, the update unit can adjust the frequency and content of updates by reflecting user feedback. In this way, the update unit can optimize the update method based on user feedback.

[0062] The update unit can select an appropriate update method based on the user's geographic location information when updating. For example, the update unit selects the optimal update method by taking the user's geographic location information into consideration when updating. For example, if the user lives in a specific region, the update unit can prioritize updating skills and knowledge related to that region. Also, if the user works in a specific region, the update unit can prioritize updating skills and knowledge related to industry trends in that region. Furthermore, if the user is considering moving to a specific region, the update unit can prioritize updating skills and knowledge required in that region. This allows the update unit to select the optimal update method based on the user's geographic location information.

[0063] The update unit can analyze the user's social media activity at the time of updating and suggest an appropriate update means. For example, the update unit can analyze the user's social media activity at the time of updating and suggest an update means. For example, the update unit can suggest related skill and knowledge updates based on information about accounts the user follows on social media. The update unit can also analyze articles and posts the user has shared on social media and suggest related skill and knowledge updates. Furthermore, the update unit can suggest skill and knowledge updates that the user may be interested in based on the user's social media activity history. This allows the update unit to suggest the optimal update means based on the user's social media activity.

[0064] The update unit can customize an appropriate update method by reflecting the user's past feedback during an update. For example, the update unit customizes the update method by reflecting the user's past feedback during an update. For example, the update unit customizes the update method based on feedback provided by the user in the past. The update unit can also analyze the user's past feedback and select an optimal update method. Furthermore, the update unit can adjust the frequency and content of updates by reflecting the user's past feedback. This allows the update unit to optimize the update method based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The career support system can further include a lifestyle suggestion unit that suggests skill improvement based on the user's hobbies and lifestyle. The lifestyle suggestion unit collects the user's hobbies and daily activities and provides them to the analysis unit. The analysis unit identifies skills and knowledge suitable for the user's lifestyle based on this information and provides the information to the suggestion unit. The suggestion unit can suggest a skill improvement plan based on the user's hobbies and lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest a work flow that includes outdoor-related skills and knowledge. Also, if the user's hobby is cooking, the suggestion unit can suggest a career path that includes cooking-related skills and knowledge. This allows the career support system to support skill improvement that matches the user's lifestyle.

[0067] The career support system can further include a network utilization unit that utilizes the user's network to support skill development. The network utilization unit collects the user's social network and professional network and provides it to the analysis unit. Based on this network information, the analysis unit determines the skills and knowledge the user is connected to and provides the information to the suggestion unit. The suggestion unit can utilize the user's network to suggest resources and mentors for skill development. For example, if the user is connected to an experienced professional in a particular field, the suggestion unit can suggest that professional as a mentor. The online community in which the user participates can also be utilized to provide resources for skill development. In this way, the career support system can support skill development by making the most of the user's network.

[0068] The career support system can further include a learning style adaptation unit that suggests a method for improving skills based on the user's learning style. The learning style adaptation unit collects the user's learning style (e.g., visual, auditory, experiential, etc.) and provides it to the analysis unit. The analysis unit identifies the optimal learning method for the user based on the learning style data and provides the information to the suggestion unit. The suggestion unit can suggest a skill improvement plan based on the user's learning style. For example, if the user has a visual learning style, the suggestion unit can suggest a learning method using visual materials or videos. Also, if the user has an experiential learning style, the suggestion unit can suggest practical training or workshops. This allows the career support system to support skill improvement that matches the user's learning style.

[0069] The career support system can further include a time management unit that supports the user's time management. The time management unit collects the user's schedule and tasks and provides them to the analysis unit. The analysis unit evaluates how the user uses their time based on this information and provides the information to the suggestion unit. The suggestion unit can propose a skill improvement plan based on the user's time management. For example, if the user has a busy schedule, the suggestion unit can propose a skill improvement method that allows for effective learning in a short amount of time. Also, if the user has a relaxed schedule, the suggestion unit can propose a long-term skill improvement plan. This allows the career support system to support skill improvement in line with the user's time management.

[0070] The career support system can further include a learning history management unit that analyzes the user's learning history and manages the progress of skill improvement. The learning history management unit collects the user's past learning history and provides it to the analysis unit. The analysis unit evaluates the user's skill improvement progress based on the learning history data and provides the information to the suggestion unit. The suggestion unit can adjust the skill improvement plan based on the user's learning history. For example, if the user is taking a long time to master a particular skill, the suggestion unit can propose a learning plan that focuses on that skill. In addition, for skills that the user has already mastered, it can propose a new skill as the next step. This allows the career support system to manage the progress of skill improvement based on the user's learning history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects the user's skills, knowledge, and career goals, such as their work experience, educational background, and areas of interest, and stores the information entered by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit to understand the user's current skills and knowledge. For example, it uses data analysis techniques and machine learning algorithms to analyze the user's work experience, educational background, and areas of interest, and evaluates the user's skills and knowledge. Step 3: The presentation unit presents the necessary skills and knowledge based on the analysis results obtained by the analysis unit. For example, it may visually display a skill map to show the user the skills and knowledge they need. It may also provide detailed text-based explanations. Step 4: The suggestion unit proposes a workflow based on the skills and knowledge presented by the suggestion unit. For example, it can provide a step-by-step guide to help the user create a specific action plan. It can also show business procedures and process flows. Step 5: The update unit updates skills based on the workflow proposed by the proposal unit. For example, it provides skill training programs to help users acquire new skills. It can also provide knowledge updates and career development plans.

[0073] (Example 2) A career support system according to an embodiment of the present invention collects and analyzes a user's skills, knowledge, and career goals, presents necessary skills and knowledge, proposes workflows, and updates skills. The career support system collects and analyzes a user's current skills, knowledge, and career goals, and presents necessary skills and knowledge. Furthermore, the career support system updates skills based on the proposed workflows. For example, a user inputs their current skills, knowledge, and career goals. For example, the user inputs their work experience, educational background, and areas of interest. This information is then entered into the career support system. The career support system then analyzes the input information and identifies the user's current skills and knowledge. The career support system identifies necessary skills and knowledge based on the user's work experience, educational background, and areas of interest. For example, a new employee seeking a sales position may be informed that communication skills and knowledge of sales strategies are necessary. Furthermore, the career support system presents a proposed workflow in a simple and easy-to-understand manner, clearly indicating what new employees and transferees should do. For example, a new employee hoping to work in sales can first learn basic sales skills and then be suggested to take the step of participating in actual sales activities. This allows the user to have a concrete action plan. The career support system also updates skills in response to changes in work and the direction of the user's career path. For example, if a user is to participate in a new project, the career support system presents the skills and knowledge required for that project and supports the user so that the user can proceed smoothly with the work. This allows the career support system to efficiently enable new employees and people looking to change careers to improve their skills in line with their career path. This allows the career support system to efficiently promote skill improvement based on the user's skills, knowledge, and career goals. For example, a new employee hoping to work in sales can acquire the necessary skills with the support of the career support system and smoothly transition to actual work.In addition, employees who are transferred can quickly acquire the skills necessary for their new jobs and adapt to changes in work.

[0074] A career support system according to an embodiment includes a collection unit, an analysis unit, a presentation unit, a suggestion unit, and an update unit. The collection unit collects a user's skills, knowledge, and career goals. The collection unit can collect, for example, the user's work experience, educational background, and areas of interest. The collection unit, for example, stores information entered by the user in a database and provides the information to the analysis unit. The analysis unit analyzes the information collected by the collection unit to understand the user's current skills and knowledge. The analysis unit, for example, uses data analysis technology to analyze the user's work experience, educational background, and areas of interest. The analysis unit can also evaluate the user's skills and knowledge using, for example, a machine learning algorithm. The presentation unit presents the required skills and knowledge based on the analysis results obtained by the analysis unit. The presentation unit, for example, visually displays a skill map to show the user the skills and knowledge required. The presentation unit can also provide detailed text-based explanations, for example. The suggestion unit proposes a workflow based on the skills and knowledge presented by the presentation unit. The suggestion unit, for example, provides a step-by-step guide to help the user create a specific action plan. The suggestion unit can also present, for example, business procedures and process flows. The update unit updates skills based on the business flows suggested by the suggestion unit. The update unit, for example, provides skill training programs to help the user acquire new skills. The update unit can also provide, for example, knowledge updates and career development plans. As a result, the career support system according to the embodiment can efficiently promote skill development based on the user's skills, knowledge, and career goals.

[0075] The collection unit can collect the user's work experience or educational background, and areas of interest. The collection unit, for example, collects the user's work experience. For example, the collection unit can collect the job content, position, and scope of work that the user has experienced in the past. The collection unit can also collect the user's educational background. For example, the collection unit can collect the user's highest educational background, major, and degree obtained. Furthermore, the collection unit can collect the user's areas of interest. For example, the collection unit can collect the user's technical fields, business fields, hobbies, etc. in which the user is interested. This allows the collection unit to collect appropriate information based on the user's work experience, educational background, and areas of interest.

[0076] The analysis unit can grasp the user's current skills and knowledge based on the collected information. The analysis unit, for example, analyzes the collected information using data analysis technology. For example, the analysis unit analyzes the user's work experience, educational background, and areas of interest to grasp the user's current skills and knowledge. The analysis unit can also evaluate the user's skills and knowledge using a machine learning algorithm. For example, the analysis unit can identify the user's required skills and knowledge based on the user's work experience and educational background. Furthermore, the analysis unit can present related skills and knowledge based on the user's areas of interest. This allows the analysis unit to accurately grasp the user's current skills and knowledge.

[0077] The presentation unit can present the required skills and knowledge based on the analysis results. The presentation unit, for example, visually presents the required skills and knowledge to the user based on the analysis results. For example, the presentation unit displays a skill map to show the required skills and knowledge to the user. The presentation unit can also provide detailed text-based explanations. For example, the presentation unit explains the specific content and scope of the required skills and knowledge to the user. Furthermore, the presentation unit can present the required skills and knowledge to the user using audio presentations. For example, the presentation unit provides audio guidance to explain the required skills and knowledge to the user. This allows the presentation unit to appropriately present the required skills and knowledge to the user.

[0078] The suggestion unit can suggest a workflow based on the presented skills and knowledge. The suggestion unit, for example, suggests a specific workflow to the user based on the presented skills and knowledge. For example, the suggestion unit provides a step-by-step guide to assist the user in creating a specific action plan. The suggestion unit can also show work procedures and process flows. For example, the suggestion unit visually shows the work procedures and processes to the user and suggests a specific action plan. Furthermore, the suggestion unit can suggest an optimal workflow based on the user's career goals. For example, the suggestion unit suggests a workflow for acquiring necessary skills and knowledge in accordance with the user's career goals. In this way, the suggestion unit can suggest a specific workflow to the user.

[0079] The update unit can update skills in accordance with changes in work and the direction of a career path. The update unit updates a user's skills in accordance with changes in work and the direction of a career path, for example. For example, when a user is to participate in a new project, the update unit presents the skills and knowledge required for the project and supports the user so that the user can proceed with the work smoothly. The update unit can also provide a skill training program to help the user acquire new skills. For example, the update unit provides a skill training program to the user and supports the user in acquiring the required skills. Furthermore, the update unit can also provide knowledge updates and career development plans. For example, the update unit provides the user with the latest knowledge and suggests a career development plan. In this way, the update unit can keep the user's skills up to date.

[0080] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can first prioritize collecting information that provides a sense of security. Also, if the user is excited, the collection unit can prioritize collecting information related to challenging skills or knowledge. Furthermore, if the user is tired, the collection unit can prioritize collecting information that helps the user relax. This allows the collection unit to adjust the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collection unit can analyze the user's past work experience and educational background and select an appropriate collection method. The collection unit, for example, analyzes the user's past work experience and educational background and selects the optimal collection method. For example, if the user has worked in sales in the past, the collection unit can prioritize collecting skills and knowledge related to sales. Also, if the user has an academic background in IT, the collection unit can prioritize collecting IT-related skills and knowledge. Furthermore, if the user has worked in a managerial position in the past, the collection unit can prioritize collecting information on leadership and management. This allows the collection unit to collect optimal information based on the user's past work experience and educational background.

[0082] The collection unit can perform filtering based on the user's current living situation and areas of interest at the time of collection. For example, the collection unit performs filtering based on the user's current living situation and areas of interest at the time of collection. For example, if the user is currently raising a child, the collection unit can prioritize collecting skills and knowledge that can be combined with childcare. Furthermore, if the user is currently interested in data science, the collection unit can also prioritize collecting information related to data science. Furthermore, if the user is currently working remotely, the collection unit can also prioritize collecting skills and knowledge suitable for remote work. This allows the collection unit to collect appropriate information according to the user's current living situation and areas of interest.

[0083] The collection unit can select an appropriate collection means depending on the user's input method at the time of collection. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) at the time of collection. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. This allows the collection unit to collect optimal information depending on the user's input method.

[0084] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user emotions. For example, if the user feels anxious, the collection unit can provide detailed information to give the user a sense of security. If the user is excited, the collection unit can also provide concise and to-the-point information. Furthermore, if the user is tired, the collection unit can also provide simple and easy-to-understand information. This allows the collection unit to adjust the level of detail of the information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant information by taking the user's geographical location information into consideration when collecting data. For example, if the user lives in a specific area, the collection unit can prioritize collecting skills and knowledge related to that area. Also, if the user works in a specific area, the collection unit can prioritize collecting information related to industry trends in that area. Furthermore, if the user is considering moving to a specific area, the collection unit can prioritize collecting skills and knowledge required in that area. This allows the collection unit to collect highly relevant information based on the user's geographical location information.

[0086] The collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit analyzes the user's social media activities and collects related information at the time of collection. For example, the collection unit can collect related skills and knowledge based on information about accounts the user follows on social media. The collection unit can also analyze articles and posts the user shared on social media and collect related information. Furthermore, the collection unit can collect information that the user may be interested in based on the user's social media activity history. This allows the collection unit to collect related information based on the user's social media activities.

[0087] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit adjusts the type and amount of information to be collected based on feedback provided by the user in the past. The collection unit can also preferentially collect related information based on information rated by the user in the past. Furthermore, the collection unit can analyze the user's past feedback and optimize the collection method. This allows the collection unit to optimize the collection method based on the user's past feedback.

[0088] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize the analysis of skills and knowledge that are important for providing a sense of security. Also, if the user is excited, the analysis unit can prioritize the analysis of challenging skills and knowledge. Furthermore, if the user is tired, the analysis unit can prioritize the analysis of skills and knowledge that will help the user relax. This allows the analysis unit to adjust the analysis priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The analysis unit can improve the accuracy of the analysis based on the interrelationships of the collected information during the analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of the collected information during the analysis. For example, the analysis unit performs the analysis by taking into account the interrelationships between the user's work experience and educational background. The analysis unit can also perform the analysis by taking into account the interrelationships between the user's fields of interest and past work experience. Furthermore, the analysis unit can perform the analysis by taking into account the interrelationships between the user's current living situation and past work experience. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information.

[0090] The analysis unit can perform the analysis based on the user's work experience and educational background during the analysis. For example, the analysis unit performs the analysis taking into consideration the user's work experience and educational background during the analysis. For example, the analysis unit analyzes related skills and knowledge based on the user's work experience. The analysis unit can also analyze related skills and knowledge based on the user's educational background. Furthermore, the analysis unit can perform the analysis by comprehensively considering the user's work experience and educational background. This allows the analysis unit to perform an appropriate analysis based on the user's work experience and educational background.

[0091] The analysis unit can weight the analysis based on the frequency of user submissions during analysis. For example, the analysis unit weights the analysis based on the frequency of user submissions during analysis. For example, the analysis unit prioritizes information frequently submitted by the user during analysis. The analysis unit can also weight the analysis based on the frequency of information previously submitted by the user. Furthermore, the analysis unit can prioritize analysis of information with high importance according to the frequency of user submissions. This allows the analysis unit to adjust the weighting of the analysis according to the frequency of user submissions.

[0092] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, if the user is feeling anxious, the analysis unit can provide a display method that gives a sense of security. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. Furthermore, if the user is tired, the analysis unit can provide a simple, highly visible display method. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The analysis unit can perform analysis based on the geographical distribution of the collected information during analysis. For example, the analysis unit performs analysis taking into account the geographical distribution of the collected information during analysis. For example, if a user lives in a specific region, the analysis unit can prioritize analyzing information related to that region. Also, if a user works in a specific region, the analysis unit can prioritize analyzing information related to industry trends in that region. Furthermore, if a user is considering relocating to a specific region, the analysis unit can prioritize analyzing skills and knowledge required in that region. This allows the analysis unit to perform appropriate analysis based on the geographical distribution of the collected information.

[0094] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis, for example. For example, the analysis unit performs the analysis by referring to literature related to the user's work experience. The analysis unit can also perform the analysis by referring to literature related to the user's educational background. Furthermore, the analysis unit can perform the analysis by referring to literature related to the user's field of interest. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0095] The analysis unit can perform the analysis based on the market value of the information during the analysis. For example, the analysis unit performs the analysis taking into account the market value of the information during the analysis. For example, the analysis unit performs the analysis taking into account the market value of skills and knowledge in which the user is interested. The analysis unit can also perform the analysis taking into account the market value of skills and knowledge related to the user's work experience. Furthermore, the analysis unit can perform the analysis taking into account the market value of skills and knowledge related to the user's educational background. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the market value of the information.

[0096] The presentation unit can estimate the user's emotions and determine the priority of information to be presented based on the estimated user emotions. The presentation unit, for example, estimates the user's emotions and determines the priority of information to be presented based on the estimated user emotions. For example, if the user is feeling anxious, the presentation unit can prioritize presenting information that provides a sense of security. Also, if the user is excited, the presentation unit can prioritize presenting information related to challenging skills or knowledge. Furthermore, if the user is tired, the presentation unit can prioritize presenting information that helps the user relax. In this way, the presentation unit can adjust the priority of information to be presented according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The presentation unit can adjust the level of detail of the presentation based on the priority of the analysis result when presenting the analysis result. The presentation unit adjusts the level of detail of the presentation based on, for example, the importance of the analysis result when presenting the analysis result. For example, the presentation unit presents information about skills and knowledge with high importance in detail. The presentation unit can also present information about skills and knowledge with low importance in a concise manner. Furthermore, the presentation unit can adjust the level of detail of the information according to the importance of the analysis result. This allows the presentation unit to adjust the level of detail of the presentation according to the importance of the analysis result.

[0098] The presentation unit can apply different presentation algorithms depending on the type of information when presenting the information. For example, the presentation unit applies different presentation algorithms depending on the type of information when presenting the information. For example, the presentation unit applies a visual presentation method using a skill map when presenting information about skills. The presentation unit can also apply detailed text-based explanations when presenting information about knowledge. Furthermore, the presentation unit can apply a step-by-step guide when presenting information about career goals. This allows the presentation unit to select the optimal presentation method depending on the type of information.

[0099] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting information. For example, the presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting information. For example, the presentation unit preferentially presents related information based on information that the user has previously rated highly. The presentation unit can also exclude and present related information based on information that the user has previously rated poorly. Furthermore, the presentation unit can analyze the user's past presentation results and present optimal information. This allows the presentation unit to improve the accuracy of presentation based on the user's past presentation results.

[0100] The presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. For example, the presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. For example, if the user is feeling anxious, the presentation unit can provide detailed information to reassure the user. If the user is excited, the presentation unit can also provide concise and to-the-point information. Furthermore, if the user is tired, the presentation unit can also provide simple and easy-to-understand information. This allows the presentation unit to adjust the length of the presentation according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The presentation unit can determine the order of presentation based on the time of submission of the information at the time of presentation. For example, the presentation unit determines the priority of presentation based on the time of submission of the information at the time of presentation. For example, the presentation unit preferentially presents the most recent information. The presentation unit can also present information that has been submitted earlier as needed. Furthermore, the presentation unit can adjust the priority of information based on the time of submission. This allows the presentation unit to adjust the priority of presentation based on the time of submission of the information.

[0102] The presentation unit can adjust the order of presentation based on the relationship between information when presenting the information. For example, the presentation unit adjusts the order of presentation based on the relationship between information when presenting the information. For example, the presentation unit prioritizes presenting information that is most relevant to the user's career goals. The presentation unit can also prioritize presenting information that is relevant to the user's current skills and knowledge. Furthermore, the presentation unit can adjust the order of presentation based on the relationship between information. This allows the presentation unit to adjust the order of presentation based on the relationship between information.

[0103] The presentation unit can adjust the use of terminology when presenting information according to the user's level of expertise. For example, the presentation unit adjusts the use of technical terminology when presenting information according to the user's level of expertise. For example, if the user is a beginner, the presentation unit can present information in easy-to-understand language, avoiding technical terminology. Furthermore, if the user is an intermediate user, the presentation unit can present information using technical terminology appropriately. Furthermore, if the user is an advanced user, the presentation unit can present detailed information using a lot of technical terminology. This allows the presentation unit to use appropriate technical terminology according to the user's level of expertise.

[0104] The suggestion unit can estimate the user's emotion and adjust the way in which the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the way in which the suggestion is expressed based on the estimated user's emotion. For example, if the user is feeling anxious, the suggestion unit can make a suggestion using an expression that gives a sense of security. Furthermore, if the user is excited, the suggestion unit can make a suggestion using a challenging expression. Furthermore, if the user is tired, the suggestion unit can make a suggestion using an expression that makes the user feel relaxed. In this way, the suggestion unit can adjust the way in which the suggestion is expressed according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The suggestion unit can adjust the level of detail of the proposal based on the priority of the skills and knowledge when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the skills and knowledge when making the proposal. For example, the suggestion unit makes detailed suggestions regarding skills and knowledge with high importance. The suggestion unit can also make concise suggestions regarding skills and knowledge with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance of the skills and knowledge. This allows the suggestion unit to adjust the level of detail of the proposal according to the importance of the skills and knowledge.

[0106] The suggestion unit can apply different suggestion algorithms depending on the type of skill or knowledge when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the type of skill or knowledge when making a suggestion. For example, the suggestion unit applies a visual suggestion method using a skill map when making a suggestion regarding skills. Furthermore, the suggestion unit can apply detailed text-based explanations when making a suggestion regarding knowledge. Furthermore, the suggestion unit can apply a step-by-step guide when making a suggestion regarding career goals. This allows the suggestion unit to select the optimal suggestion method depending on the type of skill or knowledge.

[0107] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit prioritizes related suggestions based on suggestions that the user has previously given a high rating. The suggestion unit can also exclude related suggestions based on suggestions that the user has previously given a low rating. Furthermore, the suggestion unit can analyze the user's past suggestion results and make optimal suggestions. This allows the suggestion unit to improve the accuracy of the suggestion based on the user's past suggestion results.

[0108] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user feels anxious, the suggestion unit can provide detailed suggestions to reassure the user. If the user feels excited, the suggestion unit can also provide concise and to-the-point suggestions. If the user feels tired, the suggestion unit can also provide simple and easy-to-understand suggestions. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The suggestion unit can determine the order of proposals based on the time of submission of skills and knowledge when making a proposal. For example, the suggestion unit determines the priority of proposals based on the time of submission of skills and knowledge when making a proposal. For example, the suggestion unit gives priority to proposals related to the latest skills and knowledge. The suggestion unit can also make proposals related to skills and knowledge that were submitted earlier as needed. Furthermore, the suggestion unit can adjust the priority of proposals based on the time of submission. This allows the suggestion unit to adjust the priority of proposals based on the time of submission of skills and knowledge.

[0110] The suggestion unit can adjust the order of the proposals based on the relationships between skills and knowledge when making a proposal. For example, the suggestion unit adjusts the order of the proposals based on the relationships between skills and knowledge when making a proposal. For example, the suggestion unit prioritizes proposals related to skills and knowledge that are most relevant to the user's career goals. The suggestion unit can also prioritize proposals related to the user's current skills and knowledge. Furthermore, the suggestion unit can adjust the order of the proposals based on the relationships between skills and knowledge. This allows the suggestion unit to adjust the order of the proposals based on the relationships between skills and knowledge.

[0111] The suggestion unit can adjust the use of terminology in the suggestion according to the user's level of expertise when making a suggestion. For example, the suggestion unit adjusts the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. For example, if the user is a beginner, the suggestion unit can make a suggestion using easy-to-understand language while avoiding technical terminology. Furthermore, if the user is an intermediate user, the suggestion unit can make a suggestion using technical terminology appropriately. Furthermore, if the user is an advanced user, the suggestion unit can make a detailed suggestion using a lot of technical terminology. This allows the suggestion unit to use appropriate technical terminology according to the user's level of expertise.

[0112] The update unit can estimate the user's emotion and adjust the update method based on the estimated user's emotion. For example, the update unit can estimate the user's emotion and adjust the update method based on the estimated user's emotion. For example, if the user is feeling anxious, the update unit can provide an update method that gives a sense of security. Furthermore, if the user is excited, the update unit can provide a challenging update method. Furthermore, if the user is tired, the update unit can provide an update method that allows the user to relax. This allows the update unit to adjust the update method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The update unit can select the optimal update method by analyzing past changes in the user's skills and knowledge during an update. For example, the update unit selects the optimal update method by analyzing past changes in the user's skills and knowledge during an update. For example, the update unit selects the optimal update method based on past changes in the user's skills and knowledge. The update unit can also analyze past changes in the user's skills and knowledge and prioritize updating necessary skills and knowledge. Furthermore, the update unit can adjust the frequency of updates by taking into account past changes in the user's skills and knowledge. This allows the update unit to select the optimal update method based on past changes in the user's skills and knowledge.

[0114] The update unit can select an appropriate update means based on the user's current living situation during an update. For example, the update unit customizes the update means based on the user's current living situation during an update. For example, if the user is currently raising a child, the update unit provides an update method that can be combined with childcare. Furthermore, if the user's current living situation is remote work, the update unit can also provide an update method that is suitable for remote work. Furthermore, the update unit can select the optimal update means based on the user's current living situation. This allows the update unit to select the optimal update means according to the user's current living situation.

[0115] The update unit can improve the update method by reflecting user feedback during an update. For example, the update unit improves the update method by reflecting user feedback during an update. For example, the update unit improves the update method based on feedback provided by the user. The update unit can also analyze user feedback and select an optimal update method. Furthermore, the update unit can adjust the frequency and content of updates by reflecting user feedback. In this way, the update unit can optimize the update method based on user feedback.

[0116] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated user emotions. For example, the update unit can estimate the user's emotions and determine the priority of updates based on the estimated user emotions. For example, if the user is feeling anxious, the update unit can prioritize updating skills and knowledge that provide a sense of security. Also, if the user is excited, the update unit can prioritize updating challenging skills and knowledge. Furthermore, if the user is tired, the update unit can prioritize updating relaxing skills and knowledge. This allows the update unit to adjust the priority of updates according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The update unit can select an appropriate update method based on the user's geographic location information when updating. For example, the update unit selects the optimal update method by taking the user's geographic location information into consideration when updating. For example, if the user lives in a specific region, the update unit can prioritize updating skills and knowledge related to that region. Also, if the user works in a specific region, the update unit can prioritize updating skills and knowledge related to industry trends in that region. Furthermore, if the user is considering moving to a specific region, the update unit can prioritize updating skills and knowledge required in that region. This allows the update unit to select the optimal update method based on the user's geographic location information.

[0118] The update unit can analyze the user's social media activity at the time of updating and suggest an appropriate update means. For example, the update unit can analyze the user's social media activity at the time of updating and suggest an update means. For example, the update unit can suggest related skill and knowledge updates based on information about accounts the user follows on social media. The update unit can also analyze articles and posts the user has shared on social media and suggest related skill and knowledge updates. Furthermore, the update unit can suggest skill and knowledge updates that the user may be interested in based on the user's social media activity history. This allows the update unit to suggest the optimal update means based on the user's social media activity.

[0119] The update unit can customize an appropriate update method by reflecting the user's past feedback during an update. For example, the update unit customizes the update method by reflecting the user's past feedback during an update. For example, the update unit customizes the update method based on feedback provided by the user in the past. The update unit can also analyze the user's past feedback and select an optimal update method. Furthermore, the update unit can adjust the frequency and content of updates by reflecting the user's past feedback. This allows the update unit to optimize the update method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, suggestion unit, and update unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's skills, knowledge, and career goals using the reception device 38 or camera 42 of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and grasps the user's current skills and knowledge. The presentation unit, realized, for example, by the display 40A of the smart device 14, visually displays a skill map to indicate the skills and knowledge required by the user. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests a workflow. The update unit, realized, for example, by the control unit 46A of the smart device 14, provides a skill training program. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, suggestion unit, and update unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's skills, knowledge, and career goals using the microphone 238 and camera 42 of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and grasps the user's current skills and knowledge. The presentation unit, for example, visually displays a skill map using the display of the smart glasses 214 to indicate the skills and knowledge required by the user. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests a workflow. The update unit, realized, for example, by the control unit 46A of the smart glasses 214, provides a skill training program. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, suggestion unit, and update unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects the user's skills, knowledge, and career goals using the microphone 238 and camera 42 of the headset terminal 314, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and grasps the user's current skills and knowledge. The presentation unit, for example, visually displays a skill map using the display 343 of the headset terminal 314 to indicate the skills and knowledge required by the user. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests a workflow. The update unit, realized, for example, by the control unit 46A of the headset terminal 314, provides a skill training program. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, suggestion unit, and update unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's skills, knowledge, and career goals using the microphone 238 and camera 42 of the robot 414, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and grasps the user's current skills and knowledge. The presentation unit, for example, visually displays a skill map using the display of the robot 414, indicating the skills and knowledge required by the user. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests a workflow. The update unit, realized, for example, by the control unit 46A of the robot 414, provides a skill training program.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The career support system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, sleep patterns, amount of exercise, etc.) and provides it to the analysis unit. The analysis unit evaluates the user's stress level and fatigue level based on the health data and provides the information to the suggestion unit. The suggestion unit can propose appropriate work flows and skill improvement plans based on the user's health condition. For example, if the user shows a high stress level, the suggestion unit can propose a work flow that includes relaxing tasks and breaks. Furthermore, if the user is not getting enough sleep, the suggestion unit can adjust the skill improvement plan to allow the user to proceed at a comfortable pace. This allows the career support system to provide comprehensive support that takes the user's health condition into consideration.

[0122] The career support system can further include a lifestyle suggestion unit that suggests skill improvement based on the user's hobbies and lifestyle. The lifestyle suggestion unit collects the user's hobbies and daily activities and provides them to the analysis unit. The analysis unit identifies skills and knowledge suitable for the user's lifestyle based on this information and provides the information to the suggestion unit. The suggestion unit can suggest a skill improvement plan based on the user's hobbies and lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest a work flow that includes outdoor-related skills and knowledge. Also, if the user's hobby is cooking, the suggestion unit can suggest a career path that includes cooking-related skills and knowledge. This allows the career support system to support skill improvement that matches the user's lifestyle.

[0123] The career support system can further include a network utilization unit that utilizes the user's network to support skill development. The network utilization unit collects the user's social network and professional network and provides it to the analysis unit. Based on this network information, the analysis unit determines the skills and knowledge the user is connected to and provides the information to the suggestion unit. The suggestion unit can utilize the user's network to suggest resources and mentors for skill development. For example, if the user is connected to an experienced professional in a particular field, the suggestion unit can suggest that professional as a mentor. The online community in which the user participates can also be utilized to provide resources for skill development. In this way, the career support system can support skill development by making the most of the user's network.

[0124] The career support system may further include a motivation management unit that estimates the user's emotions and increases the motivation to improve skills based on the estimated emotions. The motivation management unit collects the user's emotion data and provides it to the analysis unit. The analysis unit evaluates the user's motivation level based on the emotion data and provides the information to the suggestion unit. The suggestion unit can adjust the skill improvement plan based on the user's motivation level. For example, if the user's motivation is low, the suggestion unit can suggest short-term goals or tasks that will give the user a sense of accomplishment. Also, if the user is highly motivated, the suggestion unit can suggest challenging tasks or long-term goals. This allows the career support system to increase the user's motivation to improve skills according to the user's emotions.

[0125] The career support system can further include a learning style adaptation unit that suggests a method for improving skills based on the user's learning style. The learning style adaptation unit collects the user's learning style (e.g., visual, auditory, experiential, etc.) and provides it to the analysis unit. The analysis unit identifies the optimal learning method for the user based on the learning style data and provides the information to the suggestion unit. The suggestion unit can suggest a skill improvement plan based on the user's learning style. For example, if the user has a visual learning style, the suggestion unit can suggest a learning method using visual materials or videos. Also, if the user has an experiential learning style, the suggestion unit can suggest practical training or workshops. This allows the career support system to support skill improvement that matches the user's learning style.

[0126] The career support system can further include a feedback management unit that estimates the user's emotions and provides feedback based on the estimated emotions. The feedback management unit collects user emotion data and provides it to the analysis unit. The analysis unit evaluates the user's reaction to the feedback based on the emotion data and provides the information to the suggestion unit. The suggestion unit can adjust the content and method of feedback based on the user's emotions. For example, if the user feels anxious about the feedback, the suggestion unit can provide positive feedback or constructive advice. Also, if the user is excited about the feedback, the suggestion unit can provide challenging feedback or suggest the next step. This allows the career support system to provide feedback according to the user's emotions.

[0127] The career support system can further include a time management unit that supports the user's time management. The time management unit collects the user's schedule and tasks and provides them to the analysis unit. The analysis unit evaluates how the user uses their time based on this information and provides the information to the suggestion unit. The suggestion unit can propose a skill improvement plan based on the user's time management. For example, if the user has a busy schedule, the suggestion unit can propose a skill improvement method that allows for effective learning in a short amount of time. Also, if the user has a relaxed schedule, the suggestion unit can propose a long-term skill improvement plan. This allows the career support system to support skill improvement in line with the user's time management.

[0128] The career support system can further include a career goal management unit that estimates the user's emotions and adjusts the career goals based on the estimated emotions. The career goal management unit collects the user's emotional data and provides it to the analysis unit. The analysis unit evaluates the user's motivation and satisfaction with their career goals based on the emotional data and provides the information to the suggestion unit. The suggestion unit can adjust the career goals based on the user's emotions. For example, if the user feels anxious about their career goals, the suggestion unit can suggest realistic and achievable goals. Also, if the user is excited about their career goals, the suggestion unit can suggest challenging and ambitious goals. This allows the career support system to set career goals according to the user's emotions.

[0129] The career support system can further include a learning history management unit that analyzes the user's learning history and manages the progress of skill improvement. The learning history management unit collects the user's past learning history and provides it to the analysis unit. The analysis unit evaluates the user's skill improvement progress based on the learning history data and provides the information to the suggestion unit. The suggestion unit can adjust the skill improvement plan based on the user's learning history. For example, if the user is taking a long time to master a particular skill, the suggestion unit can propose a learning plan that focuses on that skill. In addition, for skills that the user has already mastered, it can propose a new skill as the next step. This allows the career support system to manage the progress of skill improvement based on the user's learning history.

[0130] The career support system can further include a learning environment management unit that estimates the user's emotions and adjusts the learning environment based on the estimated emotions. The learning environment management unit collects the user's emotional data and provides it to the analysis unit. The analysis unit evaluates the user's satisfaction with the learning environment and stress level based on the emotional data, and provides the information to the suggestion unit. The suggestion unit can adjust the learning environment based on the user's emotions. For example, if the user feels stressed about the learning environment, the suggestion unit can suggest an environment that is relaxing or easy to concentrate in. Furthermore, if the user is satisfied with the learning environment, the suggestion unit can provide advice on maintaining that environment. This allows the career support system to provide an optimal learning environment according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects the user's skills, knowledge, and career goals, such as their work experience, educational background, and areas of interest, and stores the information entered by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit to understand the user's current skills and knowledge. For example, it uses data analysis techniques and machine learning algorithms to analyze the user's work experience, educational background, and areas of interest, and evaluates the user's skills and knowledge. Step 3: The presentation unit presents the necessary skills and knowledge based on the analysis results obtained by the analysis unit. For example, it may visually display a skill map to show the user the skills and knowledge they need. It may also provide detailed text-based explanations. Step 4: The suggestion unit proposes a workflow based on the skills and knowledge presented by the suggestion unit. For example, it can provide a step-by-step guide to help the user create a specific action plan. It can also show business procedures and process flows. Step 5: The update unit updates skills based on the workflow proposed by the proposal unit. For example, it provides skill training programs to help users acquire new skills. It can also provide knowledge updates and career development plans.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects a user's skills, knowledge, and career goals; an analysis unit that analyzes the information collected by the collection unit and grasps the user's current skills and knowledge; a presentation unit that presents necessary skills and knowledge based on the analysis results obtained by the analysis unit; a proposal unit that proposes a business flow based on the skills and knowledge presented by the presentation unit; and an update unit that updates skills based on the business flow proposed by the proposal unit.

2. The system according to claim 1 , wherein the collection unit collects the user's work experience, educational background, and areas of interest.

3. The analysis unit Understand the user's current skills and knowledge based on the collected information 2. The system of claim 1.

4. The presentation unit Present the necessary skills and knowledge based on the analysis results 2. The system of claim 1.

5. The proposal unit Propose workflows based on skills and knowledge presented 2. The system of claim 1.

6. The update unit Update your skills according to changes in your work and career path 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

8. The system according to claim 1 , wherein the collection unit analyzes the user's past work experience and educational background and selects an appropriate collection method.

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