System

Abstract

An object of the system according to the embodiment is to provide appropriate counseling for an occupation or worries of a user and to identify and overcome a skill gap.SOLUTION: A system includes a hearing part, a counseling part, a success map creation part, a skill gap identification part, and a training plan creation part. The hearing unit hears the occupation and concern of the user. The counseling unit provides interactive AI counseling based on the information heard by the hearing unit. The success map creation unit creates an occupation success map based on the counseling information provided by the counseling unit. The skill-gap identification unit identifies a skill gap based on the occupation success map created by the success map creation unit. The training plan generator generates a personalized training plan for overcoming the skill gap identified by the skill-gap identifier.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 technologies do not adequately provide appropriate counseling for users' occupations and concerns or identify skill gaps, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate counseling for the user's occupation and concerns, and to identify and overcome skill gaps. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, a counseling unit, a success map creation unit, a skill gap identification unit, and a training plan creation unit. The hearing unit hears about a user's occupation and concerns. The counseling unit provides interactive AI counseling based on the information heard by the hearing unit. The success map creation unit creates a career success map based on the counseling information provided by the counseling unit. The skill gap identification unit identifies skill gaps based on the career success map created by the success map creation unit. The training plan creation unit creates an individualized training plan to overcome the skill gaps identified by the skill gap identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate counseling for the user's occupation and concerns, and identify and overcome skill gaps. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 advancement support system according to an embodiment of the present invention is a system in which AI listens to a user's career and concerns, provides interactive AI counseling, creates a career success map, identifies skill gaps, and creates an individualized training plan, thereby providing comprehensive support for the user's career advancement.

[0029] A career advancement support system according to an embodiment includes a hearing unit, a counseling unit, a success map creation unit, a skill gap identification unit, and a training plan creation unit. The hearing unit hears about a user's occupation and worries. For example, the generation AI receives input from the user and hears about their current occupation, worries, anxieties, and concerns. The generation AI asks questions such as, "What is your current occupation?" and "What skills are you worried about?" to understand the user's situation. The counseling unit provides interactive AI counseling based on the information heard by the hearing unit. For example, the generation AI provides interactive AI counseling to address the user's anxieties and concerns. The generation AI provides appropriate advice in response to questions such as, "If I'm worried about my lack of skills, how should I deal with it?" The success map creation unit creates a career success map based on the counseling information provided by the counseling unit. For example, the generation AI creates a career success map based on the user's occupation and goals. The generation AI maps the skills and experience required for engineers and compares them with the user's current skill set. The skill gap identification unit identifies skill gaps based on the career success map created by the success map creation unit. For example, the generation AI identifies the user's skill gaps based on the career success map. If the user feels that they lack programming skills, the generation AI identifies the skill gaps and clearly indicates which skills are lacking. The training plan creation unit creates an individualized training plan to overcome the skill gaps identified by the skill gap identification unit. For example, the generation AI creates an individualized training plan to overcome the identified skill gaps. The generation AI suggests specific online courses or teaching materials to improve programming skills. This allows the career advancement support system according to the embodiment to comprehensively support the user's career advancement. For example, the user can acquire the necessary skills and achieve their goals with the support of the generation AI.Additionally, receiving psychological support can help reduce anxiety and concerns and help people approach their careers in a positive manner.

[0030] The hearing unit can automatically analyze the user's past work history and skill set, and generate specific questions based on that information during the hearing. For example, the hearing unit can automatically analyze the user's past work history and skill set, and the generation AI can generate specific questions based on that information. For example, it can analyze what projects the user has been involved in in the past and ask questions based on that experience. In this way, more detailed information can be collected by generating specific questions based on the user's work history and skill set.

[0031] The counseling unit can analyze past counseling history and provide ongoing support based on that history. For example, the counseling unit analyzes the user's past counseling history, and the generation AI provides ongoing support based on that information. For example, support tailored to the current situation is provided based on advice from past counseling sessions. This makes it possible to provide ongoing support based on past counseling history, thereby strengthening the user's psychological support.

[0032] When creating a career success map, the success map creation unit can refer to past success stories and failure stories to suggest the optimal career path. For example, the generation AI in the success map creation unit creates a career success map for the user by referencing past success stories. For example, the optimal career path for the user can be suggested based on the career paths of people who have been successful in the same occupation. This makes it possible to suggest the optimal career path for the user by referring to past success stories and failure stories.

[0033] When creating a career success map, the success map creation unit can suggest skills with future potential by taking into account the latest trends in the industry and technological trends. For example, the success map creation unit uses a generation AI to analyze the latest trends in the industry and create a career success map for the user. For example, the success map creation unit suggests skills with future potential to the user based on currently popular technologies and skills. This allows the system to suggest skills with future potential to the user by taking into account the latest trends in the industry and technological trends.

[0034] The skill gap identification unit can analyze past learning history and evaluation data to identify specific areas for improvement. For example, the skill gap identification unit analyzes the user's past learning history, and the generation AI identifies skill gaps based on that information. For example, specific areas for improvement are identified based on the courses the user has taken in the past and the qualifications they have obtained. This makes it possible to identify specific areas for improvement by analyzing the user's past learning history and evaluation data.

[0035] The skill gap identification unit can identify skills with potential for the future, taking into account the latest trends in the industry and technological trends. For example, the generative AI analyzes the latest trends in the industry and identifies the user's skill gaps. For example, it identifies the user's skills with potential for the future, based on technologies and skills that are currently attracting attention. This makes it possible to identify the user's skills with potential for the future, taking into account the latest trends in the industry and technological trends.

[0036] The training plan creation unit can analyze the user's learning style and past learning history to propose the optimal training method. For example, the training plan creation unit analyzes the user's learning style, and the generation AI proposes the optimal training method based on that information. For example, if the user prefers visual learning, visual learning materials will be proposed. In this way, the optimal training method can be proposed by analyzing the user's learning style and past learning history.

[0037] The training plan creation unit can incorporate the latest educational technologies and teaching materials to support effective learning. For example, the generative AI in the training plan creation unit incorporates the latest educational technologies to create a training plan for the user. For example, it suggests online courses and interactive teaching materials. This allows the incorporation of the latest educational technologies and teaching materials to support effective learning.

[0038] The training plan creation unit can propose a balanced study plan by taking into account the user's lifestyle and home environment. For example, the training plan creation unit analyzes the user's lifestyle, and the generation AI proposes a balanced study plan based on that information. For example, if the user places importance on a balance between home and work, a study plan that meets that need will be proposed. This makes it possible to propose a balanced study plan by taking into account the user's lifestyle and home environment.

[0039] The training plan creation unit can refer to the success stories of other users and incorporate best practices. For example, the training plan creation unit refers to the success stories of other users, and the generation AI creates a training plan for the user based on that information. For example, it makes suggestions based on how users with the same skill set improved their skills. This makes it possible to create a training plan that incorporates best practices by referring to the success stories of other users.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The career advancement support system can further include a health management unit that monitors the user's health condition. For example, the health management unit analyzes the user's sleep patterns and stress levels, and the generation AI uses that information to understand the user's health condition. This makes it possible to provide career advancement support tailored to the user's health condition. For example, if the user is under a lot of stress, it can suggest relaxation methods.

[0042] The career advancement support system can further include a hobby analysis unit that analyzes the user's hobbies and interests. For example, the hobby analysis unit analyzes the user's hobbies and interests, and the generation AI uses that information to suggest hobbies and interests related to career advancement. This makes it possible to provide career advancement support based on the user's hobbies and interests. For example, if the user is interested in programming, related projects and communities can be introduced.

[0043] The career advancement support system may further include a networking support unit that supports the user's networking activities. The networking support unit, for example, introduces relevant experts and communities based on the user's occupation or interests. This allows the user to network effectively and build connections that will be useful for career advancement. For example, if the user is an engineer, the networking support unit may introduce technical conferences and online forums.

[0044] The career advancement support system can further include a time management unit that supports the user's time management. For example, the time management unit analyzes the user's schedule, and the generation AI proposes an efficient time management method based on that information. This allows the user to effectively manage their time and focus on activities necessary for career advancement. For example, if the user is busy, a prioritized task management method can be proposed.

[0045] The career advancement support system can further include a self-assessment support unit that supports the user's self-assessment. For example, the self-assessment support unit analyzes the user's past achievements and feedback, and the generation AI supports the self-assessment based on that information. This allows the user to self-assess and understand their strengths and areas for improvement. For example, it can suggest a method for self-assessment based on feedback the user received in past projects.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The hearing section listens to the user's occupation and concerns. For example, the generation AI receives input from the user and listens to their current occupation, worries, anxieties, and concerns. The generation AI asks questions such as, "What is your current occupation?" and "What skills are you worried about?" to understand the user's situation. Step 2: The counseling department provides interactive AI counseling based on the information gathered by the hearing department. For example, the generation AI provides interactive AI counseling to address the user's anxieties and concerns. The generation AI provides appropriate advice in response to questions such as, "If I'm worried about my lack of skills, how should I deal with it?" Step 3: The success map creation unit creates a career success map based on the counseling information provided by the counseling unit. For example, the generation AI creates a career success map based on the user's occupation and goals. The generation AI maps the skills and experience required for engineers and compares them with the user's current skill set. Step 4: The skill gap identification unit identifies skill gaps based on the career success map created by the success map creation unit. For example, the generation AI identifies the user's skill gaps based on the career success map. If the user feels they lack programming skills, the generation AI identifies the skill gaps and indicates which skills are lacking. Step 5: The training plan generator generates a personalized training plan to address the skill gaps identified by the skill gap identifier. For example, the generative AI generates a personalized training plan to address the identified skill gaps. The generative AI may suggest specific online courses or materials to improve programming skills.

[0048] (Example 2) A career advancement support system according to an embodiment of the present invention is a system in which AI listens to a user's career and concerns, provides interactive AI counseling, creates a career success map, identifies skill gaps, and creates an individualized training plan, thereby providing comprehensive support for the user's career advancement.

[0049] A career advancement support system according to an embodiment includes a hearing unit, a counseling unit, a success map creation unit, a skill gap identification unit, and a training plan creation unit. The hearing unit hears about a user's occupation and worries. For example, the generation AI receives input from the user and hears about their current occupation, worries, anxieties, and concerns. The generation AI asks questions such as, "What is your current occupation?" and "What skills are you worried about?" to understand the user's situation. The counseling unit provides interactive AI counseling based on the information heard by the hearing unit. For example, the generation AI provides interactive AI counseling to address the user's anxieties and concerns. The generation AI provides appropriate advice in response to questions such as, "If I'm worried about my lack of skills, how should I deal with it?" The success map creation unit creates a career success map based on the counseling information provided by the counseling unit. For example, the generation AI creates a career success map based on the user's occupation and goals. The generation AI maps the skills and experience required for engineers and compares them with the user's current skill set. The skill gap identification unit identifies skill gaps based on the career success map created by the success map creation unit. For example, the generation AI identifies the user's skill gaps based on the career success map. If the user feels that they lack programming skills, the generation AI identifies the skill gaps and clearly indicates which skills are lacking. The training plan creation unit creates an individualized training plan to overcome the skill gaps identified by the skill gap identification unit. For example, the generation AI creates an individualized training plan to overcome the identified skill gaps. The generation AI suggests specific online courses or teaching materials to improve programming skills. This allows the career advancement support system according to the embodiment to comprehensively support the user's career advancement. For example, the user can acquire the necessary skills and achieve their goals with the support of the generation AI.Additionally, receiving psychological support can help reduce anxiety and concerns and help people approach their careers in a positive manner.

[0050] The hearing unit can automatically analyze the user's past work history and skill set, and generate specific questions based on that information during the hearing. For example, the hearing unit can automatically analyze the user's past work history and skill set, and the generation AI can generate specific questions based on that information. For example, it can analyze what projects the user has been involved in in the past and ask questions based on that experience. In this way, more detailed information can be collected by generating specific questions based on the user's work history and skill set.

[0051] The hearing unit analyzes the tone of voice and facial expressions to understand the user's emotional state and adjust the questions accordingly. For example, the hearing unit analyzes the user's tone of voice, and the generation AI adjusts the questions based on that information. For example, if the user is nervous, it will ask questions that will relax them. This allows for more effective hearing by adjusting the questions according to the user's emotional state.

[0052] The hearing unit can use the emotion estimation function to analyze the user's emotional state in real time and generate questions to elicit positive emotions. For example, the hearing unit uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI generates questions to elicit positive emotions. For example, if the user is feeling down, the hearing unit can ask questions about successful experiences. This elicits positive emotions from the user, enabling more effective hearing.

[0053] The counseling unit can analyze past counseling history and provide ongoing support based on that history. For example, the counseling unit analyzes the user's past counseling history, and the generation AI provides ongoing support based on that information. For example, support tailored to the current situation is provided based on advice from past counseling sessions. This makes it possible to provide ongoing support based on past counseling history, thereby strengthening the user's psychological support.

[0054] The counseling unit can analyze reactions during counseling in real time and provide encouragement and advice at the appropriate time. For example, the counseling unit analyzes the user's reactions during counseling in real time, and the generation AI provides encouragement and advice based on that information. For example, if the user is feeling down, the generation AI will offer words of encouragement. This increases the effectiveness of counseling by providing encouragement and advice at the appropriate time according to the user's reactions during counseling.

[0055] The counseling unit can use the emotion estimation function to analyze the user's emotional state and apply counseling techniques to elicit positive emotions. For example, the counseling unit uses the emotion estimation function to analyze the user's emotional state, and the generation AI applies counseling techniques to elicit positive emotions. For example, if the user is feeling down, it will provide positive topics to talk about. This will elicit positive emotions from the user, thereby increasing the effectiveness of the counseling.

[0056] When creating a career success map, the success map creation unit can refer to past success stories and failure stories to suggest the optimal career path. For example, the generation AI in the success map creation unit creates a career success map for the user by referencing past success stories. For example, the optimal career path for the user can be suggested based on the career paths of people who have been successful in the same occupation. This makes it possible to suggest the optimal career path for the user by referring to past success stories and failure stories.

[0057] When creating a career success map, the success map creation unit can suggest skills with future potential by taking into account the latest trends in the industry and technological trends. For example, the success map creation unit uses a generation AI to analyze the latest trends in the industry and create a career success map for the user. For example, the success map creation unit suggests skills with future potential to the user based on currently popular technologies and skills. This allows the system to suggest skills with future potential to the user by taking into account the latest trends in the industry and technological trends.

[0058] The success map creation unit can use the emotion estimation function to identify the career path that the user finds most motivating and create a success map based on that path. The success map creation unit, for example, can use the emotion estimation function to identify the career path that the user finds most motivating and create a success map based on that path. For example, it can identify a field that the user can work on with passion. This allows for the creation of an effective success map by identifying the career path that the user finds most motivating.

[0059] The skill gap identification unit can analyze past learning history and evaluation data to identify specific areas for improvement. For example, the skill gap identification unit analyzes the user's past learning history, and the generation AI identifies skill gaps based on that information. For example, specific areas for improvement are identified based on the courses the user has taken in the past and the qualifications they have obtained. This makes it possible to identify specific areas for improvement by analyzing the user's past learning history and evaluation data.

[0060] The skill gap identification unit can identify skills with potential for the future, taking into account the latest trends in the industry and technological trends. For example, the generative AI analyzes the latest trends in the industry and identifies the user's skill gaps. For example, it identifies the user's skills with potential for the future, based on technologies and skills that are currently attracting attention. This makes it possible to identify the user's skills with potential for the future, taking into account the latest trends in the industry and technological trends.

[0061] The skill gap identification unit can use the emotion estimation function to identify the skill for which the user feels most motivated and identify a gap based on that skill. The skill gap identification unit, for example, uses the emotion estimation function to identify the skill for which the user feels most motivated and identify a gap based on that skill. For example, it identifies a skill that the user can work on with passion. This allows for effective identification of skill gaps by identifying the skill for which the user feels most motivated.

[0062] The training plan creation unit can analyze the user's learning style and past learning history to propose the optimal training method. For example, the training plan creation unit analyzes the user's learning style, and the generation AI proposes the optimal training method based on that information. For example, if the user prefers visual learning, visual learning materials will be proposed. In this way, the optimal training method can be proposed by analyzing the user's learning style and past learning history.

[0063] The training plan creation unit can incorporate the latest educational technologies and teaching materials to support effective learning. For example, the generative AI in the training plan creation unit incorporates the latest educational technologies to create a training plan for the user. For example, it suggests online courses and interactive teaching materials. This allows the incorporation of the latest educational technologies and teaching materials to support effective learning.

[0064] The training plan creation unit can use the emotion estimation function to identify the training method that most motivates the user and create a plan based on that method. The training plan creation unit, for example, can use the emotion estimation function to identify the training method that most motivates the user and create a plan based on that method. For example, if the user prefers gamified learning, the unit can suggest training that incorporates gamification. This allows the unit to create an effective training plan by identifying the training method that most motivates the user.

[0065] The training plan creation unit can propose a balanced study plan by taking into account the user's lifestyle and home environment. For example, the training plan creation unit analyzes the user's lifestyle, and the generation AI proposes a balanced study plan based on that information. For example, if the user places importance on a balance between home and work, a study plan that meets that need will be proposed. This makes it possible to propose a balanced study plan by taking into account the user's lifestyle and home environment.

[0066] The training plan creation unit can refer to the success stories of other users and incorporate best practices. For example, the training plan creation unit refers to the success stories of other users, and the generation AI creates a training plan for the user based on that information. For example, it makes suggestions based on how users with the same skill set improved their skills. This makes it possible to create a training plan that incorporates best practices by referring to the success stories of other users.

[0067] The training plan creation unit can use the emotion estimation function to identify the environment and timing in which the user can be most relaxed, and create a training plan for that situation. The training plan creation unit, for example, uses the emotion estimation function to identify the environment in which the user can be most relaxed, and create a training plan for that situation. For example, a training plan is created for the time period when the user is relaxing at home. In this way, an effective training plan can be created by identifying the environment and timing in which the user can be most relaxed.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The career advancement support system can further include a health management unit that monitors the user's health condition. For example, the health management unit analyzes the user's sleep patterns and stress levels, and the generation AI uses that information to understand the user's health condition. This makes it possible to provide career advancement support tailored to the user's health condition. For example, if the user is under a lot of stress, it can suggest relaxation methods.

[0070] The career advancement support system can further include a hobby analysis unit that analyzes the user's hobbies and interests. For example, the hobby analysis unit analyzes the user's hobbies and interests, and the generation AI uses that information to suggest hobbies and interests related to career advancement. This makes it possible to provide career advancement support based on the user's hobbies and interests. For example, if the user is interested in programming, related projects and communities can be introduced.

[0071] The career advancement support system may further include a networking support unit that supports the user's networking activities. The networking support unit, for example, introduces relevant experts and communities based on the user's occupation or interests. This allows the user to network effectively and build connections that will be useful for career advancement. For example, if the user is an engineer, the networking support unit may introduce technical conferences and online forums.

[0072] The career advancement support system can further include a time management unit that supports the user's time management. For example, the time management unit analyzes the user's schedule, and the generation AI proposes an efficient time management method based on that information. This allows the user to effectively manage their time and focus on activities necessary for career advancement. For example, if the user is busy, a prioritized task management method can be proposed.

[0073] The career advancement support system can further include a self-assessment support unit that supports the user's self-assessment. For example, the self-assessment support unit analyzes the user's past achievements and feedback, and the generation AI supports the self-assessment based on that information. This allows the user to self-assess and understand their strengths and areas for improvement. For example, it can suggest a method for self-assessment based on feedback the user received in past projects.

[0074] The career advancement support system may further include a feedback providing unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user feels anxious, the feedback providing unit provides encouraging words. By providing appropriate feedback according to the user's emotions, psychological support can be enhanced. For example, if the user is losing confidence, the system can encourage the user to reflect on past successes.

[0075] The career advancement support system can further include a progress adjustment unit that estimates the user's emotions and adjusts the learning progress based on the estimated emotions. For example, if the user is tired, the progress adjustment unit suggests slowing down the pace of learning. This allows for effective learning support by adjusting the learning progress according to the user's emotions. For example, if the user is feeling motivated, it can provide challenging tasks.

[0076] The career advancement support system may further include a break suggestion unit that estimates the user's emotions and suggests appropriate break times based on the estimated emotions. For example, the break suggestion unit suggests taking a break if the user is losing concentration. This makes it possible to support effective learning and work by suggesting appropriate break times according to the user's emotions. For example, if the user is feeling stressed, it can suggest relaxation methods.

[0077] The career advancement support system may further include an advice providing unit that estimates the user's emotions and provides appropriate career advice based on the estimated emotions. For example, if the user feels anxious, the advice providing unit provides advice that gives the user a sense of security. This allows for providing appropriate career advice according to the user's emotions, thereby enhancing psychological support. For example, if the user lacks confidence, the system may encourage the user to reflect on past successes.

[0078] The career advancement support system can further include a motivation improvement unit that estimates the user's emotions and suggests appropriate motivation improvement measures based on the estimated emotions. For example, if the user is losing motivation, the motivation improvement unit provides words of encouragement or success stories. This allows for effective career advancement support by suggesting appropriate motivation improvement measures according to the user's emotions. For example, if the user is feeling anxious about their goals, specific steps can be shown.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The hearing section listens to the user's occupation and concerns. For example, the generation AI receives input from the user and listens to their current occupation, worries, anxieties, and concerns. The generation AI asks questions such as, "What is your current occupation?" and "What skills are you worried about?" to understand the user's situation. Step 2: The counseling department provides interactive AI counseling based on the information gathered by the hearing department. For example, the generation AI provides interactive AI counseling to address the user's anxieties and concerns. The generation AI provides appropriate advice in response to questions such as, "If I'm worried about my lack of skills, how should I deal with it?" Step 3: The success map creation unit creates a career success map based on the counseling information provided by the counseling unit. For example, the generation AI creates a career success map based on the user's occupation and goals. The generation AI maps the skills and experience required for engineers and compares them with the user's current skill set. Step 4: The skill gap identification unit identifies skill gaps based on the career success map created by the success map creation unit. For example, the generation AI identifies the user's skill gaps based on the career success map. If the user feels they lack programming skills, the generation AI identifies the skill gaps and indicates which skills are lacking. Step 5: The training plan generator generates a personalized training plan to address the skill gaps identified by the skill gap identifier. For example, the generative AI generates a personalized training plan to address the identified skill gaps. The generative AI may suggest specific online courses or materials to improve programming skills.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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. [Explanation of symbols]

[0148] 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 hearing department that listens to users' occupations and concerns, a counseling unit that provides interactive AI counseling based on the information heard by the hearing unit; a success map creation unit that creates a career success map based on the counseling information provided by the counseling unit; a skill gap identification unit that identifies a skill gap based on the occupational success map created by the success map creation unit; a training plan creation unit that creates an individualized training plan to overcome the skill gap identified by the skill gap identification unit. A system characterized by:

2. The hearing section Analyze the user's tone of voice and facial expressions to understand their emotional state and adjust the questions asked 2. The system of claim 1.

3. The counseling department Analyzing the user's past counseling history and providing ongoing support based on that history 2. The system of claim 1.

4. The success map creation unit When creating a career success map for the user, the system refers to past successes and failures and suggests the best career path.

2. The system of claim 1.

5. The skill gap identification unit Analyze the user's past learning history and evaluation data to identify specific areas for improvement 2. The system of claim 1.

6. The training plan creation unit Analyze the user's learning style and past learning history to suggest the most suitable training method 2. The system of claim 1.

7. The counseling department The user's reaction during counseling is analyzed in real time, and the encouragement and advice are provided at an appropriate timing.

2. The system of claim 1.

8. The success map creation unit Using emotion estimation, identify the career path that motivates the user most and create a success map based on that path.

2. The system of claim 1.

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