System

Abstract

An object of the system according to the embodiment is to support growth by providing an appropriate role model or plan for a business person to achieve his / her career goal.SOLUTION: A system includes a question reception unit, an analysis unit, a proposal unit, an information provision unit, a matching unit, and an analysis unit. The question reception unit receives a question related to a self-image, a short-term goal, a medium-term goal, and a long-term goal that the user wants to have, and a life carrier. The analysis unit analyzes the information received by the question reception unit. The suggestion unit suggests a role model optimal for the user or a goal for improving career on the basis of the information analyzed by the analysis unit. The information providing unit provides a plan for qualification acquisition or learning based on the goal proposed by the proposal unit. The matching unit matches an appropriate mentor based on the plan provided by the information providing unit. An analysis part analyzes the macro data, business environment and market trend of the employee.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] With conventional technology, it was difficult for business people to find appropriate role models and plans to achieve their career goals, and there was a problem of insufficient support for growth.

[0005] The system according to the embodiment aims to support the growth of business people by providing them with appropriate role models and plans to help them achieve their career goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an analysis unit, a proposal unit, an information providing unit, a matching unit, and an analysis unit. The question receiving unit receives questions about the user's desired self-image, short-term, medium-term, and long-term goals, and life career. The analysis unit analyzes the information received by the question receiving unit. The proposal unit proposes optimal role models and career advancement goals for the user based on the information analyzed by the analysis unit. The information providing unit provides qualification acquisition and learning plans based on the goals proposed by the proposal unit. The matching unit matches appropriate mentors based on the plans provided by the information providing unit. The analysis unit analyzes employee macro data, the business environment, and market trends. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate role models and plans for business people to achieve their career goals, and can support their growth. [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) The AI ​​system according to an embodiment of the present invention is a system that supports reskilling and growth by answering questions about the self-image that businesspeople want to become, their short-, medium-, and long-term goals, and their life careers, and by providing reference role models and goal setting for career advancement, planning for qualification acquisition and learning, advice, push-type provision of useful information, matching with mentors, etc. In this way, the AI ​​system efficiently supports businesspeople's personal growth, helping them get closer to the self-image they want to become.

[0029] The AI ​​system according to the embodiment includes a question receiving unit, an analysis unit, a proposal unit, an information providing unit, a matching unit, and an analysis unit. The question receiving unit accepts questions about the user's desired self-image, short-term, medium-term, and long-term goals, and life career. For example, a user can input a goal such as "I want to be a manager in five years." The question receiving unit also allows the user to enter goals and questions in free-form. The analysis unit analyzes the information received by the question receiving unit. For example, the analysis unit may analyze the user's goals and questions using data mining technology. The analysis unit may also extract patterns for achieving the user's goals using statistical analysis technology. The proposal unit proposes optimal role models and career advancement goals for the user based on the information analyzed by the analysis unit. For example, the proposal unit may propose industry leaders or successful entrepreneurs as role models. The proposal unit may also propose specific career advancement steps based on the user's goals. The information providing unit provides qualification acquisition and learning plans based on the goals proposed by the proposal unit. For example, the information providing unit may suggest online courses or methods for obtaining professional qualifications. The information providing unit may also suggest optimal learning methods based on the user's learning style. The matching unit may match the user with an appropriate mentor based on the plan provided by the information providing unit. For example, the matching unit may suggest a mentor with marketing expertise. The matching unit may also suggest mentors in different industries depending on the user's goals. The analysis unit may analyze employee macro data, the business environment, and market trends. For example, the analysis unit may provide the latest trends and required skill sets in a specific industry. The analysis unit may also predict changes in the business environment and provide appropriate career advice to the user. This allows the AI ​​system according to the embodiment to efficiently support the user's career advancement and reskilling. For example, the user can clarify specific steps toward their goals and efficiently acquire the necessary skills and qualifications. Furthermore, by constantly keeping up with the latest information on industry trends and business skills and receiving expert advice, a more effective career plan can be realized.

[0030] The analysis unit can analyze the user's past behavioral history and performance data, extract success patterns, and reflect them in goal setting. For example, the analysis unit analyzes the user's past project history and performance data to extract commonalities between successful projects. For example, if specific skills or team composition contribute to success, goal setting is based on that information. The analysis unit also analyzes the user's past behavioral history to identify successful behavior patterns. For example, if actions at specific times or the use of specific resources lead to success, that pattern is reflected in goal setting. The analysis unit also analyzes the user's past goals and results based on performance data to extract success patterns. For example, it identifies the steps and resources necessary to achieve a specific goal and sets new goals based on those. This makes it possible to set goals based on the user's past success patterns.

[0031] The analysis unit can suggest an optimal career path based on the user's personality diagnosis results. For example, the analysis unit analyzes the user's personality diagnosis results and suggests a career path based on their personality traits. For example, it suggests a management position for a user with strong leadership skills, and a design position for a creative user. The analysis unit also identifies the job types and environments in which the user is least likely to feel stressed based on the personality diagnosis results, and suggests a career path based on that information. For example, it suggests a job that involves a lot of remote work or individual work for an introverted user. The analysis unit also uses the personality diagnosis results to identify the job types and work content that the user finds most motivating, and suggests a career path based on that information. For example, it suggests new business development for a user who enjoys challenges. This makes it possible to suggest career paths based on the user's personality.

[0032] The suggestion unit proposes a career path that takes into account the user's hobbies and interests, enabling the user to balance work and private life. The suggestion unit, for example, analyzes the user's hobbies and interests and proposes a career path based on them. For example, it proposes jobs that involve a lot of fieldwork to a user who likes the outdoors. The suggestion unit also proposes a career path that takes into account the user's hobbies and interests, thereby balancing work and private life. For example, it proposes jobs and work content that can make use of hobbies. The suggestion unit also proposes specific steps to balance work and private life based on the user's hobbies and interests. For example, it proposes jobs that can make use of skills related to hobbies. This makes it possible to propose a career path that takes into account the user's hobbies and interests.

[0033] The suggestion unit can introduce role models from different industries and provide users with new perspectives. For example, the suggestion unit can introduce successful people and role models from different industries and provide users with new perspectives. For example, it can introduce leadership examples from different industries. The suggestion unit can also suggest new career paths to users based on role models from different industries. For example, it can propose career plans that refer to successful examples from different industries. The suggestion unit can also introduce role models from different industries and provide users with opportunities to learn new skills and knowledge. For example, it can promote interaction with experts from different industries. This makes it possible to provide users with new perspectives.

[0034] The information providing unit can analyze the user's learning style and suggest the optimal learning method. For example, the information providing unit analyzes the user's past learning history and performance data and suggests the optimal learning method. For example, visual learning materials are suggested for a user for whom visual learning is effective. The information providing unit also diagnoses the learning style and identifies the optimal learning method for the user. For example, audio learning materials are suggested for a user for whom auditory learning is effective. The information providing unit also builds a system that suggests the optimal learning method based on the user's learning style. For example, a hands-on workshop is suggested for a user for whom practical learning is effective. This makes it possible to suggest the optimal learning method based on the user's learning style.

[0035] The information providing unit can predict the learning progress based on the user's past learning history and provide learning resources at the appropriate time. For example, the information providing unit analyzes the user's past learning history and develops an algorithm to predict learning progress. For example, it suggests the next learning step based on past learning data. The information providing unit also builds a system that provides learning resources at the appropriate time based on the learning history. For example, it suggests new learning materials or courses according to the learning progress. The information providing unit also analyzes the past learning history and predicts learning progress to provide the user with optimal learning resources. For example, it provides feedback and support according to the learning progress. This makes it possible to provide learning resources at the appropriate time according to the learning progress.

[0036] The information providing unit can recommend acquiring qualifications in different fields, thereby diversifying the user's skill set. For example, the information providing unit recommends acquiring qualifications in different fields based on the user's goals and interests. For example, it might suggest marketing qualifications to a user in the IT field. The information providing unit also diversifies the user's skill set by recommending acquiring qualifications in different fields. For example, it might suggest business skill qualifications to an engineer. The information providing unit also builds a system that recommends acquiring qualifications in different fields according to the user's career path. For example, it might suggest leadership qualifications to a user aiming for a managerial position. This makes it possible to diversify the user's skill set.

[0037] The information providing unit provides opportunities for group learning, allowing users to advance their studies in cooperation with other users. The information providing unit, for example, provides opportunities for group learning and builds a system in which users advance their studies in cooperation with other users. For example, it suggests online study groups or study circles. The information providing unit also provides a group learning platform to promote cooperation with other users. For example, it sets up joint projects or discussion forums. The information providing unit also provides opportunities for group learning, providing a place where users can share knowledge and skills with other users. For example, it recommends peer review or group work. This provides group learning opportunities that promote cooperation with other users.

[0038] The information providing unit can analyze a user's past browsing history and provide information that is likely to be of interest to the user on a priority basis. The information providing unit, for example, analyzes a user's past browsing history and builds a system that provides information that is likely to be of interest to the user on a priority basis. For example, it suggests information related to articles or videos that the user has viewed in the past. The information providing unit also automatically filters information that is likely to be of interest to the user based on the browsing history and provides it on a priority basis. For example, it notifies the user of information related to specific topics or keywords. The information providing unit also analyzes a user's past browsing history and develops an algorithm that provides information that is likely to be of interest to the user on a priority basis. For example, it analyzes browsing history patterns and provides highly relevant information. This makes it possible to provide information that is likely to be of interest to the user on a priority basis.

[0039] The information providing unit can provide information at the optimal timing based on the user's schedule. The information providing unit, for example, analyzes the user's schedule and builds a system that provides information at the optimal timing. For example, information is notified in accordance with meetings or break times. The information providing unit also identifies times when the user is most likely to receive information based on schedule data and provides the information at those times. For example, information is notified during commuting time or lunch breaks. The information providing unit also develops an algorithm that takes the user's schedule into consideration and provides information at the optimal timing. For example, information is provided in accordance with free time in the schedule. This makes it possible to provide information at the optimal timing according to the user's schedule.

[0040] The information providing unit can provide information in different media formats to deepen the user's understanding. For example, the information providing unit builds a system that provides information in different media formats to deepen the user's understanding. For example, the information providing unit provides information in formats such as text, video, and podcast. The information providing unit also provides information in different media formats according to the user's preferences. For example, if visual information is preferred, it provides video, and if auditory information is preferred, it provides podcast. The information providing unit also deepens the user's understanding by providing information in different media formats. For example, it provides the same information in both text and video, allowing the user to select. This makes it possible to deepen the user's understanding by providing information in different media formats.

[0041] The information providing unit can analyze information shared within a user's network and provide highly relevant information. The information providing unit, for example, analyzes information shared within a user's network and builds a system that provides highly relevant information. For example, related information is provided based on information shared by colleagues and friends. The information providing unit also analyzes information within the network, identifies information that is highly relevant to the user, and provides that information. For example, information related to the same project is provided preferentially. The information providing unit also analyzes information shared within a user's network and develops an algorithm that provides highly relevant information. For example, related information is provided based on trend information within the network. This makes it possible to analyze information shared within a user's network and provide highly relevant information.

[0042] The matching unit can analyze a user's past feedback and select the most suitable mentor. For example, the matching unit analyzes a user's past feedback and builds a system for selecting the most suitable mentor. For example, it selects a mentor based on evaluations of past mentoring sessions. The matching unit also identifies the most suitable mentor for the user based on the feedback data and suggests that mentor. For example, it prioritizes the selection of mentors who have received a lot of positive feedback. The matching unit also analyzes a user's past feedback and develops an algorithm for selecting the most suitable mentor. For example, it analyzes the content of the feedback and identifies an appropriate mentor. This makes it possible to select the most suitable mentor based on a user's past feedback.

[0043] The matching unit can develop an algorithm that matches the mentor's expertise with the user's goals in detail. For example, the matching unit develops an algorithm that matches the mentor's expertise with the user's goals in detail. For example, it selects a mentor with specific skills and experience to match the user's goals. The matching unit also builds an algorithm that identifies the most suitable mentor based on the user's goals. For example, it suggests a mentor with expertise related to the user's goals. The matching unit also develops a system that matches the mentor's expertise with the user's goals in detail. For example, it evaluates the mentor's skill set according to the user's goals and selects the most suitable mentor. This makes it possible to match the mentor's expertise with the user's goals in detail.

[0044] The matching unit can introduce mentors from different industries and provide users with new perspectives. For example, the matching unit builds a system that introduces mentors from different industries and provides users with new perspectives. For example, it suggests experts from different industries as mentors. The matching unit also suggests new career paths to users based on mentors from different industries. For example, it suggests career plans that refer to success stories from different industries. The matching unit also introduces mentors from different industries and provides users with opportunities to learn new skills and knowledge. For example, it promotes interaction with experts from different industries. This makes it possible to provide users with new perspectives by introducing them to mentors from different industries.

[0045] The matching unit provides group mentoring opportunities, allowing users to receive feedback from multiple mentors. The matching unit, for example, builds a system that provides group mentoring opportunities and allows users to receive feedback from multiple mentors. For example, it holds online mentoring sessions. The matching unit also provides users with a multifaceted perspective by receiving feedback from multiple mentors. For example, users can receive advice from mentors with different fields of expertise. The matching unit also provides group mentoring opportunities, providing a forum for users to interact with multiple mentors. For example, it sets up mentoring workshops and discussion forums. This makes it possible to receive feedback from multiple mentors and provide users with a multifaceted perspective.

[0046] The analysis unit can analyze market trends related to the user's career path in real time and provide optimal advice. The analysis unit, for example, builds a system that analyzes market trends related to the user's career path in real time and provides optimal advice. For example, career advice is provided based on the latest trends in a specific industry. The analysis unit also suggests the optimal career path for the user based on market trend data. For example, it suggests industries and job types that are expected to grow. The analysis unit also develops an algorithm that analyzes market trends related to the user's career path in real time and provides optimal advice. For example, career advice is provided based on market demand and trends. This makes it possible to analyze market trends related to the user's career path in real time and provide optimal advice.

[0047] The analysis unit can predict changes in the business environment and provide appropriate career advice to users. The analysis unit, for example, builds a system that predicts changes in the business environment and provides appropriate career advice to users. For example, it provides career advice based on economic indicators and industry trends. The analysis unit also develops an algorithm that predicts changes in the business environment and provides career advice to users based on the results. For example, it provides advice that takes technological innovation and market fluctuations into consideration. The analysis unit also analyzes changes in the business environment in real time and provides appropriate career advice to users. For example, it proposes career plans based on new business opportunities and risks. This makes it possible to predict changes in the business environment and provide appropriate career advice to users.

[0048] The analysis unit can compare market trends in different regions and provide users with a global perspective. The analysis unit, for example, builds a system that compares market trends in different regions and provides users with a global perspective. For example, it performs comparative analysis based on regional economic indicators and industry trends. The analysis unit also analyzes market trends in different regions and provides users with global career advice. For example, it proposes career plans based on growth opportunities and risks in overseas markets. The analysis unit also develops algorithms that compare market trends in different regions and provides users with a global perspective based on the results. For example, it compares business opportunities and competitive situations in each region. This makes it possible to compare market trends in different regions and provide users with a global perspective.

[0049] The analysis unit can analyze trends by industry in detail and provide specific career advice to users. The analysis unit, for example, builds a system that analyzes trends by industry in detail and provides specific career advice to users. For example, it proposes a career plan based on growth forecasts and skill demand for a specific industry. The analysis unit also analyzes trends by industry and provides specific career advice to users. For example, it proposes a career plan based on the latest trends and technological innovations in the industry. The analysis unit also develops an algorithm that analyzes trends by industry in detail and provides specific career advice to users based on the results. For example, it proposes a career plan based on the competitive situation and market needs in the industry. This makes it possible to analyze trends by industry in detail and provide specific career advice to users.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The suggestion unit proposes a career path that takes into account the user's hobbies and interests, enabling the user to balance work and private life. The suggestion unit, for example, analyzes the user's hobbies and interests and proposes a career path based on them. For example, it proposes jobs that involve a lot of fieldwork to a user who likes the outdoors. The suggestion unit also proposes a career path that takes into account the user's hobbies and interests, thereby balancing work and private life. For example, it proposes jobs and work content that can make use of hobbies. The suggestion unit also proposes specific steps to balance work and private life based on the user's hobbies and interests. For example, it proposes jobs that can make use of skills related to hobbies. This makes it possible to propose a career path that takes into account the user's hobbies and interests.

[0052] The suggestion unit can introduce role models from different industries and provide users with new perspectives. For example, the suggestion unit can introduce successful people and role models from different industries and provide users with new perspectives. For example, it can introduce leadership examples from different industries. The suggestion unit can also suggest new career paths to users based on role models from different industries. For example, it can propose career plans that refer to successful examples from different industries. The suggestion unit can also introduce role models from different industries and provide users with opportunities to learn new skills and knowledge. For example, it can promote interaction with experts from different industries. This makes it possible to provide users with new perspectives.

[0053] The information providing unit can analyze the user's learning style and suggest the optimal learning method. For example, the information providing unit analyzes the user's past learning history and performance data and suggests the optimal learning method. For example, visual learning materials are suggested for a user for whom visual learning is effective. The information providing unit also diagnoses the learning style and identifies the optimal learning method for the user. For example, audio learning materials are suggested for a user for whom auditory learning is effective. The information providing unit also builds a system that suggests the optimal learning method based on the user's learning style. For example, a hands-on workshop is suggested for a user for whom practical learning is effective. This makes it possible to suggest the optimal learning method based on the user's learning style.

[0054] The information providing unit can predict the learning progress based on the user's past learning history and provide learning resources at the appropriate time. For example, the information providing unit analyzes the user's past learning history and develops an algorithm to predict learning progress. For example, it suggests the next learning step based on past learning data. The information providing unit also builds a system that provides learning resources at the appropriate time based on the learning history. For example, it suggests new learning materials or courses according to the learning progress. The information providing unit also analyzes the past learning history and predicts learning progress to provide the user with optimal learning resources. For example, it provides feedback and support according to the learning progress. This makes it possible to provide learning resources at the appropriate time according to the learning progress.

[0055] The information providing unit can recommend acquiring qualifications in different fields, thereby diversifying the user's skill set. For example, the information providing unit recommends acquiring qualifications in different fields based on the user's goals and interests. For example, it might suggest marketing qualifications to a user in the IT field. The information providing unit also diversifies the user's skill set by recommending acquiring qualifications in different fields. For example, it might suggest business skill qualifications to an engineer. The information providing unit also builds a system that recommends acquiring qualifications in different fields according to the user's career path. For example, it might suggest leadership qualifications to a user aiming for a managerial position. This makes it possible to diversify the user's skill set.

[0056] The information providing unit provides opportunities for group learning, allowing users to advance their studies in cooperation with other users. The information providing unit, for example, provides opportunities for group learning and builds a system in which users advance their studies in cooperation with other users. For example, it suggests online study groups or study circles. The information providing unit also provides a group learning platform to promote cooperation with other users. For example, it sets up joint projects or discussion forums. The information providing unit also provides opportunities for group learning, providing a place where users can share knowledge and skills with other users. For example, it recommends peer review or group work. This provides group learning opportunities that promote cooperation with other users.

[0057] The information providing unit can analyze a user's past browsing history and provide information that is likely to be of interest to the user on a priority basis. The information providing unit, for example, analyzes a user's past browsing history and builds a system that provides information that is likely to be of interest to the user on a priority basis. For example, it suggests information related to articles or videos that the user has viewed in the past. The information providing unit also automatically filters information that is likely to be of interest to the user based on the browsing history and provides it on a priority basis. For example, it notifies the user of information related to specific topics or keywords. The information providing unit also analyzes a user's past browsing history and develops an algorithm that provides information that is likely to be of interest to the user on a priority basis. For example, it analyzes browsing history patterns and provides highly relevant information. This makes it possible to provide information that is likely to be of interest to the user on a priority basis.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The question receiving unit accepts questions about the user's desired self-image, short-term, medium-term, and long-term goals, and life career. For example, a user can input a goal such as "I want to become a manager in five years." The question receiving unit also allows users to enter goals and questions in free-form. Step 2: The analysis unit analyzes the information received by the question receiving unit. For example, the analysis unit may use data mining techniques to analyze the user's goals and questions. The analysis unit may also use statistical analysis techniques to extract patterns for achieving the user's goals. Step 3: The suggestion unit suggests the user the most suitable role model and career advancement goals based on the information analyzed by the analysis unit. For example, the suggestion unit may suggest industry leaders or successful entrepreneurs as role models. The suggestion unit may also suggest specific career advancement steps based on the user's goals. Step 4: The information provider provides a qualification or learning plan based on the goals proposed by the suggestion provider. For example, the information provider may suggest online courses or ways to obtain professional qualifications. The information provider may also suggest optimal learning methods based on the user's learning style. Step 5: The matching unit matches the user with an appropriate mentor based on the plan provided by the information providing unit. For example, the matching unit may suggest a mentor with expertise in marketing. The matching unit may also suggest mentors in different industries depending on the user's goals. Step 6: The analytics department analyzes employee macro data, the business environment, and market trends. For example, the analytics department can provide the latest trends and required skill sets for a specific industry. The analytics department can also predict changes in the business environment and provide appropriate career advice to users.

[0060] (Example 2) The AI ​​system according to an embodiment of the present invention is a system that supports reskilling and growth by answering questions about the self-image that businesspeople want to become, their short-, medium-, and long-term goals, and their life careers, and by providing reference role models and goal setting for career advancement, planning for qualification acquisition and learning, advice, push-type provision of useful information, matching with mentors, etc. In this way, the AI ​​system efficiently supports businesspeople's personal growth, helping them get closer to the self-image they want to become.

[0061] The AI ​​system according to the embodiment includes a question receiving unit, an analysis unit, a proposal unit, an information providing unit, a matching unit, and an analysis unit. The question receiving unit accepts questions about the user's desired self-image, short-term, medium-term, and long-term goals, and life career. For example, a user can input a goal such as "I want to be a manager in five years." The question receiving unit also allows the user to enter goals and questions in free-form. The analysis unit analyzes the information received by the question receiving unit. For example, the analysis unit may analyze the user's goals and questions using data mining technology. The analysis unit may also extract patterns for achieving the user's goals using statistical analysis technology. The proposal unit proposes optimal role models and career advancement goals for the user based on the information analyzed by the analysis unit. For example, the proposal unit may propose industry leaders or successful entrepreneurs as role models. The proposal unit may also propose specific career advancement steps based on the user's goals. The information providing unit provides qualification acquisition and learning plans based on the goals proposed by the proposal unit. For example, the information providing unit may suggest online courses or methods for obtaining professional qualifications. The information providing unit may also suggest optimal learning methods based on the user's learning style. The matching unit may match the user with an appropriate mentor based on the plan provided by the information providing unit. For example, the matching unit may suggest a mentor with marketing expertise. The matching unit may also suggest mentors in different industries depending on the user's goals. The analysis unit may analyze employee macro data, the business environment, and market trends. For example, the analysis unit may provide the latest trends and required skill sets in a specific industry. The analysis unit may also predict changes in the business environment and provide appropriate career advice to the user. This allows the AI ​​system according to the embodiment to efficiently support the user's career advancement and reskilling. For example, the user can clarify specific steps toward their goals and efficiently acquire the necessary skills and qualifications. Furthermore, by constantly keeping up with the latest information on industry trends and business skills and receiving expert advice, a more effective career plan can be realized.

[0062] The analysis unit can analyze the user's past behavioral history and performance data, extract success patterns, and reflect them in goal setting. For example, the analysis unit analyzes the user's past project history and performance data to extract commonalities between successful projects. For example, if specific skills or team composition contribute to success, goal setting is based on that information. The analysis unit also analyzes the user's past behavioral history to identify successful behavior patterns. For example, if actions at specific times or the use of specific resources lead to success, that pattern is reflected in goal setting. The analysis unit also analyzes the user's past goals and results based on performance data to extract success patterns. For example, it identifies the steps and resources necessary to achieve a specific goal and sets new goals based on those. This makes it possible to set goals based on the user's past success patterns.

[0063] The analysis unit can suggest an optimal career path based on the user's personality diagnosis results. For example, the analysis unit analyzes the user's personality diagnosis results and suggests a career path based on their personality traits. For example, it suggests a management position for a user with strong leadership skills, and a design position for a creative user. The analysis unit also identifies the job types and environments in which the user is least likely to feel stressed based on the personality diagnosis results, and suggests a career path based on that information. For example, it suggests a job that involves a lot of remote work or individual work for an introverted user. The analysis unit also uses the personality diagnosis results to identify the job types and work content that the user finds most motivating, and suggests a career path based on that information. For example, it suggests new business development for a user who enjoys challenges. This makes it possible to suggest career paths based on the user's personality.

[0064] The analysis unit can use the emotion estimation function to identify the goal that the user finds most motivating and suggest steps toward that goal. For example, the analysis unit uses the emotion estimation function to analyze the emotions felt by the user when setting a goal and identify the goal that motivates the user most. For example, it prioritizes suggesting goals that evoke strong positive emotions. The analysis unit also monitors the user's emotional reactions in real time as they take steps toward the goal and suggests steps that will increase motivation. For example, it prioritizes setting steps with high emotion scores. The analysis unit also identifies the goal that the user finds most motivating based on the emotion estimation data and suggests specific steps toward that goal. For example, it sets tasks related to goals with high emotion scores. This makes it possible to set goals that increase the user's motivation.

[0065] The suggestion unit proposes a career path that takes into account the user's hobbies and interests, enabling the user to balance work and private life. The suggestion unit, for example, analyzes the user's hobbies and interests and proposes a career path based on them. For example, it proposes jobs that involve a lot of fieldwork to a user who likes the outdoors. The suggestion unit also proposes a career path that takes into account the user's hobbies and interests, thereby balancing work and private life. For example, it proposes jobs and work content that can make use of hobbies. The suggestion unit also proposes specific steps to balance work and private life based on the user's hobbies and interests. For example, it proposes jobs that can make use of skills related to hobbies. This makes it possible to propose a career path that takes into account the user's hobbies and interests.

[0066] The suggestion unit can introduce role models from different industries and provide users with new perspectives. For example, the suggestion unit can introduce successful people and role models from different industries and provide users with new perspectives. For example, it can introduce leadership examples from different industries. The suggestion unit can also suggest new career paths to users based on role models from different industries. For example, it can propose career plans that refer to successful examples from different industries. The suggestion unit can also introduce role models from different industries and provide users with opportunities to learn new skills and knowledge. For example, it can promote interaction with experts from different industries. This makes it possible to provide users with new perspectives.

[0067] The suggestion unit can use the emotion estimation function to provide advice to reduce the anxiety and stress the user feels when setting goals. For example, the suggestion unit uses the emotion estimation function to analyze the anxiety and stress the user feels when setting goals and propose ways to reduce them. For example, it proposes relaxation techniques and stress management methods. The suggestion unit also monitors the anxiety and stress the user feels when setting goals in real time and provides appropriate advice. For example, it suggests relaxation methods if the emotion score is low. The suggestion unit also provides specific advice to reduce the anxiety and stress the user feels when setting goals based on the emotion estimation data. For example, it suggests ways to elicit positive emotions. This makes it possible to provide advice to reduce the user's anxiety and stress.

[0068] The information providing unit can analyze the user's learning style and suggest the optimal learning method. For example, the information providing unit analyzes the user's past learning history and performance data and suggests the optimal learning method. For example, visual learning materials are suggested for a user for whom visual learning is effective. The information providing unit also diagnoses the learning style and identifies the optimal learning method for the user. For example, audio learning materials are suggested for a user for whom auditory learning is effective. The information providing unit also builds a system that suggests the optimal learning method based on the user's learning style. For example, a hands-on workshop is suggested for a user for whom practical learning is effective. This makes it possible to suggest the optimal learning method based on the user's learning style.

[0069] The information providing unit can predict the learning progress based on the user's past learning history and provide learning resources at the appropriate time. For example, the information providing unit analyzes the user's past learning history and develops an algorithm to predict learning progress. For example, it suggests the next learning step based on past learning data. The information providing unit also builds a system that provides learning resources at the appropriate time based on the learning history. For example, it suggests new learning materials or courses according to the learning progress. The information providing unit also analyzes the past learning history and predicts learning progress to provide the user with optimal learning resources. For example, it provides feedback and support according to the learning progress. This makes it possible to provide learning resources at the appropriate time according to the learning progress.

[0070] The information providing unit can use the emotion estimation function to monitor fluctuations in motivation felt by the user while studying in real time and provide appropriate support. The information providing unit, for example, uses the emotion estimation function to develop a system that monitors fluctuations in motivation felt by the user while studying in real time. For example, it analyzes emotion scores during studying and provides support when motivation drops. The information providing unit also provides specific support to maintain motivation while studying based on the user's emotional response. For example, it sends encouraging messages or suggests taking a break. The information providing unit also builds a system that analyzes fluctuations in motivation felt by the user while studying based on the emotion estimation data and provides appropriate support. For example, it suggests ways to refresh yourself when motivation drops. This makes it possible to provide appropriate support according to fluctuations in motivation during studying.

[0071] The information providing unit can recommend acquiring qualifications in different fields, thereby diversifying the user's skill set. For example, the information providing unit recommends acquiring qualifications in different fields based on the user's goals and interests. For example, it might suggest marketing qualifications to a user in the IT field. The information providing unit also diversifies the user's skill set by recommending acquiring qualifications in different fields. For example, it might suggest business skill qualifications to an engineer. The information providing unit also builds a system that recommends acquiring qualifications in different fields according to the user's career path. For example, it might suggest leadership qualifications to a user aiming for a managerial position. This makes it possible to diversify the user's skill set.

[0072] The information providing unit provides opportunities for group learning, allowing users to advance their studies in cooperation with other users. The information providing unit, for example, provides opportunities for group learning and builds a system in which users advance their studies in cooperation with other users. For example, it suggests online study groups or study circles. The information providing unit also provides a group learning platform to promote cooperation with other users. For example, it sets up joint projects or discussion forums. The information providing unit also provides opportunities for group learning, providing a place where users can share knowledge and skills with other users. For example, it recommends peer review or group work. This provides group learning opportunities that promote cooperation with other users.

[0073] The information providing unit can use the emotion estimation function to provide content that increases the user's interest and concern in learning. For example, the information providing unit uses the emotion estimation function to analyze the user's interest and concern in learning and provides optimal content based on the data. For example, it suggests interesting topics or examples. The information providing unit also provides specific content that increases the user's interest and concern in learning based on the user's emotional response. For example, it suggests interactive learning materials or game-style learning. The information providing unit also builds a system that provides content that increases the user's interest and concern in learning based on the emotion estimation data. For example, it provides learning materials related to topics with high emotion scores. This makes it possible to provide content that increases the user's interest and concern in learning.

[0074] The information providing unit can analyze a user's past browsing history and provide information that is likely to be of interest to the user on a priority basis. The information providing unit, for example, analyzes a user's past browsing history and builds a system that provides information that is likely to be of interest to the user on a priority basis. For example, it suggests information related to articles or videos that the user has viewed in the past. The information providing unit also automatically filters information that is likely to be of interest to the user based on the browsing history and provides it on a priority basis. For example, it notifies the user of information related to specific topics or keywords. The information providing unit also analyzes a user's past browsing history and develops an algorithm that provides information that is likely to be of interest to the user on a priority basis. For example, it analyzes browsing history patterns and provides highly relevant information. This makes it possible to provide information that is likely to be of interest to the user on a priority basis.

[0075] The information providing unit can provide information at the optimal timing based on the user's schedule. The information providing unit, for example, analyzes the user's schedule and builds a system that provides information at the optimal timing. For example, information is notified in accordance with meetings or break times. The information providing unit also identifies times when the user is most likely to receive information based on schedule data and provides the information at those times. For example, information is notified during commuting time or lunch breaks. The information providing unit also develops an algorithm that takes the user's schedule into consideration and provides information at the optimal timing. For example, information is provided in accordance with free time in the schedule. This makes it possible to provide information at the optimal timing according to the user's schedule.

[0076] The information providing unit can use the emotion estimation function to identify information in which the user is most interested and prioritize push notification of that information. For example, the information providing unit uses the emotion estimation function to identify information in which the user is most interested and build a system that prioritizes push notification of that information. For example, information with a high emotion score is prioritized. The information providing unit also identifies information in which the user is most interested based on the user's emotional response and prioritizes push notification of that information. For example, information with a strong positive emotion is prioritized. The information providing unit also develops an algorithm that identifies information in which the user is most interested based on emotion estimation data and prioritizes push notification of that information. For example, information with a high emotion score is prioritized. This makes it possible to prioritize push notification of information in which the user is most interested.

[0077] The information providing unit can provide information in different media formats to deepen the user's understanding. For example, the information providing unit builds a system that provides information in different media formats to deepen the user's understanding. For example, the information providing unit provides information in formats such as text, video, and podcast. The information providing unit also provides information in different media formats according to the user's preferences. For example, if visual information is preferred, it provides video, and if auditory information is preferred, it provides podcast. The information providing unit also deepens the user's understanding by providing information in different media formats. For example, it provides the same information in both text and video, allowing the user to select. This makes it possible to deepen the user's understanding by providing information in different media formats.

[0078] The information providing unit can analyze information shared within a user's network and provide highly relevant information. The information providing unit, for example, analyzes information shared within a user's network and builds a system that provides highly relevant information. For example, related information is provided based on information shared by colleagues and friends. The information providing unit also analyzes information within the network, identifies information that is highly relevant to the user, and provides that information. For example, information related to the same project is provided preferentially. The information providing unit also analyzes information shared within a user's network and develops an algorithm that provides highly relevant information. For example, related information is provided based on trend information within the network. This makes it possible to analyze information shared within a user's network and provide highly relevant information.

[0079] The information providing unit can use the emotion estimation function to provide information at optimal timing based on the emotional state of the user when receiving the information. For example, the information providing unit uses the emotion estimation function to analyze the emotional state of the user when receiving the information and build a system that provides information at optimal timing. For example, it notifies the user when the emotional state is positive. The information providing unit also monitors the user's emotional state in real time and provides information at optimal timing. For example, it notifies the user when the emotional score is high. The information providing unit also develops an algorithm that provides information at optimal timing based on the emotion estimation data, taking into account the emotional state of the user when receiving the information. For example, it provides information when the emotional score is high. This makes it possible to provide information at optimal timing taking into account the user's emotional state.

[0080] The matching unit can analyze a user's past feedback and select the most suitable mentor. For example, the matching unit analyzes a user's past feedback and builds a system for selecting the most suitable mentor. For example, it selects a mentor based on evaluations of past mentoring sessions. The matching unit also identifies the most suitable mentor for the user based on the feedback data and suggests that mentor. For example, it prioritizes the selection of mentors who have received a lot of positive feedback. The matching unit also analyzes a user's past feedback and develops an algorithm for selecting the most suitable mentor. For example, it analyzes the content of the feedback and identifies an appropriate mentor. This makes it possible to select the most suitable mentor based on a user's past feedback.

[0081] The matching unit can develop an algorithm that matches the mentor's expertise with the user's goals in detail. For example, the matching unit develops an algorithm that matches the mentor's expertise with the user's goals in detail. For example, it selects a mentor with specific skills and experience to match the user's goals. The matching unit also builds an algorithm that identifies the most suitable mentor based on the user's goals. For example, it suggests a mentor with expertise related to the user's goals. The matching unit also develops a system that matches the mentor's expertise with the user's goals in detail. For example, it evaluates the mentor's skill set according to the user's goals and selects the most suitable mentor. This makes it possible to match the mentor's expertise with the user's goals in detail.

[0082] The matching unit can use the emotion estimation function to identify the mentor that the user feels most trustworthy and suggest that mentor. For example, the matching unit uses the emotion estimation function to build a system that identifies the mentor that the user feels most trustworthy. For example, it preferentially suggests mentors with high emotion scores. The matching unit also identifies the mentor that the user feels most trustworthy based on the user's emotional response and suggests that mentor. For example, it preferentially selects mentors with strong positive emotions. The matching unit also develops an algorithm that identifies the mentor that the user feels most trustworthy based on the emotion estimation data and suggests that mentor. For example, it preferentially provides mentors with high emotion scores. This makes it possible to identify the mentor that the user feels most trustworthy and suggest that mentor.

[0083] The matching unit can introduce mentors from different industries and provide users with new perspectives. For example, the matching unit builds a system that introduces mentors from different industries and provides users with new perspectives. For example, it suggests experts from different industries as mentors. The matching unit also suggests new career paths to users based on mentors from different industries. For example, it suggests career plans that refer to success stories from different industries. The matching unit also introduces mentors from different industries and provides users with opportunities to learn new skills and knowledge. For example, it promotes interaction with experts from different industries. This makes it possible to provide users with new perspectives by introducing them to mentors from different industries.

[0084] The matching unit provides group mentoring opportunities, allowing users to receive feedback from multiple mentors. The matching unit, for example, builds a system that provides group mentoring opportunities and allows users to receive feedback from multiple mentors. For example, it holds online mentoring sessions. The matching unit also provides users with a multifaceted perspective by receiving feedback from multiple mentors. For example, users can receive advice from mentors with different fields of expertise. The matching unit also provides group mentoring opportunities, providing a forum for users to interact with multiple mentors. For example, it sets up mentoring workshops and discussion forums. This makes it possible to receive feedback from multiple mentors and provide users with a multifaceted perspective.

[0085] The matching unit can use the emotion estimation function to provide advice to reduce the anxiety the user feels when communicating with the mentor. For example, the matching unit uses the emotion estimation function to analyze the anxiety the user feels when communicating with the mentor and propose ways to reduce it. For example, it proposes relaxation techniques and stress management methods. The matching unit also monitors the anxiety the user feels when communicating with the mentor in real time and provides appropriate advice. For example, it suggests relaxation methods if the emotion score is low. The matching unit also provides specific advice to reduce the anxiety the user feels when communicating with the mentor based on the emotion estimation data. For example, it proposes ways to elicit positive emotions. This makes it possible to provide advice to reduce the anxiety the user feels when communicating with the mentor.

[0086] The analysis unit can analyze market trends related to the user's career path in real time and provide optimal advice. The analysis unit, for example, builds a system that analyzes market trends related to the user's career path in real time and provides optimal advice. For example, career advice is provided based on the latest trends in a specific industry. The analysis unit also suggests the optimal career path for the user based on market trend data. For example, it suggests industries and job types that are expected to grow. The analysis unit also develops an algorithm that analyzes market trends related to the user's career path in real time and provides optimal advice. For example, career advice is provided based on market demand and trends. This makes it possible to analyze market trends related to the user's career path in real time and provide optimal advice.

[0087] The analysis unit can predict changes in the business environment and provide appropriate career advice to users. The analysis unit, for example, builds a system that predicts changes in the business environment and provides appropriate career advice to users. For example, it provides career advice based on economic indicators and industry trends. The analysis unit also develops an algorithm that predicts changes in the business environment and provides career advice to users based on the results. For example, it provides advice that takes technological innovation and market fluctuations into consideration. The analysis unit also analyzes changes in the business environment in real time and provides appropriate career advice to users. For example, it proposes career plans based on new business opportunities and risks. This makes it possible to predict changes in the business environment and provide appropriate career advice to users.

[0088] The analysis unit can use the emotion estimation function to provide information to reduce the anxiety the user feels about market trends. For example, the analysis unit uses the emotion estimation function to analyze the anxiety the user feels about market trends and propose ways to reduce that anxiety. For example, it provides positive market data and success stories. The analysis unit also monitors the anxiety the user feels about market trends in real time and provides appropriate information. For example, it provides information that gives a sense of security when the emotion score is low. The analysis unit also provides specific information to reduce the anxiety the user feels about market trends based on the emotion estimation data. For example, it provides a market analysis report that elicits positive emotions. This makes it possible to provide information to reduce the anxiety the user feels about market trends.

[0089] The analysis unit can compare market trends in different regions and provide users with a global perspective. The analysis unit, for example, builds a system that compares market trends in different regions and provides users with a global perspective. For example, it performs comparative analysis based on regional economic indicators and industry trends. The analysis unit also analyzes market trends in different regions and provides users with global career advice. For example, it proposes career plans based on growth opportunities and risks in overseas markets. The analysis unit also develops algorithms that compare market trends in different regions and provides users with a global perspective based on the results. For example, it compares business opportunities and competitive situations in each region. This makes it possible to compare market trends in different regions and provide users with a global perspective.

[0090] The analysis unit can analyze trends by industry in detail and provide specific career advice to users. The analysis unit, for example, builds a system that analyzes trends by industry in detail and provides specific career advice to users. For example, it proposes a career plan based on growth forecasts and skill demand for a specific industry. The analysis unit also analyzes trends by industry and provides specific career advice to users. For example, it proposes a career plan based on the latest trends and technological innovations in the industry. The analysis unit also develops an algorithm that analyzes trends by industry in detail and provides specific career advice to users based on the results. For example, it proposes a career plan based on the competitive situation and market needs in the industry. This makes it possible to analyze trends by industry in detail and provide specific career advice to users.

[0091] The analysis unit can use the emotion estimation function to provide information that increases the user's interest and concern in market trends. For example, the analysis unit uses the emotion estimation function to analyze the user's interest and concern in market trends and provide optimal information based on the data. For example, the analysis unit may suggest interesting market trends or success stories. The analysis unit also provides specific information that increases the user's interest and concern in market trends based on the user's emotional response. For example, the analysis unit may provide interactive market analysis reports or visual data. The analysis unit also builds a system that provides information that increases the user's interest and concern in market trends based on the emotion estimation data. For example, the analysis unit may provide information related to market trends with high emotion scores. This makes it possible to provide information that increases the user's interest and concern in market trends.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The suggestion unit proposes a career path that takes into account the user's hobbies and interests, enabling the user to balance work and private life. The suggestion unit, for example, analyzes the user's hobbies and interests and proposes a career path based on them. For example, it proposes jobs that involve a lot of fieldwork to a user who likes the outdoors. The suggestion unit also proposes a career path that takes into account the user's hobbies and interests, thereby balancing work and private life. For example, it proposes jobs and work content that can make use of hobbies. The suggestion unit also proposes specific steps to balance work and private life based on the user's hobbies and interests. For example, it proposes jobs that can make use of skills related to hobbies. This makes it possible to propose a career path that takes into account the user's hobbies and interests.

[0094] The suggestion unit can introduce role models from different industries and provide users with new perspectives. For example, the suggestion unit can introduce successful people and role models from different industries and provide users with new perspectives. For example, it can introduce leadership examples from different industries. The suggestion unit can also suggest new career paths to users based on role models from different industries. For example, it can propose career plans that refer to successful examples from different industries. The suggestion unit can also introduce role models from different industries and provide users with opportunities to learn new skills and knowledge. For example, it can promote interaction with experts from different industries. This makes it possible to provide users with new perspectives.

[0095] The suggestion unit can use the emotion estimation function to provide advice to reduce the anxiety and stress the user feels when setting goals. For example, the suggestion unit uses the emotion estimation function to analyze the anxiety and stress the user feels when setting goals and propose ways to reduce them. For example, it proposes relaxation techniques and stress management methods. The suggestion unit also monitors the anxiety and stress the user feels when setting goals in real time and provides appropriate advice. For example, it suggests relaxation methods if the emotion score is low. The suggestion unit also provides specific advice to reduce the anxiety and stress the user feels when setting goals based on the emotion estimation data. For example, it suggests ways to elicit positive emotions. This makes it possible to provide advice to reduce the user's anxiety and stress.

[0096] The information providing unit can analyze the user's learning style and suggest the optimal learning method. For example, the information providing unit analyzes the user's past learning history and performance data and suggests the optimal learning method. For example, visual learning materials are suggested for a user for whom visual learning is effective. The information providing unit also diagnoses the learning style and identifies the optimal learning method for the user. For example, audio learning materials are suggested for a user for whom auditory learning is effective. The information providing unit also builds a system that suggests the optimal learning method based on the user's learning style. For example, a hands-on workshop is suggested for a user for whom practical learning is effective. This makes it possible to suggest the optimal learning method based on the user's learning style.

[0097] The information providing unit can predict the learning progress based on the user's past learning history and provide learning resources at the appropriate time. For example, the information providing unit analyzes the user's past learning history and develops an algorithm to predict learning progress. For example, it suggests the next learning step based on past learning data. The information providing unit also builds a system that provides learning resources at the appropriate time based on the learning history. For example, it suggests new learning materials or courses according to the learning progress. The information providing unit also analyzes the past learning history and predicts learning progress to provide the user with optimal learning resources. For example, it provides feedback and support according to the learning progress. This makes it possible to provide learning resources at the appropriate time according to the learning progress.

[0098] The information providing unit can use the emotion estimation function to monitor fluctuations in motivation felt by the user while studying in real time and provide appropriate support. The information providing unit, for example, uses the emotion estimation function to develop a system that monitors fluctuations in motivation felt by the user while studying in real time. For example, it analyzes emotion scores during studying and provides support when motivation drops. The information providing unit also provides specific support to maintain motivation while studying based on the user's emotional response. For example, it sends encouraging messages or suggests taking a break. The information providing unit also builds a system that analyzes fluctuations in motivation felt by the user while studying based on the emotion estimation data and provides appropriate support. For example, it suggests ways to refresh yourself when motivation drops. This makes it possible to provide appropriate support according to fluctuations in motivation during studying.

[0099] The information providing unit can recommend acquiring qualifications in different fields, thereby diversifying the user's skill set. For example, the information providing unit recommends acquiring qualifications in different fields based on the user's goals and interests. For example, it might suggest marketing qualifications to a user in the IT field. The information providing unit also diversifies the user's skill set by recommending acquiring qualifications in different fields. For example, it might suggest business skill qualifications to an engineer. The information providing unit also builds a system that recommends acquiring qualifications in different fields according to the user's career path. For example, it might suggest leadership qualifications to a user aiming for a managerial position. This makes it possible to diversify the user's skill set.

[0100] The information providing unit provides opportunities for group learning, allowing users to advance their studies in cooperation with other users. The information providing unit, for example, provides opportunities for group learning and builds a system in which users advance their studies in cooperation with other users. For example, it suggests online study groups or study circles. The information providing unit also provides a group learning platform to promote cooperation with other users. For example, it sets up joint projects or discussion forums. The information providing unit also provides opportunities for group learning, providing a place where users can share knowledge and skills with other users. For example, it recommends peer review or group work. This provides group learning opportunities that promote cooperation with other users.

[0101] The information providing unit can use the emotion estimation function to provide content that increases the user's interest and concern in learning. For example, the information providing unit uses the emotion estimation function to analyze the user's interest and concern in learning and provides optimal content based on the data. For example, it suggests interesting topics or examples. The information providing unit also provides specific content that increases the user's interest and concern in learning based on the user's emotional response. For example, it suggests interactive learning materials or game-style learning. The information providing unit also builds a system that provides content that increases the user's interest and concern in learning based on the emotion estimation data. For example, it provides learning materials related to topics with high emotion scores. This makes it possible to provide content that increases the user's interest and concern in learning.

[0102] The information providing unit can analyze a user's past browsing history and provide information that is likely to be of interest to the user on a priority basis. The information providing unit, for example, analyzes a user's past browsing history and builds a system that provides information that is likely to be of interest to the user on a priority basis. For example, it suggests information related to articles or videos that the user has viewed in the past. The information providing unit also automatically filters information that is likely to be of interest to the user based on the browsing history and provides it on a priority basis. For example, it notifies the user of information related to specific topics or keywords. The information providing unit also analyzes a user's past browsing history and develops an algorithm that provides information that is likely to be of interest to the user on a priority basis. For example, it analyzes browsing history patterns and provides highly relevant information. This makes it possible to provide information that is likely to be of interest to the user on a priority basis.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The question receiving unit accepts questions about the user's desired self-image, short-term, medium-term, and long-term goals, and life career. For example, a user can input a goal such as "I want to become a manager in five years." The question receiving unit also allows users to enter goals and questions in free-form. Step 2: The analysis unit analyzes the information received by the question receiving unit. For example, the analysis unit may use data mining techniques to analyze the user's goals and questions. The analysis unit may also use statistical analysis techniques to extract patterns for achieving the user's goals. Step 3: The suggestion unit suggests the user the most suitable role model and career advancement goals based on the information analyzed by the analysis unit. For example, the suggestion unit may suggest industry leaders or successful entrepreneurs as role models. The suggestion unit may also suggest specific career advancement steps based on the user's goals. Step 4: The information provider provides a qualification or learning plan based on the goals proposed by the suggestion provider. For example, the information provider may suggest online courses or ways to obtain professional qualifications. The information provider may also suggest optimal learning methods based on the user's learning style. Step 5: The matching unit matches the user with an appropriate mentor based on the plan provided by the information providing unit. For example, the matching unit may suggest a mentor with expertise in marketing. The matching unit may also suggest mentors in different industries depending on the user's goals. Step 6: The analytics department analyzes employee macro data, the business environment, and market trends. For example, the analytics department can provide the latest trends and required skill sets for a specific industry. The analytics department can also predict changes in the business environment and provide appropriate career advice to users.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 question reception section that receives questions about the user's desired self-image, short-term, medium-term, and long-term goals, and life career; an analysis unit that analyzes the information received by the question receiving unit; a suggestion unit that suggests optimal role models and career advancement goals to the user based on the information analyzed by the analysis unit; an information providing unit that provides qualification acquisition and learning plans based on the goals proposed by the proposal unit; a matching unit that matches an appropriate mentor based on the plan provided by the information providing unit; Equipped with an analysis department that analyzes employee macro data, business environment, and market trends. A system characterized by:

2. The analysis unit Identify the goals that motivate the user most and suggest steps toward those goals 2. The system of claim 1.

3. The proposal unit Provide advice to reduce anxiety and stress experienced by the user when setting goals 2. The system of claim 1.

4. The information providing unit The fluctuation of motivation felt by the user during learning is monitored in real time, and appropriate support is provided.

2. The system of claim 1.

5. The matching unit Identify the mentor the user feels most confident in and suggest that mentor to the user.

2. The system of claim 1.

6. The analysis unit To provide information to reduce the anxiety felt by the user regarding the market trends 2. The system of claim 1.

7. The proposal unit Introduce role models from different industries to provide users with new perspectives 2. The system of claim 1.

8. The information providing unit Based on the user's past learning history, the learning progress is predicted and learning resources are provided at an appropriate time.

2. The system of claim 1.

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