System

Abstract

An object of the system according to the embodiment is to statistically determine and propose an occupation based on the aptitude of an individual.SOLUTION: A system includes a data collection unit, an analysis unit, a determination unit, and a proposal unit. The data collection unit collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The determination unit statistically determines the suitable occupation based on the personal data analyzed by the analysis unit. The proposal unit proposes the suitable occupation determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately statistically determine occupations based on individual aptitudes, and there is room for improvement.

[0005] The system according to the embodiment aims to statistically determine and suggest occupations based on an individual's aptitude. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a determination unit, and a suggestion unit. The data collection unit collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The determination unit statistically determines an appropriate occupation based on the personal data analyzed by the analysis unit. The suggestion unit suggests the appropriate occupation determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can statistically determine and suggest occupations based on an individual's aptitude. [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 career aptitude assessment system according to an embodiment of the present invention is a system that analyzes an individual's characteristics and skills and statistically determines the most suitable career for that individual. This system uses generative AI to analyze individual data and propose suitable careers. As a result, the career aptitude assessment system can statistically determine and propose suitable careers based on the individual's characteristics and skills.

[0029] An occupational aptitude assessment system according to an embodiment includes a data collection unit, an analysis unit, a determination unit, and a suggestion unit. The data collection unit collects personal data. For example, the data collection unit collects information such as an individual's educational background, work history, skills, interests, and personality traits. The data collection unit can also collect information such as the individual's past occupations, skills, and interests. The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit analyzes data related to the individual's characteristics and skills using generative AI. The analysis unit can also analyze data using statistical analysis or machine learning algorithms. The determination unit statistically determines an appropriate occupation based on the personal data analyzed by the analysis unit. For example, the determination unit statistically analyzes whether the individual's skills and interests are suitable for a specific occupation and determines the appropriate occupation based on the results. The determination unit can also match the individual's characteristics with occupational aptitude using past data and statistical models. The suggestion unit suggests the appropriate occupation determined by the determination unit. For example, the suggestion unit may suggest, for example, "Based on your skills and interests, the following occupations are suitable." The suggestion unit may also provide information such as specific job content, required skills, and future career paths for the suggested occupations. This allows the occupational aptitude assessment system according to the embodiment to statistically determine and suggest suitable occupations based on individual characteristics and skills. For example, it can help individuals find occupations that match their aptitudes and broaden career options. It can also help companies find suitable personnel, thereby improving the efficiency of recruitment.

[0030] The data collection unit can analyze public information from an individual's social media or blog and combine it with private data to extract more detailed characteristics. For example, the data collection unit analyzes an individual's social media posts or blog articles to extract information such as hobbies, interests, and values. For example, it analyzes frequently posted topics and used keywords to understand individual characteristics. The data collection unit also combines and analyzes public information with private data (e.g., resumes and CVs) to evaluate an individual's skills and experience in more detail. For example, it compares social media activity with work history. The data collection unit also performs sentiment analysis of social media and blogs to analyze the situations in which an individual feels positive emotions. For example, it calculates sentiment scores for specific topics and uses them to determine occupational suitability. This allows for more detailed characteristics to be extracted by combining public information and private data.

[0031] The data collection department can analyze an individual's past projects or deliverables to evaluate specific skills or abilities. For example, the data collection department collects details of projects that an individual has been involved in in the past and analyzes the project deliverables and roles. For example, skills are evaluated based on project reports and presentation materials. The data collection department can also analyze the technologies and tools used in past projects to evaluate an individual's technical skill set. For example, the data collection department can identify the programming languages ​​and software used. The data collection department can also analyze project deliverables (for example, finished products and demo videos) to evaluate an individual's creativity and problem-solving ability. For example, skills are determined based on the project's completeness and innovativeness. In this way, specific skills and abilities can be evaluated by analyzing past projects and deliverables.

[0032] The data collection unit can also include audio or video data in its analysis targets to perform a more multifaceted characteristic evaluation. The data collection unit, for example, analyzes audio data provided by an individual to evaluate characteristics based on the content and tone of speech. For example, it analyzes the audio of a presentation or interview to evaluate communication skills. The data collection unit can also analyze video data to evaluate emotions and characteristics based on an individual's facial expressions and gestures. For example, it can analyze an interview video to evaluate non-verbal communication skills. The data collection unit can also analyze a combination of audio and video data to perform a more detailed characteristic evaluation. For example, it can evaluate the degree of agreement between the content of speech and facial expressions to determine trustworthiness and sincerity. This allows for a more multifaceted characteristic evaluation to be performed by including audio and video data in its analysis targets.

[0033] The data collection unit can collect data from different cultural spheres or regions and analyze vocational aptitude taking cultural background into consideration. The data collection unit, for example, collects data from different cultural spheres and analyzes vocational aptitude taking cultural background into consideration. For example, it reflects values ​​and views on work that are unique to each culture. The data collection unit also collects data for each region and analyzes vocational aptitude taking regional characteristics into consideration. For example, it reflects the characteristics of the regional industrial structure and labor market. The data collection unit also incorporates the opinions of experts from different cultural spheres in order to analyze vocational aptitude taking cultural background into consideration. For example, it identifies culture-specific vocational aptitude factors. This makes it possible to collect data from different cultural spheres and regions and analyze vocational aptitude taking cultural background into consideration.

[0034] The determination unit can also take into account an individual's lifestyle or values ​​to make more personalized suggestions. The determination unit, for example, collects data about an individual's lifestyle and reflects this in determining an appropriate occupation. For example, it takes into account work preferences and daily routines. The determination unit also collects data about an individual's values ​​and reflects this in determining an appropriate occupation. For example, it takes into account values ​​and goals regarding work. The determination unit also conducts individual interviews and questionnaires to determine an appropriate occupation taking into account lifestyles and values. For example, it grasps an individual's values ​​through detailed questions. This allows for more personalized suggestions to be made, taking into account an individual's lifestyle and values.

[0035] The judgment unit can compare past occupational aptitude assessment results or actual occupational history to continuously improve the accuracy of the judgment algorithm. For example, the judgment unit compares past occupational aptitude assessment results with actual occupational history to evaluate the accuracy of the judgment algorithm. For example, it verifies whether the aptitude assessment was accurate. The judgment unit also builds a feedback loop based on past data to continuously improve the accuracy of the judgment algorithm. For example, it periodically reviews the aptitude assessment results. The judgment unit also analyzes the differences between the actual occupational history and the aptitude assessment results to identify areas for improvement in the algorithm. For example, it improves the judgment accuracy for specific occupations. This allows the accuracy of the judgment algorithm to be continuously improved by comparing past occupational aptitude assessment results with actual occupational history.

[0036] The determination unit can integrate data from different industries or occupations to determine cross-industry occupational aptitude. For example, the determination unit integrates data from different industries to build a system for determining cross-industry occupational aptitude. For example, it combines technical and creative data. The determination unit also integrates data from different occupations and reflects this in determining occupational aptitude. For example, it combines data from sales and technical occupations and analyzes it. The determination unit also incorporates the opinions of experts from different industries to determine cross-industry occupational aptitude. For example, it takes into account industry-specific skills and characteristics. This makes it possible to integrate data from different industries and occupations to determine cross-industry occupational aptitude.

[0037] The assessment unit can suggest promising occupations by taking into account future technological trends or market trends. For example, the assessment unit analyzes future technological trends and market trends and reflects them in its assessment of suitable occupations. For example, it can suggest occupations related to new technologies such as AI and blockchain. The assessment unit also collects the latest market data and reflects it in its analysis to assess occupational aptitude taking market trends into account. For example, it identifies growth industries and occupations with increasing demand. The assessment unit also incorporates the opinions of experts on technological trends and market trends in order to suggest promising occupations. For example, it predicts technological innovations and market fluctuations. This makes it possible to suggest promising occupations by taking into account future technological trends and market trends.

[0038] The suggestion unit can present detailed career paths or growth opportunities for the proposed occupation and support the individual's future planning. The suggestion unit, for example, presents detailed career paths for the proposed occupation and supports the individual's future planning. For example, it specifically indicates promotion steps and required skills. The suggestion unit also presents growth opportunities in the proposed occupation and supports the individual's career plan. For example, it introduces training programs and opportunities to obtain qualifications. The suggestion unit also visually displays the career paths and growth opportunities to make it easier for the individual to imagine their future career. For example, it provides a career map or growth graph. This makes it possible to present detailed career paths and growth opportunities for the proposed occupation and support the individual's future planning.

[0039] The suggestion unit can provide a specific study plan for acquiring the skills or qualifications required for the proposed occupation. The suggestion unit, for example, provides a specific study plan for acquiring the skills or qualifications required for the proposed occupation. For example, it can introduce online courses or training programs. The suggestion unit can also customize the study plan to fit an individual's schedule, allowing them to efficiently acquire skills. For example, it can provide weekly study goals and progress management. The suggestion unit can also provide resources and teaching materials related to the study plan, supporting the individual in effectively acquiring the required skills. For example, it can provide reference books and video tutorials. In this way, it is possible to provide a specific study plan for acquiring the skills or qualifications required for the proposed occupation.

[0040] The suggestion unit can share other users' feedback or experiences on the proposed occupations and provide reference information. The suggestion unit, for example, builds a system that collects and shares other users' feedback and experiences on proposed occupations. For example, it displays reviews and comments on occupations. The suggestion unit also provides a concrete image of the proposed occupation based on the experiences of other users. For example, it introduces the daily schedule and work content of the occupation. The suggestion unit also visually displays the feedback and experiences to make it easier for individuals to understand the proposed occupations. For example, it visualizes the feedback using graphs and charts. This makes it possible to share other users' feedback and experiences on the proposed occupations and provide reference information.

[0041] The suggestion department may introduce community or networking opportunities related to the proposed occupation and provide social support. For example, the suggestion department may introduce community or networking opportunities related to the proposed occupation and provide social support. For example, the suggestion department may introduce industry events or study groups. The suggestion department may also enable individuals to obtain information about the proposed occupation through community or networking opportunities. For example, the suggestion department may introduce online forums or social media groups. The suggestion department may also provide networking opportunities with experts or mentors related to the proposed occupation to support the individual's career. For example, the suggestion department may introduce mentoring programs or coaching sessions. This may allow the suggestion department to introduce community or networking opportunities related to the proposed occupation and provide social support.

[0042] The feedback collection unit can analyze individual feedback on proposed occupations in detail and improve the accuracy of the proposal algorithm. The feedback collection unit, for example, builds a system that collects individual feedback on proposed occupations and analyzes it in detail. For example, it performs text analysis on the feedback content and identifies areas for improvement. The feedback collection unit also improves the accuracy of the proposal algorithm based on the feedback data. For example, it adjusts the algorithm by reflecting the feedback content. The feedback collection unit also improves the proposal content based on the feedback analysis results and suggests an occupation that is more suitable for the individual. For example, it suggests a new occupation based on the feedback content. This allows for a detailed analysis of individual feedback on proposed occupations and improves the accuracy of the proposal algorithm.

[0043] The feedback collection unit can dynamically adjust the parameters for vocational aptitude assessment based on the feedback data. The feedback collection unit, for example, builds a system that dynamically adjusts the parameters for vocational aptitude assessment based on the feedback data. For example, the parameters are adjusted by reflecting the feedback content. The feedback collection unit also analyzes the feedback data in real time and dynamically adjusts the parameters for vocational aptitude assessment. For example, the assessment criteria are changed depending on the feedback content. The feedback collection unit also continuously improves the parameters for vocational aptitude assessment based on the feedback data. For example, the algorithm is adjusted by reflecting the feedback content. This allows the parameters for vocational aptitude assessment to be dynamically adjusted based on the feedback data.

[0044] The feedback collection unit can share the feedback data with other users and extract common issues or success stories. The feedback collection unit, for example, builds a system for sharing the feedback data with other users and extracting common issues and success stories. For example, the feedback content is shared on the cloud. The feedback collection unit also analyzes the feedback data and identifies common issues and success stories. For example, it performs text analysis on the feedback content and extracts common themes. The feedback collection unit also shares common issues and success stories based on the feedback data and provides reference for other users. For example, it proposes improvement measures based on the feedback content. In this way, the feedback data can be shared with other users and common issues and success stories can be extracted.

[0045] The feedback collection unit can introduce new evaluation criteria for career aptitude assessment based on the feedback. The feedback collection unit, for example, builds a system that introduces new evaluation criteria for career aptitude assessment based on feedback data. For example, it updates the evaluation criteria by reflecting the feedback content. The feedback collection unit also analyzes the feedback data and introduces new evaluation criteria. For example, it sets new evaluation criteria based on the feedback content. The feedback collection unit also continuously improves the evaluation criteria for career aptitude assessment based on the feedback data. For example, it adjusts the evaluation criteria by reflecting the feedback content. This makes it possible to introduce new evaluation criteria for career aptitude assessment based on feedback.

[0046] The data update unit can track an individual's career progress or new skill acquisition in real time and reflect it in suggestions of suitable occupations. The data update unit, for example, builds a system that tracks an individual's career progress or new skill acquisition in real time. For example, it automatically updates resumes and skill sheets. The data update unit also dynamically adjusts the content of suitable occupation suggestions based on career progress and skill acquisition data. For example, it updates occupation suggestions based on new skills. The data update unit also suggests occupations that are optimal for the individual based on the career data tracked in real time. For example, it suggests occupations based on the latest skill set. This allows an individual's career progress and new skill acquisition to be tracked in real time and reflected in suggestions of suitable occupations.

[0047] The data update unit can take into account an individual's life events in the continuous data update and reevaluate occupational aptitude. The data update unit, for example, builds a system that takes into account an individual's life events (marriage, childbirth, etc.) and reevaluates occupational aptitude. For example, it makes occupational suggestions based on life events. The data update unit also reevaluates occupational aptitude based on life event data. For example, it suggests occupations that are suitable for a lifestyle after marriage or childbirth. The data update unit also conducts individual interviews and questionnaires to reevaluate occupational aptitude taking into account life events in the continuous data update. For example, it grasps an individual's situation through detailed questions about life events. This makes it possible to take into account an individual's life events in the continuous data update and reevaluate occupational aptitude.

[0048] The data update unit can compare the data with other users' data during continuous data updates and provide a benchmark. The data update unit, for example, builds a system that compares the data with other users' data during continuous data updates and provides a benchmark. For example, it compares the skills and experience of other users in the same occupation. The data update unit also benchmarks an individual's skills and experience based on the data of other users and evaluates occupational aptitude. For example, it identifies skill gaps by comparing with other users in the same industry. The data update unit also optimizes an individual's career plan based on the benchmark data. For example, it suggests a career path by referring to the success stories of other users. This makes it possible to compare the data with other users' data during continuous data updates and provide a benchmark.

[0049] The data update unit can customize the frequency or method of data updates to suit the needs of an individual. For example, the data update unit builds a system that customizes the frequency and method of data updates to suit the needs of an individual. For example, it adjusts the timing of data updates according to an individual's schedule. The data update unit also customizes the method of data updates to suit the needs of an individual. For example, it provides a choice between manual input and automatic collection. The data update unit also adjusts the frequency and method of data updates based on individual feedback. For example, it reduces notifications if the update frequency is too high. This makes it possible to customize the frequency and method of data updates to suit the needs of an individual.

[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 data collection unit can collect an individual's health data and reflect it in determining occupational aptitude. For example, it can collect data from a fitness tracker or smartwatch to evaluate an individual's physical strength and health condition. The data collection unit can also analyze an individual's sleep patterns and stress levels to determine occupational aptitude based on their health condition. Furthermore, the data collection unit can evaluate occupational health risks based on the health data and reflect this in suggesting suitable occupations. This makes it possible to determine occupational aptitude taking into account an individual's health condition.

[0052] The analysis unit can collect data on an individual's hobbies and leisure activities and reflect this in determining career aptitude. For example, it can collect and analyze information on an individual's hobbies and clubs they participate in. The analysis unit can also evaluate skills and interests related to hobbies and leisure activities and reflect this in determining career aptitude. Furthermore, the analysis unit can suggest careers that an individual can enjoy based on their hobbies and leisure activities. This makes it possible to determine career aptitude that takes into account an individual's hobbies and leisure activities.

[0053] The assessment unit can assess occupational aptitude by taking into account an individual's learning style and learning speed. For example, it can evaluate how an individual is good at learning and how quickly they can acquire new skills. The assessment unit can also suggest occupational aptitude based on the individual's learning style and learning speed. Furthermore, the assessment unit can suggest a vocational training program tailored to the individual's learning style. This makes it possible to assess occupational aptitude by taking into account an individual's learning style and learning speed.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects personal data. For example, the data collection unit collects information such as an individual's educational background, work history, skills, interests, and personality traits. It also collects information such as what jobs an individual has had in the past, what skills they have, and what interests they have. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may use generative AI to analyze data related to personal characteristics and skills. The analysis unit may also use statistical analysis or machine learning algorithms to analyze the data. Step 3: The determination unit statistically determines the appropriate occupation based on the personal data analyzed by the analysis unit. For example, the determination unit statistically analyzes whether an individual's skills and interests are suitable for a particular occupation, and determines the appropriate occupation based on the results. It can also match individual characteristics with occupational aptitude using past data and statistical models. Step 4: The suggestion unit proposes suitable occupations based on the assessment made by the assessment unit. For example, the suggestion unit may suggest, "Based on your skills and interests, the following occupations are suitable for you." It can also provide information such as the specific job content, required skills, and future career paths for the proposed occupations.

[0056] (Example 2) The career aptitude assessment system according to an embodiment of the present invention is a system that analyzes an individual's characteristics and skills and statistically determines the most suitable career for that individual. This system uses generative AI to analyze individual data and propose suitable careers. As a result, the career aptitude assessment system can statistically determine and propose suitable careers based on the individual's characteristics and skills.

[0057] An occupational aptitude assessment system according to an embodiment includes a data collection unit, an analysis unit, a determination unit, and a suggestion unit. The data collection unit collects personal data. For example, the data collection unit collects information such as an individual's educational background, work history, skills, interests, and personality traits. The data collection unit can also collect information such as the individual's past occupations, skills, and interests. The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit analyzes data related to the individual's characteristics and skills using generative AI. The analysis unit can also analyze data using statistical analysis or machine learning algorithms. The determination unit statistically determines an appropriate occupation based on the personal data analyzed by the analysis unit. For example, the determination unit statistically analyzes whether the individual's skills and interests are suitable for a specific occupation and determines the appropriate occupation based on the results. The determination unit can also match the individual's characteristics with occupational aptitude using past data and statistical models. The suggestion unit suggests the appropriate occupation determined by the determination unit. For example, the suggestion unit may suggest, for example, "Based on your skills and interests, the following occupations are suitable." The suggestion unit may also provide information such as specific job content, required skills, and future career paths for the suggested occupations. This allows the occupational aptitude assessment system according to the embodiment to statistically determine and suggest suitable occupations based on individual characteristics and skills. For example, it can help individuals find occupations that match their aptitudes and broaden career options. It can also help companies find suitable personnel, thereby improving the efficiency of recruitment.

[0058] The data collection unit can analyze public information from an individual's social media or blog and combine it with private data to extract more detailed characteristics. For example, the data collection unit analyzes an individual's social media posts or blog articles to extract information such as hobbies, interests, and values. For example, it analyzes frequently posted topics and used keywords to understand individual characteristics. The data collection unit also combines and analyzes public information with private data (e.g., resumes and CVs) to evaluate an individual's skills and experience in more detail. For example, it compares social media activity with work history. The data collection unit also performs sentiment analysis of social media and blogs to analyze the situations in which an individual feels positive emotions. For example, it calculates sentiment scores for specific topics and uses them to determine occupational suitability. This allows for more detailed characteristics to be extracted by combining public information and private data.

[0059] The data collection department can analyze an individual's past projects or deliverables to evaluate specific skills or abilities. For example, the data collection department collects details of projects that an individual has been involved in in the past and analyzes the project deliverables and roles. For example, skills are evaluated based on project reports and presentation materials. The data collection department can also analyze the technologies and tools used in past projects to evaluate an individual's technical skill set. For example, the data collection department can identify the programming languages ​​and software used. The data collection department can also analyze project deliverables (for example, finished products and demo videos) to evaluate an individual's creativity and problem-solving ability. For example, skills are determined based on the project's completeness and innovativeness. In this way, specific skills and abilities can be evaluated by analyzing past projects and deliverables.

[0060] The data collection unit can use the emotion estimation function to analyze how an individual feels about their past occupations or skills, and identify occupations that are emotionally suited to them. The data collection unit, for example, analyzes past resumes and interview records to estimate the emotions an individual had about specific occupations and skills. For example, it identifies occupations with a high proportion of positive emotions. The data collection unit also uses the emotion estimation function to analyze the emotions an individual had about their past occupations and identify occupations that are emotionally suited to them. For example, it evaluates satisfaction with job content and stress levels. The data collection unit also analyzes feedback and reviews about an individual's past occupations to estimate emotions. For example, it determines occupational suitability based on emotions toward the work environment and colleagues. This makes it possible to analyze emotions toward past occupations and skills, and identify occupations that are emotionally suited to them.

[0061] The data collection unit can also include audio or video data in its analysis targets to perform a more multifaceted characteristic evaluation. The data collection unit, for example, analyzes audio data provided by an individual to evaluate characteristics based on the content and tone of speech. For example, it analyzes the audio of a presentation or interview to evaluate communication skills. The data collection unit can also analyze video data to evaluate emotions and characteristics based on an individual's facial expressions and gestures. For example, it can analyze an interview video to evaluate non-verbal communication skills. The data collection unit can also analyze a combination of audio and video data to perform a more detailed characteristic evaluation. For example, it can evaluate the degree of agreement between the content of speech and facial expressions to determine trustworthiness and sincerity. This allows for a more multifaceted characteristic evaluation to be performed by including audio and video data in its analysis targets.

[0062] The data collection unit can collect data from different cultural spheres or regions and analyze vocational aptitude taking cultural background into consideration. The data collection unit, for example, collects data from different cultural spheres and analyzes vocational aptitude taking cultural background into consideration. For example, it reflects values ​​and views on work that are unique to each culture. The data collection unit also collects data for each region and analyzes vocational aptitude taking regional characteristics into consideration. For example, it reflects the characteristics of the regional industrial structure and labor market. The data collection unit also incorporates the opinions of experts from different cultural spheres in order to analyze vocational aptitude taking cultural background into consideration. For example, it identifies culture-specific vocational aptitude factors. This makes it possible to collect data from different cultural spheres and regions and analyze vocational aptitude taking cultural background into consideration.

[0063] The data collection unit can use the emotion estimation function to provide an interface for reducing stress or anxiety felt by individuals when entering data. For example, the data collection unit uses the emotion estimation function to monitor in real time the stress or anxiety felt by individuals when entering data and adjust the interface. For example, relaxing music or a message can be displayed. The data collection unit also provides an interface for reducing stress or anxiety based on the emotion estimation results when entering data. For example, an encouraging message can be displayed according to the content of the data entry. The data collection unit also uses the emotion estimation function to customize the interface for reducing stress or anxiety when entering data. For example, the interface can be adjusted according to the input speed and content. In this way, an interface for reducing stress and anxiety when entering data can be provided.

[0064] The determination unit can also take into account an individual's lifestyle or values ​​to make more personalized suggestions. The determination unit, for example, collects data about an individual's lifestyle and reflects this in determining an appropriate occupation. For example, it takes into account work preferences and daily routines. The determination unit also collects data about an individual's values ​​and reflects this in determining an appropriate occupation. For example, it takes into account values ​​and goals regarding work. The determination unit also conducts individual interviews and questionnaires to determine an appropriate occupation taking into account lifestyles and values. For example, it grasps an individual's values ​​through detailed questions. This allows for more personalized suggestions to be made, taking into account an individual's lifestyle and values.

[0065] The judgment unit can compare past occupational aptitude assessment results or actual occupational history to continuously improve the accuracy of the judgment algorithm. For example, the judgment unit compares past occupational aptitude assessment results with actual occupational history to evaluate the accuracy of the judgment algorithm. For example, it verifies whether the aptitude assessment was accurate. The judgment unit also builds a feedback loop based on past data to continuously improve the accuracy of the judgment algorithm. For example, it periodically reviews the aptitude assessment results. The judgment unit also analyzes the differences between the actual occupational history and the aptitude assessment results to identify areas for improvement in the algorithm. For example, it improves the judgment accuracy for specific occupations. This allows the accuracy of the judgment algorithm to be continuously improved by comparing past occupational aptitude assessment results with actual occupational history.

[0066] The determination unit can use the emotion estimation function to analyze the emotions an individual has toward a specific occupation and preferentially suggest occupations that are emotionally suitable. For example, the determination unit can use the emotion estimation function to analyze the emotions an individual has toward a specific occupation and identify occupations that are emotionally suitable. For example, it can preferentially suggest occupations that have strong positive emotions. The determination unit also evaluates the emotional suitability for a specific occupation based on the individual's emotion data. For example, it can analyze emotion scores for past occupations. The determination unit also uses the emotion estimation function to monitor the emotions an individual has toward a specific occupation in real time and dynamically adjust the content of the suggestions. For example, it can preferentially display occupations with high emotion scores. This makes it possible to analyze the emotions an individual has toward a specific occupation and preferentially suggest occupations that are emotionally suitable.

[0067] The determination unit can integrate data from different industries or occupations to determine cross-industry occupational aptitude. For example, the determination unit integrates data from different industries to build a system for determining cross-industry occupational aptitude. For example, it combines technical and creative data. The determination unit also integrates data from different occupations and reflects this in determining occupational aptitude. For example, it combines data from sales and technical occupations and analyzes it. The determination unit also incorporates the opinions of experts from different industries to determine cross-industry occupational aptitude. For example, it takes into account industry-specific skills and characteristics. This makes it possible to integrate data from different industries and occupations to determine cross-industry occupational aptitude.

[0068] The assessment unit can suggest promising occupations by taking into account future technological trends or market trends. For example, the assessment unit analyzes future technological trends and market trends and reflects them in its assessment of suitable occupations. For example, it can suggest occupations related to new technologies such as AI and blockchain. The assessment unit also collects the latest market data and reflects it in its analysis to assess occupational aptitude taking market trends into account. For example, it identifies growth industries and occupations with increasing demand. The assessment unit also incorporates the opinions of experts on technological trends and market trends in order to suggest promising occupations. For example, it predicts technological innovations and market fluctuations. This makes it possible to suggest promising occupations by taking into account future technological trends and market trends.

[0069] The determination unit uses the emotion estimation function to monitor in real time the emotional response of an individual when they receive a career suggestion, and can dynamically adjust the content of the suggestion. For example, the determination unit uses the emotion estimation function to build a system that monitors in real time the emotional response of an individual when they receive a career suggestion. For example, it analyzes an emotion score for the content of the suggestion. The determination unit also develops a system that dynamically adjusts the content of the career suggestion based on the individual's emotional response data. For example, it avoids suggestions that have strong negative emotions. The determination unit also uses the emotion estimation function to provide feedback in real time on the emotional response of an individual when they receive a career suggestion, and optimizes the content of the suggestion. For example, it prioritizes suggestions that have strong positive emotions. This makes it possible to monitor in real time the emotional response of an individual when they receive a career suggestion, and dynamically adjust the content of the suggestion.

[0070] The suggestion unit can present detailed career paths or growth opportunities for the proposed occupation and support the individual's future planning. The suggestion unit, for example, presents detailed career paths for the proposed occupation and supports the individual's future planning. For example, it specifically indicates promotion steps and required skills. The suggestion unit also presents growth opportunities in the proposed occupation and supports the individual's career plan. For example, it introduces training programs and opportunities to obtain qualifications. The suggestion unit also visually displays the career paths and growth opportunities to make it easier for the individual to imagine their future career. For example, it provides a career map or growth graph. This makes it possible to present detailed career paths and growth opportunities for the proposed occupation and support the individual's future planning.

[0071] The suggestion unit can provide a specific study plan for acquiring the skills or qualifications required for the proposed occupation. The suggestion unit, for example, provides a specific study plan for acquiring the skills or qualifications required for the proposed occupation. For example, it can introduce online courses or training programs. The suggestion unit can also customize the study plan to fit an individual's schedule, allowing them to efficiently acquire skills. For example, it can provide weekly study goals and progress management. The suggestion unit can also provide resources and teaching materials related to the study plan, supporting the individual in effectively acquiring the required skills. For example, it can provide reference books and video tutorials. In this way, it is possible to provide a specific study plan for acquiring the skills or qualifications required for the proposed occupation.

[0072] The suggestion unit can use the emotion estimation function to analyze an individual's emotional response to a proposed occupation and make suggestions that elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze an individual's emotional response to a proposed occupation and make suggestions that elicit positive emotions. For example, the suggestion unit can prioritize suggesting occupations with high emotion scores. The suggestion unit can also dynamically adjust the content of the suggestion based on the individual's emotional data to elicit positive emotions. For example, the suggestion unit can provide real-time feedback on the content of the suggestion. The suggestion unit can also use the emotion estimation function to monitor the emotional response to a proposed occupation and provide advice to elicit positive emotions. For example, the suggestion unit can emphasize the appeal and benefits of the occupation. This makes it possible to analyze an individual's emotional response to a proposed occupation and make suggestions that elicit positive emotions.

[0073] The suggestion unit can share other users' feedback or experiences on the proposed occupations and provide reference information. The suggestion unit, for example, builds a system that collects and shares other users' feedback and experiences on proposed occupations. For example, it displays reviews and comments on occupations. The suggestion unit also provides a concrete image of the proposed occupation based on the experiences of other users. For example, it introduces the daily schedule and work content of the occupation. The suggestion unit also visually displays the feedback and experiences to make it easier for individuals to understand the proposed occupations. For example, it visualizes the feedback using graphs and charts. This makes it possible to share other users' feedback and experiences on the proposed occupations and provide reference information.

[0074] The suggestion department may introduce community or networking opportunities related to the proposed occupation and provide social support. For example, the suggestion department may introduce community or networking opportunities related to the proposed occupation and provide social support. For example, the suggestion department may introduce industry events or study groups. The suggestion department may also enable individuals to obtain information about the proposed occupation through community or networking opportunities. For example, the suggestion department may introduce online forums or social media groups. The suggestion department may also provide networking opportunities with experts or mentors related to the proposed occupation to support the individual's career. For example, the suggestion department may introduce mentoring programs or coaching sessions. This may allow the suggestion department to introduce community or networking opportunities related to the proposed occupation and provide social support.

[0075] The suggestion unit can use the emotion estimation function to customize the proposed content based on an individual's emotional response to the proposed occupation. For example, the suggestion unit uses the emotion estimation function to analyze an individual's emotional response to the proposed occupation and customize the proposed content. For example, it prioritizes suggesting occupations with high emotion scores. The suggestion unit also dynamically adjusts the proposed content based on the individual's emotion data to elicit positive emotions. For example, it provides real-time feedback on the proposed content. The suggestion unit also uses the emotion estimation function to monitor the emotional response to the proposed occupation and provide advice to elicit positive emotions. For example, it emphasizes the appeal and benefits of the occupation. This allows the proposed content to be customized based on an individual's emotional response to the proposed occupation.

[0076] The feedback collection unit can analyze individual feedback on proposed occupations in detail and improve the accuracy of the proposal algorithm. The feedback collection unit, for example, builds a system that collects individual feedback on proposed occupations and analyzes it in detail. For example, it performs text analysis on the feedback content and identifies areas for improvement. The feedback collection unit also improves the accuracy of the proposal algorithm based on the feedback data. For example, it adjusts the algorithm by reflecting the feedback content. The feedback collection unit also improves the proposal content based on the feedback analysis results and suggests an occupation that is more suitable for the individual. For example, it suggests a new occupation based on the feedback content. This allows for a detailed analysis of individual feedback on proposed occupations and improves the accuracy of the proposal algorithm.

[0077] The feedback collection unit can dynamically adjust the parameters for vocational aptitude assessment based on the feedback data. The feedback collection unit, for example, builds a system that dynamically adjusts the parameters for vocational aptitude assessment based on the feedback data. For example, the parameters are adjusted by reflecting the feedback content. The feedback collection unit also analyzes the feedback data in real time and dynamically adjusts the parameters for vocational aptitude assessment. For example, the assessment criteria are changed depending on the feedback content. The feedback collection unit also continuously improves the parameters for vocational aptitude assessment based on the feedback data. For example, the algorithm is adjusted by reflecting the feedback content. This allows the parameters for vocational aptitude assessment to be dynamically adjusted based on the feedback data.

[0078] The feedback collection unit can use the emotion estimation function to analyze an individual's emotions at the time of feedback and propose improvement measures to reduce negative emotions. The feedback collection unit, for example, uses the emotion estimation function to analyze an individual's emotions at the time of feedback and propose improvement measures to reduce negative emotions. For example, it proposes a positive approach to the feedback content. The feedback collection unit also provides an interface for reducing negative emotions based on emotional data at the time of feedback. For example, it displays an encouraging message according to the feedback content. The feedback collection unit also uses the emotion estimation function to monitor emotional reactions at the time of feedback in real time and provide advice to reduce negative emotions. For example, it provides a positive perspective on the feedback content. This makes it possible to analyze an individual's emotions at the time of feedback and propose improvement measures to reduce negative emotions.

[0079] The feedback collection unit can share the feedback data with other users and extract common issues or success stories. The feedback collection unit, for example, builds a system for sharing the feedback data with other users and extracting common issues and success stories. For example, the feedback content is shared on the cloud. The feedback collection unit also analyzes the feedback data and identifies common issues and success stories. For example, it performs text analysis on the feedback content and extracts common themes. The feedback collection unit also shares common issues and success stories based on the feedback data and provides reference for other users. For example, it proposes improvement measures based on the feedback content. In this way, the feedback data can be shared with other users and common issues and success stories can be extracted.

[0080] The feedback collection unit can introduce new evaluation criteria for career aptitude assessment based on the feedback. The feedback collection unit, for example, builds a system that introduces new evaluation criteria for career aptitude assessment based on feedback data. For example, it updates the evaluation criteria by reflecting the feedback content. The feedback collection unit also analyzes the feedback data and introduces new evaluation criteria. For example, it sets new evaluation criteria based on the feedback content. The feedback collection unit also continuously improves the evaluation criteria for career aptitude assessment based on the feedback data. For example, it adjusts the evaluation criteria by reflecting the feedback content. This makes it possible to introduce new evaluation criteria for career aptitude assessment based on feedback.

[0081] The feedback collection unit can use the emotion estimation function to monitor the emotions of individuals at the time of feedback in real time and improve the feedback process. The feedback collection unit, for example, uses the emotion estimation function to build a system that monitors the emotions of individuals at the time of feedback in real time. For example, it analyzes the emotion score for the feedback content. The feedback collection unit also improves the feedback process based on the emotion data at the time of feedback. For example, it suggests a positive approach when negative emotions are strong. The feedback collection unit also uses the emotion estimation function to provide feedback on emotional reactions at the time of feedback in real time and optimize the feedback process. For example, it prioritizes feedback content with a high emotion score. This makes it possible to monitor the emotions of individuals at the time of feedback in real time and improve the feedback process.

[0082] The data update unit can track an individual's career progress or new skill acquisition in real time and reflect it in suggestions of suitable occupations. The data update unit, for example, builds a system that tracks an individual's career progress or new skill acquisition in real time. For example, it automatically updates resumes and skill sheets. The data update unit also dynamically adjusts the content of suitable occupation suggestions based on career progress and skill acquisition data. For example, it updates occupation suggestions based on new skills. The data update unit also suggests occupations that are optimal for the individual based on the career data tracked in real time. For example, it suggests occupations based on the latest skill set. This allows an individual's career progress and new skill acquisition to be tracked in real time and reflected in suggestions of suitable occupations.

[0083] The data update unit can take into account an individual's life events in the continuous data update and reevaluate occupational aptitude. The data update unit, for example, builds a system that takes into account an individual's life events (marriage, childbirth, etc.) and reevaluates occupational aptitude. For example, it makes occupational suggestions based on life events. The data update unit also reevaluates occupational aptitude based on life event data. For example, it suggests occupations that are suitable for a lifestyle after marriage or childbirth. The data update unit also conducts individual interviews and questionnaires to reevaluate occupational aptitude taking into account life events in the continuous data update. For example, it grasps an individual's situation through detailed questions about life events. This makes it possible to take into account an individual's life events in the continuous data update and reevaluate occupational aptitude.

[0084] The data update unit can use the emotion estimation function to analyze the emotion of an individual when updating data and provide an interface for eliciting positive emotions. The data update unit, for example, uses the emotion estimation function to monitor the emotion of an individual when updating data in real time and adjust the interface. For example, it displays relaxing music or a message. The data update unit also provides an interface for eliciting positive emotions based on the emotion estimation results when updating data. For example, it displays an encouraging message according to the update content. The data update unit also uses the emotion estimation function to customize the interface for eliciting positive emotions when updating data. For example, it adjusts the interface according to the update speed and content. In this way, it is possible to analyze the emotion of an individual when updating data and provide an interface for eliciting positive emotions.

[0085] The data update unit can compare the data with other users' data during continuous data updates and provide a benchmark. The data update unit, for example, builds a system that compares the data with other users' data during continuous data updates and provides a benchmark. For example, it compares the skills and experience of other users in the same occupation. The data update unit also benchmarks an individual's skills and experience based on the data of other users and evaluates occupational aptitude. For example, it identifies skill gaps by comparing with other users in the same industry. The data update unit also optimizes an individual's career plan based on the benchmark data. For example, it suggests a career path by referring to the success stories of other users. This makes it possible to compare the data with other users' data during continuous data updates and provide a benchmark.

[0086] The data update unit can customize the frequency or method of data updates to suit the needs of an individual. For example, the data update unit builds a system that customizes the frequency and method of data updates to suit the needs of an individual. For example, it adjusts the timing of data updates according to an individual's schedule. The data update unit also customizes the method of data updates to suit the needs of an individual. For example, it provides a choice between manual input and automatic collection. The data update unit also adjusts the frequency and method of data updates based on individual feedback. For example, it reduces notifications if the update frequency is too high. This makes it possible to customize the frequency and method of data updates to suit the needs of an individual.

[0087] The data update unit uses the emotion estimation function to monitor the emotions of individuals at the time of data update in real time and optimize the update process. The data update unit, for example, uses the emotion estimation function to build a system that monitors the emotions of individuals at the time of data update in real time. For example, it analyzes the emotion score for the update content. The data update unit also optimizes the update process based on the emotion data at the time of data update. For example, it adjusts the update content if negative emotions are strong. The data update unit also uses the emotion estimation function to provide feedback on the emotional response at the time of data update in real time and optimize the update process. For example, it prioritizes update content with a high emotion score. This makes it possible to monitor the emotions of individuals at the time of data update in real time and optimize the update process.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The data collection unit can collect an individual's health data and reflect it in determining occupational aptitude. For example, it can collect data from a fitness tracker or smartwatch to evaluate an individual's physical strength and health condition. The data collection unit can also analyze an individual's sleep patterns and stress levels to determine occupational aptitude based on their health condition. Furthermore, the data collection unit can evaluate occupational health risks based on the health data and reflect this in suggesting suitable occupations. This makes it possible to determine occupational aptitude taking into account an individual's health condition.

[0090] The analysis unit can collect data on an individual's hobbies and leisure activities and reflect this in determining career aptitude. For example, it can collect and analyze information on an individual's hobbies and clubs they participate in. The analysis unit can also evaluate skills and interests related to hobbies and leisure activities and reflect this in determining career aptitude. Furthermore, the analysis unit can suggest careers that an individual can enjoy based on their hobbies and leisure activities. This makes it possible to determine career aptitude that takes into account an individual's hobbies and leisure activities.

[0091] The assessment unit can assess occupational aptitude by taking into account an individual's learning style and learning speed. For example, it can evaluate how an individual is good at learning and how quickly they can acquire new skills. The assessment unit can also suggest occupational aptitude based on the individual's learning style and learning speed. Furthermore, the assessment unit can suggest a vocational training program tailored to the individual's learning style. This makes it possible to assess occupational aptitude by taking into account an individual's learning style and learning speed.

[0092] The suggestion unit can suggest a stress management method related to the proposed occupation based on the individual's emotional reaction to the proposed occupation. For example, the suggestion unit can use the emotion estimation function to analyze the individual's stress level for the proposed occupation and suggest an appropriate stress management method. The suggestion unit can also identify stress factors related to the occupation and suggest countermeasures for those factors. Furthermore, the suggestion unit can provide resources and tools that are useful for stress management. This allows the suggestion unit to suggest a stress management method for the proposed occupation.

[0093] The suggestion unit can provide occupation-related mental health support based on the individual's emotional response to the proposed occupation. For example, the suggestion unit can use the emotion estimation function to analyze the individual's mental health status for the proposed occupation and suggest appropriate support. The suggestion unit can also identify occupation-related mental health risks and suggest countermeasures. Furthermore, the suggestion unit can provide resources and tools useful for mental health support. This allows mental health support to be provided for the proposed occupation.

[0094] The suggestion unit can make work-life balance suggestions related to the occupation based on the individual's emotional response to the proposed occupation. For example, the suggestion unit can use the emotion estimation function to analyze the individual's satisfaction with work-life balance for the proposed occupation and make appropriate suggestions. The suggestion unit can also suggest ways to improve work-life balance related to the occupation. Furthermore, the suggestion unit can provide resources and tools that are useful for improving work-life balance. This allows for work-life balance suggestions to be made for the proposed occupation.

[0095] The suggestion unit can provide career counseling related to the occupation based on the individual's emotional response to the proposed occupation. For example, the suggestion unit can use the emotion estimation function to analyze the individual's career-related emotions regarding the proposed occupation and suggest appropriate counseling. The suggestion unit can also identify career worries and challenges related to the occupation and suggest measures to address them. Furthermore, the suggestion unit can provide resources and tools that are useful for career counseling. This makes it possible to provide career counseling for the proposed occupation.

[0096] The suggestion unit can suggest career-related motivation-enhancing measures based on the individual's emotional response to the proposed career. For example, the suggestion unit can use the emotion estimation function to analyze the individual's motivation level for the proposed career and suggest appropriate improvement measures. The suggestion unit can also identify career-related factors that decrease motivation and suggest measures to address them. Furthermore, the suggestion unit can provide resources and tools that are useful for improving motivation. This makes it possible to suggest motivation-enhancing measures for the proposed career.

[0097] The suggestion unit can provide career-related feedback based on the individual's emotional response to the proposed career. For example, the suggestion unit can use the emotion estimation function to analyze the individual's feedback on the proposed career and provide appropriate feedback. The suggestion unit can also identify the content of the career-related feedback and propose countermeasures for it. Furthermore, the suggestion unit can provide resources and tools that are useful for providing feedback. This allows the suggestion unit to provide feedback on the proposed career.

[0098] The suggestion unit can provide a career plan related to the occupation based on the individual's emotional response to the proposed occupation. For example, the suggestion unit can use the emotion estimation function to analyze the individual's career plan for the proposed occupation and provide an appropriate plan. The suggestion unit can also identify the content of the career plan related to the occupation and propose measures for it. Furthermore, the suggestion unit can provide resources and tools that are useful for the career plan. This makes it possible to provide a career plan for the proposed occupation.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The data collection unit collects personal data. For example, the data collection unit collects information such as an individual's educational background, work history, skills, interests, and personality traits. It also collects information such as what jobs an individual has had in the past, what skills they have, and what interests they have. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may use generative AI to analyze data related to personal characteristics and skills. The analysis unit may also use statistical analysis or machine learning algorithms to analyze the data. Step 3: The determination unit statistically determines the appropriate occupation based on the personal data analyzed by the analysis unit. For example, the determination unit statistically analyzes whether an individual's skills and interests are suitable for a particular occupation, and determines the appropriate occupation based on the results. It can also match individual characteristics with occupational aptitude using past data and statistical models. Step 4: The suggestion unit proposes suitable occupations based on the assessment made by the assessment unit. For example, the suggestion unit may suggest, "Based on your skills and interests, the following occupations are suitable for you." It can also provide information such as the specific job content, required skills, and future career paths for the proposed occupations.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 data collection unit that collects personal data; an analysis unit that analyzes the personal data collected by the data collection unit; a determination unit that statistically determines an aptitude for a job based on the personal data analyzed by the analysis unit; a suggestion unit that suggests the suitable occupation determined by the determination unit. A system characterized by:

2. The data collection unit Analyze how individuals feel about their past jobs or skills to identify jobs that are emotionally suitable 2. The system of claim 1.

3. The data collection unit Collect data from different cultural spheres or regions and analyze occupational aptitude taking cultural background into account 2. The system of claim 1.

4. The determination unit Analyzing an individual's feelings about a particular occupation and prioritizing suggestions for occupations that are emotionally suitable 2. The system of claim 1.

5. The proposal unit Analyze an individual's emotional response to a proposed job and make suggestions that elicit positive emotions 2. The system of claim 1.

6. The feedback collection unit: Analyze individual emotions during feedback and suggest improvements to reduce negative emotions 2. The system of claim 1.

7. The data update section is Analyzing personal emotions when updating data and providing an interface to elicit positive emotions 2. The system of claim 1.

8. The data update section is Monitor personal sentiment in real time when updating data and optimize the update process 2. The system of claim 1.

Images