System

Abstract

An object of a system according to an embodiment is to provide an optimal interview preparation of a company based on the work experience and skill of a user.SOLUTION: A system according to an embodiment includes a work history analysis unit, a tendency analysis unit, an approach customization unit, and a feedback provision unit. The work experience analysis unit analyzes the work experience or skill of the user. A tendency analysis part analyzes the past adoption tendency of the enterprise or an element to be emphasized. The approach customization unit customizes an optimum approach for the user based on the analysis results of the work history analysis unit and the tendency analysis unit. The feedback providing unit provides personalized feedback to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty providing optimal interview preparation for companies based on a user's work history and skills, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal preparation for interviews at companies based on the user's work history and skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a work history analysis unit, a trend analysis unit, an approach customization unit, and a feedback provision unit. The work history analysis unit analyzes the user's work history or skills. The trend analysis unit analyzes a company's past hiring trends or important factors. The approach customization unit customizes an optimal approach for the user based on the analysis results of the work history analysis unit and the trend analysis unit. The feedback provision unit provides individual feedback to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal interview preparation for companies based on the user's work history and skills. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An interview preparation system according to an embodiment of the present invention analyzes a user's work history and skills, and a generation AI analyzes companies' hiring trends, customizes an optimal approach, and provides individualized feedback. As a result, the interview preparation system provides optimal interview preparation based on the user's work history and skills, suggests an approach tailored to companies' hiring trends, and enables the user to improve their self-expression through individualized feedback.

[0029] An interview preparation system according to an embodiment includes a work history analysis unit, a trend analysis unit, an approach customization unit, and a feedback provision unit. The work history analysis unit analyzes a user's work history or skills. For example, it analyzes information about the work history and skills provided by the user and performs a detailed analysis of the user's past work experience and skills. The trend analysis unit analyzes a company's past hiring trends or factors that the company values. For example, it analyzes the company's past hiring data and public information to identify factors that the company values. The approach customization unit customizes an optimal approach for the user based on the analysis results of the work history analysis unit and the trend analysis unit. For example, it proposes an approach that emphasizes factors that the company values ​​based on the user's work history and skills. The feedback provision unit provides individual feedback to the user. For example, it analyzes how the user answered questions in an interview and specifically points out areas for improvement and strengthening. As a result, the interview preparation system according to an embodiment can effectively support the user's interview preparation by analyzing the user's work history and skills, customizing an optimal approach based on the company's hiring trends, and providing individual feedback.

[0030] The employment history analysis unit performs a detailed analysis of past projects or deliverables in addition to the user's employment history or skills, and can extract specific success stories. For example, the generation AI performs a detailed analysis of past projects and deliverables in addition to the user's employment history and skills. For example, it analyzes deliverables and reports of projects in which the user was involved and extracts specific success stories. The employment history analysis unit also collects detailed data on projects the user has previously worked on, and the generation AI analyzes that data to identify success stories. For example, it evaluates the progress of the project and the quality of the deliverables. The employment history analysis unit also performs a detailed analysis of past projects related to the user's employment history and skills and extracts specific success stories. For example, it identifies success stories based on project deliverables and customer feedback. This allows the user to analyze the user's past projects and deliverables in detail and extract specific success stories, thereby strengthening their selling points in interviews.

[0031] The employment history analysis unit can analyze the latest industry trends or technological trends related to the user's employment history or skills and evaluate the market value of the user's skill set. For example, the generation AI analyzes the latest industry trends and technological trends related to the user's employment history or skills to evaluate the market value of the user's skill set. For example, it evaluates the demand for skills based on the latest technological trends and market needs. The employment history analysis unit also collects the latest industry trends and technological trends related to the user's employment history or skills, and the generation AI analyzes the data to evaluate the market value of the user's skill set. For example, it takes into account the industry growth rate and the impact of technological innovation. The employment history analysis unit also analyzes the latest industry trends and technological trends related to the user's employment history or skills to evaluate the market value of the user's skill set. For example, it evaluates the market value of skills based on industry recruitment trends and salary levels. This allows the user's career strategy to be optimized by evaluating the market value of the user's skill set.

[0032] The work history analysis unit can reconstruct the analysis results of the user's work history or skills into a skill set that can be transferred to a different industry or occupation, and suggest the possibility of a career change. For example, the work history analysis unit reconstructs the results of the analysis of the user's work history and skills using a generation AI into a skill set that can be transferred to a different industry or occupation, and suggests ways to utilize the user's skills in other industries. Furthermore, the work history analysis unit reconstructs the results of the analysis of the user's work history and skills into a skill set that can be transferred to a different industry or occupation, and suggests the possibility of a career change. For example, it suggests ways to apply the user's skills to a new occupation. Furthermore, the work history analysis unit reconstructs the results of the analysis of the user's work history and skills using a generation AI into a skill set that can be transferred to a different industry or occupation, and suggests the possibility of a career change. For example, it provides specific advice on how to utilize the user's skills in other industries. This can expand the possibilities for a career change by transferring the user's skill set to a different industry or occupation.

[0033] The work history analysis unit can support skill development by suggesting appropriate online courses or qualification acquisition based on the user's work history or skills. For example, the generation AI of the work history analysis unit can recommend appropriate online courses or qualification acquisition based on the user's work history and skills. For example, the generation AI can recommend online courses to strengthen the user's skill set. The work history analysis unit can also support skill development by suggesting appropriate online courses or qualification acquisition based on the user's work history and skills. For example, the generation AI can recommend qualification acquisition that matches the user's career goals. The work history analysis unit can also support skill development by suggesting appropriate online courses or qualification acquisition based on the user's work history and skills. For example, the generation AI can recommend specific courses to fill the user's skill gaps. This can promote career growth by supporting the user's skill development.

[0034] The trend analysis unit analyzes corporate culture or values ​​in addition to companies' hiring trends, and can identify companies that match the user's values. For example, the generation AI analyzes corporate culture and values ​​in addition to companies' hiring trends. For example, it analyzes publicly available information about companies and employee feedback to identify corporate culture and values. The trend analysis unit also takes corporate culture and values ​​into consideration when analyzing companies' hiring trends, and identifies companies that match the user's values. For example, it analyzes a company's mission statement and vision. The trend analysis unit also analyzes corporate culture and values ​​in addition to companies' hiring trends, and identifies companies that match the user's values. For example, it analyzes a company's internal events and employee benefits. This allows the system to identify companies that match the user's values, thereby improving user satisfaction.

[0035] When analyzing a company's hiring trends, the trend analysis unit takes into account not only past hiring data but also the company's performance or market trends, making it possible to predict future hiring needs. For example, when the generation AI analyzes a company's hiring trends, the trend analysis unit takes into account not only past hiring data but also the company's performance and market trends. For example, it analyzes the company's financial reports and market reports. Furthermore, when analyzing a company's hiring trends, the trend analysis unit takes into account the company's performance and market trends to predict future hiring needs. For example, it analyzes the company's growth strategy and the competitive situation in the market. Furthermore, when the generation AI analyzes a company's hiring trends, the trend analysis unit takes into account not only past hiring data but also the company's performance and market trends to predict future hiring needs. For example, it analyzes the company's new business and market expansion plans. This makes it possible to predict a company's future hiring needs, thereby improving the accuracy of advice provided to users.

[0036] When analyzing a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions, allowing the user to receive advice from a multifaceted perspective. For example, when the generation AI analyzes a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions. For example, it analyzes hiring trends that differ depending on the industry or region, even for the same job type. Furthermore, when analyzing a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions, allowing the user to receive advice from a multifaceted perspective. For example, it compares the hiring standards and prioritized skills of different industries. Furthermore, when the generation AI analyzes a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions, allowing the user to receive advice from a multifaceted perspective. For example, it analyzes hiring trends and market needs by region. This allows the user to receive advice from a multifaceted perspective by comparing it with companies in different industries or regions.

[0037] The trend analysis unit can provide the user with information on appropriate networking events or career fairs based on companies' hiring trends. For example, the generation AI provides the user with information on appropriate networking events or career fairs based on companies' hiring trends. For example, it recommends events related to a specific industry or job type. The trend analysis unit also analyzes companies' hiring trends and provides the user with information on appropriate networking events or career fairs. For example, it provides information on events and fairs in which companies participate. The trend analysis unit also provides the user with information on appropriate networking events or career fairs based on companies' hiring trends. For example, it recommends events that match the user's career goals. This makes it possible to support the user's career growth by providing them with information on appropriate networking events and career fairs.

[0038] The approach customization unit can suggest specific interview anecdotes or examples based on the user's work history or skills, thereby providing a method for effectively highlighting the user's strengths. For example, the approach customization unit allows the generation AI to suggest specific interview anecdotes or examples based on the user's work history or skills. For example, it provides a method for the user to specifically explain past successful projects or goals they have achieved. The approach customization unit also allows the generation AI to suggest specific interview anecdotes or examples based on the user's work history or skills, thereby providing a method for effectively highlighting the user's strengths. For example, it introduces examples of when the user's skills were actually utilized. The approach customization unit also allows the generation AI to suggest specific interview anecdotes or examples based on the user's work history or skills, thereby providing a method for effectively highlighting the user's strengths. For example, it provides a method for showing the user's experience with specific figures or results. This makes it possible to suggest specific anecdotes and examples for effectively highlighting the user's strengths.

[0039] The approach customization unit can analyze the user's past interview experience, compare and analyze successful and unsuccessful approaches, and customize an optimal interview strategy. In the approach customization unit, for example, the generation AI analyzes the user's past interview experience, compare and analyze successful and unsuccessful approaches. For example, it identifies success factors and failure factors based on the user's interview results and feedback. The approach customization unit also analyzes the user's past interview experience, and the generation AI compares and analyzes successful and unsuccessful approaches to customize an optimal interview strategy. For example, it strengthens successful approaches and improves unsuccessful approaches. The approach customization unit also analyzes the user's past interview experience, compare and analyze successful and unsuccessful approaches to customize an optimal interview strategy. For example, it provides specific advice to improve the user's interview performance. In this way, the success rate of interviews can be improved by analyzing the user's past interview experience and customizing an optimal interview strategy.

[0040] The approach customization unit can support career diversity by suggesting interview strategies for different industries or occupations based on the user's work history or skills. In the approach customization unit, for example, the generation AI suggests interview strategies for different industries or occupations based on the user's work history and skills. For example, it provides interview strategies when the user changes jobs to a different industry. The approach customization unit also supports career diversity by suggesting interview strategies for different industries or occupations based on the user's work history and skills. For example, it provides interview strategies for utilizing the user's skills in other industries. The approach customization unit also supports career diversity by suggesting interview strategies for different industries or occupations based on the user's work history and skills. For example, it provides interview strategies when the user tries a new occupation. In this way, it is possible to support career diversity by suggesting interview strategies for different industries and occupations.

[0041] The approach customization unit provides users with opportunities for online mock interviews, enabling them to improve their interview skills through real-time feedback. In the approach customization unit, for example, a generation AI provides users with opportunities for online mock interviews. For example, a system is constructed that allows users to take mock interviews in an environment similar to a real interview. The approach customization unit also provides users with opportunities for online mock interviews, enabling them to improve their interview skills through real-time feedback. For example, specific areas for improvement are pointed out based on the results of the mock interview. The approach customization unit also provides users with opportunities for online mock interviews, enabling them to improve their interview skills through real-time feedback. For example, the generation AI analyzes recordings of mock interviews and provides detailed feedback. In this way, the user's interview performance can be improved by providing opportunities for online mock interviews and improving their interview skills through real-time feedback.

[0042] The feedback providing unit can analyze the user's interview results in detail and provide a report that visually shows specific areas for improvement or success. For example, the feedback providing unit uses a generation AI to analyze the user's interview results in detail and provide a report that visually shows specific areas for improvement or success. For example, the report is created based on the interview answers and the interviewer's feedback. The feedback providing unit also analyzes the user's interview results in detail and provides a report that visually shows specific areas for improvement or success. For example, the report is presented visually in an easy-to-understand manner using graphs and charts. The feedback providing unit also analyzes the user's interview results in detail and provides a report that visually shows specific areas for improvement or success. For example, the report shows an evaluation of each interview question and areas for improvement in detail. This allows the user's interview results to be analyzed in detail and specific areas for improvement or success to be visually presented, thereby deepening the user's understanding.

[0043] The feedback providing unit can propose a specific practice plan for the next interview based on the user's interview results, thereby supporting continuous skill improvement. For example, the generation AI in the feedback providing unit proposes a specific practice plan for the next interview based on the user's interview results. For example, the generation AI proposes practicing answers to specific questions or conducting a mock interview. The feedback providing unit also proposes a specific practice plan for the next interview based on the user's interview results, thereby supporting continuous skill improvement. For example, the generation AI provides interview simulations and feedback. The feedback providing unit also proposes a specific practice plan for the next interview based on the user's interview results, thereby supporting continuous skill improvement. For example, the generation AI proposes an interview practice schedule and specific practice content. This allows the generation AI to propose a specific practice plan for the next interview and support continuous skill improvement, thereby improving the user's interview performance.

[0044] The feedback providing unit can clarify the user's strengths or weaknesses by comparing the user's interview results with other users and providing a relative evaluation. In the feedback providing unit, for example, the generation AI compares the user's interview results with other users and provides a relative evaluation. For example, the user's strengths and weaknesses are clarified by comparison with other users in the same occupation or industry. The feedback providing unit also compares the user's interview results with other users and the generation AI provides a relative evaluation, thereby clarifying the user's strengths and weaknesses. For example, the user's performance is evaluated by comparing with the interview results of other users. The feedback providing unit also compares the user's interview results with other users and provides a relative evaluation, thereby clarifying the user's strengths and weaknesses. For example, the generation AI refers to success stories and areas for improvement of other users. In this way, the user's interview results are compared with other users and the relative evaluation is provided, thereby clarifying the user's strengths and weaknesses.

[0045] The feedback providing unit can introduce an appropriate mentor or coach based on the user's interview results and provide individualized support. For example, the generation AI introduces an appropriate mentor or coach based on the user's interview results. For example, the generation AI recommends a mentor who is familiar with the user's job type or industry. The feedback providing unit also introduces an appropriate mentor or coach based on the user's interview results and provides individualized support. For example, the generation AI provides specific advice to improve the user's interview skills. The feedback providing unit also introduces an appropriate mentor or coach based on the user's interview results and provides individualized support. For example, the generation AI recommends a mentor that matches the user's career goals. This makes it possible to support the user's skill improvement by introducing an appropriate mentor or coach based on the user's interview results and providing individualized support.

[0046] The feedback providing unit can analyze the content of the user's self-introduction or presentation and suggest specific areas for improvement or enhancement. For example, the generation AI analyzes the content of the user's self-introduction or presentation and suggests specific areas for improvement or enhancement. For example, improving the structure of the self-introduction or the slide design of the presentation. The feedback providing unit also analyzes the content of the user's self-introduction or presentation and suggests specific areas for improvement or enhancement. For example, providing advice to improve speaking style or expression method. The feedback providing unit also analyzes the content of the user's self-introduction or presentation and suggests specific areas for improvement or enhancement. For example, providing advice to make the content of the self-introduction more specific. In this way, by analyzing the content of the user's self-introduction or presentation and suggesting specific areas for improvement or enhancement, it is possible to improve self-expression skills.

[0047] The feedback providing unit can provide an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, the feedback providing unit provides an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, it provides methods for practicing speaking and gestures. The feedback providing unit also provides an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, it provides methods for improving eye contact and posture. The feedback providing unit also provides an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, it provides advice for improving the tone of voice and the use of facial expressions. In this way, by providing an appropriate communication skill or body language training program to improve the user's ability to express themselves, it is possible to improve the user's ability to express themselves.

[0048] The feedback providing unit can provide opportunities for the user to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides opportunities for the generation AI to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides an intercultural communication training program. The feedback providing unit also provides opportunities for the generation AI to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides foreign language speaking practice or a workshop on intercultural understanding. The feedback providing unit also provides opportunities for the generation AI to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides opportunities for intercultural exchange events or international networking opportunities. This allows the user's ability to express themselves to be improved by providing opportunities to learn communication skills in different cultures or language areas.

[0049] The feedback providing unit can provide the user with information about online workshops or seminars to improve self-expression skills, thereby providing an opportunity to acquire practical skills. For example, the generation AI provides the user with information about online workshops or seminars to improve self-expression skills. For example, the feedback providing unit recommends workshops for improving self-introduction or presentation skills. The feedback providing unit also provides the user with information about online workshops or seminars to improve self-expression skills, thereby providing an opportunity to acquire practical skills. For example, the feedback providing unit provides training in communication skills or body language. The feedback providing unit also provides the user with information about online workshops or seminars to improve self-expression skills, thereby providing an opportunity to acquire practical skills. For example, the feedback providing unit provides specific practice methods for improving self-expression skills. In this way, the user's self-expression skills can be improved by providing information about online workshops or seminars to improve self-expression skills and providing an opportunity to acquire practical skills.

[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 work history analysis unit analyzes the user's hobbies and interests in addition to their work history and skills, and by linking these to their work experience, they can strengthen their selling points in an interview. For example, if a user's hobby strengthens their work-related skills, the system extracts specific examples and suggests them as selling points in an interview. Also, if the user's interests are related to their work, the system suggests specific approaches that utilize those interests. Furthermore, even if the user's hobbies and interests are not directly related to their work, they can be used as examples of soft skills or leadership. In this way, by linking the user's hobbies and interests to their work experience, they can strengthen their selling points in an interview.

[0052] The trend analysis unit can analyze companies' hiring trends, as well as their corporate social responsibility (CSR) activities and environmental initiatives, to identify companies that align with the user's values. For example, it can analyze a company's CSR report or environmental report to identify the type of social responsibility the company is fulfilling. It can also collect information on companies' environmental initiatives and sustainability to identify companies that align with the user's values. Furthermore, if a company's social responsibility or environmental initiatives align with the user's values, it can recommend that company, thereby improving user satisfaction. In this way, it is possible to identify companies that align with the user's values ​​by analyzing a company's social responsibility and environmental initiatives.

[0053] The approach customization unit suggests interview strategies for different cultures and countries based on the user's work history and skills, thereby expanding the possibilities for an international career. For example, if the user has work experience overseas, it suggests a specific approach that makes use of that experience. Also, when the user attempts an interview in a different culture or country, it provides strategies that take into account the interview style and expected skills of that culture or country. Furthermore, if the user is aiming for an international career, it suggests specific steps and preparation methods for achieving that. In this way, by suggesting interview strategies for different cultures and countries, it can expand the possibilities for the user's international career.

[0054] The feedback providing unit can analyze the user's interview results, evaluate the effectiveness of the words and phrases used by the user in the interview, and suggest more effective words and phrases. For example, it can analyze the words and phrases used by the user in the interview and evaluate their effectiveness. It can also analyze the impression the words and phrases used by the user made on the interviewer and suggest areas for improvement. It can also provide specific advice to make the words and phrases used by the user more effective. This can improve the success rate of the interview by improving the words and phrases used by the user in the interview.

[0055] The feedback providing unit can analyze the user's interview results, evaluate the type of body language used by the user in the interview, and suggest more effective body language. For example, it can analyze the body language used by the user in the interview and evaluate its effectiveness. It can also analyze the impression the body language used by the user made on the interviewer and suggest areas for improvement. It can also provide specific advice to make the body language used by the user in the interview more effective. This can improve the success rate of the interview by improving the body language used by the user in the interview.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The work history analysis unit analyzes the user's work history or skills. For example, it analyzes the information on the work history and skills provided by the user and analyzes the user's past work experience and skills in detail. Step 2: The trend analysis unit analyzes the company's past hiring trends or the factors it values. For example, it analyzes the company's past hiring data and public information to identify the factors it values. Step 3: The approach customization unit customizes the optimal approach for the user based on the analysis results of the employment history analysis unit and the trend analysis unit. For example, it proposes an approach that emphasizes the factors that companies value based on the user's employment history and skills. Step 4: The feedback provider provides individual feedback to the user. For example, it analyzes how the user answered questions in an interview and specifically points out areas for improvement or strengthening.

[0058] (Example 2) An interview preparation system according to an embodiment of the present invention analyzes a user's work history and skills, and a generation AI analyzes companies' hiring trends, customizes an optimal approach, and provides individualized feedback. As a result, the interview preparation system provides optimal interview preparation based on the user's work history and skills, suggests an approach tailored to companies' hiring trends, and enables the user to improve their self-expression through individualized feedback.

[0059] An interview preparation system according to an embodiment includes a work history analysis unit, a trend analysis unit, an approach customization unit, and a feedback provision unit. The work history analysis unit analyzes a user's work history or skills. For example, it analyzes information about the work history and skills provided by the user and performs a detailed analysis of the user's past work experience and skills. The trend analysis unit analyzes a company's past hiring trends or factors that the company values. For example, it analyzes the company's past hiring data and public information to identify factors that the company values. The approach customization unit customizes an optimal approach for the user based on the analysis results of the work history analysis unit and the trend analysis unit. For example, it proposes an approach that emphasizes factors that the company values ​​based on the user's work history and skills. The feedback provision unit provides individual feedback to the user. For example, it analyzes how the user answered questions in an interview and specifically points out areas for improvement and strengthening. As a result, the interview preparation system according to an embodiment can effectively support the user's interview preparation by analyzing the user's work history and skills, customizing an optimal approach based on the company's hiring trends, and providing individual feedback.

[0060] The employment history analysis unit performs a detailed analysis of past projects or deliverables in addition to the user's employment history or skills, and can extract specific success stories. For example, the generation AI performs a detailed analysis of past projects and deliverables in addition to the user's employment history and skills. For example, it analyzes deliverables and reports of projects in which the user was involved and extracts specific success stories. The employment history analysis unit also collects detailed data on projects the user has previously worked on, and the generation AI analyzes that data to identify success stories. For example, it evaluates the progress of the project and the quality of the deliverables. The employment history analysis unit also performs a detailed analysis of past projects related to the user's employment history and skills and extracts specific success stories. For example, it identifies success stories based on project deliverables and customer feedback. This allows the user to analyze the user's past projects and deliverables in detail and extract specific success stories, thereby strengthening their selling points in interviews.

[0061] The employment history analysis unit can analyze the latest industry trends or technological trends related to the user's employment history or skills and evaluate the market value of the user's skill set. For example, the generation AI analyzes the latest industry trends and technological trends related to the user's employment history or skills to evaluate the market value of the user's skill set. For example, it evaluates the demand for skills based on the latest technological trends and market needs. The employment history analysis unit also collects the latest industry trends and technological trends related to the user's employment history or skills, and the generation AI analyzes the data to evaluate the market value of the user's skill set. For example, it takes into account the industry growth rate and the impact of technological innovation. The employment history analysis unit also analyzes the latest industry trends and technological trends related to the user's employment history or skills to evaluate the market value of the user's skill set. For example, it evaluates the market value of skills based on industry recruitment trends and salary levels. This allows the user's career strategy to be optimized by evaluating the market value of the user's skill set.

[0062] The work history analysis unit uses the emotion estimation function to analyze how the user feels about their past work experience and can propose an approach that emphasizes positive experiences. In the work history analysis unit, for example, the generation AI uses the emotion estimation function to analyze how the user feels about their past work experience. For example, it analyzes text data related to the user's work experience and calculates an emotion score. The work history analysis unit also uses the emotion estimation function to analyze how the user feels about their past work experience and proposes an approach that emphasizes positive experiences. For example, it emphasizes work experiences with strong positive emotions in an interview. The work history analysis unit also uses the emotion estimation function to analyze the user's emotions about their past work experience and proposes an approach that emphasizes positive experiences. For example, it highlights work experiences with high emotion scores in an interview. In this way, emphasizing the user's positive work experience can strengthen their selling points in an interview.

[0063] The work history analysis unit can reconstruct the analysis results of the user's work history or skills into a skill set that can be transferred to a different industry or occupation, and suggest the possibility of a career change. For example, the work history analysis unit reconstructs the results of the analysis of the user's work history and skills using a generation AI into a skill set that can be transferred to a different industry or occupation, and suggests ways to utilize the user's skills in other industries. Furthermore, the work history analysis unit reconstructs the results of the analysis of the user's work history and skills into a skill set that can be transferred to a different industry or occupation, and suggests the possibility of a career change. For example, it suggests ways to apply the user's skills to a new occupation. Furthermore, the work history analysis unit reconstructs the results of the analysis of the user's work history and skills using a generation AI into a skill set that can be transferred to a different industry or occupation, and suggests the possibility of a career change. For example, it provides specific advice on how to utilize the user's skills in other industries. This can expand the possibilities for a career change by transferring the user's skill set to a different industry or occupation.

[0064] The work history analysis unit can support skill development by suggesting appropriate online courses or qualification acquisition based on the user's work history or skills. For example, the generation AI of the work history analysis unit can recommend appropriate online courses or qualification acquisition based on the user's work history and skills. For example, the generation AI can recommend online courses to strengthen the user's skill set. The work history analysis unit can also support skill development by suggesting appropriate online courses or qualification acquisition based on the user's work history and skills. For example, the generation AI can recommend qualification acquisition that matches the user's career goals. The work history analysis unit can also support skill development by suggesting appropriate online courses or qualification acquisition based on the user's work history and skills. For example, the generation AI can recommend specific courses to fill the user's skill gaps. This can promote career growth by supporting the user's skill development.

[0065] The work history analysis unit uses the emotion estimation function to identify the skills or experience in which the user is most confident and can suggest an approach to make the most of those strengths. In the work history analysis unit, for example, the generation AI uses the emotion estimation function to identify the skills and experience in which the user is most confident. For example, it analyzes text data related to the user's work experience and calculates an emotion score. The work history analysis unit also uses the emotion estimation function to identify the skills and experience in which the user is most confident and suggests an approach to make the most of those strengths. For example, it emphasizes the skills in which the user is confident in during an interview. In addition, the work history analysis unit uses the emotion estimation function to identify the skills and experience in which the user is most confident and suggests an approach to make the most of those strengths. For example, it highlights skills with high emotion scores during an interview. In this way, by emphasizing the skills and experience in which the user is confident, it is possible to strengthen the selling points in an interview.

[0066] The trend analysis unit analyzes corporate culture or values ​​in addition to companies' hiring trends, and can identify companies that match the user's values. For example, the generation AI analyzes corporate culture and values ​​in addition to companies' hiring trends. For example, it analyzes publicly available information about companies and employee feedback to identify corporate culture and values. The trend analysis unit also takes corporate culture and values ​​into consideration when analyzing companies' hiring trends, and identifies companies that match the user's values. For example, it analyzes a company's mission statement and vision. The trend analysis unit also analyzes corporate culture and values ​​in addition to companies' hiring trends, and identifies companies that match the user's values. For example, it analyzes a company's internal events and employee benefits. This allows the system to identify companies that match the user's values, thereby improving user satisfaction.

[0067] When analyzing a company's hiring trends, the trend analysis unit takes into account not only past hiring data but also the company's performance or market trends, making it possible to predict future hiring needs. For example, when the generation AI analyzes a company's hiring trends, the trend analysis unit takes into account not only past hiring data but also the company's performance and market trends. For example, it analyzes the company's financial reports and market reports. Furthermore, when analyzing a company's hiring trends, the trend analysis unit takes into account the company's performance and market trends to predict future hiring needs. For example, it analyzes the company's growth strategy and the competitive situation in the market. Furthermore, when the generation AI analyzes a company's hiring trends, the trend analysis unit takes into account not only past hiring data but also the company's performance and market trends to predict future hiring needs. For example, it analyzes the company's new business and market expansion plans. This makes it possible to predict a company's future hiring needs, thereby improving the accuracy of advice provided to users.

[0068] The trend analysis unit uses the emotion estimation function to analyze what emotions a company's recruiters had during past interviews and can suggest approaches to making a good impression during the interview. For example, the generation AI in the trend analysis unit uses the emotion estimation function to analyze what emotions a company's recruiters had during past interviews. For example, it analyzes interview recording data and interviewer feedback. The trend analysis unit also uses the emotion estimation function to analyze what emotions a company's recruiters had during past interviews and suggests approaches to making a good impression during the interview. For example, it provides example answers that elicit positive emotions. The trend analysis unit also uses the emotion estimation function to analyze what emotions a company's recruiters had during past interviews and suggests approaches to making a good impression during the interview. For example, it provides example answers with high emotion scores. In this way, by analyzing the emotions of a company's recruiters, it is possible to suggest approaches to making a good impression during the interview.

[0069] When analyzing a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions, allowing the user to receive advice from a multifaceted perspective. For example, when the generation AI analyzes a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions. For example, it analyzes hiring trends that differ depending on the industry or region, even for the same job type. Furthermore, when analyzing a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions, allowing the user to receive advice from a multifaceted perspective. For example, it compares the hiring standards and prioritized skills of different industries. Furthermore, when the generation AI analyzes a company's hiring trends, the trend analysis unit compares it with companies in different industries or regions, allowing the user to receive advice from a multifaceted perspective. For example, it analyzes hiring trends and market needs by region. This allows the user to receive advice from a multifaceted perspective by comparing it with companies in different industries or regions.

[0070] The trend analysis unit can provide the user with information on appropriate networking events or career fairs based on companies' hiring trends. For example, the generation AI provides the user with information on appropriate networking events or career fairs based on companies' hiring trends. For example, it recommends events related to a specific industry or job type. The trend analysis unit also analyzes companies' hiring trends and provides the user with information on appropriate networking events or career fairs. For example, it provides information on events and fairs in which companies participate. The trend analysis unit also provides the user with information on appropriate networking events or career fairs based on companies' hiring trends. For example, it recommends events that match the user's career goals. This makes it possible to support the user's career growth by providing them with information on appropriate networking events and career fairs.

[0071] The trend analysis unit uses the emotion estimation function to analyze the user's emotional response to factors that are important to corporate recruiters and can propose the optimal method. For example, the generation AI in the trend analysis unit uses the emotion estimation function to analyze the user's emotional response to factors that are important to corporate recruiters. For example, it analyzes how the user feels about specific skills or experience. The trend analysis unit also uses the emotion estimation function to analyze the user's emotional response to factors that are important to corporate recruiters and proposes the optimal method. For example, it proposes an approach that emphasizes factors about which the user has positive feelings. The generation AI also uses the emotion estimation function to analyze the user's emotional response to factors that are important to corporate recruiters and proposes the optimal method. For example, it proposes an approach that emphasizes factors with high emotion scores in interviews. In this way, the optimal approach can be proposed by analyzing the user's emotional response to factors that are important to corporate recruiters.

[0072] The approach customization unit can suggest specific interview anecdotes or examples based on the user's work history or skills, thereby providing a method for effectively highlighting the user's strengths. For example, the approach customization unit allows the generation AI to suggest specific interview anecdotes or examples based on the user's work history or skills. For example, it provides a method for the user to specifically explain past successful projects or goals they have achieved. The approach customization unit also allows the generation AI to suggest specific interview anecdotes or examples based on the user's work history or skills, thereby providing a method for effectively highlighting the user's strengths. For example, it introduces examples of when the user's skills were actually utilized. The approach customization unit also allows the generation AI to suggest specific interview anecdotes or examples based on the user's work history or skills, thereby providing a method for effectively highlighting the user's strengths. For example, it provides a method for showing the user's experience with specific figures or results. This makes it possible to suggest specific anecdotes and examples for effectively highlighting the user's strengths.

[0073] The approach customization unit can analyze the user's past interview experience, compare and analyze successful and unsuccessful approaches, and customize an optimal interview strategy. In the approach customization unit, for example, the generation AI analyzes the user's past interview experience, compare and analyze successful and unsuccessful approaches. For example, it identifies success factors and failure factors based on the user's interview results and feedback. The approach customization unit also analyzes the user's past interview experience, and the generation AI compares and analyzes successful and unsuccessful approaches to customize an optimal interview strategy. For example, it strengthens successful approaches and improves unsuccessful approaches. The approach customization unit also analyzes the user's past interview experience, compare and analyze successful and unsuccessful approaches to customize an optimal interview strategy. For example, it provides specific advice to improve the user's interview performance. In this way, the success rate of interviews can be improved by analyzing the user's past interview experience and customizing an optimal interview strategy.

[0074] The approach customization unit can use the emotion estimation function to suggest relaxation techniques or mental support to reduce the tension or anxiety the user feels during an interview. For example, the generation AI in the approach customization unit uses the emotion estimation function to suggest relaxation techniques to reduce the tension or anxiety the user feels during an interview. For example, relaxation methods such as deep breathing or meditation are provided. The approach customization unit also uses the emotion estimation function to suggest mental support to reduce the tension or anxiety the user feels during an interview. For example, positive autosuggestion or mental rehearsal methods are provided. The generation AI also uses the emotion estimation function to suggest relaxation techniques or mental support to reduce the tension or anxiety the user feels during an interview. For example, advice on how to relax or prepare mentally before an interview is provided. This allows the user's performance during an interview to be improved by suggesting relaxation techniques or mental support to reduce tension and anxiety.

[0075] The approach customization unit can support career diversity by suggesting interview strategies for different industries or occupations based on the user's work history or skills. In the approach customization unit, for example, the generation AI suggests interview strategies for different industries or occupations based on the user's work history and skills. For example, it provides interview strategies when the user changes jobs to a different industry. The approach customization unit also supports career diversity by suggesting interview strategies for different industries or occupations based on the user's work history and skills. For example, it provides interview strategies for utilizing the user's skills in other industries. The approach customization unit also supports career diversity by suggesting interview strategies for different industries or occupations based on the user's work history and skills. For example, it provides interview strategies when the user tries a new occupation. In this way, it is possible to support career diversity by suggesting interview strategies for different industries and occupations.

[0076] The approach customization unit provides users with opportunities for online mock interviews, enabling them to improve their interview skills through real-time feedback. In the approach customization unit, for example, a generation AI provides users with opportunities for online mock interviews. For example, a system is constructed that allows users to take mock interviews in an environment similar to a real interview. The approach customization unit also provides users with opportunities for online mock interviews, enabling them to improve their interview skills through real-time feedback. For example, specific areas for improvement are pointed out based on the results of the mock interview. The approach customization unit also provides users with opportunities for online mock interviews, enabling them to improve their interview skills through real-time feedback. For example, the generation AI analyzes recordings of mock interviews and provides detailed feedback. In this way, the user's interview performance can be improved by providing opportunities for online mock interviews and improving their interview skills through real-time feedback.

[0077] The approach customization unit can use the emotion estimation function to identify the approach in which the user is most confident and provide specific advice to strengthen that approach. For example, the generation AI in the approach customization unit uses the emotion estimation function to identify the approach in which the user is most confident. For example, the generation AI analyzes the emotion scores for the user's interview answers to identify the approach in which the user is confident. The approach customization unit also uses the emotion estimation function to identify the approach in which the user is most confident and provide specific advice to strengthen that approach. For example, the generation AI suggests a practice method to further strengthen the approach in which the user is confident. The generation AI in the approach customization unit also uses the emotion estimation function to identify the approach in which the user is most confident and provide specific advice to strengthen that approach. For example, the generation AI provides a method to emphasize approaches with high emotion scores in interviews. This allows the user to identify the approach in which the user is most confident and strengthen that approach, thereby improving performance in the interview.

[0078] The feedback providing unit can analyze the user's interview results in detail and provide a report that visually shows specific areas for improvement or success. For example, the feedback providing unit uses a generation AI to analyze the user's interview results in detail and provide a report that visually shows specific areas for improvement or success. For example, the report is created based on the interview answers and the interviewer's feedback. The feedback providing unit also analyzes the user's interview results in detail and provides a report that visually shows specific areas for improvement or success. For example, the report is presented visually in an easy-to-understand manner using graphs and charts. The feedback providing unit also analyzes the user's interview results in detail and provides a report that visually shows specific areas for improvement or success. For example, the report shows an evaluation of each interview question and areas for improvement in detail. This allows the user's interview results to be analyzed in detail and specific areas for improvement or success to be visually presented, thereby deepening the user's understanding.

[0079] The feedback providing unit can propose a specific practice plan for the next interview based on the user's interview results, thereby supporting continuous skill improvement. For example, the generation AI in the feedback providing unit proposes a specific practice plan for the next interview based on the user's interview results. For example, the generation AI proposes practicing answers to specific questions or conducting a mock interview. The feedback providing unit also proposes a specific practice plan for the next interview based on the user's interview results, thereby supporting continuous skill improvement. For example, the generation AI provides interview simulations and feedback. The feedback providing unit also proposes a specific practice plan for the next interview based on the user's interview results, thereby supporting continuous skill improvement. For example, the generation AI proposes an interview practice schedule and specific practice content. This allows the generation AI to propose a specific practice plan for the next interview and support continuous skill improvement, thereby improving the user's interview performance.

[0080] The feedback providing unit can use the emotion estimation function to analyze the emotions felt by the user during the interview and provide feedback to elicit positive emotions. In the feedback providing unit, for example, the generation AI uses the emotion estimation function to analyze the emotions felt by the user during the interview. For example, the feedback providing unit analyzes facial expressions and voice during the interview and calculates an emotion score. The feedback providing unit also uses the emotion estimation function to analyze the emotions felt by the user during the interview and provide feedback to elicit positive emotions. For example, the feedback providing unit provides advice to reinforce positive emotions during the interview. The feedback providing unit also uses the emotion estimation function to analyze the emotions felt by the user during the interview and provide feedback to elicit positive emotions. For example, the feedback providing unit provides feedback that emphasizes parts with high emotion scores. In this way, the user's emotions felt during the interview can be analyzed and feedback to elicit positive emotions can be provided, thereby improving interview performance.

[0081] The feedback providing unit can clarify the user's strengths or weaknesses by comparing the user's interview results with other users and providing a relative evaluation. In the feedback providing unit, for example, the generation AI compares the user's interview results with other users and provides a relative evaluation. For example, the user's strengths and weaknesses are clarified by comparison with other users in the same occupation or industry. The feedback providing unit also compares the user's interview results with other users and the generation AI provides a relative evaluation, thereby clarifying the user's strengths and weaknesses. For example, the user's performance is evaluated by comparing with the interview results of other users. The feedback providing unit also compares the user's interview results with other users and provides a relative evaluation, thereby clarifying the user's strengths and weaknesses. For example, the generation AI refers to success stories and areas for improvement of other users. In this way, the user's interview results are compared with other users and the relative evaluation is provided, thereby clarifying the user's strengths and weaknesses.

[0082] The feedback providing unit can introduce an appropriate mentor or coach based on the user's interview results and provide individualized support. For example, the generation AI introduces an appropriate mentor or coach based on the user's interview results. For example, the generation AI recommends a mentor who is familiar with the user's job type or industry. The feedback providing unit also introduces an appropriate mentor or coach based on the user's interview results and provides individualized support. For example, the generation AI provides specific advice to improve the user's interview skills. The feedback providing unit also introduces an appropriate mentor or coach based on the user's interview results and provides individualized support. For example, the generation AI recommends a mentor that matches the user's career goals. This makes it possible to support the user's skill improvement by introducing an appropriate mentor or coach based on the user's interview results and providing individualized support.

[0083] The feedback providing unit can use the emotion estimation function to suggest emotion management methods for the next interview based on the emotions the user felt during the interview. For example, the generation AI in the feedback providing unit uses the emotion estimation function to analyze the emotions the user felt during the interview and suggest emotion management methods for the next interview. For example, the feedback providing unit provides specific methods for reducing tension and anxiety during the interview. The feedback providing unit also uses the emotion estimation function to suggest emotion management methods for the next interview based on the emotions the user felt during the interview. For example, the feedback providing unit provides relaxation techniques for eliciting positive emotions. The feedback providing unit also uses the emotion estimation function to suggest emotion management methods for the next interview based on the emotions the user felt during the interview. For example, the feedback providing unit provides methods for mental preparation before the interview or autosuggestion. In this way, the user can improve their interview performance by suggesting emotion management methods for the next interview based on the emotions they felt during the interview.

[0084] The feedback providing unit can analyze the content of the user's self-introduction or presentation and suggest specific areas for improvement or enhancement. For example, the generation AI analyzes the content of the user's self-introduction or presentation and suggests specific areas for improvement or enhancement. For example, improving the structure of the self-introduction or the slide design of the presentation. The feedback providing unit also analyzes the content of the user's self-introduction or presentation and suggests specific areas for improvement or enhancement. For example, providing advice to improve speaking style or expression method. The feedback providing unit also analyzes the content of the user's self-introduction or presentation and suggests specific areas for improvement or enhancement. For example, providing advice to make the content of the self-introduction more specific. In this way, by analyzing the content of the user's self-introduction or presentation and suggesting specific areas for improvement or enhancement, it is possible to improve self-expression skills.

[0085] The feedback providing unit can provide an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, the feedback providing unit provides an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, it provides methods for practicing speaking and gestures. The feedback providing unit also provides an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, it provides methods for improving eye contact and posture. The feedback providing unit also provides an appropriate communication skill or body language training program to improve the user's ability to express themselves. For example, it provides advice for improving the tone of voice and the use of facial expressions. In this way, by providing an appropriate communication skill or body language training program to improve the user's ability to express themselves, it is possible to improve the user's ability to express themselves.

[0086] The feedback providing unit can use the emotion estimation function to analyze the emotions the user feels toward self-expression and suggest an approach to elicit positive self-expression. In the feedback providing unit, for example, the generation AI uses the emotion estimation function to analyze the emotions the user feels toward self-expression. For example, it calculates an emotion score during a self-introduction or presentation. The feedback providing unit also uses the emotion estimation function to analyze the emotions the user feels toward self-expression and suggest an approach to elicit positive self-expression. For example, it provides a specific method for strengthening positive emotions. The feedback providing unit also uses the emotion estimation function to analyze the emotions the user feels toward self-expression and suggest an approach to elicit positive self-expression. For example, it provides advice that emphasizes parts with high emotion scores. In this way, the user's emotions toward self-expression are analyzed and an approach to elicit positive self-expression is suggested, thereby improving self-expression.

[0087] The feedback providing unit can provide opportunities for the user to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides opportunities for the generation AI to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides an intercultural communication training program. The feedback providing unit also provides opportunities for the generation AI to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides foreign language speaking practice or a workshop on intercultural understanding. The feedback providing unit also provides opportunities for the generation AI to learn communication skills in different cultures or language areas in order to improve the user's ability to express themselves. For example, the feedback providing unit provides opportunities for intercultural exchange events or international networking opportunities. This allows the user's ability to express themselves to be improved by providing opportunities to learn communication skills in different cultures or language areas.

[0088] The feedback providing unit can provide the user with information about online workshops or seminars to improve self-expression skills, thereby providing an opportunity to acquire practical skills. For example, the generation AI provides the user with information about online workshops or seminars to improve self-expression skills. For example, the feedback providing unit recommends workshops for improving self-introduction or presentation skills. The feedback providing unit also provides the user with information about online workshops or seminars to improve self-expression skills, thereby providing an opportunity to acquire practical skills. For example, the feedback providing unit provides training in communication skills or body language. The feedback providing unit also provides the user with information about online workshops or seminars to improve self-expression skills, thereby providing an opportunity to acquire practical skills. For example, the feedback providing unit provides specific practice methods for improving self-expression skills. In this way, the user's self-expression skills can be improved by providing information about online workshops or seminars to improve self-expression skills and providing an opportunity to acquire practical skills.

[0089] The feedback providing unit can use the emotion estimation function to suggest an optimal method of self-expression based on the emotions the user feels about self-expression. For example, the generation AI uses the emotion estimation function to analyze the emotions the user feels about self-expression and suggest an optimal method of self-expression. For example, the feedback providing unit provides advice based on the emotion score during a self-introduction or presentation. The feedback providing unit also uses the emotion estimation function to suggest an optimal method of self-expression based on the emotions the user feels about self-expression. For example, the feedback providing unit provides a specific method for eliciting positive emotions. The generation AI also uses the emotion estimation function to suggest an optimal method of self-expression based on the emotions the user feels about self-expression. For example, the feedback providing unit provides advice that emphasizes parts with high emotion scores. This allows the user's ability to express themselves to be improved by suggesting an optimal method of self-expression based on the emotions they feel about self-expression.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The work history analysis unit analyzes the user's hobbies and interests in addition to their work history and skills, and by linking these to their work experience, they can strengthen their selling points in an interview. For example, if a user's hobby strengthens their work-related skills, the system extracts specific examples and suggests them as selling points in an interview. Also, if the user's interests are related to their work, the system suggests specific approaches that utilize those interests. Furthermore, even if the user's hobbies and interests are not directly related to their work, they can be used as examples of soft skills or leadership. In this way, by linking the user's hobbies and interests to their work experience, they can strengthen their selling points in an interview.

[0092] The trend analysis unit can analyze companies' hiring trends, as well as their corporate social responsibility (CSR) activities and environmental initiatives, to identify companies that align with the user's values. For example, it can analyze a company's CSR report or environmental report to identify the type of social responsibility the company is fulfilling. It can also collect information on companies' environmental initiatives and sustainability to identify companies that align with the user's values. Furthermore, if a company's social responsibility or environmental initiatives align with the user's values, it can recommend that company, thereby improving user satisfaction. In this way, it is possible to identify companies that align with the user's values ​​by analyzing a company's social responsibility and environmental initiatives.

[0093] The approach customization unit suggests interview strategies for different cultures and countries based on the user's work history and skills, thereby expanding the possibilities for an international career. For example, if the user has work experience overseas, it suggests a specific approach that makes use of that experience. Also, when the user attempts an interview in a different culture or country, it provides strategies that take into account the interview style and expected skills of that culture or country. Furthermore, if the user is aiming for an international career, it suggests specific steps and preparation methods for achieving that. In this way, by suggesting interview strategies for different cultures and countries, it can expand the possibilities for the user's international career.

[0094] The feedback providing unit can analyze the user's interview results, evaluate the effectiveness of the words and phrases used by the user in the interview, and suggest more effective words and phrases. For example, it can analyze the words and phrases used by the user in the interview and evaluate their effectiveness. It can also analyze the impression the words and phrases used by the user made on the interviewer and suggest areas for improvement. It can also provide specific advice to make the words and phrases used by the user more effective. This can improve the success rate of the interview by improving the words and phrases used by the user in the interview.

[0095] The feedback providing unit can analyze the user's interview results, evaluate the type of body language used by the user in the interview, and suggest more effective body language. For example, it can analyze the body language used by the user in the interview and evaluate its effectiveness. It can also analyze the impression the body language used by the user made on the interviewer and suggest areas for improvement. It can also provide specific advice to make the body language used by the user in the interview more effective. This can improve the success rate of the interview by improving the body language used by the user in the interview.

[0096] The work history analysis unit uses the emotion estimation function to analyze how the user feels about their past work experience and propose an approach that emphasizes positive experiences. For example, it analyzes text data related to the user's work experience and calculates an emotion score. It also uses the emotion estimation function to analyze the user's emotions about their past work experience and proposes an approach that emphasizes positive experiences. It also proposes a method for highlighting work experience with a high emotion score in an interview. This allows the user to emphasize their positive work experience and strengthen their selling points in the interview.

[0097] The approach customization unit can use the emotion estimation function to identify the skills and experience in which the user is most confident and propose an approach to make the most of those strengths. For example, it can analyze text data related to the user's work experience and calculate an emotion score. It can also use the emotion estimation function to identify the skills and experience in which the user is most confident and propose an approach to make the most of those strengths. It can also suggest ways to highlight skills and experiences with high emotion scores in an interview. This allows the user to emphasize the skills and experiences in which they are confident, thereby strengthening their selling points in the interview.

[0098] The trend analysis unit uses the emotion estimation function to analyze the emotions felt by company recruiters in past interviews and can suggest approaches to leaving a good impression in the interview. For example, it analyzes interview recording data and interviewer feedback to calculate an emotion score. It also uses the emotion estimation function to analyze the emotions felt by company recruiters in past interviews and suggests approaches to leaving a good impression in the interview. It also suggests approaches to leaving a good impression in the interview by providing example answers with high emotion scores. In this way, by analyzing the emotions of company recruiters, it is possible to suggest approaches to leaving a good impression in the interview.

[0099] The feedback providing unit can use the emotion estimation function to analyze the emotions felt by the user during the interview and provide feedback to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by the user during the interview and provide feedback to elicit positive emotions. Furthermore, by providing feedback that emphasizes parts with high emotion scores, it is possible to improve interview performance. In this way, by analyzing the emotions felt by the user during the interview and providing feedback to elicit positive emotions, it is possible to improve interview performance.

[0100] The feedback providing unit can use the emotion estimation function to analyze the emotions felt by the user regarding self-expression and suggest approaches to elicit positive self-expression. For example, the emotion estimation function can calculate an emotion score during a self-introduction or presentation. The emotion estimation function can also be used to analyze the emotions felt by the user regarding self-expression and suggest approaches to elicit positive self-expression. Furthermore, by providing advice that emphasizes parts with high emotion scores, self-expression skills can be improved. In this way, self-expression skills can be improved by analyzing the emotions felt by the user regarding self-expression and suggesting approaches to elicit positive self-expression.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The work history analysis unit analyzes the user's work history or skills. For example, it analyzes the information on the work history and skills provided by the user and analyzes the user's past work experience and skills in detail. Step 2: The trend analysis unit analyzes the company's past hiring trends or the factors it values. For example, it analyzes the company's past hiring data and public information to identify the factors it values. Step 3: The approach customization unit customizes the optimal approach for the user based on the analysis results of the employment history analysis unit and the trend analysis unit. For example, it proposes an approach that emphasizes the factors that companies value based on the user's employment history and skills. Step 4: The feedback provider provides individual feedback to the user. For example, it analyzes how the user answered questions in an interview and specifically points out areas for improvement or strengthening.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

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

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

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

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

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 work history analysis unit that analyzes the work history or skills of a user; A trend analysis department that analyzes companies' past hiring trends and priorities; an approach customization unit that customizes an optimal approach for a user based on the analysis results of the work history analysis unit and the tendency analysis unit; a feedback providing unit that provides individual feedback to the user. A system characterized by:

2. The work history analysis unit Conduct a detailed analysis of the user's work history or skills, as well as past projects or deliverables, to extract specific success stories.

2. The system of claim 1.

3. The work history analysis unit Analyzing the latest trends in the industry or technology related to the user's work history or skills and assessing the market value of the user's skill set 2. The system of claim 1.

4. The work history analysis unit Analyze how the user feels about their past work experiences and propose an approach that emphasizes positive experiences.

2. The system of claim 1.

5. The work history analysis unit The results of the analysis of the user's work history or skills are reconstructed as a skill set that can be transferred to a different industry or occupation, and the possibility of a career change is suggested.

2. The system of claim 1.

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