System

Abstract

An object of a system according to an embodiment is to recommend an optimal company based on profile information of a user and consistently support creation and interview preparation of application form.SOLUTION: A system includes a collection part, an analysis part, a recommendation part, a creation support part, and an interview support part. The collection unit collects profile information of a user. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends a company based on the analysis result obtained by the analysis unit. The creation support unit creates application form for the company recommended by the recommendation unit. The interview preparation supporting unit is configured to conduct an interview based on the application form created by the creation supporting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend the most suitable companies based on a user's profile information, nor do they provide consistent support for preparing application documents and preparing for interviews, so there is room for improvement.

[0005] The system according to the embodiment aims to recommend the most suitable companies based on the user's profile information and to provide consistent support for preparing application documents and preparing for interviews. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, a creation support unit, and an interview support unit. The collection unit collects profile information of users. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends companies based on the analysis results obtained by the analysis unit. The creation support unit prepares application documents for companies recommended by the recommendation unit. The interview support unit prepares interview preparation based on the application documents prepared by the creation support unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable companies based on the user's profile information and provide consistent support for preparing application documents and preparing for interviews. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A job change / employment support system according to an embodiment of the present invention uses a generation AI to collect and analyze a user's profile information, recommend companies, prepare application documents, and provide interview preparation. In the job change / employment support system, a user inputs their profile information, and the generation AI analyzes that information to recommend the most suitable companies. For example, if a user is interested in the IT industry, the generation AI recommends the most suitable IT companies. Furthermore, the generation AI also assists in preparing application documents for the recommended companies and provides interview preparation. For example, the generation AI generates resume and curriculum vitae templates based on the user's profile information, allowing the user to easily prepare application documents. The generation AI also analyzes past interview data, conducts mock interviews for the user, and provides feedback. This allows users to effectively prepare for interviews. This allows new graduates and working adults to efficiently find companies that suit them, smoothly prepare application documents, and prepare for interviews. This allows the job change / employment support system to effectively support users' job changes and job hunting activities. For example, if a user is interested in the IT industry, the generative AI will recommend the most suitable IT companies and assist with preparing application documents and preparing for interviews, allowing the user to effectively progress with their job search or career change.

[0029] The job change / employment support system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, a creation support unit, and an interview support unit. The collection unit collects user profile information. The user profile information includes, but is not limited to, educational background, work history, skills, and interests. The collection unit, for example, stores information entered by the user in a database. The collection unit can also collect information such as the user's social media activities and past application history. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, uses data mining technology to analyze the user's profile information and identify companies that are best suited to the user. The analysis unit can also use a machine learning algorithm to identify companies based on the user's skills and interests. The recommendation unit recommends companies based on the analysis results obtained by the analysis unit. The recommendation unit recommends companies that are suitable for the user based on data such as the company's job information, corporate culture, and performance. The recommendation unit can also use a matching algorithm to evaluate the compatibility between the user and companies and recommend the best company. The creation support unit prepares application documents for companies recommended by the recommendation unit. The creation support unit generates resume and curriculum vitae templates based on the user's profile information, for example. The creation support unit can also provide a template customization function to enable the user to easily create application documents. The interview support unit prepares for interviews based on the application documents created by the creation support unit. The interview support unit can, for example, analyze past interview data, conduct mock interviews for the user, and provide feedback. The interview support unit can also provide training programs to improve the user's interview skills. This allows the job change / employment support system according to the embodiment to effectively support the user's job change and job hunting. For example, if the user is interested in the IT industry, the generation AI can recommend the most suitable IT company and provide support for creating application documents and interview preparation, thereby effectively advancing the user's job change and job hunting.

[0030] The job change / employment support system includes a privacy protection unit that protects the user's privacy. The privacy protection unit provides a function for protecting the user's privacy. For example, the privacy protection unit protects the user's information using data encryption technology. The privacy protection unit also provides an access control function to prevent unauthorized access to the user's information. Furthermore, the privacy protection unit can anonymize the user's information using anonymization technology to protect the user's privacy. This allows the user's privacy to be protected. For example, the privacy protection unit encrypts the user's information to prevent unauthorized access by third parties. The privacy protection unit also anonymizes the user's information to protect the user's privacy. This allows the user to use the service with peace of mind.

[0031] The collection unit can collect information on the user's educational background, work history, skills, and interests. The collection unit, for example, collects educational background information input by the user. The educational background information includes the highest level of education, degree obtained, field of major, etc. The collection unit can also collect the user's employment history information. The employment history information includes past employers, job titles, and job duties. The collection unit can also collect the user's skill information. The skill information includes programming skills, language skills, specialized knowledge, etc. The collection unit can also collect the user's interest information. The interest information includes hobbies, industries and fields of interest, etc. This allows the collection unit to collect detailed profile information of the user. For example, the collection unit stores the educational background information input by the user in a database, and the analysis unit analyzes the information. The collection unit also collects the user's employment history information, and the analysis unit identifies companies based on the information. This allows the collection unit to collect the user's detailed profile information and the analysis unit to analyze the information, thereby identifying the most suitable company for the user.

[0032] The analysis unit analyzes the information collected by the collection unit and can identify companies that are suitable for the user. The analysis unit analyzes the collected information using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and is used to analyze user profile information. The analysis unit can also analyze the information using statistical analysis technology. Statistical analysis technology is a technology for analyzing data trends and patterns and is used to identify companies based on the user's skills and interests. The analysis unit can also analyze the information using machine learning algorithms. Machine learning algorithms are technologies that learn from data and make predictions and classifications and are used to identify companies that are suitable for the user. In this way, the analysis unit can analyze the information collected by the collection unit and identify companies that are suitable for the user. For example, the analysis unit analyzes the user's profile information using data mining technology and identifies companies that are suitable for the user. In addition, the analysis unit can use machine learning algorithms to identify companies based on the user's skills and interests. In this way, the analysis unit can identify companies that are suitable for the user.

[0033] The recommendation unit can recommend companies that suit the user based on data on the company's job information, corporate culture, and performance. The recommendation unit, for example, recommends companies based on the company's job information. The job information includes the job type, work location, salary, etc. The recommendation unit can also recommend companies based on the company's culture. The company's values, work style, internal atmosphere, etc. The recommendation unit can also recommend companies based on the company's performance data. The performance data includes sales, profits, growth rate, etc. The recommendation unit recommends companies that suit the user based on this data. For example, the recommendation unit analyzes the company's job information to recommend companies that suit the user's skills and interests. The recommendation unit also evaluates the corporate culture and recommends companies that suit the user. The recommendation unit also analyzes the company's performance data to recommend companies with high growth potential. This allows the recommendation unit to recommend companies that suit the user. For example, the recommendation unit recommends companies that are optimal for the user based on the company's job information. The recommendation unit also evaluates the corporate culture and recommends companies that suit the user. Furthermore, the recommendation unit analyzes business performance data of companies and recommends companies with high growth potential, thereby enabling the recommendation unit to recommend companies that suit the user.

[0034] The creation support unit can generate a resume or career history template based on the user's profile information. The creation support unit generates a resume template based on, for example, the user's educational background information. The resume template includes items such as educational background, work history, skills, and self-promotion. The creation support unit can also generate a career history template based on the user's work history information. The career history template includes items such as past employers, job titles, job duties, and achievements. The creation support unit can also customize the template based on the user's skill information. This allows the user to easily create application documents. For example, the creation support unit generates a resume template based on the user's educational background information, allowing the user to complete the template simply by entering information. The creation support unit can also generate a career history template based on the user's work history information, allowing the user to easily create application documents. This allows the user to easily create application documents.

[0035] The interview support unit can analyze past interview data, conduct a mock interview with the user, and provide feedback. The interview support unit, for example, analyzes past interview data and conducts a mock interview with the user. The past interview data includes interview questions, answers, evaluation results, and the like. The interview support unit can also provide feedback based on the results of the mock interview. The feedback includes areas for improvement in answers, interview attitude, speaking style, and the like. Furthermore, the interview support unit can also provide a training program for improving the user's interview skills. This allows the interview support unit to effectively prepare for the interview. For example, the interview support unit analyzes past interview data, conducts a mock interview with the user, and provides feedback. The interview support unit also provides a training program for improving the user's interview skills. This allows the interview support unit to effectively prepare for the interview.

[0036] The collection unit can analyze the user's past profile information and select the optimal collection method. The collection unit, for example, suggests the optimal collection method based on information previously input by the user. The collection unit can analyze the user's past input history and select an efficient collection method. For example, the collection unit collects information in the form of a questionnaire based on information previously input by the user. The collection unit can also collect information in the form of an interview based on the user's past input history. Furthermore, the collection unit can analyze the user's past profile information and automatically complete necessary information. This allows the collection unit to select the optimal collection method based on the user's past information. For example, the collection unit suggests the optimal collection method based on information previously input by the user. The collection unit selects an efficient collection method based on the user's past input history. This allows the collection unit to select the optimal collection method based on the user's past information.

[0037] When collecting profile information, the collection unit may perform filtering based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the collection unit may preferentially collect IT-related information. The collection unit may filter and collect related information based on the user's current occupation or field of interest. For example, if the user is engaged in the education industry, the collection unit may preferentially collect education-related information. The collection unit may also filter and collect related information based on the user's field of interest. This allows the collection unit to collect related information based on the user's field of interest. For example, if the user is interested in the IT industry, the collection unit may preferentially collect IT-related information. Also, if the user is engaged in the education industry, the collection unit may preferentially collect education-related information. This allows the collection unit to collect related information based on the user's field of interest.

[0038] When collecting profile information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit preferentially uses voice input. The collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers text input, the collection unit preferentially uses text input. Furthermore, if the user prefers image input, the collection unit can also preferentially use image input. This allows the collection unit to select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit preferentially uses voice input. Furthermore, if the user prefers text input, the collection unit preferentially uses text input. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0039] When collecting profile information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting information related to the area. The collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific city, the collection unit prioritizes collecting information related to the city. The collection unit can also prioritize collecting highly relevant information based on the user's geographical location information. This allows the collection unit to collect highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting information related to the area. Also, if the user lives in a specific city, the collection unit prioritizes collecting information related to the city. This allows the collection unit to collect highly relevant information taking into account the user's geographical location information.

[0040] When collecting the profile information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related profile information based on information shared by the user on social media. The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit analyzes the user's social media activities and collects related information. The collection unit can also refer to the activities of the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related profile information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. In this way, the collection unit can analyze the user's social media activities and collect related information.

[0041] When collecting profile information, the collection unit can customize the collection method based on the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can customize the collection means by reflecting the user's past feedback. For example, the collection unit collects information in the form of a questionnaire based on the user's past feedback. The collection unit can also collect information in the form of an interview by reflecting the user's past feedback. Furthermore, the collection unit can optimize the collection process by referring to the user's past feedback. This allows the collection unit to customize the collection method by reflecting the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit customizes the collection means by reflecting the user's past feedback. This allows the collection unit to customize the collection method by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit performs a detailed analysis based on important profile information. The analysis unit can adjust the level of detail of the analysis based on the importance of the profile information. For example, the analysis unit performs a simplified analysis based on profile information with a low level of importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the profile information. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit performs a detailed analysis based on important profile information. The analysis unit also performs a simplified analysis based on profile information with a low level of importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information.

[0043] During analysis, the analysis unit can apply different analysis algorithms based on the category of the profile information. For example, the analysis unit applies an analysis algorithm specialized for educational background to educational background information. The analysis unit can apply different analysis algorithms based on the category of the profile information. For example, the analysis unit applies an analysis algorithm specialized for educational background to employment history information. The analysis unit can also apply an analysis algorithm specialized for skills to skill information. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of the profile information. For example, the analysis unit applies an analysis algorithm specialized for educational background to educational background information. The analysis unit also applies an analysis algorithm specialized for employment history to employment history information. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of the profile information.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the profile information. For example, the analysis unit prioritizes analysis of profile information that has been submitted most recently. The analysis unit can determine the priority of analysis based on the time of submission of the profile information. For example, the analysis unit analyzes profile information that has been submitted more recently with a lower priority. The analysis unit can also adjust the priority of analysis based on the time of submission of the profile information. This allows the analysis unit to determine the priority of analysis based on the time of submission of the profile information. For example, the analysis unit analyzes profile information that has been submitted more recently with a lower priority. The analysis unit analyzes profile information that has been submitted more recently with a lower priority. This allows the analysis unit to determine the priority of analysis based on the time of submission of the profile information.

[0046] The analysis unit can adjust the order of analysis according to the relevance of the profile information during analysis. For example, the analysis unit prioritizes analysis of highly relevant profile information. The analysis unit can adjust the order of analysis based on the relevance of the profile information. For example, the analysis unit postpones analysis of less relevant profile information. The analysis unit can also adjust the order of analysis based on the relevance of the profile information. This allows the analysis unit to adjust the order of analysis based on the relevance of the profile information. For example, the analysis unit prioritizes analysis of highly relevant profile information. Also, the analysis unit postpones analysis of less relevant profile information. This allows the analysis unit to adjust the order of analysis based on the relevance of the profile information.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user does not have technical expertise, the analysis unit provides analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed based on the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit provides analysis results in simple language. This allows the analysis unit to adjust the use of technical terms in the analysis based on the user's level of expertise.

[0048] The recommendation unit can adjust the level of detail of the recommendation according to the importance of the company information when making a recommendation. The recommendation unit, for example, makes a detailed recommendation based on important company information. The recommendation unit can adjust the level of detail of the recommendation based on the importance of the company information. For example, the recommendation unit makes a simplified recommendation based on company information with low importance. The recommendation unit can also adjust the level of detail of the recommendation according to the importance of the company information. This allows the recommendation unit to adjust the level of detail of the recommendation according to the importance of the company information. For example, the recommendation unit makes a detailed recommendation based on important company information. The recommendation unit makes a simplified recommendation based on company information with low importance. This allows the recommendation unit to adjust the level of detail of the recommendation according to the importance of the company information.

[0049] The recommendation unit can apply different recommendation algorithms based on the category of company information when making recommendations. For example, the recommendation unit applies a recommendation algorithm specialized for IT to an IT company. The recommendation unit can apply different recommendation algorithms based on the category of company information. For example, the recommendation unit can apply a recommendation algorithm specialized for education to an education company. Furthermore, the recommendation unit can apply a recommendation algorithm specialized for medical care to a medical company. This allows the recommendation unit to apply an optimal recommendation algorithm depending on the category of company information. For example, the recommendation unit can apply a recommendation algorithm specialized for IT to an IT company. Furthermore, the recommendation unit can apply a recommendation algorithm specialized for education to an education company. This allows the recommendation unit to apply an optimal recommendation algorithm depending on the category of company information.

[0050] The recommendation unit can improve the accuracy of recommendations based on the user's past recommendation results when making recommendations. The recommendation unit, for example, adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results. For example, the recommendation unit adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past recommendation results. This allows the recommendation unit to improve the accuracy of recommendations by referring to the user's past recommendation results. For example, the recommendation unit adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past recommendation results. This allows the recommendation unit to improve the accuracy of recommendations by referring to the user's past recommendation results.

[0051] The recommendation unit can determine the priority of recommendations based on the update time of the company information when making recommendations. For example, the recommendation unit preferentially recommends company information that has been updated recently. The recommendation unit can determine the priority of recommendations based on the update time of the company information. For example, the recommendation unit recommends company information that has been updated recently with a lower priority. The recommendation unit can also adjust the priority of recommendations based on the update time of the company information. This allows the recommendation unit to determine the priority of recommendations based on the update time of the company information. For example, the recommendation unit preferentially recommends company information that has been updated recently. The recommendation unit recommends company information that has been updated recently with a lower priority. This allows the recommendation unit to determine the priority of recommendations based on the update time of the company information.

[0052] The recommendation unit can adjust the order of recommendation based on the relevance of the company information when making a recommendation. For example, the recommendation unit preferentially recommends highly relevant company information. The recommendation unit can adjust the order of recommendation based on the relevance of the company information. For example, the recommendation unit may recommend less relevant company information later. The recommendation unit can also adjust the order of recommendation based on the relevance of the company information. This allows the recommendation unit to adjust the order of recommendation based on the relevance of the company information. For example, the recommendation unit preferentially recommends highly relevant company information. The recommendation unit may recommend less relevant company information later. This allows the recommendation unit to adjust the order of recommendation based on the relevance of the company information.

[0053] The recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise when making a recommendation. For example, if the user has technical expertise, the recommendation unit provides a recommendation result that uses a lot of technical terms. The recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise. For example, if the user does not have technical expertise, the recommendation unit provides a recommendation result in simple language. The recommendation unit may also adjust the way in which the recommendation result is expressed based on the user's level of expertise. In this way, the recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise. For example, if the user has technical expertise, the recommendation unit provides a recommendation result that uses a lot of technical terms. In addition, if the user does not have technical expertise, the recommendation unit provides a recommendation result in simple language. In this way, the recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise.

[0054] During creation support, the creation support unit can select an optimal template based on the user's past application documents. For example, the creation support unit suggests an optimal template based on templates the user has used in the past. The creation support unit can analyze the user's past application documents and select an optimal template. For example, the creation support unit suggests an optimal template based on the user's past application documents. The creation support unit can also suggest an optimal template based on the success rate of the user's past application documents. This allows the creation support unit to analyze the user's past application documents and select an optimal template. For example, the creation support unit suggests an optimal template based on templates the user has used in the past. The creation support unit analyzes the user's past application documents and selects an optimal template. This allows the creation support unit to analyze the user's past application documents and select an optimal template.

[0055] During creation support, the creation support unit can customize the template based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the creation support unit provides a template for the IT industry. The creation support unit can customize the template based on the user's current occupation or field of interest. For example, if the user is engaged in the education industry, the creation support unit provides a template for the education industry. The creation support unit can also customize the template based on the user's field of interest. This allows the creation support unit to customize the template based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the creation support unit provides a template for the IT industry. Also, if the user is engaged in the education industry, the creation support unit provides a template for the education industry. This allows the creation support unit to customize the template based on the user's current occupation or field of interest.

[0056] The creation support unit can improve the template based on user feedback during creation support. For example, the creation support unit improves the template based on feedback provided by the user. The creation support unit can customize the template by reflecting the user's feedback. For example, the creation support unit improves the template based on user feedback. The creation support unit can also customize the template by reflecting the user's feedback. This allows the creation support unit to improve the template by reflecting the user's feedback. For example, the creation support unit improves the template based on feedback provided by the user. The creation support unit also customizes the template by reflecting the user's feedback. This allows the creation support unit to improve the template by reflecting the user's feedback.

[0057] During creation assistance, the creation support unit can select an optimal template based on the user's geographical location information. For example, if the user lives in a specific area, the creation support unit provides a template related to that area. The creation support unit can select an optimal template by taking the user's geographical location information into consideration. For example, if the user lives in a specific city, the creation support unit provides a template related to that city. The creation support unit can also select an optimal template based on the user's geographical location information. This allows the creation support unit to select an optimal template by taking the user's geographical location information into consideration. For example, if the user lives in a specific area, the creation support unit provides a template related to that area. Also, if the user lives in a specific city, the creation support unit provides a template related to that city. This allows the creation support unit to select an optimal template by taking the user's geographical location information into consideration.

[0058] During creation support, the creation support unit can analyze the user's social media activity and suggest a template. For example, the creation support unit suggests an optimal template based on information shared by the user on social media. The creation support unit can analyze the user's social media activity and suggest a related template. For example, the creation support unit analyzes the user's social media activity and suggests a related template. The creation support unit can also suggest an optimal template by referring to the activity of the user's friends on social media. In this way, the creation support unit can analyze the user's social media activity and suggest an optimal template. For example, the creation support unit suggests an optimal template based on information shared by the user on social media. In addition, the creation support unit analyzes the user's social media activity and suggests a related template. In this way, the creation support unit can analyze the user's social media activity and suggest an optimal template.

[0059] During creation assistance, the creation assistance unit can customize the template based on the user's past feedback. For example, the creation assistance unit improves the template based on feedback provided by the user in the past. The creation assistance unit can customize the template by reflecting the user's past feedback. For example, the creation assistance unit improves the template based on the user's past feedback. The creation assistance unit can also customize the template by reflecting the user's past feedback. This allows the creation assistance unit to customize the template by reflecting the user's past feedback. For example, the creation assistance unit improves the template based on feedback provided by the user in the past. The creation assistance unit customizes the template by reflecting the user's past feedback. This allows the creation assistance unit to customize the template by reflecting the user's past feedback.

[0060] The interview support unit can select optimal strategies based on the user's past interview history when supporting an interview. The interview support unit, for example, suggests optimal interview strategies based on the user's past interview history. The interview support unit can analyze the user's past interview history and select optimal strategies. For example, the interview support unit suggests optimal interview strategies based on the user's past interview history. The interview support unit can also suggest optimal strategies based on the success rate of the user's past interview history. This allows the interview support unit to analyze the user's past interview history and select optimal strategies. For example, the interview support unit suggests optimal interview strategies based on the user's past interview history. The interview support unit analyzes the user's past interview history and selects optimal strategies. This allows the interview support unit to analyze the user's past interview history and select optimal strategies.

[0061] The interview support unit can customize the countermeasures based on the user's current occupation or field of interest during interview support. For example, if the user is interested in the IT industry, the interview support unit provides interview countermeasures for the IT industry. The interview support unit can customize the countermeasures based on the user's current occupation or field of interest. For example, if the user is working in the education industry, the interview support unit provides interview countermeasures for the education industry. The interview support unit can also customize the countermeasures based on the user's field of interest. This allows the interview support unit to customize the countermeasures based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the interview support unit provides interview countermeasures for the IT industry. Also, if the user is working in the education industry, the interview support unit provides interview countermeasures for the education industry. This allows the interview support unit to customize the countermeasures based on the user's current occupation or field of interest.

[0062] The interview support unit can improve the countermeasures based on user feedback during interview support. The interview support unit improves the interview countermeasures based on, for example, feedback provided by the user. The interview support unit can customize the countermeasures by reflecting the user's feedback. For example, the interview support unit improves the interview countermeasures based on the user's feedback. The interview support unit can also customize the countermeasures by reflecting the user's feedback. This allows the interview support unit to improve the countermeasures by reflecting the user's feedback. For example, the interview support unit improves the interview countermeasures based on the feedback provided by the user. The interview support unit also customizes the countermeasures by reflecting the user's feedback. This allows the interview support unit to improve the countermeasures by reflecting the user's feedback.

[0063] The interview support unit can select optimal strategies based on the user's geographical location information when supporting an interview. For example, if the user lives in a specific area, the interview support unit provides interview strategies related to the area. The interview support unit can select optimal strategies by taking the user's geographical location information into consideration. For example, if the user lives in a specific city, the interview support unit provides interview strategies related to the city. The interview support unit can also select optimal strategies for the interview based on the user's geographical location information. This allows the interview support unit to select optimal strategies by taking the user's geographical location information into consideration. For example, if the user lives in a specific area, the interview support unit provides interview strategies related to the area. Also, if the user lives in a specific city, the interview support unit provides interview strategies related to the city. This allows the interview support unit to select optimal strategies by taking the user's geographical location information into consideration.

[0064] The interview support unit can analyze the user's social media activity and suggest strategies when supporting an interview. For example, the interview support unit suggests optimal interview strategies based on information shared by the user on social media. The interview support unit can analyze the user's social media activity and suggest related interview strategies. For example, the interview support unit analyzes the user's social media activity and suggest related interview strategies. The interview support unit can also suggest optimal interview strategies by referring to the activities of the user's friends on social media. In this way, the interview support unit can analyze the user's social media activity and suggest optimal interview strategies. For example, the interview support unit suggests optimal interview strategies based on information shared by the user on social media. The interview support unit also analyzes the user's social media activity and suggest related interview strategies. In this way, the interview support unit can analyze the user's social media activity and suggest optimal interview strategies.

[0065] The interview support unit can customize the countermeasures based on the user's past feedback when supporting the interview. The interview support unit, for example, improves the interview countermeasures based on feedback provided by the user in the past. The interview support unit can customize the countermeasures by reflecting the user's past feedback. For example, the interview support unit improves the interview countermeasures based on the user's past feedback. The interview support unit can also customize the countermeasures by reflecting the user's past feedback. This allows the interview support unit to customize the countermeasures by reflecting the user's past feedback. For example, the interview support unit improves the interview countermeasures based on feedback provided by the user in the past. The interview support unit customizes the countermeasures by reflecting the user's past feedback. This allows the interview support unit to customize the countermeasures by reflecting the user's past feedback.

[0066] The privacy protection unit can select an optimal protection method based on the user's past privacy settings during privacy protection. For example, the privacy protection unit suggests an optimal protection method based on the user's past privacy settings. The privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. For example, the privacy protection unit suggests an optimal protection method based on the user's past privacy settings. The privacy protection unit can also suggest an optimal protection method based on the success rate of the user's past privacy settings. In this way, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. For example, the privacy protection unit suggests an optimal protection method based on the user's past privacy settings. In addition, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. In this way, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method.

[0067] The privacy protection unit can customize protection measures based on the user's current occupation or field of interest during privacy protection. For example, if the user is employed in the IT industry, the privacy protection unit provides privacy protection measures for the IT industry. The privacy protection unit can customize protection measures based on the user's current occupation or field of interest. For example, if the user is employed in the education industry, the privacy protection unit provides privacy protection measures for the education industry. The privacy protection unit can also customize protection measures based on the user's field of interest. This allows the privacy protection unit to customize protection measures based on the user's current occupation or field of interest. For example, if the user is employed in the IT industry, the privacy protection unit provides privacy protection measures for the IT industry. Also, if the user is employed in the education industry, the privacy protection unit provides privacy protection measures for the education industry. This allows the privacy protection unit to customize protection measures based on the user's current occupation or field of interest.

[0068] The privacy protection unit may select an optimal protection method based on the geographical location information of a user during privacy protection. For example, if the user lives in a specific area, the privacy protection unit may provide a privacy protection method related to the area. The privacy protection unit may select an optimal protection method in consideration of the geographical location information of the user. For example, if the user lives in a specific city, the privacy protection unit may provide a privacy protection method related to the city. The privacy protection unit may also select an optimal privacy protection method based on the geographical location information of the user. In this way, the privacy protection unit may select an optimal protection method in consideration of the geographical location information of the user. For example, if the user lives in a specific area, the privacy protection unit may provide a privacy protection method related to the area. In addition, if the user lives in a specific city, the privacy protection unit may provide a privacy protection method related to the city. In this way, the privacy protection unit may select an optimal protection method in consideration of the geographical location information of the user.

[0069] The privacy protection unit may suggest privacy protection measures based on the user's social media activities during privacy protection. For example, the privacy protection unit may suggest optimal privacy protection measures based on information shared by the user on social media. The privacy protection unit may analyze the user's social media activities and suggest related privacy protection measures. For example, the privacy protection unit may analyze the user's social media activities and suggest related privacy protection measures. The privacy protection unit may also suggest optimal privacy protection measures with reference to the activities of the user's friends on social media. In this way, the privacy protection unit may analyze the user's social media activities and suggest optimal privacy protection measures. For example, the privacy protection unit may suggest optimal privacy protection measures based on information shared by the user on social media. The privacy protection unit may also analyze the user's social media activities and suggest related privacy protection measures. In this way, the privacy protection unit may analyze the user's social media activities and suggest optimal privacy protection measures.

[0070] The privacy protection unit can customize the protection measures based on the user's past feedback during privacy protection. For example, the privacy protection unit improves the privacy protection measures based on feedback provided by the user in the past. The privacy protection unit can customize the protection measures by reflecting the user's past feedback. For example, the privacy protection unit improves the privacy protection measures based on the user's past feedback. The privacy protection unit can also customize the protection measures by reflecting the user's past feedback. In this way, the privacy protection unit can customize the protection measures by reflecting the user's past feedback. For example, the privacy protection unit improves the privacy protection measures based on feedback provided by the user in the past. The privacy protection unit can customize the protection measures by reflecting the user's past feedback. In this way, the privacy protection unit can customize the protection measures by reflecting the user's past feedback.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The collection unit can analyze the user's past profile information and select the optimal collection method. For example, the collection unit can suggest the optimal collection method based on information the user has input in the past. The collection unit can also analyze the user's past input history and select an efficient collection method. This allows the collection unit to select the optimal collection method based on the user's past information. For example, the collection unit can collect information in the form of a questionnaire based on information the user has input in the past. The collection unit can also collect information in the form of an interview based on the user's past input history. This allows the collection unit to select the optimal collection method based on the user's past information.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit can perform a detailed analysis based on important profile information. Also, the analysis unit can perform a simplified analysis based on less important profile information. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit can perform a detailed analysis based on important profile information. Also, the analysis unit can perform a simplified analysis based on less important profile information. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information.

[0074] When making a recommendation, the recommendation unit can apply different recommendation algorithms based on the category of company information. For example, a recommendation algorithm specialized for IT can be applied to an IT company. Also, a recommendation algorithm specialized for education can be applied to an education company. This allows the recommendation unit to apply the optimal recommendation algorithm depending on the category of company information. For example, the recommendation unit can apply a recommendation algorithm specialized for medical care to a medical company. Also, a recommendation algorithm specialized for finance can be applied to a financial company. This allows the recommendation unit to apply the optimal recommendation algorithm depending on the category of company information.

[0075] When providing support for creation, the creation support unit can select an optimal template based on the user's past application documents. For example, the creation support unit can suggest an optimal template based on templates the user has used in the past. The creation support unit can also analyze the user's past application documents and select an optimal template. This allows the creation support unit to analyze the user's past application documents and select an optimal template. For example, the creation support unit can suggest an optimal template based on templates the user has used in the past. The creation support unit can also suggest an optimal template based on the success rate of the user's past application documents. This allows the creation support unit to analyze the user's past application documents and select an optimal template.

[0076] When supporting an interview, the interview support unit can select the optimal countermeasure based on the user's past interview history. For example, the optimal interview countermeasure can be suggested based on the user's past interview history. The interview support unit can also analyze the user's past interview history and select the optimal countermeasure. This allows the interview support unit to analyze the user's past interview history and select the optimal countermeasure. For example, the interview support unit can suggest the optimal interview countermeasure based on the user's past interview history. The interview support unit can also suggest the optimal countermeasure based on the success rate of the user's past interview history. This allows the interview support unit to analyze the user's past interview history and select the optimal countermeasure.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection unit collects the user's profile information, which includes information such as educational background, work history, skills, and interests. The collection unit stores the information entered by the user in a database. It may also collect information such as the user's social media activity and past application history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's profile information using data mining technology and machine learning algorithms to identify the company that is best suited to the user. Step 3: The recommendation unit recommends companies based on the analysis results obtained by the analysis unit. The recommendation unit recommends companies that suit the user based on data such as company recruitment information, corporate culture, and performance. It can also use a matching algorithm to evaluate the compatibility between the user and companies and recommend the most suitable company. Step 4: The creation support unit creates application documents for companies recommended by the recommendation unit. The creation support unit generates resume and curriculum vitae templates based on the user's profile information. In addition, the creation support unit provides a template customization function to enable users to easily create application documents. Step 5: The Interview Support Department prepares users for interviews based on the application documents prepared by the Creation Support Department. The Interview Support Department analyzes past interview data, conducts mock interviews with users, and provides feedback. It also provides training programs to improve users' interview skills.

[0079] (Example 2) A job change / employment support system according to an embodiment of the present invention uses a generation AI to collect and analyze a user's profile information, recommend companies, prepare application documents, and provide interview preparation. In the job change / employment support system, a user inputs their profile information, and the generation AI analyzes that information to recommend the most suitable companies. For example, if a user is interested in the IT industry, the generation AI recommends the most suitable IT companies. Furthermore, the generation AI also assists in preparing application documents for the recommended companies and provides interview preparation. For example, the generation AI generates resume and curriculum vitae templates based on the user's profile information, allowing the user to easily prepare application documents. The generation AI also analyzes past interview data, conducts mock interviews for the user, and provides feedback. This allows users to effectively prepare for interviews. This allows new graduates and working adults to efficiently find companies that suit them, smoothly prepare application documents, and prepare for interviews. This allows the job change / employment support system to effectively support users' job changes and job hunting activities. For example, if a user is interested in the IT industry, the generative AI will recommend the most suitable IT companies and assist with preparing application documents and preparing for interviews, allowing the user to effectively progress with their job search or career change.

[0080] The job change / employment support system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, a creation support unit, and an interview support unit. The collection unit collects user profile information. The user profile information includes, but is not limited to, educational background, work history, skills, and interests. The collection unit, for example, stores information entered by the user in a database. The collection unit can also collect information such as the user's social media activities and past application history. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, uses data mining technology to analyze the user's profile information and identify companies that are best suited to the user. The analysis unit can also use a machine learning algorithm to identify companies based on the user's skills and interests. The recommendation unit recommends companies based on the analysis results obtained by the analysis unit. The recommendation unit recommends companies that are suitable for the user based on data such as the company's job information, corporate culture, and performance. The recommendation unit can also use a matching algorithm to evaluate the compatibility between the user and companies and recommend the best company. The creation support unit prepares application documents for companies recommended by the recommendation unit. The creation support unit generates resume and curriculum vitae templates based on the user's profile information, for example. The creation support unit can also provide a template customization function to enable the user to easily create application documents. The interview support unit prepares for interviews based on the application documents created by the creation support unit. The interview support unit can, for example, analyze past interview data, conduct mock interviews for the user, and provide feedback. The interview support unit can also provide training programs to improve the user's interview skills. This allows the job change / employment support system according to the embodiment to effectively support the user's job change and job hunting. For example, if the user is interested in the IT industry, the generation AI can recommend the most suitable IT company and provide support for creating application documents and interview preparation, thereby effectively advancing the user's job change and job hunting.

[0081] The job change / employment support system includes a privacy protection unit that protects the user's privacy. The privacy protection unit provides a function for protecting the user's privacy. For example, the privacy protection unit protects the user's information using data encryption technology. The privacy protection unit also provides an access control function to prevent unauthorized access to the user's information. Furthermore, the privacy protection unit can anonymize the user's information using anonymization technology to protect the user's privacy. This allows the user's privacy to be protected. For example, the privacy protection unit encrypts the user's information to prevent unauthorized access by third parties. The privacy protection unit also anonymizes the user's information to protect the user's privacy. This allows the user to use the service with peace of mind.

[0082] The collection unit can collect information on the user's educational background, work history, skills, and interests. The collection unit, for example, collects educational background information input by the user. The educational background information includes the highest level of education, degree obtained, field of major, etc. The collection unit can also collect the user's employment history information. The employment history information includes past employers, job titles, and job duties. The collection unit can also collect the user's skill information. The skill information includes programming skills, language skills, specialized knowledge, etc. The collection unit can also collect the user's interest information. The interest information includes hobbies, industries and fields of interest, etc. This allows the collection unit to collect detailed profile information of the user. For example, the collection unit stores the educational background information input by the user in a database, and the analysis unit analyzes the information. The collection unit also collects the user's employment history information, and the analysis unit identifies companies based on the information. This allows the collection unit to collect the user's detailed profile information and the analysis unit to analyze the information, thereby identifying the most suitable company for the user.

[0083] The analysis unit analyzes the information collected by the collection unit and can identify companies that are suitable for the user. The analysis unit analyzes the collected information using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and is used to analyze user profile information. The analysis unit can also analyze the information using statistical analysis technology. Statistical analysis technology is a technology for analyzing data trends and patterns and is used to identify companies based on the user's skills and interests. The analysis unit can also analyze the information using machine learning algorithms. Machine learning algorithms are technologies that learn from data and make predictions and classifications and are used to identify companies that are suitable for the user. In this way, the analysis unit can analyze the information collected by the collection unit and identify companies that are suitable for the user. For example, the analysis unit analyzes the user's profile information using data mining technology and identifies companies that are suitable for the user. In addition, the analysis unit can use machine learning algorithms to identify companies based on the user's skills and interests. In this way, the analysis unit can identify companies that are suitable for the user.

[0084] The recommendation unit can recommend companies that suit the user based on data on the company's job information, corporate culture, and performance. The recommendation unit, for example, recommends companies based on the company's job information. The job information includes the job type, work location, salary, etc. The recommendation unit can also recommend companies based on the company's culture. The company's values, work style, internal atmosphere, etc. The recommendation unit can also recommend companies based on the company's performance data. The performance data includes sales, profits, growth rate, etc. The recommendation unit recommends companies that suit the user based on this data. For example, the recommendation unit analyzes the company's job information to recommend companies that suit the user's skills and interests. The recommendation unit also evaluates the corporate culture and recommends companies that suit the user. The recommendation unit also analyzes the company's performance data to recommend companies with high growth potential. This allows the recommendation unit to recommend companies that suit the user. For example, the recommendation unit recommends companies that are optimal for the user based on the company's job information. The recommendation unit also evaluates the corporate culture and recommends companies that suit the user. Furthermore, the recommendation unit analyzes business performance data of companies and recommends companies with high growth potential, thereby enabling the recommendation unit to recommend companies that suit the user.

[0085] The creation support unit can generate a resume or career history template based on the user's profile information. The creation support unit generates a resume template based on, for example, the user's educational background information. The resume template includes items such as educational background, work history, skills, and self-promotion. The creation support unit can also generate a career history template based on the user's work history information. The career history template includes items such as past employers, job titles, job duties, and achievements. The creation support unit can also customize the template based on the user's skill information. This allows the user to easily create application documents. For example, the creation support unit generates a resume template based on the user's educational background information, allowing the user to complete the template simply by entering information. The creation support unit can also generate a career history template based on the user's work history information, allowing the user to easily create application documents. This allows the user to easily create application documents.

[0086] The interview support unit can analyze past interview data, conduct a mock interview with the user, and provide feedback. The interview support unit, for example, analyzes past interview data and conducts a mock interview with the user. The past interview data includes interview questions, answers, evaluation results, and the like. The interview support unit can also provide feedback based on the results of the mock interview. The feedback includes areas for improvement in answers, interview attitude, speaking style, and the like. Furthermore, the interview support unit can also provide a training program for improving the user's interview skills. This allows the interview support unit to effectively prepare for the interview. For example, the interview support unit analyzes past interview data, conducts a mock interview with the user, and provides feedback. The interview support unit also provides a training program for improving the user's interview skills. This allows the interview support unit to effectively prepare for the interview.

[0087] The collection unit can estimate the user's emotions and adjust the timing of collecting profile information based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit collects profile information during a time period when the user is able to relax. The collection unit can analyze the user's facial expressions and voice to estimate the user's emotions. For example, facial expression recognition technology can be used to analyze the user's facial expressions to estimate the level of stress. Voice analysis technology can also be used to analyze the tone and speed of the user's voice to estimate the user's emotions. This allows the collection unit to collect profile information at the optimal timing depending on the user's emotions. For example, if the user is relaxed, the collection unit collects detailed profile information. Also, if the user is in a hurry, the collection unit collects the minimum necessary profile information. This allows the collection unit to collect profile information at the optimal timing depending on the user's emotions.

[0088] The collection unit can analyze the user's past profile information and select the optimal collection method. The collection unit, for example, suggests the optimal collection method based on information previously input by the user. The collection unit can analyze the user's past input history and select an efficient collection method. For example, the collection unit collects information in the form of a questionnaire based on information previously input by the user. The collection unit can also collect information in the form of an interview based on the user's past input history. Furthermore, the collection unit can analyze the user's past profile information and automatically complete necessary information. This allows the collection unit to select the optimal collection method based on the user's past information. For example, the collection unit suggests the optimal collection method based on information previously input by the user. The collection unit selects an efficient collection method based on the user's past input history. This allows the collection unit to select the optimal collection method based on the user's past information.

[0089] When collecting profile information, the collection unit may perform filtering based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the collection unit may preferentially collect IT-related information. The collection unit may filter and collect related information based on the user's current occupation or field of interest. For example, if the user is engaged in the education industry, the collection unit may preferentially collect education-related information. The collection unit may also filter and collect related information based on the user's field of interest. This allows the collection unit to collect related information based on the user's field of interest. For example, if the user is interested in the IT industry, the collection unit may preferentially collect IT-related information. Also, if the user is engaged in the education industry, the collection unit may preferentially collect education-related information. This allows the collection unit to collect related information based on the user's field of interest.

[0090] When collecting profile information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit preferentially uses voice input. The collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers text input, the collection unit preferentially uses text input. Furthermore, if the user prefers image input, the collection unit can also preferentially use image input. This allows the collection unit to select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit preferentially uses voice input. Furthermore, if the user prefers text input, the collection unit preferentially uses text input. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0091] The collection unit can estimate the user's emotions and determine the priority of profile information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important information. The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting the minimum necessary information. In this way, the collection unit can determine the priority of information to be collected according to the user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important information. Furthermore, when the user is relaxed, the collection unit prioritizes collecting detailed information. In this way, the collection unit can determine the priority of information to be collected according to the user's emotions.

[0092] When collecting profile information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting information related to the area. The collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific city, the collection unit prioritizes collecting information related to the city. The collection unit can also prioritize collecting highly relevant information based on the user's geographical location information. This allows the collection unit to collect highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting information related to the area. Also, if the user lives in a specific city, the collection unit prioritizes collecting information related to the city. This allows the collection unit to collect highly relevant information taking into account the user's geographical location information.

[0093] When collecting the profile information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related profile information based on information shared by the user on social media. The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit analyzes the user's social media activities and collects related information. The collection unit can also refer to the activities of the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related profile information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. In this way, the collection unit can analyze the user's social media activities and collect related information.

[0094] When collecting profile information, the collection unit can customize the collection method based on the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can customize the collection means by reflecting the user's past feedback. For example, the collection unit collects information in the form of a questionnaire based on the user's past feedback. The collection unit can also collect information in the form of an interview by reflecting the user's past feedback. Furthermore, the collection unit can optimize the collection process by referring to the user's past feedback. This allows the collection unit to customize the collection method by reflecting the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit customizes the collection means by reflecting the user's past feedback. This allows the collection unit to customize the collection method by reflecting the user's past feedback.

[0095] The analysis unit can estimate the user's emotions and adjust the method of presentation of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. The analysis unit can estimate the user's emotions and adjust the method of presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can also provide an analysis result that focuses on the main points. This allows the analysis unit to adjust the method of presentation of the analysis according to the user's emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit provides a detailed analysis result. This allows the analysis unit to adjust the method of presentation of the analysis according to the user's emotions.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit performs a detailed analysis based on important profile information. The analysis unit can adjust the level of detail of the analysis based on the importance of the profile information. For example, the analysis unit performs a simplified analysis based on profile information with a low level of importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the profile information. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit performs a detailed analysis based on important profile information. The analysis unit also performs a simplified analysis based on profile information with a low level of importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information.

[0097] During analysis, the analysis unit can apply different analysis algorithms based on the category of the profile information. For example, the analysis unit applies an analysis algorithm specialized for educational background to educational background information. The analysis unit can apply different analysis algorithms based on the category of the profile information. For example, the analysis unit applies an analysis algorithm specialized for educational background to employment history information. The analysis unit can also apply an analysis algorithm specialized for skills to skill information. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of the profile information. For example, the analysis unit applies an analysis algorithm specialized for educational background to educational background information. The analysis unit also applies an analysis algorithm specialized for employment history to employment history information. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of the profile information.

[0098] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0099] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit provides a detailed analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions.

[0100] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the profile information. For example, the analysis unit prioritizes analysis of profile information that has been submitted most recently. The analysis unit can determine the priority of analysis based on the time of submission of the profile information. For example, the analysis unit analyzes profile information that has been submitted more recently with a lower priority. The analysis unit can also adjust the priority of analysis based on the time of submission of the profile information. This allows the analysis unit to determine the priority of analysis based on the time of submission of the profile information. For example, the analysis unit analyzes profile information that has been submitted more recently with a lower priority. The analysis unit analyzes profile information that has been submitted more recently with a lower priority. This allows the analysis unit to determine the priority of analysis based on the time of submission of the profile information.

[0101] The analysis unit can adjust the order of analysis according to the relevance of the profile information during analysis. For example, the analysis unit prioritizes analysis of highly relevant profile information. The analysis unit can adjust the order of analysis based on the relevance of the profile information. For example, the analysis unit postpones analysis of less relevant profile information. The analysis unit can also adjust the order of analysis based on the relevance of the profile information. This allows the analysis unit to adjust the order of analysis based on the relevance of the profile information. For example, the analysis unit prioritizes analysis of highly relevant profile information. Also, the analysis unit postpones analysis of less relevant profile information. This allows the analysis unit to adjust the order of analysis based on the relevance of the profile information.

[0102] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user does not have technical expertise, the analysis unit provides analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed based on the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit provides analysis results in simple language. This allows the analysis unit to adjust the use of technical terms in the analysis based on the user's level of expertise.

[0103] The recommendation unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user's emotions. For example, if the user is nervous, the recommendation unit provides a simple and highly visible recommendation result. The recommendation unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated emotions. For example, if the user is relaxed, the recommendation unit provides a detailed recommendation result. Furthermore, if the user is in a hurry, the recommendation unit can also provide a recommendation result that focuses on the main points. This allows the recommendation unit to adjust the way recommendations are expressed based on the user's emotions. For example, if the user is nervous, the recommendation unit provides a simple and highly visible recommendation result. Furthermore, if the user is relaxed, the recommendation unit provides a detailed recommendation result. This allows the recommendation unit to adjust the way recommendations are expressed based on the user's emotions.

[0104] The recommendation unit can adjust the level of detail of the recommendation according to the importance of the company information when making a recommendation. The recommendation unit, for example, makes a detailed recommendation based on important company information. The recommendation unit can adjust the level of detail of the recommendation based on the importance of the company information. For example, the recommendation unit makes a simplified recommendation based on company information with low importance. The recommendation unit can also adjust the level of detail of the recommendation according to the importance of the company information. This allows the recommendation unit to adjust the level of detail of the recommendation according to the importance of the company information. For example, the recommendation unit makes a detailed recommendation based on important company information. The recommendation unit makes a simplified recommendation based on company information with low importance. This allows the recommendation unit to adjust the level of detail of the recommendation according to the importance of the company information.

[0105] The recommendation unit can apply different recommendation algorithms based on the category of company information when making recommendations. For example, the recommendation unit applies a recommendation algorithm specialized for IT to an IT company. The recommendation unit can apply different recommendation algorithms based on the category of company information. For example, the recommendation unit can apply a recommendation algorithm specialized for education to an education company. Furthermore, the recommendation unit can apply a recommendation algorithm specialized for medical care to a medical company. This allows the recommendation unit to apply an optimal recommendation algorithm depending on the category of company information. For example, the recommendation unit can apply a recommendation algorithm specialized for IT to an IT company. Furthermore, the recommendation unit can apply a recommendation algorithm specialized for education to an education company. This allows the recommendation unit to apply an optimal recommendation algorithm depending on the category of company information.

[0106] The recommendation unit can improve the accuracy of recommendations based on the user's past recommendation results when making recommendations. The recommendation unit, for example, adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results. For example, the recommendation unit adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past recommendation results. This allows the recommendation unit to improve the accuracy of recommendations by referring to the user's past recommendation results. For example, the recommendation unit adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past recommendation results. This allows the recommendation unit to improve the accuracy of recommendations by referring to the user's past recommendation results.

[0107] The recommendation unit may estimate a user's emotion and adjust the length of the recommendation based on the estimated user's emotion. For example, if the user is in a hurry, the recommendation unit may provide a short and to-the-point recommendation result. The recommendation unit may estimate a user's emotion and adjust the length of the recommendation based on the estimated emotion. For example, if the user is relaxed, the recommendation unit may provide a detailed recommendation result. Furthermore, if the user is excited, the recommendation unit may provide a visually stimulating recommendation result. In this way, the recommendation unit can adjust the length of the recommendation according to the user's emotion. For example, if the user is in a hurry, the recommendation unit may provide a short and to-the-point recommendation result. Furthermore, if the user is relaxed, the recommendation unit may provide a detailed recommendation result. In this way, the recommendation unit can adjust the length of the recommendation according to the user's emotion.

[0108] The recommendation unit can determine the priority of recommendations based on the update time of the company information when making recommendations. For example, the recommendation unit preferentially recommends company information that has been updated recently. The recommendation unit can determine the priority of recommendations based on the update time of the company information. For example, the recommendation unit recommends company information that has been updated recently with a lower priority. The recommendation unit can also adjust the priority of recommendations based on the update time of the company information. This allows the recommendation unit to determine the priority of recommendations based on the update time of the company information. For example, the recommendation unit preferentially recommends company information that has been updated recently. The recommendation unit recommends company information that has been updated recently with a lower priority. This allows the recommendation unit to determine the priority of recommendations based on the update time of the company information.

[0109] The recommendation unit can adjust the order of recommendation based on the relevance of the company information when making a recommendation. For example, the recommendation unit preferentially recommends highly relevant company information. The recommendation unit can adjust the order of recommendation based on the relevance of the company information. For example, the recommendation unit may recommend less relevant company information later. The recommendation unit can also adjust the order of recommendation based on the relevance of the company information. This allows the recommendation unit to adjust the order of recommendation based on the relevance of the company information. For example, the recommendation unit preferentially recommends highly relevant company information. The recommendation unit may recommend less relevant company information later. This allows the recommendation unit to adjust the order of recommendation based on the relevance of the company information.

[0110] The recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise when making a recommendation. For example, if the user has technical expertise, the recommendation unit provides a recommendation result that uses a lot of technical terms. The recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise. For example, if the user does not have technical expertise, the recommendation unit provides a recommendation result in simple language. The recommendation unit may also adjust the way in which the recommendation result is expressed based on the user's level of expertise. In this way, the recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise. For example, if the user has technical expertise, the recommendation unit provides a recommendation result that uses a lot of technical terms. In addition, if the user does not have technical expertise, the recommendation unit provides a recommendation result in simple language. In this way, the recommendation unit may adjust the use of technical terms in the recommendation based on the user's level of expertise.

[0111] The creation support unit can estimate the user's emotions and adjust the method for creating application documents based on the estimated user's emotions. For example, if the user is nervous, the creation support unit provides a simple, highly visible template. The creation support unit can estimate the user's emotions and adjust the method for creating application documents based on the estimated emotions. For example, if the user is relaxed, the creation support unit provides a template that can be customized in detail. Furthermore, if the user is in a hurry, the creation support unit can also provide a template that can be created quickly. In this way, the creation support unit can adjust the method for creating application documents according to the user's emotions. For example, if the user is nervous, the creation support unit provides a simple, highly visible template. Furthermore, if the user is relaxed, the creation support unit provides a template that can be customized in detail. In this way, the creation support unit can adjust the method for creating application documents according to the user's emotions.

[0112] During creation support, the creation support unit can select an optimal template based on the user's past application documents. For example, the creation support unit suggests an optimal template based on templates the user has used in the past. The creation support unit can analyze the user's past application documents and select an optimal template. For example, the creation support unit suggests an optimal template based on the user's past application documents. The creation support unit can also suggest an optimal template based on the success rate of the user's past application documents. This allows the creation support unit to analyze the user's past application documents and select an optimal template. For example, the creation support unit suggests an optimal template based on templates the user has used in the past. The creation support unit analyzes the user's past application documents and selects an optimal template. This allows the creation support unit to analyze the user's past application documents and select an optimal template.

[0113] During creation support, the creation support unit can customize the template based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the creation support unit provides a template for the IT industry. The creation support unit can customize the template based on the user's current occupation or field of interest. For example, if the user is engaged in the education industry, the creation support unit provides a template for the education industry. The creation support unit can also customize the template based on the user's field of interest. This allows the creation support unit to customize the template based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the creation support unit provides a template for the IT industry. Also, if the user is engaged in the education industry, the creation support unit provides a template for the education industry. This allows the creation support unit to customize the template based on the user's current occupation or field of interest.

[0114] The creation support unit can improve the template based on user feedback during creation support. For example, the creation support unit improves the template based on feedback provided by the user. The creation support unit can customize the template by reflecting the user's feedback. For example, the creation support unit improves the template based on user feedback. The creation support unit can also customize the template by reflecting the user's feedback. This allows the creation support unit to improve the template by reflecting the user's feedback. For example, the creation support unit improves the template based on feedback provided by the user. The creation support unit also customizes the template by reflecting the user's feedback. This allows the creation support unit to improve the template by reflecting the user's feedback.

[0115] The creation support unit can estimate the user's emotions and determine the priority of application documents based on the estimated user's emotions. For example, if the user is feeling stressed, the creation support unit prioritizes creating important application documents. The creation support unit can estimate the user's emotions and determine the priority of application documents based on the estimated emotions. For example, if the user is relaxed, the creation support unit prioritizes creating detailed application documents. Furthermore, if the user is in a hurry, the creation support unit can also prioritize creating the minimum necessary application documents. In this way, the creation support unit can determine the priority of application documents according to the user's emotions. For example, if the user is feeling stressed, the creation support unit prioritizes creating important application documents. Furthermore, if the user is relaxed, the creation support unit prioritizes creating detailed application documents. In this way, the creation support unit can determine the priority of application documents according to the user's emotions.

[0116] During creation assistance, the creation support unit can select an optimal template based on the user's geographical location information. For example, if the user lives in a specific area, the creation support unit provides a template related to that area. The creation support unit can select an optimal template by taking the user's geographical location information into consideration. For example, if the user lives in a specific city, the creation support unit provides a template related to that city. The creation support unit can also select an optimal template based on the user's geographical location information. This allows the creation support unit to select an optimal template by taking the user's geographical location information into consideration. For example, if the user lives in a specific area, the creation support unit provides a template related to that area. Also, if the user lives in a specific city, the creation support unit provides a template related to that city. This allows the creation support unit to select an optimal template by taking the user's geographical location information into consideration.

[0117] During creation support, the creation support unit can analyze the user's social media activity and suggest a template. For example, the creation support unit suggests an optimal template based on information shared by the user on social media. The creation support unit can analyze the user's social media activity and suggest a related template. For example, the creation support unit analyzes the user's social media activity and suggests a related template. The creation support unit can also suggest an optimal template by referring to the activity of the user's friends on social media. In this way, the creation support unit can analyze the user's social media activity and suggest an optimal template. For example, the creation support unit suggests an optimal template based on information shared by the user on social media. In addition, the creation support unit analyzes the user's social media activity and suggests a related template. In this way, the creation support unit can analyze the user's social media activity and suggest an optimal template.

[0118] During creation assistance, the creation assistance unit can customize the template based on the user's past feedback. For example, the creation assistance unit improves the template based on feedback provided by the user in the past. The creation assistance unit can customize the template by reflecting the user's past feedback. For example, the creation assistance unit improves the template based on the user's past feedback. The creation assistance unit can also customize the template by reflecting the user's past feedback. This allows the creation assistance unit to customize the template by reflecting the user's past feedback. For example, the creation assistance unit improves the template based on feedback provided by the user in the past. The creation assistance unit customizes the template by reflecting the user's past feedback. This allows the creation assistance unit to customize the template by reflecting the user's past feedback.

[0119] The interview support unit can estimate the user's emotions and adjust the interview preparation method based on the estimated user's emotions. For example, if the user is nervous, the interview support unit provides interview preparation that will allow the user to relax. The interview support unit can estimate the user's emotions and adjust the interview preparation method based on the estimated emotions. For example, if the user is relaxed, the interview support unit provides detailed interview preparation. Furthermore, if the user is in a hurry, the interview support unit can also provide interview preparation that focuses on the main points. This allows the interview support unit to adjust the interview preparation method according to the user's emotions. For example, if the user is nervous, the interview support unit provides interview preparation that will allow the user to relax. Furthermore, if the user is relaxed, the interview support unit provides detailed interview preparation. This allows the interview support unit to adjust the interview preparation method according to the user's emotions.

[0120] The interview support unit can select optimal strategies based on the user's past interview history when supporting an interview. The interview support unit, for example, suggests optimal interview strategies based on the user's past interview history. The interview support unit can analyze the user's past interview history and select optimal strategies. For example, the interview support unit suggests optimal interview strategies based on the user's past interview history. The interview support unit can also suggest optimal strategies based on the success rate of the user's past interview history. This allows the interview support unit to analyze the user's past interview history and select optimal strategies. For example, the interview support unit suggests optimal interview strategies based on the user's past interview history. The interview support unit analyzes the user's past interview history and selects optimal strategies. This allows the interview support unit to analyze the user's past interview history and select optimal strategies.

[0121] The interview support unit can customize the countermeasures based on the user's current occupation or field of interest during interview support. For example, if the user is interested in the IT industry, the interview support unit provides interview countermeasures for the IT industry. The interview support unit can customize the countermeasures based on the user's current occupation or field of interest. For example, if the user is working in the education industry, the interview support unit provides interview countermeasures for the education industry. The interview support unit can also customize the countermeasures based on the user's field of interest. This allows the interview support unit to customize the countermeasures based on the user's current occupation or field of interest. For example, if the user is interested in the IT industry, the interview support unit provides interview countermeasures for the IT industry. Also, if the user is working in the education industry, the interview support unit provides interview countermeasures for the education industry. This allows the interview support unit to customize the countermeasures based on the user's current occupation or field of interest.

[0122] The interview support unit can improve the countermeasures based on user feedback during interview support. The interview support unit improves the interview countermeasures based on, for example, feedback provided by the user. The interview support unit can customize the countermeasures by reflecting the user's feedback. For example, the interview support unit improves the interview countermeasures based on the user's feedback. The interview support unit can also customize the countermeasures by reflecting the user's feedback. This allows the interview support unit to improve the countermeasures by reflecting the user's feedback. For example, the interview support unit improves the interview countermeasures based on the feedback provided by the user. The interview support unit also customizes the countermeasures by reflecting the user's feedback. This allows the interview support unit to improve the countermeasures by reflecting the user's feedback.

[0123] The interview support unit can estimate the user's emotions and determine the priority of interview preparations based on the estimated user's emotions. For example, if the user is feeling stressed, the interview support unit prioritizes providing important interview preparations. The interview support unit can estimate the user's emotions and determine the priority of interview preparations based on the estimated emotions. For example, if the user is relaxed, the interview support unit prioritizes providing detailed interview preparations. Furthermore, if the user is in a hurry, the interview support unit can also prioritize providing the minimum necessary interview preparations. In this way, the interview support unit can determine the priority of interview preparations according to the user's emotions. For example, if the user is feeling stressed, the interview support unit prioritizes providing important interview preparations. Furthermore, if the user is relaxed, the interview support unit prioritizes providing detailed interview preparations. In this way, the interview support unit can determine the priority of interview preparations according to the user's emotions.

[0124] The interview support unit can select optimal strategies based on the user's geographical location information when supporting an interview. For example, if the user lives in a specific area, the interview support unit provides interview strategies related to the area. The interview support unit can select optimal strategies by taking the user's geographical location information into consideration. For example, if the user lives in a specific city, the interview support unit provides interview strategies related to the city. The interview support unit can also select optimal strategies for the interview based on the user's geographical location information. This allows the interview support unit to select optimal strategies by taking the user's geographical location information into consideration. For example, if the user lives in a specific area, the interview support unit provides interview strategies related to the area. Also, if the user lives in a specific city, the interview support unit provides interview strategies related to the city. This allows the interview support unit to select optimal strategies by taking the user's geographical location information into consideration.

[0125] The interview support unit can analyze the user's social media activity and suggest strategies when supporting an interview. For example, the interview support unit suggests optimal interview strategies based on information shared by the user on social media. The interview support unit can analyze the user's social media activity and suggest related interview strategies. For example, the interview support unit analyzes the user's social media activity and suggest related interview strategies. The interview support unit can also suggest optimal interview strategies by referring to the activities of the user's friends on social media. In this way, the interview support unit can analyze the user's social media activity and suggest optimal interview strategies. For example, the interview support unit suggests optimal interview strategies based on information shared by the user on social media. The interview support unit also analyzes the user's social media activity and suggest related interview strategies. In this way, the interview support unit can analyze the user's social media activity and suggest optimal interview strategies.

[0126] The interview support unit can customize the countermeasures based on the user's past feedback when supporting the interview. The interview support unit, for example, improves the interview countermeasures based on feedback provided by the user in the past. The interview support unit can customize the countermeasures by reflecting the user's past feedback. For example, the interview support unit improves the interview countermeasures based on the user's past feedback. The interview support unit can also customize the countermeasures by reflecting the user's past feedback. This allows the interview support unit to customize the countermeasures by reflecting the user's past feedback. For example, the interview support unit improves the interview countermeasures based on feedback provided by the user in the past. The interview support unit customizes the countermeasures by reflecting the user's past feedback. This allows the interview support unit to customize the countermeasures by reflecting the user's past feedback.

[0127] The privacy protection unit can estimate a user's emotion and adjust a privacy protection method based on the estimated user's emotion. For example, if the user is feeling anxious, the privacy protection unit provides a strong privacy protection setting. The privacy protection unit can estimate a user's emotion and adjust a privacy protection method based on the estimated emotion. For example, if the user is relaxed, the privacy protection unit provides a flexible privacy protection setting. Furthermore, if the user is in a hurry, the privacy protection unit can provide a privacy protection method that can be easily set. In this way, the privacy protection unit can adjust a privacy protection method according to the user's emotion. For example, if the user is feeling anxious, the privacy protection unit provides a strong privacy protection setting. Furthermore, if the user is relaxed, the privacy protection unit provides a flexible privacy protection setting. In this way, the privacy protection unit can adjust a privacy protection method according to the user's emotion.

[0128] The privacy protection unit can select an optimal protection method based on the user's past privacy settings during privacy protection. For example, the privacy protection unit suggests an optimal protection method based on the user's past privacy settings. The privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. For example, the privacy protection unit suggests an optimal protection method based on the user's past privacy settings. The privacy protection unit can also suggest an optimal protection method based on the success rate of the user's past privacy settings. In this way, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. For example, the privacy protection unit suggests an optimal protection method based on the user's past privacy settings. In addition, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. In this way, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method.

[0129] The privacy protection unit can customize protection measures based on the user's current occupation or field of interest during privacy protection. For example, if the user is employed in the IT industry, the privacy protection unit provides privacy protection measures for the IT industry. The privacy protection unit can customize protection measures based on the user's current occupation or field of interest. For example, if the user is employed in the education industry, the privacy protection unit provides privacy protection measures for the education industry. The privacy protection unit can also customize protection measures based on the user's field of interest. This allows the privacy protection unit to customize protection measures based on the user's current occupation or field of interest. For example, if the user is employed in the IT industry, the privacy protection unit provides privacy protection measures for the IT industry. Also, if the user is employed in the education industry, the privacy protection unit provides privacy protection measures for the education industry. This allows the privacy protection unit to customize protection measures based on the user's current occupation or field of interest.

[0130] The privacy protection unit can estimate a user's emotion and determine the priority of privacy protection based on the estimated user's emotion. For example, if the user feels anxious, the privacy protection unit can prioritize providing important privacy protection. The privacy protection unit can estimate a user's emotion and determine the priority of privacy protection based on the estimated emotion. For example, if the user feels relaxed, the privacy protection unit can prioritize providing detailed privacy protection. Furthermore, if the user is in a hurry, the privacy protection unit can prioritize providing the minimum necessary privacy protection. In this way, the privacy protection unit can determine the priority of privacy protection according to the user's emotion. For example, if the user feels anxious, the privacy protection unit can prioritize providing important privacy protection. Furthermore, if the user is relaxed, the privacy protection unit can prioritize providing detailed privacy protection. In this way, the privacy protection unit can determine the priority of privacy protection according to the user's emotion.

[0131] The privacy protection unit may select an optimal protection method based on the geographical location information of a user during privacy protection. For example, if the user lives in a specific area, the privacy protection unit may provide a privacy protection method related to the area. The privacy protection unit may select an optimal protection method in consideration of the geographical location information of the user. For example, if the user lives in a specific city, the privacy protection unit may provide a privacy protection method related to the city. The privacy protection unit may also select an optimal privacy protection method based on the geographical location information of the user. In this way, the privacy protection unit may select an optimal protection method in consideration of the geographical location information of the user. For example, if the user lives in a specific area, the privacy protection unit may provide a privacy protection method related to the area. In addition, if the user lives in a specific city, the privacy protection unit may provide a privacy protection method related to the city. In this way, the privacy protection unit may select an optimal protection method in consideration of the geographical location information of the user.

[0132] The privacy protection unit may suggest privacy protection measures based on the user's social media activities during privacy protection. For example, the privacy protection unit may suggest optimal privacy protection measures based on information shared by the user on social media. The privacy protection unit may analyze the user's social media activities and suggest related privacy protection measures. For example, the privacy protection unit may analyze the user's social media activities and suggest related privacy protection measures. The privacy protection unit may also suggest optimal privacy protection measures with reference to the activities of the user's friends on social media. In this way, the privacy protection unit may analyze the user's social media activities and suggest optimal privacy protection measures. For example, the privacy protection unit may suggest optimal privacy protection measures based on information shared by the user on social media. The privacy protection unit may also analyze the user's social media activities and suggest related privacy protection measures. In this way, the privacy protection unit may analyze the user's social media activities and suggest optimal privacy protection measures.

[0133] The privacy protection unit can customize the protection measures based on the user's past feedback during privacy protection. For example, the privacy protection unit improves the privacy protection measures based on feedback provided by the user in the past. The privacy protection unit can customize the protection measures by reflecting the user's past feedback. For example, the privacy protection unit improves the privacy protection measures based on the user's past feedback. The privacy protection unit can also customize the protection measures by reflecting the user's past feedback. In this way, the privacy protection unit can customize the protection measures by reflecting the user's past feedback. For example, the privacy protection unit improves the privacy protection measures based on feedback provided by the user in the past. The privacy protection unit can customize the protection measures by reflecting the user's past feedback. In this way, the privacy protection unit can customize the protection measures by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, creation support unit, interview support unit, and privacy protection unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 38B of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The recommendation unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and recommends companies based on the analysis results. The creation support unit is implemented, for example, in the control unit 46A of the smart device 14 and creates application documents based on the user's profile information. The interview support unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and conducts mock interviews and provides feedback. The privacy protection unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The collection unit estimates the user's emotions using, for example, the camera 42 and microphone 38B of the smart device 14, and adjusts the collection timing using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, creation support unit, interview support unit, and privacy protection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The recommendation unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and recommends companies based on the analysis results. The creation support unit is implemented, for example, in the control unit 46A of the smart glasses 214 and creates application documents based on the user's profile information. The interview support unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and conducts mock interviews and provides feedback. The privacy protection unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The collection unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the smart glasses 214, and adjusts the collection timing using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, creation support unit, interview support unit, and privacy protection unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the headset-type terminal 314, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and recommends companies based on the analysis results. The creation support unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and creates application documents based on the user's profile information. The interview support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and conducts mock interviews and provides feedback. The privacy protection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The collection unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the headset terminal 314, and adjusts the collection timing using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, creation support unit, interview support unit, and privacy protection unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the robot 414, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and recommends companies based on the analysis results. The creation support unit is implemented, for example, by the control unit 46A of the robot 414 and creates application documents based on the user's profile information. The interview support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and conducts mock interviews and provides feedback. The privacy protection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The collection unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the robot 414, and adjusts the collection timing using the specific processing unit 290 of the data processing device 12.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize analyzing only important information. Also, if the user is relaxed, it can prioritize analyzing detailed information. This allows the analysis unit to adjust the analysis priority according to the user's emotions. For example, if the user is in a hurry, the analysis unit can prioritize analyzing the minimum necessary information. Also, if the user is excited, it can provide visually stimulating analysis results. This allows the analysis unit to determine the analysis priority according to the user's emotions.

[0136] The recommendation unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user's emotions. For example, if the user is nervous, simple and highly visible recommendation results can be provided. Also, if the user is relaxed, detailed recommendation results can be provided. This allows the recommendation unit to adjust the way recommendations are expressed according to the user's emotions. For example, if the user is in a hurry, recommendation results that are concise can be provided. Also, if the user is excited, visually stimulating recommendation results can be provided. This allows the recommendation unit to adjust the way recommendations are expressed according to the user's emotions.

[0137] The creation support unit can estimate the user's emotions and adjust the method for creating application documents based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible template can be provided. Also, if the user is relaxed, a template that can be further customized can be provided. This allows the creation support unit to adjust the method for creating application documents according to the user's emotions. For example, if the user is in a hurry, a template that can be created quickly can be provided. Also, if the user is excited, a visually stimulating template can be provided. This allows the creation support unit to adjust the method for creating application documents according to the user's emotions.

[0138] The interview support unit can estimate the user's emotions and adjust the interview preparation method based on the estimated user's emotions. For example, if the user is nervous, it can provide relaxing interview preparation. Also, if the user is relaxed, it can provide detailed interview preparation. This allows the interview support unit to adjust the interview preparation method according to the user's emotions. For example, if the user is in a hurry, it can provide interview preparation that focuses on the main points. Also, if the user is excited, it can provide visually stimulating interview preparation. This allows the interview support unit to adjust the interview preparation method according to the user's emotions.

[0139] The privacy protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user's emotions. For example, if the user feels anxious, a strong privacy protection setting can be provided. Also, if the user feels relaxed, a flexible privacy protection setting can be provided. This allows the privacy protection unit to adjust the privacy protection method according to the user's emotions. For example, if the user is in a hurry, an easily settable privacy protection method can be provided. Also, if the user is excited, a visually stimulating privacy protection setting can be provided. This allows the privacy protection unit to adjust the privacy protection method according to the user's emotions.

[0140] The collection unit can analyze the user's past profile information and select the optimal collection method. For example, the collection unit can suggest the optimal collection method based on information the user has input in the past. The collection unit can also analyze the user's past input history and select an efficient collection method. This allows the collection unit to select the optimal collection method based on the user's past information. For example, the collection unit can collect information in the form of a questionnaire based on information the user has input in the past. The collection unit can also collect information in the form of an interview based on the user's past input history. This allows the collection unit to select the optimal collection method based on the user's past information.

[0141] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit can perform a detailed analysis based on important profile information. Also, the analysis unit can perform a simplified analysis based on less important profile information. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information. For example, the analysis unit can perform a detailed analysis based on important profile information. Also, the analysis unit can perform a simplified analysis based on less important profile information. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the profile information.

[0142] When making a recommendation, the recommendation unit can apply different recommendation algorithms based on the category of company information. For example, a recommendation algorithm specialized for IT can be applied to an IT company. Also, a recommendation algorithm specialized for education can be applied to an education company. This allows the recommendation unit to apply the optimal recommendation algorithm depending on the category of company information. For example, the recommendation unit can apply a recommendation algorithm specialized for medical care to a medical company. Also, a recommendation algorithm specialized for finance can be applied to a financial company. This allows the recommendation unit to apply the optimal recommendation algorithm depending on the category of company information.

[0143] When providing support for creation, the creation support unit can select an optimal template based on the user's past application documents. For example, the creation support unit can suggest an optimal template based on templates the user has used in the past. The creation support unit can also analyze the user's past application documents and select an optimal template. This allows the creation support unit to analyze the user's past application documents and select an optimal template. For example, the creation support unit can suggest an optimal template based on templates the user has used in the past. The creation support unit can also suggest an optimal template based on the success rate of the user's past application documents. This allows the creation support unit to analyze the user's past application documents and select an optimal template.

[0144] When supporting an interview, the interview support unit can select the optimal countermeasure based on the user's past interview history. For example, the optimal interview countermeasure can be suggested based on the user's past interview history. The interview support unit can also analyze the user's past interview history and select the optimal countermeasure. This allows the interview support unit to analyze the user's past interview history and select the optimal countermeasure. For example, the interview support unit can suggest the optimal interview countermeasure based on the user's past interview history. The interview support unit can also suggest the optimal countermeasure based on the success rate of the user's past interview history. This allows the interview support unit to analyze the user's past interview history and select the optimal countermeasure.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The collection unit collects the user's profile information, which includes information such as educational background, work history, skills, and interests. The collection unit stores the information entered by the user in a database. It may also collect information such as the user's social media activity and past application history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's profile information using data mining technology and machine learning algorithms to identify the company that is best suited to the user. Step 3: The recommendation unit recommends companies based on the analysis results obtained by the analysis unit. The recommendation unit recommends companies that suit the user based on data such as company recruitment information, corporate culture, and performance. It can also use a matching algorithm to evaluate the compatibility between the user and companies and recommend the most suitable company. Step 4: The creation support unit creates application documents for companies recommended by the recommendation unit. The creation support unit generates resume and curriculum vitae templates based on the user's profile information. In addition, the creation support unit provides a template customization function to enable users to easily create application documents. Step 5: The Interview Support Department prepares users for interviews based on the application documents prepared by the Creation Support Department. The Interview Support Department analyzes past interview data, conducts mock interviews with users, and provides feedback. It also provides training programs to improve users' interview skills.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0175] 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.

[0176] 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.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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, in order to avoid confusion and to 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 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 collection unit that collects user profile information; an analysis unit that analyzes the information collected by the collection unit; a recommendation unit that recommends companies based on the analysis results obtained by the analysis unit; a preparation support unit that prepares application documents for companies recommended by the recommendation unit; an interview support unit that prepares applicants for interviews based on the application documents prepared by the preparation support unit; Equipped with A system characterized by:

2. Equipped with a privacy protection unit that protects user privacy 2. The system of claim 1.

3. The collecting unit Collect information about users' education, work history, skills, and interests 2. The system of claim 1.

4. The analysis unit Analyzing the information collected by the collection unit and identifying a company suitable for the user 2. The system of claim 1.

5. The recommendation unit Recommend companies that suit users based on job information, corporate culture, and performance data 2. The system of claim 1.

6. The creation support unit Generate a resume or CV template based on a user's profile information 2. The system of claim 1.

7. The interview support department Analyze past interview data, conduct mock interviews with users, and provide feedback 2. The system of claim 1.

8. The collecting unit The user's emotions are estimated, and the timing of collecting profile information is adjusted based on the estimated user's emotions.

2. The system of claim 1.

9. The collecting unit Analyze the user's past profile information and select the optimal collection method 2. The system of claim 1.

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