system

Abstract

The system according to the embodiment aims to enable even people who are not good at self-promotion or writing resumes to create appropriate content. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a resume or curriculum vitae based on the information analyzed by the analysis unit. The provision unit provides the content generated by the generation unit to the user.

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 technology has had the problem that it is difficult for people who are not good at self-promotion or writing resumes to create appropriate content.

[0005] The system according to the embodiment aims to enable even people who are not good at self-promotion or writing resumes to create appropriate content. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a resume or curriculum vitae based on the information analyzed by the analysis unit. The provision unit provides the content generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can enable even those who are not good at self-promotion or writing a resume to create appropriate content. [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 resume generation system according to an embodiment of the present invention automatically accepts and analyzes information from a user, and generates and provides a resume or a work history. In this system, the user answers questions from an AI, and the AI ​​generates a resume or a work history based on the user's answers. Using this service, users can easily create resumes and work history documents. Furthermore, by agreeing to provide the created content to third parties, third parties can identify talented individuals before any other company. For example, in a resume generation system, the user answers questions from an AI. The AI ​​then asks questions about the user's background, skills, and self-promotion. Examples of such questions include, "Please tell us about your work experience" and "Please describe yourself." The user responds to these questions in text. Next, in the resume generation system, the AI ​​analyzes the user's answers and generates a resume or a work history. The AI ​​creates a resume or a work history in an appropriate format based on the user's answers. For example, it organizes the user's work experience and skills and presents them in an easy-to-read format. It also appropriately describes self-promotion and motivation for applying. The generated resume or work history is provided to the user. Users can review the generated content and make corrections as necessary. This allows users to easily create resumes and CVs. Furthermore, users agree to provide the created content to third parties. This consent allows third parties to identify talented candidates before any other company. For example, third parties can select appropriate candidates based on information such as the user's background, skills, and self-promotion. Furthermore, third parties can obtain talent information, including information not included in resumes and CVs. For example, the user's answers can reveal the user's personality and values. This allows the resume generation system to automatically generate and provide the user's resume or CV. For example, even people who are not good at self-promotion or writing CVs can create appropriate resumes and CVs simply by answering AI questions. Third parties can also select appropriate candidates based on the user's detailed information.

[0029] A resume generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives information from a user. The information from the user includes, but is not limited to, personal information, work history, and skill information. The receiving unit receives, for example, text data entered by the user. The receiving unit can also receive voice input and image input. For example, the receiving unit converts the user's voice response into text data and receives the text data. The receiving unit can also analyze image data uploaded by the user to extract information. The analysis unit analyzes the information received by the receiving unit. The analysis can be performed using, for example, text analysis, data mining, machine learning algorithms, or the like, but is not limited to these examples. For example, the analysis unit analyzes the user's response using text analysis. The analysis unit can also extract the user's skills and experience using data mining technology. The analysis unit can also grasp the user's personality and values ​​using machine learning algorithms. The generation unit generates a resume or a curriculum vitae based on the information analyzed by the analysis unit. The generation may be performed using, for example, template-based generation, natural language generation technology, or the like, but is not limited to these examples. For example, the generation unit may use template-based generation to organize the user's work history and skills and generate a resume or career history in an easy-to-read format. The generation unit may also use natural language generation technology to generate a self-promotional statement and motivation for applying. The generation unit may also generate a resume or career history in an appropriate format based on the user's responses. The providing unit provides the content generated by the generation unit to the user. The provision may be performed using, for example, email, a web interface, a download link, or the like, but is not limited to these examples. For example, the providing unit may send the generated resume or career history to the user by email. The providing unit may also allow the user to download the generated content through a web interface. The providing unit may also allow the user to review the generated content and modify it as necessary.As a result, the resume generation system according to the embodiment can efficiently accept and analyze user information, and generate and provide a resume or a curriculum vitae. For example, the reception unit accepts text data entered by a user. The reception unit can also accept voice input or image input. For example, the reception unit converts the user's voice responses into text data and accepts the text data. The reception unit can also analyze image data uploaded by the user to extract information. The analysis unit analyzes the information accepted by the reception unit. The analysis can be performed using, for example, text analysis, data mining, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit can analyze the user's responses using text analysis. The analysis unit can also extract the user's skills and career history using data mining technology. The analysis unit can also grasp the user's personality and values ​​using machine learning algorithms. The generation unit generates a resume or a curriculum vitae based on the information analyzed by the analysis unit. The generation can be performed using, for example, template-based generation, natural language generation technology, or other methods, but is not limited to these examples. For example, the generation unit uses template-based generation to organize the user's work history and skills and generate a resume or career history in an easy-to-read format. The generation unit can also use natural language generation technology to generate self-promotional information and motivation for applying. The generation unit can also generate a resume or career history in an appropriate format based on the user's responses. The providing unit provides the content generated by the generation unit to the user. The provision can be performed, for example, via email, a web interface, a download link, or other methods, but is not limited to these examples. For example, the providing unit sends the generated resume or career history to the user via email. The providing unit can also allow the user to download the generated content through a web interface. The providing unit can also allow the user to review the generated content and modify it as necessary. This allows the resume generation system according to the embodiment to efficiently accept and analyze user information, generate, and provide a resume or career history.

[0030] The generation unit can generate a resume or a work history in a format based on the user's answers. Examples of formats include, but are not limited to, PDF, Word, and HTML. For example, the generation unit generates a resume or a work history in PDF format based on the user's answers. The generation unit can also generate a resume or a work history in Word format. The generation unit can also generate a resume or a work history in HTML format. For example, the generation unit organizes the user's work history and skills and generates a PDF file in an easy-to-read format. The generation unit can also generate a Word file based on the user's answers. The generation unit can also generate a resume or a work history in HTML format so that it can be displayed in a web browser. This allows the resume or work history to be generated in an appropriate format based on the user's answers. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's answer data into a generation AI and have the generation AI generate a resume or a work history in an appropriate format.

[0031] The providing unit may provide the generated resume or work history to the user, allowing the user to make corrections. For example, the providing unit may send the generated resume or work history to the user by email. The providing unit may also allow the user to download the generated content through a web interface. The providing unit may also allow the user to review the generated content and make corrections as necessary. For example, the providing unit may provide an online editor to allow the user to directly edit the generated resume or work history. The providing unit may also provide a feedback function to allow the user to point out corrections. This allows the user to correct the generated resume or work history, thereby providing more accurate information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit may input the generated resume or work history into a generation AI, causing the generation AI to suggest corrections.

[0032] The providing unit can provide the generated resume or work history to a third party based on the user's consent. Consent is obtained by, for example, a checkbox or an electronic signature, but is not limited to, such examples. The providing unit obtains consent, for example, by the user selecting a checkbox. The providing unit can also obtain consent using an electronic signature. For example, the providing unit obtains consent by the user signing an electronic signature. The providing unit provides the content of the user's consent to a third party. Third parties include, for example, companies, recruiters, agents, etc., but are not limited to such examples. For example, the providing unit provides the generated resume or work history to a company. The providing unit can also provide it to a recruiter. The providing unit can also provide it to an agent. In this way, the generated resume or work history can be provided to a third party based on the user's consent. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's consent data into the generation AI and have the generation AI confirm the consent.

[0033] The analysis unit can understand the user's personality and values ​​from the user's responses. The analysis unit can evaluate the user's personality using methods such as psychological tests and behavioral analysis. The analysis unit can also evaluate the user's values ​​using methods such as questionnaires and interviews. For example, the analysis unit can conduct a psychological test based on the user's responses to evaluate the user's personality. The analysis unit can also analyze the user's behavioral data to understand the user's behavioral patterns. The analysis unit can also conduct a questionnaire based on the user's responses to evaluate the user's values. This allows the user's personality and values ​​to be understood from the user's responses, thereby generating a more appropriate resume or curriculum vitae. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's response data into a generation AI and have the generation AI evaluate the user's personality and values.

[0034] The providing unit can provide information for a third party to select excellent personnel. The providing unit provides information such as the user's skill set, years of experience, and educational background to a third party. Definitions and criteria for excellent personnel include, but are not limited to, a specific skill set, a certain number of years of experience, and a high level of educational background. For example, the providing unit provides appropriate personnel information to a third party based on the user's skill set. The providing unit can also provide appropriate personnel information to a third party based on the user's years of experience. The providing unit can also provide appropriate personnel information to a third party based on the user's educational background. This allows the third party to provide information for selecting excellent personnel. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's personnel information into a generation AI, causing the generation AI to assist in the selection of excellent personnel.

[0035] The reception unit can analyze the user's past answer history and select a question. The reception unit, for example, analyzes the past answer history stored in a database and selects an optimal question. The past answer history includes, for example, the content of the user's past answers, the frequency of answers, and the importance of answers, but is not limited to these examples. For example, the reception unit selects a question that does not overlap with previous answers based on the content of the user's past answers. The reception unit can also select a question that will elicit particularly important information from the user's past answers. The reception unit can also analyze the user's past answer history and select a question that will fill in any missing answers. In this way, the optimal question can be selected by analyzing the user's past answer history. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit can input past answer history data into a generation AI and have the generation AI select an optimal question.

[0036] The reception unit may filter questions based on the user's current occupation and skill level. The reception unit may filter questions based on, for example, the user's occupation category and skill level threshold. Examples of filtering include, but are not limited to, occupation category and skill level threshold. For example, if the user is an engineer, the reception unit may prioritize questions about technical skills. If the user is a manager, the reception unit may prioritize questions about leadership and management. If the user is a recent graduate, the reception unit may prioritize questions about educational background and internship experience. By filtering questions based on the user's current occupation and skill level, more appropriate questions can be asked. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's occupation and skill level data into a generation AI, causing the generation AI to filter the questions.

[0037] When asking a question, the reception unit can select the optimal question means depending on the user's input method. The reception unit selects the optimal question means depending on the input method, such as voice input, text input, or image input. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit can ask a question that is easy to answer by voice. Furthermore, if the user selects text input, the reception unit can ask a question that requests a detailed text answer. Furthermore, if the user selects image input, the reception unit can ask a question that requests self-promotion or an explanation of work history using an image. This allows for more appropriate questions to be asked by selecting the optimal question means depending on the user's input method. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal question means.

[0038] When asking a question, the reception unit can prioritize relevant questions by taking into account the user's geographical location information. The reception unit selects relevant questions based on geographical location information such as GPS data or an IP address. Geographical location information includes, but is not limited to, the user's current location, previously visited places, and place of residence. For example, if the user lives in a specific area, the reception unit can ask about work experience related to that area. If the user lives overseas, the reception unit can also ask questions about international experience and skills. If the user lives in a specific city, the reception unit can also ask questions about work experience and activities in that city. This allows for more appropriate questions by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI select relevant questions.

[0039] When asking a question, the reception unit can analyze the user's social media activity and ask a related question. The reception unit selects a related question based on data such as the content of social media posts, the number of followers, and the engagement rate. Social media activity includes, but is not limited to, the user's publicly disclosed work experience, skills, and interests. For example, the reception unit asks a question based on the user's publicly disclosed work experience on social media. The reception unit can also analyze the user's social media activity and ask questions about related skills and experience. The reception unit can also ask questions about self-promotion and motivation for applying based on the user's social media posts. In this way, the reception unit can ask a related question by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input social media data into a generation AI and have the generation AI select a related question.

[0040] When asking a question, the reception unit can customize the question content by reflecting the user's past feedback. The reception unit customizes the question content based on feedback data, such as questionnaires and user reviews. Feedback includes, but is not limited to, opinions, evaluations, and improvements provided by the user in the past. For example, the reception unit adjusts the content and order of questions based on the user's past feedback. The reception unit can also customize questions to elicit particularly important information from the user's past feedback. The reception unit can also analyze the user's past feedback and customize questions to fill in missing answers. This allows the question content to be customized by reflecting the user's past feedback, thereby enabling more appropriate questions to be asked. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input feedback data into a generation AI and have the generation AI customize the question content.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's answer. The analysis unit adjusts the level of detail of the analysis based on, for example, the content of the user's answer, the frequency of the answer, the importance of the answer, etc. The importance of the answer includes, for example, but is not limited to, the content of the answer, the frequency of the answer, and the impact of the answer. For example, the analysis unit performs a detailed analysis on important answers to provide deep insights. The analysis unit can also perform a concise analysis on less important answers to quickly provide results. The analysis unit can also automatically evaluate the importance of the user's answer and adjust the level of detail of the analysis. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the user's answer. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the user's answer. The analysis unit applies different analysis algorithms depending on categories such as occupation category and skill category. Examples of categories include, but are not limited to, technical skills, management skills, and self-promotion. For example, the analysis unit applies a technical analysis algorithm to answers related to technical skills. The analysis unit can also apply a leadership analysis algorithm to answers related to management. The analysis unit can also apply a personality analysis algorithm to answers related to self-promotion. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the user's answer. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer data into a generation AI and have the generation AI apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past answers. The analysis unit, for example, analyzes past answer results stored in a database to improve the accuracy of the analysis. Past answer results include, but are not limited to, the content of answers provided by the user in the past, the frequency of answers, and the importance of answers. For example, the analysis unit adjusts the analysis algorithm based on the user's past answer results to improve accuracy. The analysis unit can also analyze the user's past answer results, find specific patterns, and reflect them in the analysis. The analysis unit can also adjust the level of detail and speed of the analysis by referring to the user's past answer results. In this way, the accuracy of the analysis can be improved by referring to the user's past answer results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past answer result data into a generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the user's answers. The analysis unit determines the analysis priority based on data such as a timestamp and a submission deadline. The submission time includes, but is not limited to, the answer submission date and time, the answer submission deadline, etc. For example, the analysis unit prioritizes analysis of the most recent answers and provides results quickly. The analysis unit can also postpone analysis of answers submitted earlier. The analysis unit can also automatically adjust the analysis schedule based on the submission time. This allows for more rapid analysis by determining the analysis priority based on the time of submission of the user's answers. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input submission time data into a generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the user's answers. The analysis unit adjusts the order of analysis based on data such as the degree of similarity of the answer content and common keywords. Answer relevance includes, but is not limited to, the degree of similarity of the answer content, common keywords, and answer themes. For example, the analysis unit prioritizes analysis of highly relevant answers and provides results quickly. The analysis unit can also postpone analysis of less relevant answers. The analysis unit can also automatically evaluate the relevance of the user's answers and adjust the order of analysis. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the user's answers. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input answer relevance data to a generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the user's level of expertise based on data such as qualifications, years of experience, and test results, and adjusts the use of technical terms in the analysis. Examples of expertise levels include, but are not limited to, specific qualifications, a certain number of years of experience, and test results. For example, the analysis unit provides analysis results that use a lot of technical terms to users with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to users with low levels of expertise. The analysis unit can also automatically evaluate the user's level of expertise and adjust the use of technical terms in the analysis. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input expertise level data into a generation AI and have the generation AI adjust the use of technical terms in the analysis.

[0047] The generation unit can adjust the level of detail of the generated content based on the importance of the user's answer during generation. The generation unit adjusts the level of detail of the generated content based on, for example, the content of the user's answer, the frequency of the answer, the importance of the answer, etc. The importance of the answer includes, for example, but is not limited to, the content of the answer, the frequency of the answer, and the impact of the answer. For example, the generation unit generates a resume or a curriculum vitae that includes detailed information for an important answer. The generation unit can also generate a resume or a curriculum vitae that includes concise information for an answer with low importance. The generation unit can also automatically evaluate the importance of the user's answer and adjust the level of detail of the generated content. As a result, by adjusting the level of detail of the generated content based on the importance of the user's answer, more appropriate resumes or curriculum vitae can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's answer data into a generation AI and have the generation AI adjust the level of detail of the content.

[0048] The generation unit can apply different generation algorithms depending on the category of the user's answer during generation. The generation unit applies different generation algorithms depending on categories such as occupational category and skill category. Examples of categories include, but are not limited to, technical skills, management skills, and self-promotion. For example, the generation unit uses a technical format for answers regarding technical skills. The generation unit can also use a format that emphasizes leadership for answers regarding management. The generation unit can also use a format that emphasizes personality for answers regarding self-promotion. In this way, by applying different generation algorithms depending on the category of the user's answer, more appropriate resumes and curriculum vitae can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's answer data into a generation AI and cause the generation AI to apply different generation algorithms.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit, for example, analyzes past generation results stored in a database to improve the accuracy of generation. Past generation results include, but are not limited to, the content of resumes and curriculum vitae (CVs) generated by the user in the past, the frequency of generation, and the importance of generation. For example, the generation unit adjusts the generation algorithm based on the user's past generation results to improve accuracy. The generation unit can also analyze the user's past generation results, find specific patterns, and reflect them in the generation. The generation unit can also adjust the level of detail and speed of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past generation result data into a generation AI to improve the accuracy of generation using the generation AI.

[0050] During generation, the generation unit can determine the priority of the content to be generated based on the time of submission of the user's answers. The generation unit determines the priority of the content to be generated based on data such as a timestamp and a submission deadline. The submission time includes, but is not limited to, the submission date and time of the answer and the submission deadline. For example, the generation unit prioritizes generating resumes and curriculum vitae based on the most recent answers. The generation unit can also postpone generation of answers submitted earlier. The generation unit can also automatically adjust the generation schedule based on the submission time. This allows for more rapid generation of resumes and curriculum vitae by determining the priority of the content to be generated based on the time of submission of the user's answers. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input submission time data into a generation AI and have the generation AI determine the priority.

[0051] The generation unit can adjust the order of the generated content based on the relevance of the user's answers during generation. The generation unit adjusts the order of the generated content based on data such as the degree of similarity of the answer content and common keywords. The relevance of the answers includes, but is not limited to, the degree of similarity of the answer content, common keywords, and themes of the answers. For example, the generation unit prioritizes highly relevant answers when writing them in a resume or curriculum vitae. The generation unit can also generate less relevant answers later. The generation unit can also automatically evaluate the relevance of the user's answers and adjust the order of the generated content. By adjusting the order of the generated content based on the relevance of the user's answers, a more appropriate resume or curriculum vitae can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input answer relevance data into a generation AI and have the generation AI adjust the order of the content.

[0052] The generation unit can adjust the use of technical terminology in the content to be generated according to the user's level of expertise during generation. The generation unit evaluates the user's level of expertise based on data such as qualifications, years of experience, and test results, and adjusts the use of technical terminology in the content to be generated. Examples of expertise levels include, but are not limited to, specific qualifications, a certain number of years of experience, and test results. For example, the generation unit generates a resume or CV that uses a lot of technical terminology for a user with high expertise. The generation unit can also generate a concise and easy-to-understand resume or CV for a user with low expertise. The generation unit can also automatically evaluate the user's level of expertise and adjust the use of technical terminology in the content to be generated. This allows for the generation of more appropriate resumes and CVs by adjusting the use of technical terminology in the content to be generated according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input expertise level data into a generation AI and have the generation AI adjust the use of technical terminology.

[0053] The providing unit can select the optimal display method by referring to the user's past operation history when providing the data. The providing unit selects the optimal display method based on operation history, such as log data or clickstream data. The operation history includes, but is not limited to, the user's past operations, the frequency of operations, and the success rate of operations. For example, the providing unit prioritizes and provides the display method that the user has previously preferred. The providing unit can also analyze the user's past operation history and suggest the optimal display method. The providing unit can also customize the display method based on the user's past operation history. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, or without using, AI. For example, the providing unit can input operation history data to a generation AI and have the generation AI select the optimal display method.

[0054] The providing unit can customize the display content according to the user's current task when providing the display content. The providing unit customizes the display content based on, for example, data from a task management system or the user's operation status. The current task includes, for example, the user's current work, the progress of the work, and the priority of the work, but is not limited to these examples. For example, when the user is reviewing their resume, the providing unit can concisely display the contents of the resume. Furthermore, when the user is reviewing their resume, the providing unit can also concisely display the contents of the resume. Furthermore, the providing unit can automatically customize the display content according to the user's current task. This allows for more appropriate display by customizing the display content according to the user's current task. Some or all of the above-described processing by the providing unit may be performed using, or without, for example, AI. For example, the providing unit can input current task data into a generating AI and have the generating AI customize the display content.

[0055] The providing unit can improve the display method by reflecting user feedback when providing the display. The providing unit improves the display method based on feedback data such as questionnaires and user reviews. Feedback includes, but is not limited to, opinions, evaluations, and improvements provided by users. For example, if a user provides feedback on the provided display method, the providing unit improves the display method based on the feedback. The providing unit can also analyze the user feedback and identify improvements to the display method. The providing unit can also automatically improve the display method based on the user feedback. This allows for a more appropriate display by improving the display method by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data into a generation AI and have the generation AI improve the display method.

[0056] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. The providing unit selects the optimal display method based on device information such as the device type, screen size, and OS. Device information includes, but is not limited to, the device type (e.g., smartphone, tablet, desktop), screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method including detailed information. This allows for more appropriate display by selecting the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input device information data to a generation AI and have the generation AI select the optimal display method.

[0057] The providing unit can make the display content multilingual based on the user's language setting when providing the content. The providing unit makes the display content multilingual based on data such as the browser's language setting and the user profile's language setting. Language settings include, but are not limited to, the language used by the user, the device's language setting, and the browser's language setting. For example, the providing unit automatically sets the display content based on the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This allows for more appropriate display by making the display content multilingual based on the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input language setting data to a generation AI and have the generation AI make the display content multilingual.

[0058] The providing unit can select the optimal display method by taking into account the user's geographical location information when providing the information. The providing unit selects the optimal display method based on geographical location information such as GPS data or an IP address. Geographical location information includes, but is not limited to, the user's current location, previously visited locations, and place of residence. For example, if the user lives in a specific region, the providing unit can prioritize displaying information related to that region. Furthermore, if the user lives overseas, the providing unit can prioritize displaying international information. Furthermore, if the user lives in a specific city, the providing unit can prioritize displaying information related to that city. This allows for more appropriate display by selecting the optimal display method by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input geographical location information data to a generation AI and have the generation AI select the optimal display method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can estimate the user's interests and concerns based on the user's input, and customize the content of the questions based on the estimated interests and concerns. For example, if the user has answered many questions about technical skills, the reception unit can add detailed questions about technical skills. Furthermore, if the user has answered many questions about managerial positions, the reception unit can add questions about leadership and management. Furthermore, if the user has answered many questions about a particular industry, the reception unit can add questions related to that industry. In this way, by customizing the content of the questions based on the user's interests and concerns, more appropriate questions can be asked.

[0061] The analysis unit can estimate the user's learning style based on the user's answers and adjust the analysis method based on the estimated learning style. For example, if the user prefers visual information, the analysis unit can provide analysis results that make extensive use of graphs and charts. If the user prefers text-based information, the analysis unit can also provide detailed text analysis results. Furthermore, if the user prefers practical examples, the analysis unit can also provide analysis results that include specific case studies. In this way, by adjusting the analysis method according to the user's learning style, more appropriate analysis results can be provided.

[0062] The generation unit can estimate the user's career goals based on the user's answers and customize the content of the resume or curriculum vitae based on the estimated career goals. For example, if the user is aiming for a technical position, the generation unit can generate a resume that emphasizes technical skills and project experience. If the user is aiming for a managerial position, the generation unit can generate a curriculum vitae that emphasizes leadership and management experience. Furthermore, if the user is aiming for a career in a specific industry, the generation unit can generate a resume that emphasizes experience and skills related to that industry. This makes it possible to generate more appropriate documents by customizing the content of the resume or curriculum vitae according to the user's career goals.

[0063] The providing unit can estimate the user's networking needs based on the user's answers and customize the information to be provided to third parties based on the estimated needs. For example, if the user wishes to network in a specific industry, the providing unit can provide the user's information to companies and recruiters in that industry. Also, if the user wishes to network in a specific occupation, the providing unit can provide the user's information to companies and agents related to that occupation. Furthermore, if the user wishes to network internationally, the providing unit can provide the user's information to overseas companies and recruiters. In this way, by customizing the information to be provided to third parties according to the user's networking needs, more appropriate information can be provided.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives information from the user. The information from the user includes, for example, personal information, work history, and skill information. The reception unit receives text data entered by the user, as well as voice input and image input. For example, it can convert voice responses into text data and accept them, or analyze uploaded image data to extract information. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis can be used to analyze the content of the user's responses, and data mining technology can be used to extract skills and background information. Machine learning algorithms can also be used to understand the user's personality and values. Step 3: The generation unit generates a resume or curriculum vitae based on the information analyzed by the analysis unit. This generation is performed using methods such as template-based generation or natural language generation technology. For example, template-based generation can be used to organize work history and skills, and generate a resume or curriculum vitae in an easy-to-read format. Natural language generation technology can also be used to generate self-promotional statements and motivations for applying. Step 4: The providing unit provides the content generated by the generating unit to the user. The provision is performed by email, a web interface, a download link, or other methods. For example, the generated resume or curriculum vitae can be sent by email or made available for download through a web interface. The user can also review the generated content and make corrections as necessary.

[0066] (Example 2) A resume generation system according to an embodiment of the present invention automatically accepts and analyzes information from a user, and generates and provides a resume or a work history. In this system, the user answers questions from an AI, and the AI ​​generates a resume or a work history based on the user's answers. Using this service, users can easily create resumes and work history documents. Furthermore, by agreeing to provide the created content to third parties, third parties can identify talented individuals before any other company. For example, in a resume generation system, the user answers questions from an AI. The AI ​​then asks questions about the user's background, skills, and self-promotion. Examples of such questions include, "Please tell us about your work experience" and "Please describe yourself." The user responds to these questions in text. Next, in the resume generation system, the AI ​​analyzes the user's answers and generates a resume or a work history. The AI ​​creates a resume or a work history in an appropriate format based on the user's answers. For example, it organizes the user's work experience and skills and presents them in an easy-to-read format. It also appropriately describes self-promotion and motivation for applying. The generated resume or work history is provided to the user. Users can review the generated content and make corrections as necessary. This allows users to easily create resumes and CVs. Furthermore, users agree to provide the created content to third parties. This consent allows third parties to identify talented candidates before any other company. For example, third parties can select appropriate candidates based on information such as the user's background, skills, and self-promotion. Furthermore, third parties can obtain talent information, including information not included in resumes and CVs. For example, the user's answers can reveal the user's personality and values. This allows the resume generation system to automatically generate and provide the user's resume or CV. For example, even people who are not good at self-promotion or writing CVs can create appropriate resumes and CVs simply by answering AI questions. Third parties can also select appropriate candidates based on the user's detailed information.

[0067] A resume generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives information from a user. The information from the user includes, but is not limited to, personal information, work history, and skill information. The receiving unit receives, for example, text data entered by the user. The receiving unit can also receive voice input and image input. For example, the receiving unit converts the user's voice response into text data and receives the text data. The receiving unit can also analyze image data uploaded by the user to extract information. The analysis unit analyzes the information received by the receiving unit. The analysis can be performed using, for example, text analysis, data mining, machine learning algorithms, or the like, but is not limited to these examples. For example, the analysis unit analyzes the user's response using text analysis. The analysis unit can also extract the user's skills and experience using data mining technology. The analysis unit can also grasp the user's personality and values ​​using machine learning algorithms. The generation unit generates a resume or a curriculum vitae based on the information analyzed by the analysis unit. The generation may be performed using, for example, template-based generation, natural language generation technology, or the like, but is not limited to these examples. For example, the generation unit may use template-based generation to organize the user's work history and skills and generate a resume or career history in an easy-to-read format. The generation unit may also use natural language generation technology to generate a self-promotional statement and motivation for applying. The generation unit may also generate a resume or career history in an appropriate format based on the user's responses. The providing unit provides the content generated by the generation unit to the user. The provision may be performed using, for example, email, a web interface, a download link, or the like, but is not limited to these examples. For example, the providing unit may send the generated resume or career history to the user by email. The providing unit may also allow the user to download the generated content through a web interface. The providing unit may also allow the user to review the generated content and modify it as necessary.As a result, the resume generation system according to the embodiment can efficiently accept and analyze user information, and generate and provide a resume or a curriculum vitae. For example, the reception unit accepts text data entered by a user. The reception unit can also accept voice input or image input. For example, the reception unit converts the user's voice responses into text data and accepts the text data. The reception unit can also analyze image data uploaded by the user to extract information. The analysis unit analyzes the information accepted by the reception unit. The analysis can be performed using, for example, text analysis, data mining, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit can analyze the user's responses using text analysis. The analysis unit can also extract the user's skills and career history using data mining technology. The analysis unit can also grasp the user's personality and values ​​using machine learning algorithms. The generation unit generates a resume or a curriculum vitae based on the information analyzed by the analysis unit. The generation can be performed using, for example, template-based generation, natural language generation technology, or other methods, but is not limited to these examples. For example, the generation unit uses template-based generation to organize the user's work history and skills and generate a resume or career history in an easy-to-read format. The generation unit can also use natural language generation technology to generate self-promotional information and motivation for applying. The generation unit can also generate a resume or career history in an appropriate format based on the user's responses. The providing unit provides the content generated by the generation unit to the user. The provision can be performed, for example, via email, a web interface, a download link, or other methods, but is not limited to these examples. For example, the providing unit sends the generated resume or career history to the user via email. The providing unit can also allow the user to download the generated content through a web interface. The providing unit can also allow the user to review the generated content and modify it as necessary. This allows the resume generation system according to the embodiment to efficiently accept and analyze user information, generate, and provide a resume or career history.

[0068] The generation unit can generate a resume or a work history in a format based on the user's answers. Examples of formats include, but are not limited to, PDF, Word, and HTML. For example, the generation unit generates a resume or a work history in PDF format based on the user's answers. The generation unit can also generate a resume or a work history in Word format. The generation unit can also generate a resume or a work history in HTML format. For example, the generation unit organizes the user's work history and skills and generates a PDF file in an easy-to-read format. The generation unit can also generate a Word file based on the user's answers. The generation unit can also generate a resume or a work history in HTML format so that it can be displayed in a web browser. This allows the resume or work history to be generated in an appropriate format based on the user's answers. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's answer data into a generation AI and have the generation AI generate a resume or a work history in an appropriate format.

[0069] The providing unit may provide the generated resume or work history to the user, allowing the user to make corrections. For example, the providing unit may send the generated resume or work history to the user by email. The providing unit may also allow the user to download the generated content through a web interface. The providing unit may also allow the user to review the generated content and make corrections as necessary. For example, the providing unit may provide an online editor to allow the user to directly edit the generated resume or work history. The providing unit may also provide a feedback function to allow the user to point out corrections. This allows the user to correct the generated resume or work history, thereby providing more accurate information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit may input the generated resume or work history into a generation AI, causing the generation AI to suggest corrections.

[0070] The providing unit can provide the generated resume or work history to a third party based on the user's consent. Consent is obtained by, for example, a checkbox or an electronic signature, but is not limited to, such examples. The providing unit obtains consent, for example, by the user selecting a checkbox. The providing unit can also obtain consent using an electronic signature. For example, the providing unit obtains consent by the user signing an electronic signature. The providing unit provides the content of the user's consent to a third party. Third parties include, for example, companies, recruiters, agents, etc., but are not limited to such examples. For example, the providing unit provides the generated resume or work history to a company. The providing unit can also provide it to a recruiter. The providing unit can also provide it to an agent. In this way, the generated resume or work history can be provided to a third party based on the user's consent. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's consent data into the generation AI and have the generation AI confirm the consent.

[0071] The analysis unit can understand the user's personality and values ​​from the user's responses. The analysis unit can evaluate the user's personality using methods such as psychological tests and behavioral analysis. The analysis unit can also evaluate the user's values ​​using methods such as questionnaires and interviews. For example, the analysis unit can conduct a psychological test based on the user's responses to evaluate the user's personality. The analysis unit can also analyze the user's behavioral data to understand the user's behavioral patterns. The analysis unit can also conduct a questionnaire based on the user's responses to evaluate the user's values. This allows the user's personality and values ​​to be understood from the user's responses, thereby generating a more appropriate resume or curriculum vitae. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's response data into a generation AI and have the generation AI evaluate the user's personality and values.

[0072] The providing unit can provide information for a third party to select excellent personnel. The providing unit provides information such as the user's skill set, years of experience, and educational background to a third party. Definitions and criteria for excellent personnel include, but are not limited to, a specific skill set, a certain number of years of experience, and a high level of educational background. For example, the providing unit provides appropriate personnel information to a third party based on the user's skill set. The providing unit can also provide appropriate personnel information to a third party based on the user's years of experience. The providing unit can also provide appropriate personnel information to a third party based on the user's educational background. This allows the third party to provide information for selecting excellent personnel. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's personnel information into a generation AI, causing the generation AI to assist in the selection of excellent personnel.

[0073] The reception unit can estimate the user's emotions and adjust the content and order of questions based on the estimated user's emotions. The reception unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, if the user is nervous, the reception unit can start with simple questions to relax the user. If the user is relaxed, the reception unit can ask detailed questions earlier and dig deeper. If the user is feeling stressed, the reception unit can reduce the number of questions and adjust the questions to be completed in a shorter time. This allows the content and order of questions to be adjusted according to the user's emotions, making it possible to ask more appropriate questions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data into the generation AI, which may then adjust the content and order of questions.

[0074] The reception unit can analyze the user's past answer history and select a question. The reception unit, for example, analyzes the past answer history stored in a database and selects an optimal question. The past answer history includes, for example, the content of the user's past answers, the frequency of answers, and the importance of answers, but is not limited to these examples. For example, the reception unit selects a question that does not overlap with previous answers based on the content of the user's past answers. The reception unit can also select a question that will elicit particularly important information from the user's past answers. The reception unit can also analyze the user's past answer history and select a question that will fill in any missing answers. In this way, the optimal question can be selected by analyzing the user's past answer history. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit can input past answer history data into a generation AI and have the generation AI select an optimal question.

[0075] The reception unit may filter questions based on the user's current occupation and skill level. The reception unit may filter questions based on, for example, the user's occupation category and skill level threshold. Examples of filtering include, but are not limited to, occupation category and skill level threshold. For example, if the user is an engineer, the reception unit may prioritize questions about technical skills. If the user is a manager, the reception unit may prioritize questions about leadership and management. If the user is a recent graduate, the reception unit may prioritize questions about educational background and internship experience. By filtering questions based on the user's current occupation and skill level, more appropriate questions can be asked. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's occupation and skill level data into a generation AI, causing the generation AI to filter the questions.

[0076] When asking a question, the reception unit can select the optimal question means depending on the user's input method. The reception unit selects the optimal question means depending on the input method, such as voice input, text input, or image input. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit can ask a question that is easy to answer by voice. Furthermore, if the user selects text input, the reception unit can ask a question that requests a detailed text answer. Furthermore, if the user selects image input, the reception unit can ask a question that requests self-promotion or an explanation of work history using an image. This allows for more appropriate questions to be asked by selecting the optimal question means depending on the user's input method. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal question means.

[0077] The reception unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user emotions. The reception unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, if the user is nervous, the reception unit can start with easy questions and gradually increase the difficulty. If the user is relaxed, the reception unit can ask more difficult questions earlier. If the user is feeling stressed, the reception unit can focus on asking less difficult questions. This allows the difficulty of questions to be adjusted according to the user's emotions, thereby making it possible to ask more appropriate questions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data into the generation AI, which may then adjust the difficulty level of the questions.

[0078] When asking a question, the reception unit can prioritize relevant questions by taking into account the user's geographical location information. The reception unit selects relevant questions based on geographical location information such as GPS data or an IP address. Geographical location information includes, but is not limited to, the user's current location, previously visited places, and place of residence. For example, if the user lives in a specific area, the reception unit can ask about work experience related to that area. If the user lives overseas, the reception unit can also ask questions about international experience and skills. If the user lives in a specific city, the reception unit can also ask questions about work experience and activities in that city. This allows for more appropriate questions by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI select relevant questions.

[0079] When asking a question, the reception unit can analyze the user's social media activity and ask a related question. The reception unit selects a related question based on data such as the content of social media posts, the number of followers, and the engagement rate. Social media activity includes, but is not limited to, the user's publicly disclosed work experience, skills, and interests. For example, the reception unit asks a question based on the user's publicly disclosed work experience on social media. The reception unit can also analyze the user's social media activity and ask questions about related skills and experience. The reception unit can also ask questions about self-promotion and motivation for applying based on the user's social media posts. In this way, the reception unit can ask a related question by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input social media data into a generation AI and have the generation AI select a related question.

[0080] When asking a question, the reception unit can customize the question content by reflecting the user's past feedback. The reception unit customizes the question content based on feedback data, such as questionnaires and user reviews. Feedback includes, but is not limited to, opinions, evaluations, and improvements provided by the user in the past. For example, the reception unit adjusts the content and order of questions based on the user's past feedback. The reception unit can also customize questions to elicit particularly important information from the user's past feedback. The reception unit can also analyze the user's past feedback and customize questions to fill in missing answers. This allows the question content to be customized by reflecting the user's past feedback, thereby enabling more appropriate questions to be asked. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input feedback data into a generation AI and have the generation AI customize the question content.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The analysis unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. The analysis unit can also analyze the user's text input and estimate the emotions using text analysis technology. For example, if the user is nervous, the analysis unit can reduce the level of analysis detail and provide a concise analysis result. If the user is relaxed, the analysis unit can perform a detailed analysis and provide deeper insights. If the user is feeling stressed, the analysis unit can prioritize the speed of the analysis and provide quick results. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data into the generation AI, which may then adjust the analysis method.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's answer. The analysis unit adjusts the level of detail of the analysis based on, for example, the content of the user's answer, the frequency of the answer, the importance of the answer, etc. The importance of the answer includes, for example, but is not limited to, the content of the answer, the frequency of the answer, and the impact of the answer. For example, the analysis unit performs a detailed analysis on important answers to provide deep insights. The analysis unit can also perform a concise analysis on less important answers to quickly provide results. The analysis unit can also automatically evaluate the importance of the user's answer and adjust the level of detail of the analysis. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the user's answer. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the user's answer. The analysis unit applies different analysis algorithms depending on categories such as occupation category and skill category. Examples of categories include, but are not limited to, technical skills, management skills, and self-promotion. For example, the analysis unit applies a technical analysis algorithm to answers related to technical skills. The analysis unit can also apply a leadership analysis algorithm to answers related to management. The analysis unit can also apply a personality analysis algorithm to answers related to self-promotion. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the user's answer. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer data into a generation AI and have the generation AI apply different analysis algorithms.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past answers. The analysis unit, for example, analyzes past answer results stored in a database to improve the accuracy of the analysis. Past answer results include, but are not limited to, the content of answers provided by the user in the past, the frequency of answers, and the importance of answers. For example, the analysis unit adjusts the analysis algorithm based on the user's past answer results to improve accuracy. The analysis unit can also analyze the user's past answer results, find specific patterns, and reflect them in the analysis. The analysis unit can also adjust the level of detail and speed of the analysis by referring to the user's past answer results. In this way, the accuracy of the analysis can be improved by referring to the user's past answer results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past answer result data into a generation AI, which can improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, if the user is nervous, the analysis unit can prioritize important analysis. If the user is relaxed, the analysis unit can prioritize detailed analysis. If the user is stressed, the analysis unit can prioritize quick analysis. This allows for more appropriate analysis by determining the analysis priority according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into a generation AI, which may then determine the analysis priority.

[0086] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the user's answers. The analysis unit determines the analysis priority based on data such as a timestamp and a submission deadline. The submission time includes, but is not limited to, the answer submission date and time, the answer submission deadline, etc. For example, the analysis unit prioritizes analysis of the most recent answers and provides results quickly. The analysis unit can also postpone analysis of answers submitted earlier. The analysis unit can also automatically adjust the analysis schedule based on the submission time. This allows for more rapid analysis by determining the analysis priority based on the time of submission of the user's answers. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input submission time data into a generation AI and have the generation AI determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the user's answers. The analysis unit adjusts the order of analysis based on data such as the degree of similarity of the answer content and common keywords. Answer relevance includes, but is not limited to, the degree of similarity of the answer content, common keywords, and answer themes. For example, the analysis unit prioritizes analysis of highly relevant answers and provides results quickly. The analysis unit can also postpone analysis of less relevant answers. The analysis unit can also automatically evaluate the relevance of the user's answers and adjust the order of analysis. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the user's answers. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input answer relevance data to a generation AI and have the generation AI adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the user's level of expertise based on data such as qualifications, years of experience, and test results, and adjusts the use of technical terms in the analysis. Examples of expertise levels include, but are not limited to, specific qualifications, a certain number of years of experience, and test results. For example, the analysis unit provides analysis results that use a lot of technical terms to users with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to users with low levels of expertise. The analysis unit can also automatically evaluate the user's level of expertise and adjust the use of technical terms in the analysis. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input expertise level data into a generation AI and have the generation AI adjust the use of technical terms in the analysis.

[0089] The generation unit can estimate the user's emotions and adjust the presentation style of the generated resume or CV based on the estimated user's emotions. The generation unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. The generation unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, if the user is nervous, the generation unit can use a simple, highly visible format. If the user is relaxed, the generation unit can use a format that includes detailed information. If the user is stressed, the generation unit can use a format that can be completed in a short time. This allows for the generation of more appropriate documents by adjusting the expression style of a resume or curriculum vitae according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's emotion data into the generation AI, which may then adjust the expression style.

[0090] The generation unit can adjust the level of detail of the generated content based on the importance of the user's answer during generation. The generation unit adjusts the level of detail of the generated content based on, for example, the content of the user's answer, the frequency of the answer, the importance of the answer, etc. The importance of the answer includes, for example, but is not limited to, the content of the answer, the frequency of the answer, and the impact of the answer. For example, the generation unit generates a resume or a curriculum vitae that includes detailed information for an important answer. The generation unit can also generate a resume or a curriculum vitae that includes concise information for an answer with low importance. The generation unit can also automatically evaluate the importance of the user's answer and adjust the level of detail of the generated content. As a result, by adjusting the level of detail of the generated content based on the importance of the user's answer, more appropriate resumes or curriculum vitae can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's answer data into a generation AI and have the generation AI adjust the level of detail of the content.

[0091] The generation unit can apply different generation algorithms depending on the category of the user's answer during generation. The generation unit applies different generation algorithms depending on categories such as occupational category and skill category. Examples of categories include, but are not limited to, technical skills, management skills, and self-promotion. For example, the generation unit uses a technical format for answers regarding technical skills. The generation unit can also use a format that emphasizes leadership for answers regarding management. The generation unit can also use a format that emphasizes personality for answers regarding self-promotion. In this way, by applying different generation algorithms depending on the category of the user's answer, more appropriate resumes and curriculum vitae can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's answer data into a generation AI and cause the generation AI to apply different generation algorithms.

[0092] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit, for example, analyzes past generation results stored in a database to improve the accuracy of generation. Past generation results include, but are not limited to, the content of resumes and curriculum vitae (CVs) generated by the user in the past, the frequency of generation, and the importance of generation. For example, the generation unit adjusts the generation algorithm based on the user's past generation results to improve accuracy. The generation unit can also analyze the user's past generation results, find specific patterns, and reflect them in the generation. The generation unit can also adjust the level of detail and speed of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past generation result data into a generation AI to improve the accuracy of generation using the generation AI.

[0093] The generation unit can estimate the user's emotions and adjust the length of the generated resume or work history based on the estimated user emotions. The generation unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. The generation unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, if the user is nervous, the generation unit can generate a short and to-the-point resume or work history. If the user is relaxed, the generation unit can generate a longer resume or work history with more detailed information. The generation unit can also adjust the length of the resume or work history so that it can be completed in a short time if the user is feeling stressed. This allows for the generation of more appropriate documents by adjusting the length of the resume or work history according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's emotional data into the generation AI, causing the generation AI to adjust the length.

[0094] During generation, the generation unit can determine the priority of the content to be generated based on the time of submission of the user's answers. The generation unit determines the priority of the content to be generated based on data such as a timestamp and a submission deadline. The submission time includes, but is not limited to, the submission date and time of the answer and the submission deadline. For example, the generation unit prioritizes generating resumes and curriculum vitae based on the most recent answers. The generation unit can also postpone generation of answers submitted earlier. The generation unit can also automatically adjust the generation schedule based on the submission time. This allows for more rapid generation of resumes and curriculum vitae by determining the priority of the content to be generated based on the time of submission of the user's answers. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input submission time data into a generation AI and have the generation AI determine the priority.

[0095] The generation unit can adjust the order of the generated content based on the relevance of the user's answers during generation. The generation unit adjusts the order of the generated content based on data such as the degree of similarity of the answer content and common keywords. The relevance of the answers includes, but is not limited to, the degree of similarity of the answer content, common keywords, and themes of the answers. For example, the generation unit prioritizes highly relevant answers when writing them in a resume or curriculum vitae. The generation unit can also generate less relevant answers later. The generation unit can also automatically evaluate the relevance of the user's answers and adjust the order of the generated content. By adjusting the order of the generated content based on the relevance of the user's answers, a more appropriate resume or curriculum vitae can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input answer relevance data into a generation AI and have the generation AI adjust the order of the content.

[0096] The generation unit can adjust the use of technical terminology in the content to be generated according to the user's level of expertise during generation. The generation unit evaluates the user's level of expertise based on data such as qualifications, years of experience, and test results, and adjusts the use of technical terminology in the content to be generated. Examples of expertise levels include, but are not limited to, specific qualifications, a certain number of years of experience, and test results. For example, the generation unit generates a resume or CV that uses a lot of technical terminology for a user with high expertise. The generation unit can also generate a concise and easy-to-understand resume or CV for a user with low expertise. The generation unit can also automatically evaluate the user's level of expertise and adjust the use of technical terminology in the content to be generated. This allows for the generation of more appropriate resumes and CVs by adjusting the use of technical terminology in the content to be generated according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input expertise level data into a generation AI and have the generation AI adjust the use of technical terminology.

[0097] The providing unit can estimate the user's emotions and adjust the display method of the resume or work history to be provided based on the estimated user's emotions. The providing unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can provide a display method including detailed information. If the user is feeling stressed, the providing unit can provide a display method that allows the user to quickly check the user's condition. This allows for more appropriate display by adjusting the display method of a resume or curriculum vitae according to the user's emotions. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's emotion data into the generating AI, causing the generating AI to adjust the display method.

[0098] The providing unit can select the optimal display method by referring to the user's past operation history when providing the data. The providing unit selects the optimal display method based on operation history, such as log data or clickstream data. The operation history includes, but is not limited to, the user's past operations, the frequency of operations, and the success rate of operations. For example, the providing unit prioritizes and provides the display method that the user has previously preferred. The providing unit can also analyze the user's past operation history and suggest the optimal display method. The providing unit can also customize the display method based on the user's past operation history. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, or without using, AI. For example, the providing unit can input operation history data to a generation AI and have the generation AI select the optimal display method.

[0099] The providing unit can customize the display content according to the user's current task when providing the display content. The providing unit customizes the display content based on, for example, data from a task management system or the user's operation status. The current task includes, for example, the user's current work, the progress of the work, and the priority of the work, but is not limited to these examples. For example, when the user is reviewing their resume, the providing unit can concisely display the contents of the resume. Furthermore, when the user is reviewing their resume, the providing unit can also concisely display the contents of the resume. Furthermore, the providing unit can automatically customize the display content according to the user's current task. This allows for more appropriate display by customizing the display content according to the user's current task. Some or all of the above-described processing by the providing unit may be performed using, or without, for example, AI. For example, the providing unit can input current task data into a generating AI and have the generating AI customize the display content.

[0100] The providing unit can improve the display method by reflecting user feedback when providing the display. The providing unit improves the display method based on feedback data such as questionnaires and user reviews. Feedback includes, but is not limited to, opinions, evaluations, and improvements provided by users. For example, if a user provides feedback on the provided display method, the providing unit improves the display method based on the feedback. The providing unit can also analyze the user feedback and identify improvements to the display method. The providing unit can also automatically improve the display method based on the user feedback. This allows for a more appropriate display by improving the display method by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data into a generation AI and have the generation AI improve the display method.

[0101] The providing unit can estimate the user's emotions and adjust the operation procedures for the resume or CV to be provided based on the estimated user's emotions. The providing unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, the providing unit can provide simple operation procedures when the user is nervous. The providing unit can also provide detailed operation procedures when the user is relaxed. The providing unit can also provide operation procedures that can be completed in a short time when the user is stressed. This allows the operation procedures to be adjusted according to the user's emotions, enabling more appropriate operations. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and have the generating AI adjust the operation procedure.

[0102] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. The providing unit selects the optimal display method based on device information such as the device type, screen size, and OS. Device information includes, but is not limited to, the device type (e.g., smartphone, tablet, desktop), screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method including detailed information. This allows for more appropriate display by selecting the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input device information data to a generation AI and have the generation AI select the optimal display method.

[0103] The providing unit can make the display content multilingual based on the user's language setting when providing the content. The providing unit makes the display content multilingual based on data such as the browser's language setting and the user profile's language setting. Language settings include, but are not limited to, the language used by the user, the device's language setting, and the browser's language setting. For example, the providing unit automatically sets the display content based on the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This allows for more appropriate display by making the display content multilingual based on the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input language setting data to a generation AI and have the generation AI make the display content multilingual.

[0104] The providing unit can select the optimal display method by taking into account the user's geographical location information when providing the information. The providing unit selects the optimal display method based on geographical location information such as GPS data or an IP address. Geographical location information includes, but is not limited to, the user's current location, previously visited locations, and place of residence. For example, if the user lives in a specific region, the providing unit can prioritize displaying information related to that region. Furthermore, if the user lives overseas, the providing unit can prioritize displaying international information. Furthermore, if the user lives in a specific city, the providing unit can prioritize displaying information related to that city. This allows for more appropriate display by selecting the optimal display method by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input geographical location information data to a generation AI and have the generation AI select the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives text data and voice data input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes user information using text analysis and data mining techniques. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a resume or curriculum vitae using template-based generation and natural language generation techniques. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated resume or curriculum vitae to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives text data and voice data input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes user information using text analysis and data mining techniques. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a resume or curriculum vitae using template-based generation and natural language generation techniques. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated resume or curriculum vitae to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives text data and voice data input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes user information using text analysis and data mining techniques. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a resume or curriculum vitae using template-based generation and natural language generation techniques. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated resume or curriculum vitae to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives text data and voice data input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes user information using text analysis and data mining techniques. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a resume or curriculum vitae using template-based generation and natural language generation techniques. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated resume or curriculum vitae to the user.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can estimate the user's interests and concerns based on the user's input, and customize the content of the questions based on the estimated interests and concerns. For example, if the user has answered many questions about technical skills, the reception unit can add detailed questions about technical skills. Furthermore, if the user has answered many questions about managerial positions, the reception unit can add questions about leadership and management. Furthermore, if the user has answered many questions about a particular industry, the reception unit can add questions related to that industry. In this way, by customizing the content of the questions based on the user's interests and concerns, more appropriate questions can be asked.

[0107] The analysis unit can estimate the user's learning style based on the user's answers and adjust the analysis method based on the estimated learning style. For example, if the user prefers visual information, the analysis unit can provide analysis results that make extensive use of graphs and charts. If the user prefers text-based information, the analysis unit can also provide detailed text analysis results. Furthermore, if the user prefers practical examples, the analysis unit can also provide analysis results that include specific case studies. In this way, by adjusting the analysis method according to the user's learning style, more appropriate analysis results can be provided.

[0108] The generation unit can estimate the user's career goals based on the user's answers and customize the content of the resume or curriculum vitae based on the estimated career goals. For example, if the user is aiming for a technical position, the generation unit can generate a resume that emphasizes technical skills and project experience. If the user is aiming for a managerial position, the generation unit can generate a curriculum vitae that emphasizes leadership and management experience. Furthermore, if the user is aiming for a career in a specific industry, the generation unit can generate a resume that emphasizes experience and skills related to that industry. This makes it possible to generate more appropriate documents by customizing the content of the resume or curriculum vitae according to the user's career goals.

[0109] The providing unit can estimate the user's networking needs based on the user's answers and customize the information to be provided to third parties based on the estimated needs. For example, if the user wishes to network in a specific industry, the providing unit can provide the user's information to companies and recruiters in that industry. Also, if the user wishes to network in a specific occupation, the providing unit can provide the user's information to companies and agents related to that occupation. Furthermore, if the user wishes to network internationally, the providing unit can provide the user's information to overseas companies and recruiters. In this way, by customizing the information to be provided to third parties according to the user's networking needs, more appropriate information can be provided.

[0110] The analysis unit can estimate the user's stress level based on the user's answers and adjust the analysis method based on the estimated stress level. For example, if the user indicates a high stress level, the analysis unit can reduce the level of analysis detail and provide a concise analysis result. Alternatively, if the user indicates a low stress level, the analysis unit can perform a detailed analysis and provide deeper insight. Furthermore, if the user indicates a medium stress level, the analysis unit can provide a balanced analysis result. In this way, by adjusting the analysis method according to the user's stress level, more appropriate analysis results can be provided.

[0111] The providing unit can estimate the user's emotions and adjust the design of the resume or work history to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible design. If the user is relaxed, the providing unit can also provide a design including detailed information. Furthermore, if the user is feeling stressed, the providing unit can also provide a design that can be checked in a short time. This allows the resume or work history to be displayed more appropriately by adjusting the design according to the user's emotions.

[0112] The reception unit can estimate the user's emotions and adjust the timing of questions based on the estimated user's emotions. For example, if the user is nervous, the reception unit can increase the intervals between questions to provide time for the user to relax. Also, if the user is relaxed, the reception unit can shorten the intervals between questions to efficiently collect information. Furthermore, if the user is feeling stressed, the reception unit can reduce the number of questions and adjust them to be completed in a short time. In this way, by adjusting the timing of questions according to the user's emotions, more appropriate questions can be asked.

[0113] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize important analysis. Also, if the user is relaxed, the analysis unit can prioritize detailed analysis. Furthermore, if the user is feeling stressed, the analysis unit can prioritize quick analysis. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis can be performed.

[0114] The providing unit can estimate the user's emotions and adjust the font size of the resume or work history to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can use a larger font size to improve visibility. Alternatively, if the user is relaxed, the providing unit can use a standard font size. Furthermore, if the user is feeling stressed, the providing unit can adjust the font size so that the resume or work history can be viewed in a short time. This allows for a more appropriate display by adjusting the font size of the resume or work history according to the user's emotions.

[0115] The providing unit can estimate the user's emotions and adjust the color shades of the resume or work history to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can use calm colors to relax the user. Also, if the user is relaxed, the providing unit can use bright colors. Furthermore, if the user is stressed, the providing unit can use highly visible colors. In this way, by adjusting the colors of the resume or work history according to the user's emotions, a more appropriate display can be achieved.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives information from the user. The information from the user includes, for example, personal information, work history, and skill information. The reception unit receives text data entered by the user, as well as voice input and image input. For example, it can convert voice responses into text data and accept them, or analyze uploaded image data to extract information. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis can be used to analyze the content of the user's responses, and data mining technology can be used to extract skills and background information. Machine learning algorithms can also be used to understand the user's personality and values. Step 3: The generation unit generates a resume or curriculum vitae based on the information analyzed by the analysis unit. This generation is performed using methods such as template-based generation or natural language generation technology. For example, template-based generation can be used to organize work history and skills, and generate a resume or curriculum vitae in an easy-to-read format. Natural language generation technology can also be used to generate self-promotional statements and motivations for applying. Step 4: The providing unit provides the content generated by the generating unit to the user. The provision is performed by email, a web interface, a download link, or other methods. For example, the generated resume or curriculum vitae can be sent by email or made available for download through a web interface. The user can also review the generated content and make corrections as necessary.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a resume or curriculum vitae based on the information analyzed by the analysis unit; a providing unit that provides the content generated by the generating unit to a user; Equipped with A system characterized by:

2. The generation unit Generate a formatted resume or curriculum vitae based on the user's answers 2. The system of claim 1.

3. The providing unit Provide the generated resume or curriculum vitae to the user and allow the user to modify it 2. The system of claim 1.

4. The providing unit Provide generated resumes and curriculum vitae to third parties with your consent 2. The system of claim 1.

5. The analysis unit Understand the user's personality and values ​​from their answers 2. The system of claim 1.

6. The providing unit Providing information for third parties to select talented personnel 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the content and order of questions based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past answer history and select questions 2. The system of claim 1.

9. The reception unit Filter questions based on the user's current occupation and skill level 2. The system of claim 1.

10. The reception unit When asking a question, select the most appropriate method of questioning depending on the user's input method.

2. The system of claim 1.

Images