system

Abstract

The system according to the embodiment aims to generate English interview questions based on a resume and provide feedback to the user. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit uploads a resume. The analysis unit analyzes the resume uploaded by the reception unit. The generation unit generates questions based on the resume analyzed by the analysis unit. The feedback unit analyzes the user's answers to the questions generated by the generation unit and provides feedback.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have the drawback of making it difficult to generate questions based on individual resumes and provide feedback when practicing for English interviews.

[0005] The system according to the embodiment aims to generate English interview questions based on a resume and provide feedback to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit uploads a resume. The analysis unit analyzes the resume uploaded by the reception unit. The generation unit generates questions based on the resume analyzed by the analysis unit. The feedback unit analyzes the user's answers to the questions generated by the generation unit and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can generate English interview questions based on a resume and provide feedback to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An English interview support system according to an embodiment of the present invention is a system for improving English interview skills for individuals who understand English but have difficulty speaking it, aiming to work for a foreign company. In this system, a user uploads a resume, and a generation AI generates a personalized English interview support system based on the resume. Specifically, questions tailored to the content of the resume are posed, and the user practices by answering the questions in English. This allows the user to practice with an emphasis on output, and the AI ​​allows the user to continue speaking freely, effectively improving their English interview skills. For example, when a user uploads a resume, the user uploads a PDF version of the resume to the system. This information is input into the generation AI, which analyzes the uploaded resume and generates a personalized English interview support system for the user. The generation AI understands the content of the resume and generates questions based on the user's background and skills. For example, questions are generated based on the user's past projects and skills. The generated English interview support system poses questions to the user, who then answers the questions in English. The generation AI then analyzes the user's answers and provides feedback. For example, it points out pronunciation and grammar errors and suggests areas for improvement. This allows users to understand and improve their weaknesses. In this way, the present invention provides a generation AI that targets people who can understand English but are not good at speaking it, and improves their English interview skills in order to succeed at foreign companies. Users can generate their own personalized English interview support system by simply uploading their resume, allowing them to practice with an emphasis on output. This allows users to effectively improve their English interview skills. As a result, the English interview support system can effectively practice English interviews based on the user's resume.

[0029] The English interview support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit allows a user to upload a resume. Resumes uploaded by a user may be in, but are not limited to, PDF, Word, or text format. The reception unit receives, for example, a PDF resume. The reception unit can also receive a Word resume. The reception unit can also receive a text resume. For example, the reception unit receives a PDF resume uploaded by a user and imports it into the system. The analysis unit analyzes the resume uploaded by the reception unit. The analysis unit can analyze the content of the resume using, for example, text analysis technology. The analysis unit can also extract important information from the resume using keyword extraction technology. The analysis unit can also analyze the grammatical structure of the resume using grammar analysis technology. For example, the analysis unit analyzes the text data of the resume and extracts the user's background and skills. The generation unit generates questions based on the resume analyzed by the analysis unit. The generation unit generates questions using, for example, a generation AI. The generation AI can generate appropriate questions based on the user's background and skills. For example, the generation unit generates questions about projects the user has been involved in in the past. The generation unit can also generate questions about the user's skills. The generation unit can also generate questions about the user's future career based on the user's background. For example, the generation unit generates the question "Tell me about your past projects" based on the project experience listed in the user's resume. The feedback unit analyzes the user's answers to the questions generated by the generation unit and provides feedback. The feedback unit analyzes the user's answers using, for example, a generation AI. The generation AI can analyze the user's answers and point out pronunciation or grammatical errors. For example, the feedback unit analyzes the user's answers and points out pronunciation errors. The feedback unit can also analyze the user's answers and point out grammatical errors. The feedback unit can also analyze the user's answers and suggest improvements.For example, the feedback unit provides feedback to the user's answer, such as "You should improve your pronunciation in this part." This allows the English interview support system according to the embodiment to effectively practice for an English interview based on the user's resume. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit inputs the user's answer into the generation AI, which can point out pronunciation or grammatical errors and suggest areas for improvement.

[0030] The reception unit can also accept resumes in multiple formats. For example, the reception unit accepts resumes in PDF format. The reception unit can also accept resumes in Word format. The reception unit can also accept resumes in text format. For example, the reception unit accepts resumes in PDF format uploaded by a user and imports them into the system. The reception unit can also accept resumes in Word format uploaded by a user and import them into the system. The reception unit can also accept resumes in text format uploaded by a user and import them into the system. This makes it possible to accept resumes in a variety of formats. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can input the format of the resume uploaded by the user into the generation AI, and the generation AI can analyze the format and accept the resume.

[0031] The analysis unit can analyze the content of the resume and generate questions based on the user's experience and skills. The analysis unit can analyze the content of the resume using, for example, text analysis technology. For example, the analysis unit can analyze the text data of the resume and extract the user's experience and skills. The analysis unit can also extract important information from the resume using keyword extraction technology. For example, the analysis unit can extract project experience and skill sets described in the resume. The analysis unit can also analyze the grammatical structure of the resume using grammatical analysis technology. For example, the analysis unit can evaluate the grammatical accuracy of the resume. This allows the analysis unit to generate questions based on the user's experience and skills. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the text data of the resume into a generation AI, which can extract the experience and skills and generate questions.

[0032] The feedback unit can analyze the user's answers, point out pronunciation and grammatical errors, and suggest improvements. The feedback unit, for example, uses a generation AI to analyze the user's answers. For example, the feedback unit can analyze the user's answers and point out pronunciation errors. The feedback unit can also analyze the user's answers and point out grammatical errors. The feedback unit can also analyze the user's answers and suggest improvements. For example, the feedback unit can provide feedback to the user's answers, such as, "You should improve your pronunciation in this part." The feedback unit can also provide feedback to the user's answers, such as, "You should correct this grammatical error." In this way, the feedback unit can improve the user's English interview skills by providing specific feedback to the user's answers. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's answers into a generation AI, which can point out pronunciation and grammatical errors and suggest improvements.

[0033] The reception unit can analyze the user's past resume submission history and select the optimal upload method. The reception unit, for example, stores the user's past resume submission history in a database and analyzes the data. For example, the reception unit records the format of the resumes the user has previously submitted and the submission date and time. The reception unit can also analyze the content of the resumes the user has previously submitted and select the optimal upload method. For example, if the user has previously submitted resumes in PDF format, the reception unit can recommend the same format. If the user has previously submitted resumes in multiple formats, the reception unit can recommend the most frequently used format. The reception unit can also analyze the content of the resumes the user has previously submitted and suggest the optimal upload method. For example, the reception unit can determine whether a specific format is appropriate based on the content of the resumes the user has previously submitted. This allows the reception unit to select the optimal upload method based on the user's past resume submission history. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past resume submission history into a generation AI, which can select the optimal upload method.

[0034] The reception unit can filter resumes based on the user's current work situation and areas of interest when uploading them. For example, if the user inputs their current work situation, the reception unit filters the resume based on that information. For example, if the user inputs their current job description, the reception unit prioritizes uploading information related to that job description. Furthermore, if the user selects an area of ​​interest, the reception unit can prioritize uploading information related to that area. For example, if the user is interested in a particular industry, the reception unit prioritizes uploading information related to that industry. Furthermore, the reception unit can automatically filter out unnecessary information based on the user's work situation and areas of interest. For example, the reception unit filters out information that is not related to the user's current work situation. This allows the reception unit to filter resumes based on the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the user's work situation and areas of interest into the generation AI, which can then perform the filtering.

[0035] When uploading a resume, the reception unit can prioritize accepting highly relevant resumes by taking into account the user's geographical location information. The reception unit, for example, uses GPS data to acquire the user's geographical location information. For example, the reception unit acquires GPS data from the user's smartphone or device to identify the user's current location. The reception unit can also acquire the user's geographical location information using an IP address. For example, the reception unit analyzes the user's IP address to identify the user's location. This allows the reception unit to prioritize accepting highly relevant resumes based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize accepting resumes related to that area. Also, if the user works in a specific area, the reception unit can prioritize accepting resumes related to that area. Also, if the user is interested in a specific area, the reception unit can prioritize accepting resumes related to that area. This allows the reception unit to prioritize accepting highly relevant resumes based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, allowing the generation AI to preferentially accept resumes that are highly relevant.

[0036] The reception unit can analyze the user's social media activity when uploading a resume and accept related resumes. The reception unit, for example, uses a social media API to analyze the user's social media activity. For example, the reception unit accesses the user's social media account and obtains data such as the content of posts, the number of followers, and the number of likes. The reception unit can also accept related resumes based on information shared by the user on social media. For example, if the user shares information about a specific company or industry on social media, the reception unit accepts related resumes based on that information. The reception unit can also prioritize accepting resumes related to companies or industries the user follows on social media. For example, if the user follows a specific company, the reception unit prioritizes accepting resumes related to that company. The reception unit can also accept resumes related to areas of interest based on the user's social media activity. For example, if the user frequently posts about a specific field, the reception unit accepts resumes related to that field. This allows the reception unit to accept relevant resumes based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's social media data into the generation AI, and the generation AI may accept the associated resume.

[0037] When analyzing a resume, the analysis unit can improve the accuracy of the analysis by referring to the user's past work history. The analysis unit, for example, stores the user's past work history in a database and refers to that data. For example, the analysis unit records what kind of work the user has done in the past and what skills they have. The analysis unit can also analyze related skills and experience based on the user's past work history. For example, the analysis unit extracts information related to a specific industry or job type based on the user's past work history. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past work history. For example, the analysis unit provides data to increase the reliability of the analysis results based on the user's past work history. This allows the analysis unit to improve the accuracy of the analysis based on the user's past work history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs the user's past work history data into a generation AI, which can improve the accuracy of the analysis.

[0038] When analyzing a resume, the analysis unit can apply different analysis algorithms depending on the user's skill set. The analysis unit, for example, stores the user's skill set in a database and selects an analysis algorithm based on that data. For example, the analysis unit records the skills possessed by the user and applies an analysis algorithm corresponding to those skills. The analysis unit can also select a specific analysis algorithm depending on the user's skill set. For example, if the user is in a technical position, the analysis unit can apply an analysis algorithm specialized for technical skills. If the user is in a managerial position, the analysis unit can apply an analysis algorithm specialized for managerial skills. If the user is in a creative position, the analysis unit can apply an analysis algorithm specialized for creative skills. This allows the analysis unit to apply the optimal analysis algorithm depending on the user's skill set. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's skill set data into a generation AI, which can select the optimal analysis algorithm.

[0039] When analyzing a resume, the analysis unit can determine the analysis priority based on the user's submission time. The analysis unit, for example, stores the user's resume submission time in a database and determines the analysis priority based on that data. For example, the analysis unit records the date and time when the user submitted the resume and determines the analysis priority based on that date and time. The analysis unit can also determine the analysis priority based on the frequency with which the user submitted the resume. For example, if the user submitted the resume early, the analysis unit can prioritize analyzing that resume. Also, if the user submitted the resume close to the deadline, the analysis unit can analyze it quickly. Also, if the user submitted within a specific period, the analysis unit can prioritize analyzing submissions made within that period. This allows the analysis unit to determine the analysis priority based on the user's submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's submission time data into a generation AI, which can determine the analysis priority.

[0040] When analyzing a resume, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature. For example, the analysis unit stores the user's related literature in a database and performs analysis based on that data. For example, the analysis unit can improve the accuracy of the analysis by referring to papers and articles written by the user in the past. The analysis unit can also extract related information based on literature cited by the user. For example, the analysis unit can analyze the literature cited by the user and compare it with the content of the resume to improve accuracy. The analysis unit can also provide data to improve the reliability of the analysis results based on the literature referenced by the user. For example, the analysis unit can analyze specific skills and experience based on papers and articles written by the user in the past. This allows the analysis unit to improve the accuracy of the analysis based on the user's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's related literature data into a generation AI, which can improve the accuracy of the analysis.

[0041] When generating a question, the generation unit can adjust the level of detail of the question based on the importance of the user's career. For example, the generation unit stores the importance of the user's career in a database and adjusts the level of detail of the question based on that data. For example, the generation unit records the user's years of work experience and achievements and adjusts the level of detail of the question based on the importance. The generation unit can also generate questions that focus on particularly important parts of the user's career. For example, if the user has an extensive career, the generation unit can generate detailed questions. On the other hand, if the user has a limited career, the generation unit can generate simple questions. Also, the generation unit can generate questions that focus on particularly important parts of the user's career. In this way, the generation unit can adjust the level of detail of the question based on the importance of the user's career. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's career data into the generation AI, and the generation AI can adjust the level of detail of the question.

[0042] When generating questions, the generation unit can apply different question generation algorithms depending on the user's skill category. For example, the generation unit stores the user's skill category in a database and selects a question generation algorithm based on that data. For example, the generation unit records the user's skills and applies a question generation algorithm corresponding to the skills. The generation unit can also select a specific question generation algorithm depending on the user's skill category. For example, if the user is in a technical job, the generation unit can apply an algorithm that generates technical questions. If the user is in a managerial job, the generation unit can apply an algorithm that generates questions related to managerial skills. If the user is in a creative job, the generation unit can apply an algorithm that generates questions related to creative skills. This allows the generation unit to apply an optimal question generation algorithm depending on the user's skill category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's skill category data into a generation AI, which can select an optimal question generation algorithm.

[0043] When generating questions, the generation unit can determine the priority of questions based on the time of user submission. The generation unit, for example, stores the time of user question submission in a database and determines the priority of questions based on that data. For example, the generation unit records the date and time when the user submitted a question and determines the priority of questions based on that date and time. The generation unit can also determine the priority of questions based on the frequency with which the user submitted questions. For example, if a user submitted a question early, the generation unit can generate that question with priority. Also, if a user submitted a question close to a deadline, the generation unit can generate questions quickly. Also, if a user submitted a question within a specific period, the generation unit can generate questions within that period with priority. This allows the generation unit to determine the priority of questions based on the time of user submission. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user submission time data into a generation AI, which can then determine the priority of questions.

[0044] The generation unit can adjust the order of questions based on the user's relevance when generating questions. The generation unit, for example, stores the user's relevance in a database and adjusts the order of questions based on that data. For example, the generation unit records information related to the user's career and skills and adjusts the order of questions based on the relevance. The generation unit can also adjust the order of questions based on information related to the user's interests and concerns. For example, questions related to the user's career can be preferentially generated. Questions related to the user's skills can also be preferentially generated. Questions related to the user's interests and concerns can also be preferentially generated. This allows the generation unit to adjust the order of questions based on the user's relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user relevance data into the generation AI, which can then adjust the order of questions.

[0045] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past response history. The feedback unit, for example, stores the user's past response history in a database and provides feedback based on that data. For example, the feedback unit records the user's past responses and the content of those responses. The feedback unit can also specifically point out areas for improvement based on the user's past response history. For example, the feedback unit can evaluate the user's progress based on the user's past response history. The feedback unit can also customize the content of the feedback by referring to the user's past response history. For example, the feedback unit can provide feedback regarding specific skills or experiences based on the user's past response history. This allows the feedback unit to improve the accuracy of the feedback based on the user's past response history. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's past response history data into the generation AI, which can then improve the accuracy of the feedback.

[0046] The feedback unit can apply different feedback algorithms depending on the user's skill level when providing feedback. For example, the feedback unit stores the user's skill level in a database and selects a feedback algorithm based on the data. For example, the feedback unit records the user's skill level and applies a feedback algorithm according to the skill level. The feedback unit can also select a specific feedback algorithm depending on the user's skill level. For example, if the user is a beginner, the feedback unit can provide basic feedback. If the user is an intermediate player, the feedback unit can provide detailed feedback. If the user is an advanced player, the feedback unit can provide specialized feedback. This allows the feedback unit to apply an optimal feedback algorithm depending on the user's skill level. Some or all of the above-described processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input the user's skill level data into a generation AI, which can select an optimal feedback algorithm.

[0047] When providing feedback, the feedback unit can provide optimal feedback by taking the user's geographical location information into consideration. The feedback unit, for example, uses GPS data to acquire the user's geographical location information. For example, the feedback unit acquires GPS data from the user's smartphone or device to identify the user's current location. The feedback unit can also acquire the user's geographical location information using an IP address. For example, the feedback unit analyzes the user's IP address to identify the user's location. This allows the feedback unit to provide optimal feedback based on the user's geographical location information. For example, if the user lives in a specific area, the feedback unit can provide feedback related to that area. Also, if the user works in a specific area, the feedback unit can provide feedback related to that area. Also, if the user is interested in a specific area, the feedback unit can provide feedback related to that area. This allows the feedback unit to provide optimal feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's geographical location information into a generation AI, which can then provide optimal feedback.

[0048] When providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of providing feedback. The feedback unit, for example, uses a social media API to analyze the user's social media activity. For example, the feedback unit accesses the user's social media account and obtains data such as the content of posts, the number of followers, and the number of likes. The feedback unit can also provide relevant feedback based on information shared by the user on social media. For example, if the user shares information about a specific company or industry on social media, the feedback unit provides relevant feedback based on that information. The feedback unit can also provide feedback related to companies or industries the user follows on social media. For example, if the user follows a specific company, the feedback unit provides feedback related to that company. The feedback unit can also provide feedback related to areas of interest based on the user's social media activity. For example, if the user frequently posts about a specific field, the feedback unit provides feedback related to that field. This allows the feedback unit to suggest the optimal means of providing feedback based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's social media data into the generation AI, which can then suggest the optimal means of feedback.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can analyze the user's past interview experiences and generate optimal questions. For example, the reception unit stores the contents and results of interviews the user has taken in the past in a database and generates questions based on that data. The reception unit can also analyze feedback from the user's past interviews and generate questions that reflect areas for improvement. Furthermore, the reception unit can generate relevant questions based on the industry and job type of the user's past interviews. This allows the reception unit to generate optimal questions based on the user's past interview experiences.

[0051] The generation unit can analyze the user's past answer history and generate optimal questions. For example, the generation unit stores the user's past answers in a database and generates questions based on that data. The generation unit can also analyze trends in the user's past answers and generate related questions. Furthermore, the generation unit can generate questions that reflect improvements to the user's past answers. This allows the generation unit to generate optimal questions based on the user's past answer history.

[0052] The reception unit can propose an optimal interview time by taking into account the user's geographical location information. For example, the reception unit acquires GPS data from the user's smartphone or device to identify the user's current location. The reception unit can also analyze the user's IP address to identify the user's location. This allows the reception unit to propose an optimal interview time based on the user's geographical location information. For example, if the user is in a different time zone, the reception unit can propose an interview time that matches that time zone. Furthermore, if the user lives in a specific area, the reception unit can also propose an interview time by taking into account the traffic conditions in that area. This allows the reception unit to propose an optimal interview time based on the user's geographical location information.

[0053] The generation unit can analyze the user's past interview results and generate optimal questions. For example, the generation unit stores the results of the user's past interviews in a database and generates questions based on that data. The generation unit can also analyze feedback from the user's past interviews and generate questions that reflect areas for improvement. Furthermore, the generation unit can generate relevant questions based on the industry and job type of the user's past interviews. This allows the generation unit to generate optimal questions based on the user's past interview results.

[0054] The reception unit can analyze the user's social media activity and suggest an optimal resume format. For example, the reception unit accesses the user's social media account and obtains data such as the content of posts, the number of followers, and the number of likes. The reception unit can also suggest a related resume format based on the information the user shared on social media. For example, if the user shares information about a specific company or industry, the reception unit can suggest an optimal resume format based on that information. The reception unit can also suggest a resume format related to the companies or industries the user follows on social media. This allows the reception unit to suggest an optimal resume format based on the user's social media activity.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit is a section where users upload their resumes. Resumes uploaded by users can be in PDF format, Word format, text format, etc. For example, the reception unit accepts a PDF resume uploaded by a user and imports it into the system. Step 2: The analysis unit analyzes the resume uploaded by the reception unit. The analysis unit analyzes the contents of the resume using text analysis technology, keyword extraction technology, and grammar analysis technology to extract the user's background and skills. Step 3: The generator generates questions based on the resume analyzed by the analyzer. The generator uses AI to generate appropriate questions based on the user's background and skills. For example, it generates questions about projects the user has been involved in and the skills they possess. Step 4: The feedback section analyzes the user's answers to the questions generated by the generation section and provides feedback. The feedback section uses the generation AI to analyze the user's answers, point out pronunciation and grammar errors, and suggest areas for improvement.

[0057] (Example 2) An English interview support system according to an embodiment of the present invention is a system for improving English interview skills for individuals who understand English but have difficulty speaking it, aiming to work for a foreign company. In this system, a user uploads a resume, and a generation AI generates a personalized English interview support system based on the resume. Specifically, questions tailored to the content of the resume are posed, and the user practices by answering the questions in English. This allows the user to practice with an emphasis on output, and the AI ​​allows the user to continue speaking freely, effectively improving their English interview skills. For example, when a user uploads a resume, the user uploads a PDF version of the resume to the system. This information is input into the generation AI, which analyzes the uploaded resume and generates a personalized English interview support system for the user. The generation AI understands the content of the resume and generates questions based on the user's background and skills. For example, questions are generated based on the user's past projects and skills. The generated English interview support system poses questions to the user, who then answers the questions in English. The generation AI then analyzes the user's answers and provides feedback. For example, it points out pronunciation and grammar errors and suggests areas for improvement. This allows users to understand and improve their weaknesses. In this way, the present invention provides a generation AI that targets people who can understand English but are not good at speaking it, and improves their English interview skills in order to succeed at foreign companies. Users can generate their own personalized English interview support system by simply uploading their resume, allowing them to practice with an emphasis on output. This allows users to effectively improve their English interview skills. As a result, the English interview support system can effectively practice English interviews based on the user's resume.

[0058] The English interview support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit allows a user to upload a resume. Resumes uploaded by a user may be in, but are not limited to, PDF, Word, or text format. The reception unit receives, for example, a PDF resume. The reception unit can also receive a Word resume. The reception unit can also receive a text resume. For example, the reception unit receives a PDF resume uploaded by a user and imports it into the system. The analysis unit analyzes the resume uploaded by the reception unit. The analysis unit can analyze the content of the resume using, for example, text analysis technology. The analysis unit can also extract important information from the resume using keyword extraction technology. The analysis unit can also analyze the grammatical structure of the resume using grammar analysis technology. For example, the analysis unit analyzes the text data of the resume and extracts the user's background and skills. The generation unit generates questions based on the resume analyzed by the analysis unit. The generation unit generates questions using, for example, a generation AI. The generation AI can generate appropriate questions based on the user's background and skills. For example, the generation unit generates questions about projects the user has been involved in in the past. The generation unit can also generate questions about the user's skills. The generation unit can also generate questions about the user's future career based on the user's background. For example, the generation unit generates a question such as "Tell me about your past projects" based on the project experience listed in the user's resume. The feedback unit analyzes the user's answers to the questions generated by the generation unit and provides feedback. The feedback unit analyzes the user's answers using, for example, a generation AI. The generation AI can analyze the user's answers and point out pronunciation or grammatical errors. For example, the feedback unit analyzes the user's answers and points out pronunciation errors. The feedback unit can also analyze the user's answers and point out grammatical errors. The feedback unit can also analyze the user's answers and suggest improvements.For example, the feedback unit provides feedback to the user's answer, such as "You should improve your pronunciation in this part." This allows the English interview support system according to the embodiment to effectively practice for an English interview based on the user's resume. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit inputs the user's answer into the generation AI, which can point out pronunciation or grammatical errors and suggest areas for improvement.

[0059] The reception unit can also accept resumes in multiple formats. For example, the reception unit accepts resumes in PDF format. The reception unit can also accept resumes in Word format. The reception unit can also accept resumes in text format. For example, the reception unit accepts resumes in PDF format uploaded by a user and imports them into the system. The reception unit can also accept resumes in Word format uploaded by a user and import them into the system. The reception unit can also accept resumes in text format uploaded by a user and import them into the system. This makes it possible to accept resumes in a variety of formats. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can input the format of the resume uploaded by the user into the generation AI, and the generation AI can analyze the format and accept the resume.

[0060] The analysis unit can analyze the content of the resume and generate questions based on the user's experience and skills. The analysis unit can analyze the content of the resume using, for example, text analysis technology. For example, the analysis unit can analyze the text data of the resume and extract the user's experience and skills. The analysis unit can also extract important information from the resume using keyword extraction technology. For example, the analysis unit can extract project experience and skill sets described in the resume. The analysis unit can also analyze the grammatical structure of the resume using grammatical analysis technology. For example, the analysis unit can evaluate the grammatical accuracy of the resume. This allows the analysis unit to generate questions based on the user's experience and skills. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the text data of the resume into a generation AI, which can extract the experience and skills and generate questions.

[0061] The feedback unit can analyze the user's answers, point out pronunciation and grammatical errors, and suggest improvements. The feedback unit, for example, uses a generation AI to analyze the user's answers. For example, the feedback unit can analyze the user's answers and point out pronunciation errors. The feedback unit can also analyze the user's answers and point out grammatical errors. The feedback unit can also analyze the user's answers and suggest improvements. For example, the feedback unit can provide feedback to the user's answers, such as, "You should improve your pronunciation in this part." The feedback unit can also provide feedback to the user's answers, such as, "You should correct this grammatical error." In this way, the feedback unit can improve the user's English interview skills by providing specific feedback to the user's answers. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's answers into a generation AI, which can point out pronunciation and grammatical errors and suggest improvements.

[0062] The reception unit can estimate the user's emotions and adjust the timing of resume uploading based on the estimated user's emotions. The reception unit, for example, uses facial expression analysis technology to estimate the user's emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit analyzes text data entered by the user to estimate the emotion. This allows the reception unit to upload the resume at the optimal timing depending on the user's emotions. For example, if the user is nervous, the reception unit can delay the upload timing to allow the user to relax. If the user is in a hurry, the reception unit can advance the timing to upload quickly. If the user is concentrating, the reception unit can prompt the user to upload at the optimal timing. This allows the reception unit to upload the resume at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the upload timing.

[0063] The reception unit can analyze the user's past resume submission history and select the optimal upload method. The reception unit, for example, stores the user's past resume submission history in a database and analyzes the data. For example, the reception unit records the format of the resumes the user has previously submitted and the submission date and time. The reception unit can also analyze the content of the resumes the user has previously submitted and select the optimal upload method. For example, if the user has previously submitted resumes in PDF format, the reception unit can recommend the same format. If the user has previously submitted resumes in multiple formats, the reception unit can recommend the most frequently used format. The reception unit can also analyze the content of the resumes the user has previously submitted and suggest the optimal upload method. For example, the reception unit can determine whether a specific format is appropriate based on the content of the resumes the user has previously submitted. This allows the reception unit to select the optimal upload method based on the user's past resume submission history. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past resume submission history into a generation AI, which can select the optimal upload method.

[0064] The reception unit can filter resumes based on the user's current work situation and areas of interest when uploading them. For example, if the user inputs their current work situation, the reception unit filters the resume based on that information. For example, if the user inputs their current job description, the reception unit prioritizes uploading information related to that job description. Furthermore, if the user selects an area of ​​interest, the reception unit can prioritize uploading information related to that area. For example, if the user is interested in a particular industry, the reception unit prioritizes uploading information related to that industry. Furthermore, the reception unit can automatically filter out unnecessary information based on the user's work situation and areas of interest. For example, the reception unit filters out information that is not related to the user's current work situation. This allows the reception unit to filter resumes based on the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the user's work situation and areas of interest into the generation AI, which can then perform the filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of resumes to be uploaded based on the estimated user's emotions. The reception unit, for example, uses facial expression analysis technology to estimate the user's emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using the facial expression analysis technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit analyzes text data entered by the user to estimate the emotion. This allows the reception unit to determine the priority of resumes to be uploaded based on the user's emotions. For example, if the user is nervous, the reception unit can prioritize uploading important information. Also, if the user is relaxed, the reception unit can prioritize uploading resumes containing detailed information. Also, if the user is in a hurry, the reception unit can prioritize uploading concise resumes. This allows the reception unit to determine the priority of resumes to be uploaded based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and determine the priority of the resumes to be uploaded.

[0066] When uploading a resume, the reception unit can prioritize accepting highly relevant resumes by taking into account the user's geographical location information. The reception unit, for example, uses GPS data to acquire the user's geographical location information. For example, the reception unit acquires GPS data from the user's smartphone or device to identify the user's current location. The reception unit can also acquire the user's geographical location information using an IP address. For example, the reception unit analyzes the user's IP address to identify the user's location. This allows the reception unit to prioritize accepting highly relevant resumes based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize accepting resumes related to that area. Also, if the user works in a specific area, the reception unit can prioritize accepting resumes related to that area. Also, if the user is interested in a specific area, the reception unit can prioritize accepting resumes related to that area. This allows the reception unit to prioritize accepting highly relevant resumes based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, allowing the generation AI to preferentially accept resumes that are highly relevant.

[0067] The reception unit can analyze the user's social media activity when uploading a resume and accept related resumes. The reception unit, for example, uses a social media API to analyze the user's social media activity. For example, the reception unit accesses the user's social media account and obtains data such as the content of posts, the number of followers, and the number of likes. The reception unit can also accept related resumes based on information shared by the user on social media. For example, if the user shares information about a specific company or industry on social media, the reception unit accepts related resumes based on that information. The reception unit can also prioritize accepting resumes related to companies or industries the user follows on social media. For example, if the user follows a specific company, the reception unit prioritizes accepting resumes related to that company. The reception unit can also accept resumes related to areas of interest based on the user's social media activity. For example, if the user frequently posts about a specific field, the reception unit accepts resumes related to that field. This allows the reception unit to accept relevant resumes based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's social media data into the generation AI, and the generation AI may receive the associated resume.

[0068] The analysis unit can estimate the user's emotions and adjust the resume analysis method based on the estimated user's emotions. The analysis unit, for example, uses facial expression analysis technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression analysis technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit analyzes text data entered by the user to estimate the emotions. This allows the analysis unit to apply the optimal analysis method depending on the user's emotions. For example, if the user is nervous, the analysis unit applies a simple and easy-to-understand analysis method. On the other hand, if the user is relaxed, the analysis unit applies a detailed analysis method. On the other hand, if the user is in a hurry, the analysis unit applies a method that performs analysis quickly. This allows the analysis unit to apply the optimal analysis method depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the analysis method.

[0069] When analyzing a resume, the analysis unit can improve the accuracy of the analysis by referring to the user's past work history. The analysis unit, for example, stores the user's past work history in a database and refers to that data. For example, the analysis unit records what kind of work the user has done in the past and what skills they have. The analysis unit can also analyze related skills and experience based on the user's past work history. For example, the analysis unit extracts information related to a specific industry or job type based on the user's past work history. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past work history. For example, the analysis unit provides data to increase the reliability of the analysis results based on the user's past work history. This allows the analysis unit to improve the accuracy of the analysis based on the user's past work history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs the user's past work history data into a generation AI, which can improve the accuracy of the analysis.

[0070] When analyzing a resume, the analysis unit can apply different analysis algorithms depending on the user's skill set. The analysis unit, for example, stores the user's skill set in a database and selects an analysis algorithm based on that data. For example, the analysis unit records the skills possessed by the user and applies an analysis algorithm corresponding to those skills. The analysis unit can also select a specific analysis algorithm depending on the user's skill set. For example, if the user is in a technical position, the analysis unit can apply an analysis algorithm specialized for technical skills. If the user is in a managerial position, the analysis unit can apply an analysis algorithm specialized for managerial skills. If the user is in a creative position, the analysis unit can apply an analysis algorithm specialized for creative skills. This allows the analysis unit to apply the optimal analysis algorithm depending on the user's skill set. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's skill set data into a generation AI, which can select the optimal analysis algorithm.

[0071] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, uses facial expression analysis technology to estimate the user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit analyzes text data entered by the user to estimate the emotion. This allows the analysis unit to provide an optimal display method depending on the user's emotion. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method including detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to provide an optimal display method depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the display method.

[0072] When analyzing a resume, the analysis unit can determine the analysis priority based on the user's submission time. The analysis unit, for example, stores the user's resume submission time in a database and determines the analysis priority based on that data. For example, the analysis unit records the date and time when the user submitted the resume and determines the analysis priority based on that date and time. The analysis unit can also determine the analysis priority based on the frequency with which the user submitted the resume. For example, if the user submitted the resume early, the analysis unit can prioritize analyzing that resume. Also, if the user submitted the resume close to the deadline, the analysis unit can analyze it quickly. Also, if the user submitted within a specific period, the analysis unit can prioritize analyzing submissions made within that period. This allows the analysis unit to determine the analysis priority based on the user's submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's submission time data into a generation AI, which can determine the analysis priority.

[0073] When analyzing a resume, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature. For example, the analysis unit stores the user's related literature in a database and performs analysis based on that data. For example, the analysis unit can improve the accuracy of the analysis by referring to papers and articles written by the user in the past. The analysis unit can also extract related information based on literature cited by the user. For example, the analysis unit can analyze the literature cited by the user and compare it with the content of the resume to improve accuracy. The analysis unit can also provide data to improve the reliability of the analysis results based on the literature referenced by the user. For example, the analysis unit can analyze specific skills and experience based on papers and articles written by the user in the past. This allows the analysis unit to improve the accuracy of the analysis based on the user's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's related literature data into a generation AI, which can improve the accuracy of the analysis.

[0074] The generation unit can estimate the user's emotions and adjust the question generation method based on the estimated user's emotions. The generation unit, for example, uses facial expression analysis technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using facial expression analysis technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text data entered by the user to estimate the emotions. This allows the generation unit to generate optimal questions based on the user's emotions. For example, if the user is nervous, the generation unit can generate simple and easy-to-answer questions. On the other hand, if the user is relaxed, the generation unit can generate detailed questions. On the other hand, if the user is in a hurry, the generation unit can generate short and to-the-point questions. This allows the generation unit to generate optimal questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the question generation method.

[0075] When generating a question, the generation unit can adjust the level of detail of the question based on the importance of the user's career. For example, the generation unit stores the importance of the user's career in a database and adjusts the level of detail of the question based on that data. For example, the generation unit records the user's years of work experience and achievements and adjusts the level of detail of the question based on the importance. The generation unit can also generate questions that focus on particularly important parts of the user's career. For example, if the user has an extensive career, the generation unit can generate detailed questions. On the other hand, if the user has a limited career, the generation unit can generate simple questions. Also, the generation unit can generate questions that focus on particularly important parts of the user's career. In this way, the generation unit can adjust the level of detail of the question based on the importance of the user's career. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's career data into the generation AI, and the generation AI can adjust the level of detail of the question.

[0076] When generating questions, the generation unit can apply different question generation algorithms depending on the user's skill category. For example, the generation unit stores the user's skill category in a database and selects a question generation algorithm based on that data. For example, the generation unit records the user's skills and applies a question generation algorithm corresponding to the skills. The generation unit can also select a specific question generation algorithm depending on the user's skill category. For example, if the user is in a technical job, the generation unit can apply an algorithm that generates technical questions. If the user is in a managerial job, the generation unit can apply an algorithm that generates questions related to managerial skills. If the user is in a creative job, the generation unit can apply an algorithm that generates questions related to creative skills. This allows the generation unit to apply an optimal question generation algorithm depending on the user's skill category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's skill category data into a generation AI, which can select an optimal question generation algorithm.

[0077] When generating questions, the generation unit can determine the priority of questions based on the time of user submission. The generation unit, for example, stores the time of user question submission in a database and determines the priority of questions based on that data. For example, the generation unit records the date and time when the user submitted a question and determines the priority of questions based on that date and time. The generation unit can also determine the priority of questions based on the frequency with which the user submitted questions. For example, if a user submitted a question early, the generation unit can generate that question with priority. Also, if a user submitted a question close to a deadline, the generation unit can generate questions quickly. Also, if a user submitted a question within a specific period, the generation unit can generate questions within that period with priority. This allows the generation unit to determine the priority of questions based on the time of user submission. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user submission time data into a generation AI, which can then determine the priority of questions.

[0078] The generation unit can adjust the order of questions based on the user's relevance when generating questions. The generation unit, for example, stores the user's relevance in a database and adjusts the order of questions based on that data. For example, the generation unit records information related to the user's career and skills and adjusts the order of questions based on the relevance. The generation unit can also adjust the order of questions based on information related to the user's interests and concerns. For example, questions related to the user's career can be preferentially generated. Questions related to the user's skills can also be preferentially generated. Questions related to the user's interests and concerns can also be preferentially generated. This allows the generation unit to adjust the order of questions based on the user's relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user relevance data into the generation AI, which can then adjust the order of questions.

[0079] The feedback unit can estimate the user's emotion and adjust the feedback provision method based on the estimated user's emotion. The feedback unit, for example, uses facial expression analysis technology to estimate the user's emotion. For example, the feedback unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The feedback unit can also estimate the user's emotion using voice analysis technology. For example, the feedback unit analyzes the tone and speed of the user's voice to estimate the emotion. The feedback unit can also estimate the user's emotion using text analysis technology. For example, the feedback unit analyzes text data entered by the user to estimate the emotion. This allows the feedback unit to adjust the optimal feedback provision method according to the user's emotion. For example, if the user is nervous, the feedback unit can provide feedback in gentle words. On the other hand, if the user is relaxed, the feedback unit can provide detailed feedback. On the other hand, if the user is in a hurry, the feedback unit can provide concise and to-the-point feedback. This allows the feedback unit to adjust the optimal feedback provision method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the method of providing feedback.

[0080] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past response history. The feedback unit, for example, stores the user's past response history in a database and provides feedback based on that data. For example, the feedback unit records the user's past responses and the content of those responses. The feedback unit can also specifically point out areas for improvement based on the user's past response history. For example, the feedback unit can evaluate the user's progress based on the user's past response history. The feedback unit can also customize the content of the feedback by referring to the user's past response history. For example, the feedback unit can provide feedback regarding specific skills or experiences based on the user's past response history. This allows the feedback unit to improve the accuracy of the feedback based on the user's past response history. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's past response history data into the generation AI, which can then improve the accuracy of the feedback.

[0081] The feedback unit can apply different feedback algorithms depending on the user's skill level when providing feedback. For example, the feedback unit stores the user's skill level in a database and selects a feedback algorithm based on the data. For example, the feedback unit records the user's skill level and applies a feedback algorithm according to the skill level. The feedback unit can also select a specific feedback algorithm depending on the user's skill level. For example, if the user is a beginner, the feedback unit can provide basic feedback. If the user is an intermediate player, the feedback unit can provide detailed feedback. If the user is an advanced player, the feedback unit can provide specialized feedback. This allows the feedback unit to apply an optimal feedback algorithm depending on the user's skill level. Some or all of the above-described processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input the user's skill level data into a generation AI, which can select an optimal feedback algorithm.

[0082] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. The feedback unit, for example, uses facial expression analysis technology to estimate the user's emotion. For example, the feedback unit captures the user's facial expression with a camera and estimates the emotion using the facial expression analysis technology. The feedback unit can also estimate the user's emotion using voice analysis technology. For example, the feedback unit analyzes the tone and speed of the user's voice to estimate the emotion. The feedback unit can also estimate the user's emotion using text analysis technology. For example, the feedback unit analyzes text data entered by the user to estimate the emotion. This allows the feedback unit to determine the priority of feedback according to the user's emotion. For example, if the user is nervous, the feedback unit can prioritize providing important feedback. Also, if the user is relaxed, the feedback unit can prioritize providing detailed feedback. Also, if the user is in a hurry, the feedback unit can prioritize providing concise feedback. This allows the feedback unit to determine the priority of feedback according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and determine the priority of the feedback.

[0083] When providing feedback, the feedback unit can provide optimal feedback by taking the user's geographical location information into consideration. The feedback unit, for example, uses GPS data to acquire the user's geographical location information. For example, the feedback unit acquires GPS data from the user's smartphone or device to identify the user's current location. The feedback unit can also acquire the user's geographical location information using an IP address. For example, the feedback unit analyzes the user's IP address to identify the user's location. This allows the feedback unit to provide optimal feedback based on the user's geographical location information. For example, if the user lives in a specific area, the feedback unit can provide feedback related to that area. Also, if the user works in a specific area, the feedback unit can provide feedback related to that area. Also, if the user is interested in a specific area, the feedback unit can provide feedback related to that area. This allows the feedback unit to provide optimal feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's geographical location information into a generation AI, which can then provide optimal feedback.

[0084] When providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of providing feedback. The feedback unit, for example, uses a social media API to analyze the user's social media activity. For example, the feedback unit accesses the user's social media account and obtains data such as the content of posts, the number of followers, and the number of likes. The feedback unit can also provide relevant feedback based on information shared by the user on social media. For example, if the user shares information about a specific company or industry on social media, the feedback unit provides relevant feedback based on that information. The feedback unit can also provide feedback related to companies or industries the user follows on social media. For example, if the user follows a specific company, the feedback unit provides feedback related to that company. The feedback unit can also provide feedback related to areas of interest based on the user's social media activity. For example, if the user frequently posts about a specific field, the feedback unit provides feedback related to that field. This allows the feedback unit to suggest the optimal means of providing feedback based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's social media data into the generation AI, which can then suggest the optimal means of feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 a PDF resume uploaded by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the resume. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions based on the user's background and skills. The feedback unit is realized, for example, by the control unit 46A of the smart device 14 and analyzes the user's answers and points out pronunciation and grammatical errors. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and feedback 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 a PDF-format resume uploaded by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the resume. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions based on the user's background and skills. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 and analyzes the user's answers and points out pronunciation and grammatical errors. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 a PDF-format resume uploaded by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the resume. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions based on the user's background and skills. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 and analyzes the user's answers and points out pronunciation and grammatical errors. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 a PDF-format resume uploaded by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the resume. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions based on the user's background and skills. The feedback unit is realized, for example, by the control unit 46A of the robot 414 and analyzes the user's answers and points out pronunciation and grammatical errors.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reception unit can analyze the user's past interview experiences and generate optimal questions. For example, the reception unit stores the contents and results of interviews the user has taken in the past in a database and generates questions based on that data. The reception unit can also analyze feedback from the user's past interviews and generate questions that reflect areas for improvement. Furthermore, the reception unit can generate relevant questions based on the industry and job type of the user's past interviews. This allows the reception unit to generate optimal questions based on the user's past interview experiences.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using facial expression analysis technology. The analysis unit can also estimate the emotion by analyzing the tone and speed of the user's voice. Furthermore, the analysis unit can also estimate the emotion by analyzing text data entered by the user. This allows the analysis unit to determine the priority of analysis according to the user's emotions. For example, if the user is nervous, the analysis unit can prioritize analyzing important information. On the other hand, if the user is relaxed, the analysis unit can prioritize analysis including detailed information. This allows the analysis unit to determine the priority of analysis according to the user's emotions.

[0088] The generation unit can analyze the user's past answer history and generate optimal questions. For example, the generation unit stores the user's past answers in a database and generates questions based on that data. The generation unit can also analyze trends in the user's past answers and generate related questions. Furthermore, the generation unit can generate questions that reflect improvements to the user's past answers. This allows the generation unit to generate optimal questions based on the user's past answer history.

[0089] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using facial expression analysis technology. The feedback unit can also estimate the emotion by analyzing the tone and speed of the user's voice. Furthermore, the feedback unit can also estimate the emotion by analyzing text data entered by the user. This allows the feedback unit to adjust the content of the feedback according to the user's emotion. For example, if the user is nervous, the feedback unit can provide feedback in gentle words. On the other hand, if the user is relaxed, the feedback unit can provide detailed feedback. This allows the feedback unit to provide optimal feedback content according to the user's emotion.

[0090] The reception unit can propose an optimal interview time by taking into account the user's geographical location information. For example, the reception unit acquires GPS data from the user's smartphone or device to identify the user's current location. The reception unit can also analyze the user's IP address to identify the user's location. This allows the reception unit to propose an optimal interview time based on the user's geographical location information. For example, if the user is in a different time zone, the reception unit can propose an interview time that matches that time zone. Furthermore, if the user lives in a specific area, the reception unit can also propose an interview time by taking into account the traffic conditions in that area. This allows the reception unit to propose an optimal interview time based on the user's geographical location information.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression analysis technology. The analysis unit can also estimate the emotions by analyzing the tone and speed of the user's voice. Furthermore, the analysis unit can also estimate the emotions by analyzing text data entered by the user. This allows the analysis unit to provide an optimal display method depending on the user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. On the other hand, if the user is relaxed, the analysis unit can provide a display method including detailed information. This allows the analysis unit to provide an optimal display method depending on the user's emotions.

[0092] The generation unit can analyze the user's past interview results and generate optimal questions. For example, the generation unit stores the results of the user's past interviews in a database and generates questions based on that data. The generation unit can also analyze feedback from the user's past interviews and generate questions that reflect areas for improvement. Furthermore, the generation unit can generate relevant questions based on the industry and job type of the user's past interviews. This allows the generation unit to generate optimal questions based on the user's past interview results.

[0093] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using facial expression analysis technology. The feedback unit can also estimate the emotion by analyzing the tone and speed of the user's voice. Furthermore, the feedback unit can also estimate the emotion by analyzing text data entered by the user. This allows the feedback unit to determine the priority of feedback according to the user's emotions. For example, if the user is nervous, the feedback unit can prioritize providing important feedback. Also, if the user is relaxed, the feedback unit can prioritize providing detailed feedback. This allows the feedback unit to determine the priority of feedback according to the user's emotions.

[0094] The reception unit can analyze the user's social media activity and suggest an optimal resume format. For example, the reception unit accesses the user's social media account and obtains data such as the content of posts, the number of followers, and the number of likes. The reception unit can also suggest a related resume format based on the information the user shared on social media. For example, if the user shares information about a specific company or industry, the reception unit can suggest an optimal resume format based on that information. The reception unit can also suggest a resume format related to the companies or industries the user follows on social media. This allows the reception unit to suggest an optimal resume format based on the user's social media activity.

[0095] The analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The analysis unit can also analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the analysis unit can analyze text data entered by the user to estimate the emotion. This allows the analysis unit to improve the accuracy of the analysis according to the user's emotions. For example, if the user is nervous, the analysis unit prioritizes analyzing important information. Also, if the user is relaxed, the analysis unit can prioritize analyzing information including detailed information. This allows the analysis unit to improve the accuracy of the analysis according to the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The reception unit is a section where users upload their resumes. Resumes uploaded by users can be in PDF format, Word format, text format, etc. For example, the reception unit accepts a PDF resume uploaded by a user and imports it into the system. Step 2: The analysis unit analyzes the resume uploaded by the reception unit. The analysis unit analyzes the contents of the resume using text analysis technology, keyword extraction technology, and grammar analysis technology to extract the user's background and skills. Step 3: The generator generates questions based on the resume analyzed by the analyzer. The generator uses AI to generate appropriate questions based on the user's background and skills. For example, it generates questions about projects the user has been involved in and the skills they possess. Step 4: The feedback section analyzes the user's answers to the questions generated by the generation section and provides feedback. The feedback section uses the generation AI to analyze the user's answers, point out pronunciation and grammar errors, and suggest areas for improvement.

[0098] 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.

[0099] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0145] 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.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where you can upload your resume, an analysis unit that analyzes the resume uploaded by the reception unit; a generation unit that generates questions based on the resume analyzed by the analysis unit; a feedback unit that analyzes the user's answer to the question generated by the generation unit and provides feedback. A system characterized by:

2. The reception unit We accept multiple resume formats 2. The system of claim 1.

3. The analysis unit Analyzes resume content and generates questions based on the user's experience and skills 2. The system of claim 1.

4. The feedback unit Analyzes user responses, points out pronunciation and grammar errors, and suggests areas for improvement 2. The system of claim 1.

5. The reception unit Estimate user emotions and adjust the timing of resume uploads based on the estimated user emotions 2. The system of claim 1.

6. The reception unit Analyze the user's past resume submission history and select the upload method 2. The system of claim 1.

7. The reception unit Filter resumes based on your current job status and interests when uploading them 2. The system of claim 1.

8. The reception unit Estimate user sentiment and prioritize resumes to upload based on the estimated sentiment 2. The system of claim 1.

Images