system

Abstract

The system according to this embodiment aims to provide effective interview preparation based on the contents of the application form and work history document. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an editing unit. The reception unit receives input of an entry sheet and a resume. The analysis unit analyzes the information received by the reception unit. The generation unit generates interview preparation materials based on the information analyzed by the analysis unit. The editing unit analyzes the content spoken during the interview preparation materials generated by the generation unit and edits and updates the entry sheet and resume.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to take effective interview countermeasures based on the contents of an entry sheet and a resume.

[0005] The system according to the embodiment aims to provide effective interview countermeasures based on the contents of an entry sheet and a resume.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an editing unit. The reception unit receives input of an entry sheet and a resume. The analysis unit analyzes the information received by the reception unit. The generation unit generates interview preparation materials based on the information analyzed by the analysis unit. The editing unit analyzes the content spoken during the interview preparation generated by the generation unit and edits and updates the entry sheet and resume. [Effects of the Invention]

[0007] The system according to this embodiment can provide effective interview preparation based on the contents of the application form and work history. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The interview preparation app according to an embodiment of the present invention is a system that provides individualized interview preparation to job seekers and those seeking a career change. This system allows users to upload their application forms and resumes, and an AI analyzes these documents, comparing them with company information and past interview questions to generate interview preparation tailored to each company. For example, the AI ​​analyzes what kinds of questions a particular company has asked in the past and suggests strategies to the user based on that analysis. Furthermore, the AI ​​analyzes what the user says during the interview preparation and revises and updates the content of the application form and resume. This allows users to create more effective documents. This app aims to address the current situation where the amount of time career support centers and career advisors can dedicate to each individual job seeker and career changer is decreasing year by year. Also, given the possibility of a future "deregulation of dismissal regulations," it is necessary to quickly deploy this app to the market in anticipation of an increase in job seekers. By using this app, users can join their desired companies and achieve career growth. Thus, the interview preparation app can analyze the user's application form and resume and provide individualized interview preparation based on company information and past interview questions.

[0029] The interview preparation application according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a proofreading unit. The reception unit accepts input of an entry sheet and a resume. For example, the reception unit allows users to upload their entry sheet and resume. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses AI to analyze the contents of the entry sheet and resume. The generation unit generates interview preparation based on the information analyzed by the analysis unit. For example, the generation unit analyzes questions previously asked by a particular company and proposes preparation strategies to the user based on that analysis. The proofreading unit analyzes the content spoken during the interview preparation generated by the generation unit and proofreads and updates the entry sheet and resume. For example, the proofreading unit uses AI to analyze what the user said and corrects the contents of the entry sheet and resume. As a result, the interview preparation application according to this embodiment can handle everything from inputting entry sheets and resumes to interview preparation, proofreading, and updates in a consistent manner.

[0030] The reception desk accepts the input of application forms and resumes. Specifically, it provides an interface for users to log in to the application and upload their application forms and resumes. Users can upload documents in formats such as PDF and Word files. The reception desk also has a function that allows users to create and edit application forms and resumes directly within the application. This makes it easy for users to prepare documents. Furthermore, the reception desk has a function that automatically checks the format and content of uploaded documents and notifies the user if there are any deficiencies. For example, if required fields are left blank or the file format is inappropriate, it will provide the user with specific correction instructions. This allows users to correct document deficiencies in advance and proceed smoothly to the next step.

[0031] The analysis department analyzes the information received by the reception department. Specifically, it uses AI to analyze the contents of application forms and resumes, extracting the user's skills, experience, and motivations. The AI ​​uses natural language processing technology to analyze text data and identify important keywords and phrases. For example, it extracts skill sets related to specific industries and job types from the work history written by the user, and uses this to evaluate the user's strengths and weaknesses. It also analyzes the user's values ​​and goals from the content of their motivations, and evaluates the degree of match with the company. Furthermore, the analysis department can use past data and statistical information to evaluate how well the user's documents compare to those of other applicants. This allows users to specifically understand their strengths and areas for improvement, enabling them to prepare for interviews more effectively.

[0032] The generation unit generates interview preparation materials based on information analyzed by the analysis unit. Specifically, it analyzes questions previously asked by a particular company and proposes preparation strategies to the user based on that analysis. The generation unit uses AI to analyze interview trends for each company and provides users with specific example questions and answers. For example, based on past interview data, it presents questions frequently asked by a particular company and effective example answers to those questions. It can also generate individually customized interview preparation materials based on the user's skills and experience. This allows users to receive interview preparation tailored to their needs and improve their performance in the actual interview. Furthermore, the generation unit also provides a simulation function for users to practice interviews. Users can interact with a virtual interviewer within the application and practice in an environment similar to a real interview. This allows users to repeatedly practice the flow of the interview and answers to questions, enabling them to approach the actual interview with confidence.

[0033] The editing department analyzes the content spoken during interview preparation, which is generated by the generation department, and edits and updates the application form and resume. Specifically, it uses AI to analyze what the user has said and revises the content of the application form and resume. For example, it uses speech recognition technology to transcribe what the user has said during interview practice, analyzes that text, and extracts information that should be reflected in the application form and resume. This allows the user to reflect newly discovered strengths and experiences in their documents through interview practice. The editing department also provides feedback on the user's speaking style and expression. For example, if the speaking style is unclear or important points are not easily conveyed, it points out specific areas for improvement and supports the user in more effectively promoting themselves. Furthermore, the editing department continuously updates the content of the application form and resume each time the user practices interviews, reflecting the latest information. This allows the user to always prepare the best possible documents and approach interviews with confidence.

[0034] The data collection unit can collect company information and past interview question examples. For example, the data collection unit can automatically collect company information and past interview question examples from the internet using AI. For example, the data collection unit can collect company information from official company websites and job posting sites. The data collection unit can also analyze online forums and review sites to collect past interview question examples. As a result, the data collection unit can provide more specific interview preparation by collecting company information and past interview question examples. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model for collecting company information and past interview question examples.

[0035] The generation unit can analyze past interview questions asked by a specific company and, based on that analysis, suggest countermeasures to the user. For example, the generation unit can use AI to analyze past interview questions and identify frequently asked and important questions. For example, the generation unit can analyze a database of interview questions from a specific company to understand question trends. The generation unit can also suggest specific answer examples and countermeasures to the user based on past question examples. In this way, the generation unit can provide interview preparation tailored to a specific company. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past interview question data into AI and have the AI ​​perform question analysis and suggest countermeasures.

[0036] The editing department can analyze what was said during interview preparation and edit and update the content of the application form and resume. For example, the editing department can use AI to convert what the user said during interview preparation into text using speech recognition technology and then analyze the content. For example, the editing department can compare what the user said with the content of the application form and resume and point out inconsistencies and contradictions. The editing department can also automatically revise the content of the application form and resume based on what the user said. This allows the editing department to more effectively update the application form and resume based on what was said during interview preparation. Some or all of the above processes in the editing department may be performed using AI or not. For example, the editing department can input what the user said into the AI ​​and have the AI ​​revise the application form and resume.

[0037] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or handwriting input). For example, the reception desk can analyze the user's past input speed and suggest an optimal input pace. The reception desk can also provide an auto-completion function for input, referencing the user's past input. As a result, the reception desk improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI and have the AI ​​select the optimal input method.

[0038] The reception desk can filter the input of application forms and resumes based on the user's current occupation and areas of interest. For example, the reception desk can prioritize suggesting relevant work experience based on the user's current occupation. For example, the reception desk can facilitate the input of relevant skills and experience based on the user's areas of interest. The reception desk can also omit unnecessary input fields based on the user's occupation and areas of interest. This allows the reception desk to input more relevant information by filtering the input content based on the user's occupation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's occupation and areas of interest into an AI and have the AI ​​perform the filtering.

[0039] The reception desk can prioritize inputting highly relevant information when users fill out application forms and resumes, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk can prioritize inputting work experience and skills related to that region. If a user works in a specific region, the reception desk can prioritize inputting company information in that region. Furthermore, if a user is interested in a specific region, the reception desk can prioritize inputting information related to that region. In this way, the reception desk can input more relevant information by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize highly relevant information.

[0040] The reception desk can analyze a user's social media activity and input relevant information when they fill out application forms and resumes. For example, the reception desk can analyze a user's social media activity and input relevant skills and experience. For example, the reception desk can analyze a user's social media network and input relevant companies and work history. The reception desk can also analyze a user's interests on social media and input relevant information. This allows the reception desk to input more relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input a user's social media data into an AI and have the AI ​​perform the analysis and input of relevant information.

[0041] The analysis unit can adjust the level of detail in the analysis based on the importance of the application form and resume during the analysis process. For example, the analysis unit can perform a detailed analysis on important work experience information. For example, it can perform a simplified analysis on less important information. The analysis unit can also determine the priority of the analysis according to its importance. This allows the analysis unit to perform efficient analysis by adjusting the level of detail according to importance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input importance data from the application form and resume into the AI ​​and have the AI ​​adjust the level of detail in the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the application form or resume during the analysis process. For example, the analysis unit can apply an analysis algorithm specializing in technical skills to an application form for a technical position. For example, the analysis unit can apply an analysis algorithm specializing in sales performance to a resume for a sales position. Furthermore, the analysis unit can apply an analysis algorithm specializing in leadership skills to an application form for a management position. This allows the analysis unit to perform more accurate analysis by applying an analysis algorithm appropriate to the category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input category data from the application form or resume into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the submission dates of application forms and resumes. For example, the analysis unit can prioritize the analysis of application forms with approaching submission deadlines. For example, it can postpone the analysis of resumes with later submission deadlines. The analysis unit can also adjust the analysis schedule according to the submission dates. This allows the analysis unit to perform efficient analysis by determining the priority of analysis according to the submission dates. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input application form and resume submission date data into AI and have the AI ​​determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the application forms and resumes during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, it can postpone the analysis of less relevant information. The analysis unit can also determine the order of analysis based on relevance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis according to relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of the application forms and resumes into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0045] The generation unit can adjust the level of detail in interview preparation materials based on the importance of the company information when generating them. For example, the generation unit can provide detailed preparation materials for important company information, and simplified preparation materials for less important company information. The generation unit can also determine the priority of preparation materials according to their importance. This allows the generation unit to provide efficient preparation materials by adjusting the level of detail according to the importance of the company information. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input company information importance data into AI and have the AI ​​perform the adjustment of the level of detail of the preparation materials.

[0046] The generation unit can apply different preparation algorithms depending on the company category when generating interview preparation materials. For example, the generation unit can apply a preparation algorithm specializing in technical questions to technology companies. For example, the generation unit can apply a preparation algorithm specializing in sales skills to sales companies. Furthermore, the generation unit can apply a preparation algorithm specializing in leadership skills to management companies. This allows the generation unit to provide more accurate preparation materials by applying preparation algorithms tailored to the company category. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input company category data into AI and have the AI ​​execute the application of preparation algorithms.

[0047] The generation unit can determine the priority of interview preparation based on the submission timing of company information when generating preparation materials. For example, the generation unit can prioritize preparation for company information with an approaching submission deadline. For example, it can postpone preparation for company information with a distant submission deadline. The generation unit can also adjust the preparation schedule according to the submission timing. This allows the generation unit to efficiently prepare by determining the priority of preparation based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input company information submission timing data into AI and have the AI ​​determine the priority of preparation.

[0048] The generation unit can adjust the order of interview preparation strategies based on the relevance of company information when generating them. For example, the generation unit can prioritize preparing strategies for highly relevant company information. For example, it can postpone preparing strategies for less relevant company information. The generation unit can also determine the order of strategies based on relevance. This allows the generation unit to prepare strategies efficiently by adjusting the order of strategies according to relevance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input relevance data of company information into AI and have the AI ​​adjust the order of strategies.

[0049] The editing unit can analyze the user's past interview preparation history to select the optimal editing method during the editing process. For example, the editing unit can analyze the content of past interview preparation sessions the user has received and propose the most suitable editing method. For example, the editing unit can extract effective editing points from the user's past interview preparation history. Furthermore, the editing unit can provide individually customized editing methods based on the user's past interview preparation history. This improves the accuracy of editing by allowing the editing unit to propose the most suitable editing method based on past interview preparation history. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit can input the user's past interview preparation history data into an AI and have the AI ​​select the most suitable editing method.

[0050] The editing unit can customize its editing methods based on the user's current occupational status. For example, the editing unit may prioritize editing relevant work history based on the user's current occupational status. For example, the editing unit may suggest appropriate expressions and terminology according to the user's current occupational status. The editing unit may also remove unnecessary information and highlight important information based on the user's current occupational status. In this way, the editing unit can provide more appropriate editing by customizing its editing methods based on the user's current occupational status. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit may input the user's occupational status data into AI and have the AI ​​perform the customization of the editing methods.

[0051] The editing unit can select the most appropriate editing method by considering the user's geographical location information during the editing process. For example, if the user lives in a specific region, the editing unit will prioritize editing work history related to that region. For example, if the user works in a specific region, the editing unit can consider company information in that region when editing. Furthermore, if the user is interested in a specific region, the editing unit can edit information related to that region. In this way, the editing unit can provide more relevant editing by considering geographical location information. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the user's geographical location information into the AI ​​and have the AI ​​select the most appropriate editing method.

[0052] The editing unit can analyze the user's social media activity during the editing process and propose editing methods. For example, the editing unit can analyze the user's social media activity and edit relevant skills and experience. For example, the editing unit can analyze the user's social media network and edit relevant companies and work history. The editing unit can also analyze the user's interests on social media and edit relevant information. This allows the editing unit to provide more relevant editing by analyzing social media activity. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the user's social media data into AI and have the AI ​​propose editing methods.

[0053] The data collection unit can adjust the level of detail collected based on the importance of company information and past question examples during the collection process. For example, the data collection unit can collect detailed information for important company information. For example, the data collection unit can collect simplified information for less important information. The data collection unit can also determine the priority of collection according to importance. This allows the data collection unit to efficiently collect information by adjusting the level of detail according to importance. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input importance data of company information and past question examples into the AI ​​and have the AI ​​perform the adjustment of the level of detail of collection.

[0054] The data collection unit can apply different collection algorithms depending on the category of company information and past question examples during collection. For example, the data collection unit can prioritize collecting technical question examples for information on technology companies. For example, the data collection unit can prioritize collecting question examples related to sales skills for information on sales companies. Furthermore, the data collection unit can prioritize collecting question examples related to leadership skills for information on management companies. This allows the data collection unit to collect information with higher accuracy by applying collection algorithms according to the category. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input category data of company information and past question examples into an AI and have the AI ​​execute the application of collection algorithms.

[0055] The data collection unit can determine the priority of data collection based on the submission dates of company information and past question examples. For example, the data collection unit can prioritize collecting company information with an approaching submission deadline. For example, it can postpone collecting company information with a distant submission deadline. The data collection unit can also adjust the collection schedule according to the submission date. This allows the data collection unit to efficiently collect information by determining the priority of data collection according to the submission date. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input company information and past question example submission date data into the AI ​​and have the AI ​​determine the priority of data collection.

[0056] The data collection unit can adjust the order of collection based on the relevance of company information and past question examples during the collection process. For example, the data collection unit can prioritize the collection of highly relevant information. For example, the data collection unit can postpone the collection of less relevant information. The data collection unit can also determine the order of collection according to relevance. This allows the data collection unit to efficiently collect information by adjusting the order of collection according to relevance. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input relevance data of company information and past question examples into the AI ​​and have the AI ​​perform the adjustment of the collection order.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] An interview preparation app can collect users' past interview experiences and analyze that data in its analytics department. For example, the analytics department can collect and analyze the questions and answers from past interviews to identify the user's strengths and weaknesses. The analytics department can also identify patterns of questions that the user has struggled with in the past and suggest countermeasures. Furthermore, the analytics department can analyze patterns of successful answers from the user's past and provide advice based on that. In this way, the analytics department can provide more personalized interview preparation based on the user's past interview experiences.

[0059] The interview preparation app analyzes the user's resume and application form, and the analysis unit performs skill matching based on that data. For example, the analysis unit compares the skills listed in the user's resume with the skills required by companies and evaluates the degree of matching. For example, the analysis unit can suggest appropriate companies and job types based on the user's skill set. The analysis unit can also identify the user's skill gaps and suggest learning resources and training programs to bridge those gaps. In this way, the analysis unit can provide more appropriate interview preparation by matching the user's skills with the needs of companies.

[0060] The interview preparation app analyzes the content of the user's application form and resume, and the analysis department can propose career paths based on that data. For example, the analysis department can analyze the user's work history and skill set and propose a career path they should aim for in the future. For example, the analysis department can match the user's current skills with market trends and suggest fields and job types with growth potential. Furthermore, the analysis department can specifically show the steps to acquire the necessary skills and experience based on the user's career goals. In this way, the analysis department can provide concrete advice to support the user's career growth.

[0061] The interview preparation app analyzes the content of the user's application form and resume, and the analytics department can perform industry analysis based on that data. For example, the analytics department collects and analyzes information on trends and competitors in the industry the user is interested in. For example, the analytics department can identify which industry has the highest demand for the user's skill set and propose interview preparation tailored to that industry. The analytics department can also provide the latest trends and news in the industry the user is interested in, which can be used to create topics for conversation during interviews. In this way, the analytics department can support more effective interview preparation by providing information tailored to the user's desired industry.

[0062] The interview preparation app analyzes the content of the user's application form and resume, and the analysis department can perform competitive analysis based on that data. For example, the analysis department can collect and analyze information on competitors of the company the user is applying to. The analysis department can identify the skills and experience that competitors are looking for and use that to evaluate the user's strengths and weaknesses. Furthermore, the analysis department can refer to the interview preparation strategies of competitors and suggest more effective strategies to the user. In this way, the analysis department can support users in making a stronger appeal to their target companies by conducting analysis based on information on competitors.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk accepts the entry sheet and resume. Users can upload their entry sheet and resume. Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses AI to analyze the contents of application forms and resumes. Step 3: The generation unit generates interview preparation materials based on the information analyzed by the analysis unit. For example, the generation unit analyzes questions that a particular company has asked in the past and proposes preparation materials to the user based on that analysis. Step 4: The editing unit analyzes the content spoken during interview preparation, which was generated by the generation unit, and edits and updates the application form and resume. For example, the editing unit uses AI to analyze what the user said and revise the content of the application form and resume.

[0065] (Example of form 2) The interview preparation app according to an embodiment of the present invention is a system that provides individualized interview preparation to job seekers and those seeking a career change. This system allows users to upload their application forms and resumes, and an AI analyzes these documents, comparing them with company information and past interview questions to generate interview preparation tailored to each company. For example, the AI ​​analyzes what kinds of questions a particular company has asked in the past and suggests strategies to the user based on that analysis. Furthermore, the AI ​​analyzes what the user says during the interview preparation and revises and updates the content of the application form and resume. This allows users to create more effective documents. This app aims to address the current situation where the amount of time career support centers and career advisors can dedicate to each individual job seeker and career changer is decreasing year by year. Also, given the possibility of a future "deregulation of dismissal regulations," it is necessary to quickly deploy this app to the market in anticipation of an increase in job seekers. By using this app, users can join their desired companies and achieve career growth. Thus, the interview preparation app can analyze the user's application form and resume and provide individualized interview preparation based on company information and past interview questions.

[0066] The interview preparation application according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a proofreading unit. The reception unit accepts input of an entry sheet and a resume. For example, the reception unit allows users to upload their entry sheet and resume. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses AI to analyze the contents of the entry sheet and resume. The generation unit generates interview preparation based on the information analyzed by the analysis unit. For example, the generation unit analyzes questions previously asked by a particular company and proposes preparation strategies to the user based on that analysis. The proofreading unit analyzes the content spoken during the interview preparation generated by the generation unit and proofreads and updates the entry sheet and resume. For example, the proofreading unit uses AI to analyze what the user said and corrects the contents of the entry sheet and resume. As a result, the interview preparation application according to this embodiment can handle everything from inputting entry sheets and resumes to interview preparation, proofreading, and updates in a consistent manner.

[0067] The reception desk accepts the input of application forms and resumes. Specifically, it provides an interface for users to log in to the application and upload their application forms and resumes. Users can upload documents in formats such as PDF and Word files. The reception desk also has a function that allows users to create and edit application forms and resumes directly within the application. This makes it easy for users to prepare documents. Furthermore, the reception desk has a function that automatically checks the format and content of uploaded documents and notifies the user if there are any deficiencies. For example, if required fields are left blank or the file format is inappropriate, it will provide the user with specific correction instructions. This allows users to correct document deficiencies in advance and proceed smoothly to the next step.

[0068] The analysis department analyzes the information received by the reception department. Specifically, it uses AI to analyze the contents of application forms and resumes, extracting the user's skills, experience, and motivations. The AI ​​uses natural language processing technology to analyze text data and identify important keywords and phrases. For example, it extracts skill sets related to specific industries and job types from the work history written by the user, and uses this to evaluate the user's strengths and weaknesses. It also analyzes the user's values ​​and goals from the content of their motivations, and evaluates the degree of match with the company. Furthermore, the analysis department can use past data and statistical information to evaluate how well the user's documents compare to those of other applicants. This allows users to specifically understand their strengths and areas for improvement, enabling them to prepare for interviews more effectively.

[0069] The generation unit generates interview preparation materials based on information analyzed by the analysis unit. Specifically, it analyzes questions previously asked by a particular company and proposes preparation strategies to the user based on that analysis. The generation unit uses AI to analyze interview trends for each company and provides users with specific example questions and answers. For example, based on past interview data, it presents questions frequently asked by a particular company and effective example answers to those questions. It can also generate individually customized interview preparation materials based on the user's skills and experience. This allows users to receive interview preparation tailored to their needs and improve their performance in the actual interview. Furthermore, the generation unit also provides a simulation function for users to practice interviews. Users can interact with a virtual interviewer within the application and practice in an environment similar to a real interview. This allows users to repeatedly practice the flow of the interview and answers to questions, enabling them to approach the actual interview with confidence.

[0070] The editing department analyzes the content spoken during interview preparation, which is generated by the generation department, and edits and updates the application form and resume. Specifically, it uses AI to analyze what the user has said and revises the content of the application form and resume. For example, it uses speech recognition technology to transcribe what the user has said during interview practice, analyzes that text, and extracts information that should be reflected in the application form and resume. This allows the user to reflect newly discovered strengths and experiences in their documents through interview practice. The editing department also provides feedback on the user's speaking style and expression. For example, if the speaking style is unclear or important points are not easily conveyed, it points out specific areas for improvement and supports the user in more effectively promoting themselves. Furthermore, the editing department continuously updates the content of the application form and resume each time the user practices interviews, reflecting the latest information. This allows the user to always prepare the best possible documents and approach interviews with confidence.

[0071] The data collection unit can collect company information and past interview question examples. For example, the data collection unit can automatically collect company information and past interview question examples from the internet using AI. For example, the data collection unit can collect company information from official company websites and job posting sites. The data collection unit can also analyze online forums and review sites to collect past interview question examples. As a result, the data collection unit can provide more specific interview preparation by collecting company information and past interview question examples. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model for collecting company information and past interview question examples.

[0072] The generation unit can analyze past interview questions asked by a specific company and, based on that analysis, suggest countermeasures to the user. For example, the generation unit can use AI to analyze past interview questions and identify frequently asked and important questions. For example, the generation unit can analyze a database of interview questions from a specific company to understand question trends. The generation unit can also suggest specific answer examples and countermeasures to the user based on past question examples. In this way, the generation unit can provide interview preparation tailored to a specific company. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past interview question data into AI and have the AI ​​perform question analysis and suggest countermeasures.

[0073] The editing department can analyze what was said during interview preparation and edit and update the content of the application form and resume. For example, the editing department can use AI to convert what the user said during interview preparation into text using speech recognition technology and then analyze the content. For example, the editing department can compare what the user said with the content of the application form and resume and point out inconsistencies and contradictions. The editing department can also automatically revise the content of the application form and resume based on what the user said. This allows the editing department to more effectively update the application form and resume based on what was said during interview preparation. Some or all of the above processes in the editing department may be performed using AI or not. For example, the editing department can input what the user said into the AI ​​and have the AI ​​revise the application form and resume.

[0074] The reception desk can estimate the user's emotions and adjust the timing of inputting the entry sheet and resume based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can pause the input and provide a relaxing interface. For example, if the user is focused, the reception desk can facilitate continuous input and allow for efficient completion. The reception desk can also divide the input and allow breaks if the user is tired. This allows the reception desk to enable more effective input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or handwriting input). For example, the reception desk can analyze the user's past input speed and suggest an optimal input pace. The reception desk can also provide an auto-completion function for input, referencing the user's past input. As a result, the reception desk improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI and have the AI ​​select the optimal input method.

[0076] The reception desk can filter the input of application forms and resumes based on the user's current occupation and areas of interest. For example, the reception desk can prioritize suggesting relevant work experience based on the user's current occupation. For example, the reception desk can facilitate the input of relevant skills and experience based on the user's areas of interest. The reception desk can also omit unnecessary input fields based on the user's occupation and areas of interest. This allows the reception desk to input more relevant information by filtering the input content based on the user's occupation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's occupation and areas of interest into an AI and have the AI ​​perform the filtering.

[0077] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk may postpone the input of important information and start with simpler information. If the user is relaxed, the reception desk may prioritize the input of important information and proceed efficiently. Also, if the user is in a hurry, the reception desk may prioritize the input of the most important information and complete it quickly. In this way, the reception desk enables efficient input by prioritizing the input information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize inputting highly relevant information when users fill out application forms and resumes, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk can prioritize inputting work experience and skills related to that region. If a user works in a specific region, the reception desk can prioritize inputting company information in that region. Furthermore, if a user is interested in a specific region, the reception desk can prioritize inputting information related to that region. In this way, the reception desk can input more relevant information by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize highly relevant information.

[0079] The reception desk can analyze a user's social media activity and input relevant information when they fill out application forms and resumes. For example, the reception desk can analyze a user's social media activity and input relevant skills and experience. For example, the reception desk can analyze a user's social media network and input relevant companies and work history. The reception desk can also analyze a user's interests on social media and input relevant information. This allows the reception desk to input more relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input a user's social media data into an AI and have the AI ​​perform the analysis and input of relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can provide more easily understandable analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail in the analysis based on the importance of the application form and resume during the analysis process. For example, the analysis unit can perform a detailed analysis on important work experience information. For example, it can perform a simplified analysis on less important information. The analysis unit can also determine the priority of the analysis according to its importance. This allows the analysis unit to perform efficient analysis by adjusting the level of detail according to importance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input importance data from the application form and resume into the AI ​​and have the AI ​​adjust the level of detail in the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the application form or resume during the analysis process. For example, the analysis unit can apply an analysis algorithm specializing in technical skills to an application form for a technical position. For example, the analysis unit can apply an analysis algorithm specializing in sales performance to a resume for a sales position. Furthermore, the analysis unit can apply an analysis algorithm specializing in leadership skills to an application form for a management position. This allows the analysis unit to perform more accurate analysis by applying an analysis algorithm appropriate to the category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input category data from the application form or resume into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a quick analysis result if the user is in a hurry. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the submission dates of application forms and resumes. For example, the analysis unit can prioritize the analysis of application forms with approaching submission deadlines. For example, it can postpone the analysis of resumes with later submission deadlines. The analysis unit can also adjust the analysis schedule according to the submission dates. This allows the analysis unit to perform efficient analysis by determining the priority of analysis according to the submission dates. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input application form and resume submission date data into AI and have the AI ​​determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the application forms and resumes during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, it can postpone the analysis of less relevant information. The analysis unit can also determine the order of analysis based on relevance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis according to relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of the application forms and resumes into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0086] The generation unit can estimate the user's emotions and adjust the way interview preparation is presented based on the estimated emotions. For example, if the user is nervous, the generation unit can provide simple and easy-to-understand preparation. For example, if the user is relaxed, the generation unit can provide detailed preparation. Furthermore, if the user is in a hurry, the generation unit can provide concise preparation. In this way, the generation unit can provide more easily understood preparation by adjusting the way interview preparation is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] The generation unit can adjust the level of detail in interview preparation materials based on the importance of the company information when generating them. For example, the generation unit can provide detailed preparation materials for important company information, and simplified preparation materials for less important company information. The generation unit can also determine the priority of preparation materials according to their importance. This allows the generation unit to provide efficient preparation materials by adjusting the level of detail according to the importance of the company information. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input company information importance data into AI and have the AI ​​perform the adjustment of the level of detail of the preparation materials.

[0088] The generation unit can apply different preparation algorithms depending on the company category when generating interview preparation materials. For example, the generation unit can apply a preparation algorithm specializing in technical questions to technology companies. For example, the generation unit can apply a preparation algorithm specializing in sales skills to sales companies. Furthermore, the generation unit can apply a preparation algorithm specializing in leadership skills to management companies. This allows the generation unit to provide more accurate preparation materials by applying preparation algorithms tailored to the company category. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input company category data into AI and have the AI ​​execute the application of preparation algorithms.

[0089] The generation unit can estimate the user's emotions and adjust the length of the interview preparation based on the estimated emotions. For example, if the user is nervous, the generation unit can provide short, concise preparation. For example, if the user is relaxed, the generation unit can provide detailed preparation. The generation unit can also provide quick preparation if the user is in a hurry. In this way, the generation unit can provide more appropriate preparation by adjusting the length of the interview preparation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] The generation unit can determine the priority of interview preparation based on the submission timing of company information when generating preparation materials. For example, the generation unit can prioritize preparation for company information with an approaching submission deadline. For example, it can postpone preparation for company information with a distant submission deadline. The generation unit can also adjust the preparation schedule according to the submission timing. This allows the generation unit to efficiently prepare by determining the priority of preparation based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input company information submission timing data into AI and have the AI ​​determine the priority of preparation.

[0091] The generation unit can adjust the order of interview preparation strategies based on the relevance of company information when generating them. For example, the generation unit can prioritize preparing strategies for highly relevant company information. For example, it can postpone preparing strategies for less relevant company information. The generation unit can also determine the order of strategies based on relevance. This allows the generation unit to prepare strategies efficiently by adjusting the order of strategies according to relevance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input relevance data of company information into AI and have the AI ​​adjust the order of strategies.

[0092] The editing unit can estimate the user's emotions and adjust its editing method based on the estimated emotions. For example, if the user is nervous, the editing unit can provide simple and easy-to-understand editing. If the user is relaxed, the editing unit can provide detailed editing. Also, if the user is in a hurry, the editing unit can provide concise editing. In this way, the editing unit can provide more easily understood editing by adjusting its editing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The editing unit can analyze the user's past interview preparation history to select the optimal editing method during the editing process. For example, the editing unit can analyze the content of past interview preparation sessions the user has received and propose the most suitable editing method. For example, the editing unit can extract effective editing points from the user's past interview preparation history. Furthermore, the editing unit can provide individually customized editing methods based on the user's past interview preparation history. This improves the accuracy of editing by allowing the editing unit to propose the most suitable editing method based on past interview preparation history. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit can input the user's past interview preparation history data into an AI and have the AI ​​select the most suitable editing method.

[0094] The editing unit can customize its editing methods based on the user's current occupational status. For example, the editing unit may prioritize editing relevant work history based on the user's current occupational status. For example, the editing unit may suggest appropriate expressions and terminology according to the user's current occupational status. The editing unit may also remove unnecessary information and highlight important information based on the user's current occupational status. In this way, the editing unit can provide more appropriate editing by customizing its editing methods based on the user's current occupational status. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit may input the user's occupational status data into AI and have the AI ​​perform the customization of the editing methods.

[0095] The editing unit can estimate the user's emotions and determine editing priorities based on the estimated emotions. For example, if the user is nervous, the editing unit can postpone editing important parts and start with easier ones. If the user is relaxed, the editing unit can prioritize editing important parts. Also, if the user is in a hurry, the editing unit can quickly edit the most important parts. In this way, the editing unit can efficiently edit by determining editing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0096] The editing unit can select the most appropriate editing method by considering the user's geographical location information during the editing process. For example, if the user lives in a specific region, the editing unit will prioritize editing work history related to that region. For example, if the user works in a specific region, the editing unit can consider company information in that region when editing. Furthermore, if the user is interested in a specific region, the editing unit can edit information related to that region. In this way, the editing unit can provide more relevant editing by considering geographical location information. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the user's geographical location information into the AI ​​and have the AI ​​select the most appropriate editing method.

[0097] The editing unit can analyze the user's social media activity during the editing process and propose editing methods. For example, the editing unit can analyze the user's social media activity and edit relevant skills and experience. For example, the editing unit can analyze the user's social media network and edit relevant companies and work history. The editing unit can also analyze the user's interests on social media and edit relevant information. This allows the editing unit to provide more relevant editing by analyzing social media activity. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the user's social media data into AI and have the AI ​​propose editing methods.

[0098] The data collection unit can estimate the user's emotions and adjust the method of collecting company information and past question examples based on the estimated user emotions. For example, if the user is nervous, the data collection unit will prioritize collecting simple and easy-to-understand information. For example, if the user is relaxed, the data collection unit can collect detailed information. Also, if the user is in a hurry, the data collection unit can quickly collect concise information. In this way, the data collection unit can collect more appropriate information by adjusting the collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0099] The data collection unit can adjust the level of detail collected based on the importance of company information and past question examples during the collection process. For example, the data collection unit can collect detailed information for important company information. For example, the data collection unit can collect simplified information for less important information. The data collection unit can also determine the priority of collection according to importance. This allows the data collection unit to efficiently collect information by adjusting the level of detail according to importance. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input importance data of company information and past question examples into the AI ​​and have the AI ​​perform the adjustment of the level of detail of collection.

[0100] The data collection unit can apply different collection algorithms depending on the category of company information and past question examples during collection. For example, the data collection unit can prioritize collecting technical question examples for information on technology companies. For example, the data collection unit can prioritize collecting question examples related to sales skills for information on sales companies. Furthermore, the data collection unit can prioritize collecting question examples related to leadership skills for information on management companies. This allows the data collection unit to collect information with higher accuracy by applying collection algorithms according to the category. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input category data of company information and past question examples into an AI and have the AI ​​execute the application of collection algorithms.

[0101] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is tense, the data collection unit may postpone the collection of important information and start with simpler information. For example, if the user is relaxed, the data collection unit can prioritize the collection of important information. Also, if the user is in a hurry, the data collection unit can quickly collect the most important information. In this way, the data collection unit can efficiently collect information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The data collection unit can determine the priority of data collection based on the submission dates of company information and past question examples. For example, the data collection unit can prioritize collecting company information with an approaching submission deadline. For example, it can postpone collecting company information with a distant submission deadline. The data collection unit can also adjust the collection schedule according to the submission date. This allows the data collection unit to efficiently collect information by determining the priority of data collection according to the submission date. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input company information and past question example submission date data into the AI ​​and have the AI ​​determine the priority of data collection.

[0103] The data collection unit can adjust the order of collection based on the relevance of company information and past question examples during the collection process. For example, the data collection unit can prioritize the collection of highly relevant information. For example, the data collection unit can postpone the collection of less relevant information. The data collection unit can also determine the order of collection according to relevance. This allows the data collection unit to efficiently collect information by adjusting the order of collection according to relevance. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input relevance data of company information and past question examples into the AI ​​and have the AI ​​perform the adjustment of the collection order.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] An interview preparation app can collect users' past interview experiences and analyze that data in its analytics department. For example, the analytics department can collect and analyze the questions and answers from past interviews to identify the user's strengths and weaknesses. The analytics department can also identify patterns of questions that the user has struggled with in the past and suggest countermeasures. Furthermore, the analytics department can analyze patterns of successful answers from the user's past and provide advice based on that. In this way, the analytics department can provide more personalized interview preparation based on the user's past interview experiences.

[0106] An interview preparation app can estimate the user's emotions and adjust the interview preparation content based on those emotions. For example, if the user is nervous, the generator can provide advice and practice methods to help them relax. If the user is confident, the generator can suggest strategies for more advanced questions. Furthermore, if the user is tired, the generator can provide effective strategies in a short amount of time. In this way, the generator can provide more effective interview preparation by adjusting the content according to the user's emotions.

[0107] The interview preparation app analyzes the user's resume and application form, and the analysis unit performs skill matching based on that data. For example, the analysis unit compares the skills listed in the user's resume with the skills required by companies and evaluates the degree of matching. For example, the analysis unit can suggest appropriate companies and job types based on the user's skill set. The analysis unit can also identify the user's skill gaps and suggest learning resources and training programs to bridge those gaps. In this way, the analysis unit can provide more appropriate interview preparation by matching the user's skills with the needs of companies.

[0108] An interview preparation app can estimate the user's emotions and provide feedback during interview practice based on those emotions. For example, if the user is nervous, the app might provide more positive feedback to boost their confidence. If the user is relaxed, the app might provide more detailed feedback and specifically point out areas for improvement. Furthermore, if the user is in a hurry, the app can provide concise, to-the-point feedback. In this way, the app can support more effective interview practice by adjusting the feedback content according to the user's emotions.

[0109] The interview preparation app analyzes the content of the user's application form and resume, and the analysis department can propose career paths based on that data. For example, the analysis department can analyze the user's work history and skill set and propose a career path they should aim for in the future. For example, the analysis department can match the user's current skills with market trends and suggest fields and job types with growth potential. Furthermore, the analysis department can specifically show the steps to acquire the necessary skills and experience based on the user's career goals. In this way, the analysis department can provide concrete advice to support the user's career growth.

[0110] An interview preparation app can estimate the user's emotions and adjust the interview preparation process based on those emotions. For example, if the user is nervous, the app can provide a relaxing interface and music. If the user is focused, the app can proceed with the preparation continuously for efficiency. Furthermore, if the user is tired, the app can incorporate breaks. In this way, the app can provide more effective interview preparation by adjusting the process according to the user's emotions.

[0111] The interview preparation app analyzes the content of the user's application form and resume, and the analytics department can perform industry analysis based on that data. For example, the analytics department collects and analyzes information on trends and competitors in the industry the user is interested in. For example, the analytics department can identify which industry has the highest demand for the user's skill set and propose interview preparation tailored to that industry. The analytics department can also provide the latest trends and news in the industry the user is interested in, which can be used to create topics for conversation during interviews. In this way, the analytics department can support more effective interview preparation by providing information tailored to the user's desired industry.

[0112] The interview preparation app can estimate the user's emotions and adjust the timing of interview preparation feedback based on those emotions. For example, if the user is nervous, the app can delay providing feedback slightly to give the user time to relax. If the user is focused, the app can provide immediate feedback to facilitate real-time improvement. Furthermore, if the user is tired, the app can provide feedback in segments, allowing for breaks as the process progresses. In this way, the app can support more effective interview preparation by adjusting the timing of feedback according to the user's emotions.

[0113] The interview preparation app analyzes the content of the user's application form and resume, and the analysis department can perform competitive analysis based on that data. For example, the analysis department can collect and analyze information on competitors of the company the user is applying to. The analysis department can identify the skills and experience that competitors are looking for and use that to evaluate the user's strengths and weaknesses. Furthermore, the analysis department can refer to the interview preparation strategies of competitors and suggest more effective strategies to the user. In this way, the analysis department can support users in making a stronger appeal to their target companies by conducting analysis based on information on competitors.

[0114] An interview preparation app can estimate the user's emotions and customize the interview preparation content based on those emotions. For example, if the user is nervous, the generator can provide advice and practice methods to help them relax. If the user is confident, the generator can suggest strategies for more advanced questions. Furthermore, if the user is tired, the generator can provide effective strategies in a short amount of time. In this way, the generator can provide more effective interview preparation by customizing the content according to the user's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk accepts the entry sheet and resume. Users can upload their entry sheet and resume. Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses AI to analyze the contents of application forms and resumes. Step 3: The generation unit generates interview preparation materials based on the information analyzed by the analysis unit. For example, the generation unit analyzes questions that a particular company has asked in the past and proposes preparation materials to the user based on that analysis. Step 4: The editing unit analyzes the content spoken during interview preparation, which was generated by the generation unit, and edits and updates the application form and resume. For example, the editing unit uses AI to analyze what the user said and revise the content of the application form and resume.

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, editing unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing users to upload entry sheets and resumes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the contents of entry sheets and resumes. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes questions previously asked by a specific company and proposes countermeasures to the user based on that analysis. The editing unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses AI to analyze what the user has said and corrects the contents of entry sheets and resumes. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically collects company information and past interview question examples from the internet. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 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.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, editing unit, and collection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input an entry sheet or resume by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the contents of the entry sheet or resume. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes questions previously asked by a specific company and proposes countermeasures to the user based on that analysis. The editing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which uses AI to analyze what the user has said and corrects the contents of the entry sheet or resume. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically collects company information and past interview question examples from the internet. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, editing unit, and collection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input an entry sheet or resume by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the contents of the entry sheet or resume. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes questions previously asked by a specific company and proposes countermeasures to the user based on that analysis. The editing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze what the user has said and corrects the contents of the entry sheet or resume. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically collects company information and past interview question examples from the internet. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, editing unit, and collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input an entry sheet or resume by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the contents of the entry sheet or resume. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes questions previously asked by a specific company and proposes countermeasures to the user based on that analysis. The editing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze what the user has said and corrects the contents of the entry sheet or resume. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically collects company information and past interview question examples from the internet. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0170] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A system characterized by comprising: a reception unit that receives input of entry sheets and work history documents; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates interview preparation materials based on the information analyzed by the analysis unit; and a revision unit that analyzes the content spoken during the interview preparation materials generated by the generation unit and revises and updates the entry sheets and work history documents. (Note 2) The system described in Appendix 1, characterized by having a collection unit for collecting company information and past question examples. (Note 3) The system described in Appendix 1 is characterized in that the generation unit analyzes questions previously asked by a specific company and proposes countermeasures to the user based on that analysis. (Note 4) The editing unit is the system described in Appendix 1, characterized by analyzing the content spoken during interview preparation and editing and updating the contents of the application form and resume. (Note 5) The system described in Appendix 1 is characterized in that the reception unit estimates the user's emotions and adjusts the timing of inputting the entry sheet and resume based on the estimated user emotions. (Note 6) The reception unit is characterized by analyzing the user's past input history and selecting the optimal input method, as described in Appendix 1. (Note 7) The system described in Appendix 1 is characterized in that the reception unit filters the user's current occupation and areas of interest when they input an entry sheet and a resume. (Note 8) The system according to Appendix 1, characterized in that the reception unit estimates the user's emotions and determines the priority of the information to be entered based on the estimated user's emotions. (Note 9) The reception unit is characterized in that, when inputting entry sheets and resumes, it prioritizes inputting highly relevant information, taking into account the user's geographical location information, as described in Appendix 1. (Note 10) The system described in Appendix 1 is characterized in that the reception unit analyzes the user's social media activity and inputs relevant information when the user enters their entry sheet and resume. (Note 11) The system according to Appendix 1, characterized in that the analysis unit estimates the user's emotions and adjusts the method of expressing the analysis based on the estimated user's emotions. (Note 12) The system described in Appendix 1 is characterized in that the analysis unit adjusts the level of detail of the analysis based on the importance of the entry sheet and the work history document during the analysis. (Note 13) The system described in Appendix 1 is characterized in that the analysis unit applies different analysis algorithms depending on the category of the entry sheet and the work history document during analysis. (Note 14) The system according to Appendix 1, characterized in that the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. (Note 15) The system described in Appendix 1 is characterized in that the analysis unit determines the priority of analysis based on the submission timing of the entry sheet and work history document during the analysis. (Note 16) The system described in Appendix 1 is characterized in that the analysis unit adjusts the order of analysis based on the relevance of the entry sheet and the work history document during the analysis. (Note 17) The system according to Appendix 1, characterized in that the generation unit estimates the user's emotions and adjusts the expression method of interview preparation based on the estimated user's emotions. (Note 18) The system according to Appendix 1, characterized in that the generation unit adjusts the level of detail of the preparations based on the importance of the company information when generating interview preparation materials. (Note 19) The system according to Appendix 1, characterized in that the generation unit applies different preparation algorithms depending on the company category when generating interview preparation materials. (Note 20) The system according to Appendix 1, characterized in that the generation unit estimates the user's emotions and adjusts the length of the interview preparation based on the estimated user's emotions. (Note 21) The system according to Appendix 1, characterized in that the generation unit determines the priority of interview preparations based on the timing of submission of company information when generating interview preparation materials. (Note 22) The system according to Appendix 1, characterized in that the generation unit adjusts the order of interview preparations based on the relevance of company information when generating interview preparation materials. (Note 23) The system according to Appendix 1, characterized in that the editing unit estimates the user's emotions and adjusts the editing method based on the estimated user's emotions. (Note 24) The system described in Appendix 1 is characterized in that the editing unit, when editing, analyzes the user's past interview preparation history and selects the optimal editing method. (Note 25) The system according to Appendix 1, characterized in that the editing unit customizes the editing method based on the user's current occupational status during editing. (Note 26) The system according to Appendix 1, characterized in that the editing unit estimates the user's emotions and determines the priority of editing based on the estimated user's emotions. (Note 27) The system described in Appendix 1, characterized in that the editing unit selects the optimal editing method while taking into account the user's geographical location information during editing. (Note 28) The system described in Appendix 1, wherein the editing unit analyzes the user's social media activity during editing and proposes editing methods. (Note 29) The system described in Appendix 2 is characterized in that the collection unit estimates the user's emotions and adjusts the method of collecting company information and past question examples based on the estimated user's emotions. (Note 30) The system described in Appendix 2 is characterized in that the collection unit adjusts the level of detail of the collection based on the importance of company information and past question examples during collection. (Note 31) The system described in Appendix 2 is characterized in that the collection unit applies different collection algorithms depending on the category of company information and past question examples during collection. (Note 32) The system according to Appendix 2, characterized in that the collection unit estimates the user's emotions and determines the priority of information to be collected based on the estimated user's emotions. (Note 33) The system described in Appendix 2 is characterized in that the collection unit determines the priority of collection based on the submission timing of company information and past question examples at the time of collection. (Note 34) The system described in Appendix 2 is characterized in that the collection unit adjusts the order of collection based on the relationship between company information and past question examples during collection. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area accepts the input of application forms and work history documents, An analysis unit that analyzes the information received by the reception unit, A generation unit generates interview preparation materials based on the information analyzed by the aforementioned analysis unit, The editing unit analyzes the content spoken during interview preparation, which is generated by the aforementioned generation unit, and edits and updates the entry sheet and work history document. A system characterized by comprising the following features.

2. The system according to claim 1, characterized in that it includes a collection unit for collecting company information and past question examples.

3. The system according to claim 1, characterized in that the generation unit analyzes questions previously asked by a specific company and proposes countermeasures to the user based on that analysis.

4. The system according to claim 1, characterized in that the editing unit analyzes the content spoken during interview preparation and edits and updates the contents of the application form and work history document.

5. The system according to claim 1, characterized in that the reception unit estimates the user's emotions and adjusts the timing of inputting the entry sheet and work history based on the estimated user's emotions.

6. The system according to claim 1, characterized in that the reception unit analyzes the user's past input history and selects the optimal input method.

7. The system according to claim 1, characterized in that the reception unit filters the user's current occupation and areas of interest when they input an entry sheet and a resume.

8. The system according to claim 1, characterized in that the reception unit estimates the user's emotions and determines the priority of the information to be entered based on the estimated user's emotions.

9. The system according to claim 1, characterized in that the reception unit prioritizes inputting highly relevant information, taking into account the user's geographical location information, when inputting an entry sheet and a resume.

10. The system according to claim 1, characterized in that the reception unit analyzes the user's social media activity and inputs relevant information when the user enters their entry sheet and resume.

Images