system

Abstract

The system according to this embodiment aims to efficiently match a user's strengths with information on companies they wish to work for, and to create application forms in bulk. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a batch creation unit. The reception unit receives information about the user's strengths and the companies they wish to work for. The analysis unit analyzes the information received by the reception unit and analyzes how the user's strengths match those of the companies they wish to work for. The generation unit generates text based on the analysis results obtained by the analysis unit. The batch creation unit creates application forms for multiple companies in a batch based on the text generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 efficiently match the strengths of a user and the information of a target company and create entry sheets in a batch.

[0005] The system according to the embodiment aims to efficiently match the strengths of a user and the information of a target company and create entry sheets in a batch.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a batch creation unit. The reception unit receives information about the user's strengths and desired companies. The analysis unit analyzes the information received by the reception unit and analyzes how the user's strengths match those of the desired companies. The generation unit generates text based on the analysis results obtained by the analysis unit. The batch creation unit creates application forms for multiple companies in a batch based on the text generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match a user's strengths with information on companies they wish to work for, and create application forms in bulk. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 document creation system according to an embodiment of the present invention is a system that streamlines the creation of entry sheets for students who are job hunting. The document creation system allows the user to input their own strengths and information about companies they wish to work for. A generating AI analyzes this information and determines how the user's strengths match those of the companies they wish to work for. Furthermore, the generating AI easily creates text based on the analysis results and creates entry sheets for multiple companies at once. For example, the document creation system allows the user to input their own strengths and information about companies they wish to work for. For example, the user inputs strengths such as "has leadership skills" or "is good at teamwork," and information about companies they wish to work for, such as "XX Corporation" or "△△ Company." This information is input into the generating AI. Next, the document creation system uses the generating AI to analyze the input information and determines how the user's strengths match those of the companies they wish to work for. The generating AI compares the skills and characteristics required by the companies with the user's strengths and determines how well they match. For example, if a company values ​​leadership, the AI ​​analyzes how the user's leadership experience would be useful. Furthermore, the document creation system uses the generating AI to easily create text based on the analysis results. The generation AI combines the user's strengths with the skills sought by the target company to generate application form text. For example, it might generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills sought by XX Corporation." Finally, the document creation system uses the generation AI to create application forms for multiple companies at once. The generation AI generates application forms that emphasize the user's strengths, tailored to the skills and characteristics sought by each company. This allows the user to create application forms for multiple companies with a single input. As a result, the document creation system significantly reduces the time that job-seeking students spend creating application forms. In addition, because the generation AI accurately analyzes the user's strengths and generates text that matches the target company, the quality of the application forms also improves. For example, if a user wants to highlight their leadership skills, the generation AI can concretely document that leadership experience and effectively appeal to the target company.This allows the document creation system to efficiently analyze the user's strengths and information about their target companies, enabling the creation of application forms in bulk.

[0029] The document creation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a batch creation unit. The reception unit receives information about the user's strengths and the companies they wish to work for. The user's strengths include, but are not limited to, leadership, teamwork, and problem-solving abilities. The information about the companies they wish to work for includes, but are not limited to, company name, industry, and required skills and characteristics. The reception unit accepts information entered by the user through an input form, for example. The reception unit can also accept information provided by the user using voice input or image input. For example, the user may describe their strengths verbally, and this can be converted into text and accepted. Alternatively, the user may upload a handwritten memo as an image, and the system can analyze it and accept the information. The analysis unit analyzes the information received by the reception unit and analyzes how the user's strengths match those of the companies they wish to work for. The analysis unit compares the skills and characteristics required by the companies with the user's strengths, for example, using a generation AI. For example, if the company values ​​leadership, the analysis unit analyzes how the user's leadership experience would be useful. Furthermore, the analysis unit can analyze how the user's teamwork experience will be evaluated if the target company values ​​teamwork. It can also analyze how the user's technical skills match the target company's requirements if the company values ​​technical skills. The generation unit generates text based on the analysis results obtained by the analysis unit. For example, using a generation AI, the generation unit can combine the user's strengths with the skills required by the target company to generate application form text. For instance, the generation unit might generate text such as, "I have experience demonstrating leadership and successfully guiding a team. This experience matches the leadership skills required by XX Corporation." The generation unit can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it could generate text such as, "I value teamwork and have experience successfully guiding projects. This experience matches the teamwork skills required by △△ Company."The batch creation unit creates application forms for multiple companies in bulk based on the text generated by the generation unit. The batch creation unit uses, for example, generation AI to generate application forms that emphasize the user's strengths according to the skills and characteristics required by each company. For example, the batch creation unit generates application forms that emphasize leadership experience for companies that value leadership, and application forms that emphasize teamwork experience for companies that value teamwork. The batch creation unit also enables users to create application forms for multiple companies with a single input. For example, the batch creation unit automatically generates customized application forms for multiple companies based on the information entered by the user. As a result, the document creation system according to this embodiment can efficiently analyze the user's strengths and information on target companies and create application forms in bulk.

[0030] The reception desk receives information about the user's strengths and desired companies. User strengths include, but are not limited to, leadership, teamwork, and problem-solving abilities. Desired company information includes, but is not limited to, company name, industry, and required skills and characteristics. The reception desk accepts information entered by the user through an input form. It can also accept information provided by the user using voice input or image input. For example, the reception desk can receive a voice description of the user's strengths, which is then converted into text and received. Alternatively, the reception desk can receive handwritten notes as images, which are then analyzed and received. Because the reception desk can accept information in various formats, it enhances user convenience. For example, in the case of voice input, speech recognition technology is used to convert the user's speech into text, and natural language processing technology is used to analyze its meaning. This allows the reception desk to accurately understand what the user has verbally explained and receive it as appropriate information. In the case of image input, image recognition technology is used to analyze handwritten characters and diagrams and convert them into text data. This allows the reception desk to accurately receive information provided by the user in handwriting. Furthermore, the reception department can centrally manage the information provided by users and collaborate with other departments as needed. For example, the reception department can store the information entered by users in a database, making it accessible to the analysis and generation departments. The reception department can also be equipped with functions to prompt users to confirm and correct their input in order to improve the accuracy and reliability of the information they provide. This allows the reception department to respond to the diverse needs of users and receive information efficiently and accurately.

[0031] The analysis department analyzes the information received by the reception department to determine how the user's strengths match those of the target company. For example, the analysis department uses generative AI to compare the user's strengths with the skills and characteristics required by the target company. For instance, if the target company values ​​leadership, the analysis department analyzes how the user's leadership experience would be useful. It can also analyze how the user's teamwork experience would be valued if the target company values ​​teamwork. Furthermore, if the target company values ​​technical skills, the analysis department can analyze how the user's technical skills match those requirements. The analysis department uses generative AI to compare the user's strengths with the skills and characteristics required by the target company in detail and evaluate the degree of matching. Specifically, the generative AI analyzes the information provided by the user using natural language processing technology and compares it with the skills and characteristics required by the target company. For example, if the user has leadership experience, it evaluates how well that experience matches the leadership requirements of the target company. Based on past data and statistical information, the generative AI can also predict how the user's strengths can contribute to the target company. Furthermore, the analytics unit can simultaneously evaluate how a user's strengths match those of multiple prospective employers. This allows the user to identify the most effective selling points for each company when applying to multiple businesses. The analytics unit can analyze the information provided by the user from multiple angles and find the optimal way to appeal to prospective employers. As a result, the analytics unit can provide information that maximizes the user's strengths and effectively appeals to prospective employers.

[0032] The generation unit generates text based on the analysis results obtained by the analysis unit. For example, the generation unit uses generation AI to combine the user's strengths with the skills required by the target company to generate application form text. For example, the generation unit can generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills required by XX Corporation." The generation unit can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it can generate text such as, "I value teamwork and have experience leading projects to success. This experience matches the teamwork skills required by △△ Company." The generation unit automatically generates text that combines the user's strengths with the skills required by the target company using generation AI. The generation AI uses natural language generation technology to generate appropriate text based on the information provided by the user. For example, if the user has leadership experience, it generates text that specifically describes that experience and emphasizes how it matches the leadership requirements of the target company. The generation AI can identify effective selling points based on past data and statistical information and reflect them in the text. Furthermore, the generation unit can generate customized text for multiple companies based on the information provided by the user. This allows users to create application forms for multiple companies with a single input. The generation unit can generate text that maximizes the user's strengths and effectively appeals to their target companies. In this way, the generation unit can efficiently and effectively support users in creating their application forms.

[0033] The batch creation unit creates application forms for multiple companies in bulk based on the text generated by the generation unit. The batch creation unit, for example, uses generation AI to generate application forms that emphasize the user's strengths in line with the skills and characteristics required by each company. For example, for companies that value leadership, the batch creation unit will generate application forms that emphasize leadership experience, and for companies that value teamwork, it will emphasize teamwork experience. Furthermore, the batch creation unit allows users to create application forms for multiple companies with a single input. For example, the batch creation unit automatically generates customized application forms for multiple companies based on the information entered by the user. This enables the document creation system according to the embodiment to efficiently analyze the user's strengths and information about target companies, and create application forms in bulk. The batch creation unit uses generation AI to automatically generate application forms tailored to the skills and characteristics required by each company, based on the information provided by the user. The generation AI uses natural language generation technology to emphasize the user's strengths and find the optimal selling points for each company. For example, for companies that value leadership, it generates text that specifically describes the user's leadership experience and emphasizes how that experience matches the skills required by the company. For companies that value teamwork, the system generates text that highlights the user's teamwork experience and explains how that experience contributes to the skills the company seeks. Furthermore, the batch creation section allows users to create application forms for multiple companies with a single input. This enables users to efficiently create application forms and apply to multiple companies. By automatically generating the most suitable application form for each company based on the information provided by the user, the batch creation section reduces the user's burden and streamlines the application process. In this way, the batch creation section can efficiently and effectively support users in creating their application forms.

[0034] The analysis unit can compare the skills and characteristics required by prospective employers with the user's strengths and determine how well they match. For example, the analysis unit can retrieve the skills and characteristics required by prospective employers from a database and compare them with the user's strengths. For instance, if a prospective employer values ​​leadership, the analysis unit can determine how the user's leadership experience would be useful. It can also determine how the user's teamwork experience would be valued if a prospective employer values ​​teamwork. Furthermore, if a prospective employer values ​​technical skills, the analysis unit can determine how the user's technical skills would match. This allows for a more appropriate match by comparing the skills and characteristics required by prospective employers with the user's strengths. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can determine a match using a generative AI model that takes the skills and characteristics required by prospective employers and the user's strengths as input and outputs matching results.

[0035] The generation unit can generate application form text by combining the user's strengths with the skills required by the target company. For example, the generation unit can use a generation AI to combine the user's strengths with the skills required by the target company to generate text. For example, the generation unit can generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills required by XX Corporation." The generation unit can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it can generate text such as, "I value teamwork and have experience leading projects to success. This experience matches the teamwork skills required by △△ Company." In this way, by combining the user's strengths with the skills required by the target company, effective application form text can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can generate text using a generation AI model that takes the user's strengths and the skills required by the target company as input and outputs application form text.

[0036] The batch creation unit can generate application forms that highlight the user's strengths, tailored to the skills and characteristics required by each company. For example, the batch creation unit uses a generation AI to generate application forms that match the skills and characteristics required by each company. For instance, for companies that value leadership, the batch creation unit will generate application forms that emphasize leadership experience, and for companies that value teamwork, it will emphasize teamwork experience. Furthermore, the batch creation unit can adjust the format, word choice, and order to highlight the user's strengths. For example, to emphasize leadership experience, the batch creation unit will place anecdotes related to leadership at the beginning and show specific results. Similarly, to emphasize teamwork experience, it will describe the user's role and contributions within a team in detail. This allows for the creation of optimal application forms for each company by generating application forms tailored to their specific skills and characteristics. Some or all of the above-described processes in the batch creation unit may be performed using, for example, a generation AI, or without one. For example, the batch creation unit can generate application forms using a generation AI model that takes the skills and characteristics required by each company as input and outputs application forms that highlight the user's strengths.

[0037] The batch creation section allows users to create application forms for multiple companies with a single input. For example, the batch creation section automatically generates customized application forms for multiple companies based on the information entered by the user, using a generation AI. For instance, the batch creation section generates application forms tailored to the skills and characteristics required by each company, based on the strengths and target companies entered by the user. The batch creation section can also provide input forms and templates to enable users to create application forms for multiple companies with a single input. For example, the batch creation section generates application forms for multiple companies based on the user's input into an input form. The batch creation section can also generate application forms based on a template selected by the user. This significantly reduces the user's effort by allowing them to create application forms for multiple companies with a single input. Some or all of the above-described processes in the batch creation section may be performed using, for example, a generation AI, or without a generation AI. For example, the batch creation section can generate application forms using a generation AI model that takes user-entered information as input and outputs customized application forms for multiple companies.

[0038] The generation unit can generate text by referring to past success stories. For example, the generation unit can use a generation AI to generate text by referring to past success stories. For example, the generation unit can retrieve examples of past successful application forms from a database and generate text based on them. The generation unit can also analyze past success stories and generate text using the most effective expression methods. For example, the generation unit can generate a user's application form by referring to the expression methods and structure of past successful application forms. The generation unit can also generate text that effectively highlights the user's strengths based on past success stories. For example, the generation unit can extract points that were particularly evaluated in past successful application forms and generate a user's application form based on them. In this way, by referring to past success stories, it is possible to generate more effective application form text. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can generate text using a generation AI model that takes past success stories as input and outputs application form text.

[0039] The generation unit can enable users to review and modify the generated entry sheet. For example, the generation unit can provide an interface that allows users to review and modify the entry sheet generated using a generation AI. For example, the generation unit can allow users to preview the generated entry sheet and modify it as needed. The generation unit can also provide editing functions for users to modify specific parts of the entry sheet. For example, the generation unit can provide a text editor that allows users to directly edit the text of the entry sheet. The generation unit can also provide customization options that allow users to change the structure and format of the entry sheet. For example, the generation unit can provide a template editor that allows users to change the layout and design of the entry sheet. This allows users to review and modify the generated entry sheet, ensuring that the final content matches the user's intentions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can provide an interface using a generation AI model that takes the generated entry sheet as input and outputs an interface that users can review and modify.

[0040] The reception desk can analyze a user's past input history and suggest the optimal input method. For example, the reception desk can use AI to analyze a user's past input history. For instance, the reception desk can retrieve information on strengths and desired companies that the user has previously entered from a database and suggest the optimal input method based on that information. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest strengths and desired companies to use at specific times based on the user's past input history. For example, based on information the user has entered at a specific time in the past, the reception desk can suggest the optimal input method for that time. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past input history as input and outputs the optimal input method.

[0041] The reception desk can customize input fields based on the user's current situation and areas of interest when receiving information. For example, the reception desk can use AI to analyze the user's current situation and areas of interest. For instance, if the user enters their current job search progress, the reception desk can suggest necessary input fields based on that information. The reception desk can also prioritize displaying fields related to relevant skills and experience based on the user's areas of interest. For example, if the reception desk is interested in a particular industry, it will automatically add input fields related to that industry. Furthermore, the reception desk can adjust the order and content of input fields based on the user's current situation and areas of interest. For example, if the reception desk enters the user's current occupation and educational background, it will prioritize displaying fields related to relevant skills and experience based on that information. This allows for the collection of more relevant information by customizing input fields based on the user's current situation 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 use an AI model that takes the user's current situation and areas of interest as input to customize the input fields and adjust them accordingly.

[0042] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception desk can analyze the user's geographical location using AI. For example, if the user lives in a specific region, the reception desk can prioritize displaying company information related to that region. Also, if the user is job hunting in a specific city, the reception desk can prioritize receiving job postings related to that city. For example, if the user is job hunting in a specific city, the reception desk can prioritize displaying job postings related to that city. Also, if the user is job hunting overseas, the reception desk can prioritize receiving information related to that country or region. For example, if the user is job hunting overseas, the reception desk can prioritize displaying company information related to that country or region. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can receive information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0043] The reception desk can analyze a user's social media activity and receive relevant information upon receiving it. For example, the reception desk can use AI to analyze a user's social media activity. For instance, it might prioritize receiving information about companies the user follows on social media. The reception desk can also receive relevant information based on the user's shared interests on social media. For example, it might prioritize displaying relevant company information based on the user's shared interests on social media. Furthermore, the reception desk can receive relevant information based on the groups and communities the user participates in on social media. For example, it might prioritize displaying relevant company information based on the groups and communities the user participates in on social media. This allows for the efficient reception of relevant information by analyzing the user's 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 receive information using an AI model that takes a user's social media activity as input and outputs relevant information.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the latest job postings of target companies during the analysis process. For example, the analysis unit can use AI to obtain the latest job postings of target companies. For example, the analysis unit can obtain the latest job postings from the target company's official website or job site and perform analysis based on that information. The analysis unit can also reflect the skills and characteristics listed in the target company's job postings in its analysis. For example, the analysis unit can register the skills and characteristics listed in the target company's job postings in a database and use that information to match the user's strengths. The analysis unit can also adjust the analysis results based on the latest information, taking into account the frequency of updates to the target company's job postings. For example, if the target company's job postings are frequently updated, the analysis unit can obtain that information in real time and reflect it in the analysis results. This improves the accuracy of the analysis by referring to the latest job postings of target companies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the latest job postings of target companies as input and outputs analysis results.

[0045] The analysis unit can perform analysis while considering the user's past application history. For example, the analysis unit can use AI to analyze the user's past application history. For example, the analysis unit can obtain information on companies the user has applied to in the past from a database and perform analysis based on that information. The analysis unit can also reflect patterns of successful and unsuccessful applications in the analysis based on the user's past application history. For example, the analysis unit can refer to application forms from successful applications in the past and perform analysis using similar expression methods. The analysis unit can also analyze the user's past application history and propose the optimal application strategy. For example, the analysis unit proposes the most effective application strategy based on the user's past application history. This allows for more appropriate analysis results by considering the user's past application history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the user's past application history as input and outputs analysis results.

[0046] The analysis unit can perform analysis while considering the geographical distribution of target companies. For example, the analysis unit can use AI to analyze the geographical distribution of target companies. For example, the analysis unit can adjust the analysis results considering the location of the target companies. The analysis unit can also analyze in which region a user's strengths are best valued based on the geographical distribution of target companies. For example, the analysis unit analyzes how a user's strengths are valued in a given region based on the location of the target companies. The analysis unit can also propose an application strategy for the user based on the geographical distribution of target companies. For example, the analysis unit proposes an optimal application strategy considering the location of the target companies. This allows for more appropriate analysis results by considering the geographical distribution of target companies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the geographical distribution of target companies as input and outputs analysis results.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the target company during the analysis process. For example, the analysis unit can use AI to obtain relevant literature on the target company. For example, the analysis unit can perform analysis by referring to the target company's official website and press releases. The analysis unit can also incorporate academic papers and industry reports related to the target company into its analysis. For example, the analysis unit can perform analysis based on information obtained from the target company's official website. The analysis unit can also analyze how the user's strengths are evaluated based on academic papers and industry reports related to the target company. This allows the accuracy of the analysis to be improved by referring to relevant literature on the target company. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes relevant literature on the target company as input and outputs analysis results.

[0048] The generation unit can generate text by referencing the user's past success stories. For example, the generation unit can use AI to obtain the user's past success stories. For example, the generation unit can retrieve the user's past successful application forms from a database and generate text based on them. The generation unit can also generate text using similar expression methods based on the user's past success stories. For example, the generation unit can generate text by referring to the expression methods and structure of the user's past successful application forms. The generation unit can also analyze the user's past success stories and generate text using the most effective expression methods. For example, the generation unit can extract the most effective expression methods based on the user's past success stories and generate text based on them. This allows for the generation of more effective text by referencing past success stories. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate text using an AI model that takes the user's past success stories as input and outputs text.

[0049] The generation unit can apply different generation algorithms to text generation depending on the skills required by the target company. For example, the generation unit can use AI to apply different generation algorithms depending on the skills required by the target company. For example, if the target company emphasizes leadership, the generation unit can apply an algorithm that generates text related to leadership. Also, if the target company emphasizes teamwork, the generation unit can apply an algorithm that generates text related to teamwork. Also, if the target company emphasizes technical skills, the generation unit can apply an algorithm that generates text related to technical skills. For example, the generation unit can select the optimal generation algorithm according to the skills required by the target company and generate text based on that. This makes it possible to generate text that is optimally suited to the skills required by the target company. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can apply a generation algorithm using an AI model that takes the skills required by the target company as input and outputs text.

[0050] The generation unit can prioritize documents based on the submission deadlines of the target companies when generating documents. For example, the generation unit can use AI to obtain the submission deadlines of the target companies. For example, the generation unit can obtain the submission deadlines from the target companies' official websites or job sites and use that to determine the priority of documents. The generation unit can also prioritize the generation of documents for a company if the submission deadline is approaching. For example, if the submission deadline is approaching, the generation unit will generate documents for that company with the highest priority. The generation unit can also generate documents at the optimal timing based on the submission deadlines of the target companies. For example, the generation unit can efficiently generate documents for multiple companies, taking into account the submission deadlines of the target companies. This allows for efficient creation of application forms by prioritizing documents based on the submission deadlines of the target companies. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the submission deadlines of the target companies as input and determine the priority using an AI model that determines the priority of documents.

[0051] The generation unit can adjust the order of sentences based on the relevance of the target companies when generating text. For example, the generation unit can use AI to analyze the relevance of the target companies. For example, the generation unit can adjust the order of sentences based on the importance of the target companies. The generation unit can also place the most important information first based on the relevance of the target companies. For example, the generation unit can optimize the order of sentences based on the skills and characteristics required by the target companies. The generation unit can also apply algorithms to adjust the order of sentences based on the relevance of the target companies. For example, the generation unit generates sentences in the optimal order based on the relevance of the target companies. This allows for the creation of more effective application forms by adjusting the order of sentences based on the relevance of the target companies. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the relevance of the target companies as input and adjust the order using an AI model that adjusts the order of sentences.

[0052] The batch creation unit can analyze the user's past application history and select the optimal batch creation method during batch creation. For example, the batch creation unit can use AI to analyze the user's past application history. For instance, the batch creation unit can retrieve information on companies the user has applied to in the past from a database and perform batch creation based on that information. The batch creation unit can also select the most effective batch creation method based on the user's past application history. For example, the batch creation unit can perform batch creation by referring to application forms from successful past applications. Furthermore, the batch creation unit can analyze the user's past application history and propose the optimal batch creation strategy. For example, the batch creation unit proposes the most effective batch creation strategy based on the user's past application history. This allows the optimal batch creation method to be selected by analyzing the user's past application history. Some or all of the above processes in the batch creation unit may be performed using AI, or not. For example, the batch creation unit can use an AI model that selects a batch creation method based on the user's past application history as input.

[0053] The batch creation unit can improve the accuracy of batch creation by referring to the latest job postings of target companies during the batch creation process. For example, the batch creation unit can use AI to obtain the latest job postings of target companies. For example, the batch creation unit can obtain the latest job postings from the official websites or job sites of target companies and perform batch creation based on that information. The batch creation unit can also reflect the skills and characteristics listed in the job postings of target companies in the batch creation. For example, the batch creation unit can register the skills and characteristics listed in the job postings of target companies in a database and use that information to match the user's strengths. The batch creation unit can also adjust the batch creation results based on the latest information, taking into account the update frequency of the job postings of target companies. For example, if the job postings of target companies are frequently updated, the batch creation unit can obtain that information in real time and reflect it in the batch creation results. This improves the accuracy of batch creation by referring to the latest job postings of target companies. Some or all of the above processes in the batch creation unit may be performed using AI, for example, or without using AI. For example, the batch creation unit can perform batch creation using an AI model that takes the latest job postings from target companies as input and outputs the batch creation results.

[0054] The batch creation unit can select the optimal batch creation method by considering the geographical distribution of the target companies during batch creation. For example, the batch creation unit can use AI to analyze the geographical distribution of the target companies. For example, the batch creation unit can adjust the batch creation results by considering the location of the target companies. The batch creation unit can also consider in which region the user's strengths will be best valued based on the geographical distribution of the target companies. For example, the batch creation unit considers how the user's strengths will be valued in that region based on the location of the target companies. The batch creation unit can also propose an application strategy for the user based on the geographical distribution of the target companies. For example, the batch creation unit proposes an optimal application strategy by considering the location of the target companies. In this way, the optimal batch creation method can be selected by considering the geographical distribution of the target companies. Some or all of the above processing in the batch creation unit may be performed using AI, for example, or without AI. For example, the batch creation unit can select a method using an AI model that takes the geographical distribution of the target companies as input and selects a batch creation method.

[0055] The batch creation unit can improve the accuracy of batch creation by referring to relevant literature on the target company during the batch creation process. For example, the batch creation unit can use AI to obtain relevant literature on the target company. For example, the batch creation unit can perform batch creation by referring to the target company's official website and press releases. The batch creation unit can also incorporate academic papers and industry reports related to the target company into the batch creation process. For example, the batch creation unit can perform batch creation based on information obtained from the target company's official website. The batch creation unit can also consider how the user's strengths will be evaluated based on academic papers and industry reports related to the target company. This improves the accuracy of batch creation by referring to relevant literature on the target company. Some or all of the above processing in the batch creation unit may be performed using AI, for example, or without AI. For example, the batch creation unit can perform batch creation using an AI model that takes relevant literature on the target company as input and outputs the batch creation results.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The document creation system may also include a success story analysis unit that analyzes successful past application forms submitted by the user. The success story analysis unit retrieves particularly well-received application forms submitted by the user in the past from a database and analyzes their characteristics. For example, the success story analysis unit extracts the expression methods and structure of successful application forms from the past and generates a new application form based on them. The success story analysis unit can also suggest ways to effectively highlight the user's strengths based on past success stories. For example, the success story analysis unit extracts points that were particularly well-received in successful application forms from the past and generates the user's application form based on them. This allows for the generation of more effective application form text by referring to past success stories. Some or all of the above processing in the success story analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the success story analysis unit can generate text using a generative AI model that takes past success stories as input and outputs application form text.

[0058] The document creation system may also include an application history analysis unit that analyzes the user's past application history and proposes the optimal method for creating an application form. The application history analysis unit retrieves information on companies the user has applied to in the past from a database and performs analysis based on that information. The application history analysis unit can also incorporate patterns of successful and unsuccessful applications from the user's past application history into its analysis. For example, the application history analysis unit can refer to application forms from successful applications in the past and perform analysis using similar presentation methods. The application history analysis unit can also analyze the user's past application history and propose the optimal application strategy. For example, the application history analysis unit proposes the most effective application strategy based on the user's past application history. This allows for more appropriate analysis results by considering the user's past application history. Some or all of the above processing in the application history analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the application history analysis unit can perform analysis using a generative AI model that takes the user's past application history as input and outputs analysis results.

[0059] The document creation system may further include a geographic information unit that customizes the application form by taking into account the user's geographic location. The geographic information unit, for example, acquires the user's geographic location and adjusts the content of the application form based on it. For example, if the user lives in a specific region, the geographic information unit may prioritize displaying company information related to that region. The geographic information unit may also prioritize receiving job postings related to a specific city if the user is job hunting in that city. For example, if the geographic information unit is job hunting in a specific city, it may prioritize displaying job postings related to that city. The geographic information unit may also prioritize receiving information related to a country or region if the user is job hunting overseas. For example, if the geographic information unit is job hunting overseas, it may prioritize displaying company information related to that country or region. In this way, by taking into account the user's geographic location, highly relevant information can be prioritized. Some or all of the above processing in the geographic information unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the geographic information unit can receive information using a generative AI model that takes the user's geographic location information as input and outputs highly relevant information.

[0060] The document creation system may further include a social media analysis unit that analyzes the user's social media activity and receives relevant information. The social media analysis unit, for example, analyzes the user's social media activity. For example, the social media analysis unit prioritizes receiving information about companies that the user follows on social media. The social media analysis unit can also receive relevant information based on the interests and concerns that the user has shared on social media. For example, the social media analysis unit prioritizes displaying relevant company information based on the interests and concerns that the user has shared on social media. The social media analysis unit can also receive relevant information based on the groups and communities that the user participates in on social media. For example, the social media analysis unit prioritizes displaying relevant company information based on the groups and communities that the user participates in on social media. This allows for the efficient reception of relevant information by analyzing the user's social media activity. Some or all of the above processing in the social media analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the social media analysis unit can receive information using a generative AI model that takes the user's social media activity as input and outputs relevant information.

[0061] The document creation system may also include an input history analysis unit that analyzes the user's past input history and proposes the optimal input method. The input history analysis unit, for example, analyzes the user's past input history. For example, the input history analysis unit retrieves information on strengths and desired companies that the user has entered in the past from a database and proposes the optimal input method based on that information. The input history analysis unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the input history analysis unit has used voice input in the past, it will prioritize suggesting voice input. Furthermore, the input history analysis unit can predict and propose strengths and desired companies to be used at specific time periods based on the user's past input history. For example, based on information the user has entered at specific time periods in the past, the input history analysis unit will propose the optimal input method for that time period. In this way, by analyzing past input history, the system can propose the optimal input method for the user. Some or all of the above processing in the input history analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input history analysis unit can take the user's past input history as input and propose an optimal input method using a generative AI model that outputs the optimal input method.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives information about the user's strengths and desired companies. User strengths include leadership, teamwork, and problem-solving skills, while desired company information includes company name, industry, and required skills and characteristics. The reception desk accepts information from users through input forms, as well as through voice and image input. For example, a user can describe their strengths verbally, and this can be converted into text for reception. Alternatively, a user can upload handwritten notes as images, which can then be analyzed and accepted. Step 2: The analysis department analyzes the information received by the reception department and analyzes how the user's strengths match those of the target company. The analysis department uses generative AI to compare the skills and characteristics required by the target company with the user's strengths. For example, if the target company values ​​leadership, it analyzes how the user's leadership experience would be useful. It can also analyze how the user's teamwork experience would be valued if the target company values ​​teamwork. Furthermore, if the target company values ​​technical skills, it can analyze how the user's technical skills match those requirements. Step 3: The generation unit generates text based on the analysis results obtained by the analysis unit. The generation unit uses generation AI to combine the user's strengths with the skills required by the target company to generate the text for the application form. For example, it can generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills required by XX Corporation." It can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it can generate text such as, "I value teamwork and have experience leading projects to success. This experience matches the teamwork skills required by △△ Company." Step 4: The batch creation unit creates application forms for multiple companies in bulk based on the text generated by the generation unit. The batch creation unit uses generation AI to create application forms that highlight the user's strengths according to the skills and characteristics required by each company. For example, it will generate application forms that emphasize leadership experience for companies that value leadership, and application forms that emphasize teamwork experience for companies that value teamwork. It also allows users to create application forms for multiple companies with a single input. This enables users to create application forms efficiently.

[0064] (Example of form 2) The document creation system according to an embodiment of the present invention is a system that streamlines the creation of entry sheets for students who are job hunting. The document creation system allows the user to input their own strengths and information about companies they wish to work for. A generating AI analyzes this information and determines how the user's strengths match those of the companies they wish to work for. Furthermore, the generating AI easily creates text based on the analysis results and creates entry sheets for multiple companies at once. For example, the document creation system allows the user to input their own strengths and information about companies they wish to work for. For example, the user inputs strengths such as "has leadership skills" or "is good at teamwork," and information about companies they wish to work for, such as "XX Corporation" or "△△ Company." This information is input into the generating AI. Next, the document creation system uses the generating AI to analyze the input information and determines how the user's strengths match those of the companies they wish to work for. The generating AI compares the skills and characteristics required by the companies with the user's strengths and determines how well they match. For example, if a company values ​​leadership, the AI ​​analyzes how the user's leadership experience would be useful. Furthermore, the document creation system uses the generating AI to easily create text based on the analysis results. The generation AI combines the user's strengths with the skills sought by the target company to generate application form text. For example, it might generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills sought by XX Corporation." Finally, the document creation system uses the generation AI to create application forms for multiple companies at once. The generation AI generates application forms that emphasize the user's strengths, tailored to the skills and characteristics sought by each company. This allows the user to create application forms for multiple companies with a single input. As a result, the document creation system significantly reduces the time that job-seeking students spend creating application forms. In addition, because the generation AI accurately analyzes the user's strengths and generates text that matches the target company, the quality of the application forms also improves. For example, if a user wants to highlight their leadership skills, the generation AI can concretely document that leadership experience and effectively appeal to the target company.This allows the document creation system to efficiently analyze the user's strengths and information about their target companies, enabling the creation of application forms in bulk.

[0065] The document creation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a batch creation unit. The reception unit receives information about the user's strengths and the companies they wish to work for. The user's strengths include, but are not limited to, leadership, teamwork, and problem-solving abilities. The information about the companies they wish to work for includes, but are not limited to, company name, industry, and required skills and characteristics. The reception unit accepts information entered by the user through an input form, for example. The reception unit can also accept information provided by the user using voice input or image input. For example, the user may describe their strengths verbally, and this can be converted into text and accepted. Alternatively, the user may upload a handwritten memo as an image, and the system can analyze it and accept the information. The analysis unit analyzes the information received by the reception unit and analyzes how the user's strengths match those of the companies they wish to work for. The analysis unit compares the skills and characteristics required by the companies with the user's strengths, for example, using a generation AI. For example, if the company values ​​leadership, the analysis unit analyzes how the user's leadership experience would be useful. Furthermore, the analysis unit can analyze how the user's teamwork experience will be evaluated if the target company values ​​teamwork. It can also analyze how the user's technical skills match the target company's requirements if the company values ​​technical skills. The generation unit generates text based on the analysis results obtained by the analysis unit. For example, using a generation AI, the generation unit can combine the user's strengths with the skills required by the target company to generate application form text. For instance, the generation unit might generate text such as, "I have experience demonstrating leadership and successfully guiding a team. This experience matches the leadership skills required by XX Corporation." The generation unit can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it could generate text such as, "I value teamwork and have experience successfully guiding projects. This experience matches the teamwork skills required by △△ Company."The batch creation unit creates application forms for multiple companies in bulk based on the text generated by the generation unit. The batch creation unit uses, for example, generation AI to generate application forms that emphasize the user's strengths according to the skills and characteristics required by each company. For example, the batch creation unit generates application forms that emphasize leadership experience for companies that value leadership, and application forms that emphasize teamwork experience for companies that value teamwork. The batch creation unit also enables users to create application forms for multiple companies with a single input. For example, the batch creation unit automatically generates customized application forms for multiple companies based on the information entered by the user. As a result, the document creation system according to this embodiment can efficiently analyze the user's strengths and information on target companies and create application forms in bulk.

[0066] The reception desk receives information about the user's strengths and desired companies. User strengths include, but are not limited to, leadership, teamwork, and problem-solving abilities. Desired company information includes, but is not limited to, company name, industry, and required skills and characteristics. The reception desk accepts information entered by the user through an input form. It can also accept information provided by the user using voice input or image input. For example, the reception desk can receive a voice description of the user's strengths, which is then converted into text and received. Alternatively, the reception desk can receive handwritten notes as images, which are then analyzed and received. Because the reception desk can accept information in various formats, it enhances user convenience. For example, in the case of voice input, speech recognition technology is used to convert the user's speech into text, and natural language processing technology is used to analyze its meaning. This allows the reception desk to accurately understand what the user has verbally explained and receive it as appropriate information. In the case of image input, image recognition technology is used to analyze handwritten characters and diagrams and convert them into text data. This allows the reception desk to accurately receive information provided by the user in handwriting. Furthermore, the reception department can centrally manage the information provided by users and collaborate with other departments as needed. For example, the reception department can store the information entered by users in a database, making it accessible to the analysis and generation departments. The reception department can also be equipped with functions to prompt users to confirm and correct their input in order to improve the accuracy and reliability of the information they provide. This allows the reception department to respond to the diverse needs of users and receive information efficiently and accurately.

[0067] The analysis department analyzes the information received by the reception department to determine how the user's strengths match those of the target company. For example, the analysis department uses generative AI to compare the user's strengths with the skills and characteristics required by the target company. For instance, if the target company values ​​leadership, the analysis department analyzes how the user's leadership experience would be useful. It can also analyze how the user's teamwork experience would be valued if the target company values ​​teamwork. Furthermore, if the target company values ​​technical skills, the analysis department can analyze how the user's technical skills match those requirements. The analysis department uses generative AI to compare the user's strengths with the skills and characteristics required by the target company in detail and evaluate the degree of matching. Specifically, the generative AI analyzes the information provided by the user using natural language processing technology and compares it with the skills and characteristics required by the target company. For example, if the user has leadership experience, it evaluates how well that experience matches the leadership requirements of the target company. Based on past data and statistical information, the generative AI can also predict how the user's strengths can contribute to the target company. Furthermore, the analytics unit can simultaneously evaluate how a user's strengths match those of multiple prospective employers. This allows the user to identify the most effective selling points for each company when applying to multiple businesses. The analytics unit can analyze the information provided by the user from multiple angles and find the optimal way to appeal to prospective employers. As a result, the analytics unit can provide information that maximizes the user's strengths and effectively appeals to prospective employers.

[0068] The generation unit generates text based on the analysis results obtained by the analysis unit. For example, the generation unit uses generation AI to combine the user's strengths with the skills required by the target company to generate application form text. For example, the generation unit can generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills required by XX Corporation." The generation unit can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it can generate text such as, "I value teamwork and have experience leading projects to success. This experience matches the teamwork skills required by △△ Company." The generation unit automatically generates text that combines the user's strengths with the skills required by the target company using generation AI. The generation AI uses natural language generation technology to generate appropriate text based on the information provided by the user. For example, if the user has leadership experience, it generates text that specifically describes that experience and emphasizes how it matches the leadership requirements of the target company. The generation AI can identify effective selling points based on past data and statistical information and reflect them in the text. Furthermore, the generation unit can generate customized text for multiple companies based on the information provided by the user. This allows users to create application forms for multiple companies with a single input. The generation unit can generate text that maximizes the user's strengths and effectively appeals to their target companies. In this way, the generation unit can efficiently and effectively support users in creating their application forms.

[0069] The batch creation unit creates application forms for multiple companies in bulk based on the text generated by the generation unit. The batch creation unit, for example, uses generation AI to generate application forms that emphasize the user's strengths in line with the skills and characteristics required by each company. For example, for companies that value leadership, the batch creation unit will generate application forms that emphasize leadership experience, and for companies that value teamwork, it will emphasize teamwork experience. Furthermore, the batch creation unit allows users to create application forms for multiple companies with a single input. For example, the batch creation unit automatically generates customized application forms for multiple companies based on the information entered by the user. This enables the document creation system according to the embodiment to efficiently analyze the user's strengths and information about target companies, and create application forms in bulk. The batch creation unit uses generation AI to automatically generate application forms tailored to the skills and characteristics required by each company, based on the information provided by the user. The generation AI uses natural language generation technology to emphasize the user's strengths and find the optimal selling points for each company. For example, for companies that value leadership, it generates text that specifically describes the user's leadership experience and emphasizes how that experience matches the skills required by the company. For companies that value teamwork, the system generates text that highlights the user's teamwork experience and explains how that experience contributes to the skills the company seeks. Furthermore, the batch creation section allows users to create application forms for multiple companies with a single input. This enables users to efficiently create application forms and apply to multiple companies. By automatically generating the most suitable application form for each company based on the information provided by the user, the batch creation section reduces the user's burden and streamlines the application process. In this way, the batch creation section can efficiently and effectively support users in creating their application forms.

[0070] The analysis unit can compare the skills and characteristics required by prospective employers with the user's strengths and determine how well they match. For example, the analysis unit can retrieve the skills and characteristics required by prospective employers from a database and compare them with the user's strengths. For instance, if a prospective employer values ​​leadership, the analysis unit can determine how the user's leadership experience would be useful. It can also determine how the user's teamwork experience would be valued if a prospective employer values ​​teamwork. Furthermore, if a prospective employer values ​​technical skills, the analysis unit can determine how the user's technical skills would match. This allows for a more appropriate match by comparing the skills and characteristics required by prospective employers with the user's strengths. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can determine a match using a generative AI model that takes the skills and characteristics required by prospective employers and the user's strengths as input and outputs matching results.

[0071] The generation unit can generate application form text by combining the user's strengths with the skills required by the target company. For example, the generation unit can use a generation AI to combine the user's strengths with the skills required by the target company to generate text. For example, the generation unit can generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills required by XX Corporation." The generation unit can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it can generate text such as, "I value teamwork and have experience leading projects to success. This experience matches the teamwork skills required by △△ Company." In this way, by combining the user's strengths with the skills required by the target company, effective application form text can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can generate text using a generation AI model that takes the user's strengths and the skills required by the target company as input and outputs application form text.

[0072] The batch creation unit can generate application forms that highlight the user's strengths, tailored to the skills and characteristics required by each company. For example, the batch creation unit uses a generation AI to generate application forms that match the skills and characteristics required by each company. For instance, for companies that value leadership, the batch creation unit will generate application forms that emphasize leadership experience, and for companies that value teamwork, it will emphasize teamwork experience. Furthermore, the batch creation unit can adjust the format, word choice, and order to highlight the user's strengths. For example, to emphasize leadership experience, the batch creation unit will place anecdotes related to leadership at the beginning and show specific results. Similarly, to emphasize teamwork experience, it will describe the user's role and contributions within a team in detail. This allows for the creation of optimal application forms for each company by generating application forms tailored to their specific skills and characteristics. Some or all of the above-described processes in the batch creation unit may be performed using, for example, a generation AI, or without one. For example, the batch creation unit can generate application forms using a generation AI model that takes the skills and characteristics required by each company as input and outputs application forms that highlight the user's strengths.

[0073] The batch creation section allows users to create application forms for multiple companies with a single input. For example, the batch creation section automatically generates customized application forms for multiple companies based on the information entered by the user, using a generation AI. For instance, the batch creation section generates application forms tailored to the skills and characteristics required by each company, based on the strengths and target companies entered by the user. The batch creation section can also provide input forms and templates to enable users to create application forms for multiple companies with a single input. For example, the batch creation section generates application forms for multiple companies based on the user's input into an input form. The batch creation section can also generate application forms based on a template selected by the user. This significantly reduces the user's effort by allowing them to create application forms for multiple companies with a single input. Some or all of the above-described processes in the batch creation section may be performed using, for example, a generation AI, or without a generation AI. For example, the batch creation section can generate application forms using a generation AI model that takes user-entered information as input and outputs customized application forms for multiple companies.

[0074] The generation unit can generate text by referring to past success stories. For example, the generation unit can use a generation AI to generate text by referring to past success stories. For example, the generation unit can retrieve examples of past successful application forms from a database and generate text based on them. The generation unit can also analyze past success stories and generate text using the most effective expression methods. For example, the generation unit can generate a user's application form by referring to the expression methods and structure of past successful application forms. The generation unit can also generate text that effectively highlights the user's strengths based on past success stories. For example, the generation unit can extract points that were particularly evaluated in past successful application forms and generate a user's application form based on them. In this way, by referring to past success stories, it is possible to generate more effective application form text. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can generate text using a generation AI model that takes past success stories as input and outputs application form text.

[0075] The generation unit can enable users to review and modify the generated entry sheet. For example, the generation unit can provide an interface that allows users to review and modify the entry sheet generated using a generation AI. For example, the generation unit can allow users to preview the generated entry sheet and modify it as needed. The generation unit can also provide editing functions for users to modify specific parts of the entry sheet. For example, the generation unit can provide a text editor that allows users to directly edit the text of the entry sheet. The generation unit can also provide customization options that allow users to change the structure and format of the entry sheet. For example, the generation unit can provide a template editor that allows users to change the layout and design of the entry sheet. This allows users to review and modify the generated entry sheet, ensuring that the final content matches the user's intentions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can provide an interface using a generation AI model that takes the generated entry sheet as input and outputs an interface that users can review and modify.

[0076] The reception desk can estimate the user's emotions and adjust the information input interface based on the estimated emotions. For example, the reception desk might use an emotion engine or generative AI to estimate the user's emotions. For instance, it could capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it could record the user's voice and estimate their emotions using voice analysis technology. For example, it could analyze the tone and speed of the voice to calculate an emotion score. It could also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it could calculate an emotion score based on heart rate variability. The reception desk adjusts the information input interface based on the estimated user emotions. For example, if the user is tense, it might provide a simple and intuitive interface, minimizing the input steps. If the user is relaxed, it might offer detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it might prioritize voice input to allow for quick information entry. This allows users to input information more comfortably by adjusting the input interface according to their 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 unit may be performed using AI, for example, or not using AI. For example, the reception unit can take user emotion data as input and adjust the interface using an AI model that adjusts the input interface.

[0077] The reception desk can analyze a user's past input history and suggest the optimal input method. For example, the reception desk can use AI to analyze a user's past input history. For instance, the reception desk can retrieve information on strengths and desired companies that the user has previously entered from a database and suggest the optimal input method based on that information. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest strengths and desired companies to use at specific times based on the user's past input history. For example, based on information the user has entered at a specific time in the past, the reception desk can suggest the optimal input method for that time. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past input history as input and outputs the optimal input method.

[0078] The reception desk can customize input fields based on the user's current situation and areas of interest when receiving information. For example, the reception desk can use AI to analyze the user's current situation and areas of interest. For instance, if the user enters their current job search progress, the reception desk can suggest necessary input fields based on that information. The reception desk can also prioritize displaying fields related to relevant skills and experience based on the user's areas of interest. For example, if the reception desk is interested in a particular industry, it will automatically add input fields related to that industry. Furthermore, the reception desk can adjust the order and content of input fields based on the user's current situation and areas of interest. For example, if the reception desk enters the user's current occupation and educational background, it will prioritize displaying fields related to relevant skills and experience based on that information. This allows for the collection of more relevant information by customizing input fields based on the user's current situation 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 use an AI model that takes the user's current situation and areas of interest as input to customize the input fields and adjust them accordingly.

[0079] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. The reception desk can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the reception desk can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. Alternatively, the reception desk can record the user's voice and estimate emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the voice and calculate an emotion score. The reception desk can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on heart rate fluctuations. The reception desk determines the priority of inputs based on the estimated emotions of the user. For example, if the user is stressed, the reception desk will prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk can sequentially display detailed input items, allowing the user to input freely. Furthermore, the reception desk can display only the most important input fields when the user is in a hurry, allowing for quick completion of the input. This enables more efficient information input by prioritizing inputs 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, for example, or not using AI. For example, the reception desk can take the user's emotion data as input and determine the priority of inputs using an AI model that determines the priority of inputs.

[0080] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception desk can analyze the user's geographical location using AI. For example, if the user lives in a specific region, the reception desk can prioritize displaying company information related to that region. Also, if the user is job hunting in a specific city, the reception desk can prioritize receiving job postings related to that city. For example, if the user is job hunting in a specific city, the reception desk can prioritize displaying job postings related to that city. Also, if the user is job hunting overseas, the reception desk can prioritize receiving information related to that country or region. For example, if the user is job hunting overseas, the reception desk can prioritize displaying company information related to that country or region. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can receive information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0081] The reception desk can analyze a user's social media activity and receive relevant information upon receiving it. For example, the reception desk can use AI to analyze a user's social media activity. For instance, it might prioritize receiving information about companies the user follows on social media. The reception desk can also receive relevant information based on the user's shared interests on social media. For example, it might prioritize displaying relevant company information based on the user's shared interests on social media. Furthermore, the reception desk can receive relevant information based on the groups and communities the user participates in on social media. For example, it might prioritize displaying relevant company information based on the groups and communities the user participates in on social media. This allows for the efficient reception of relevant information by analyzing the user's 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 receive information using an AI model that takes a user's social media activity as input and outputs relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. The analysis unit adjusts the analysis criteria based on the estimated emotions of the user. For example, if the user is tense, the analysis unit can relax the analysis criteria and emphasize the user's strengths. If the user is relaxed, the analysis unit can perform a detailed analysis and precisely match the user's strengths with the companies they wish to work for. Furthermore, the analysis unit can perform a rapid analysis if the user is in a hurry, prioritizing the matching of key strengths with target companies. This allows for more appropriate analysis results by adjusting the analysis criteria 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-described processing in the analysis unit may be performed using generative AI, for example, or without generative AI. For example, the analysis unit can take the user's emotion data as input and adjust the criteria using a generative AI model that adjusts the analysis criteria.

[0083] The analysis unit can improve the accuracy of its analysis by referring to the latest job postings of target companies during the analysis process. For example, the analysis unit can use AI to obtain the latest job postings of target companies. For example, the analysis unit can obtain the latest job postings from the target company's official website or job site and perform analysis based on that information. The analysis unit can also reflect the skills and characteristics listed in the target company's job postings in its analysis. For example, the analysis unit can register the skills and characteristics listed in the target company's job postings in a database and use that information to match the user's strengths. The analysis unit can also adjust the analysis results based on the latest information, taking into account the frequency of updates to the target company's job postings. For example, if the target company's job postings are frequently updated, the analysis unit can obtain that information in real time and reflect it in the analysis results. This improves the accuracy of the analysis by referring to the latest job postings of target companies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the latest job postings of target companies as input and outputs analysis results.

[0084] The analysis unit can perform analysis while considering the user's past application history. For example, the analysis unit can use AI to analyze the user's past application history. For example, the analysis unit can obtain information on companies the user has applied to in the past from a database and perform analysis based on that information. The analysis unit can also reflect patterns of successful and unsuccessful applications in the analysis based on the user's past application history. For example, the analysis unit can refer to application forms from successful applications in the past and perform analysis using similar expression methods. The analysis unit can also analyze the user's past application history and propose the optimal application strategy. For example, the analysis unit proposes the most effective application strategy based on the user's past application history. This allows for more appropriate analysis results by considering the user's past application history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the user's past application history as input and outputs analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. The analysis unit adjusts the display method of the analysis results based on the estimated emotions of the user. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows the user to view the results in a more easily understandable format by adjusting how the analysis results are displayed 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can take user emotion data as input and adjust the display method using a generative AI model that adjusts how the analysis results are displayed.

[0086] The analysis unit can perform analysis while considering the geographical distribution of target companies. For example, the analysis unit can use AI to analyze the geographical distribution of target companies. For example, the analysis unit can adjust the analysis results considering the location of the target companies. The analysis unit can also analyze in which region a user's strengths are best valued based on the geographical distribution of target companies. For example, the analysis unit analyzes how a user's strengths are valued in a given region based on the location of the target companies. The analysis unit can also propose an application strategy for the user based on the geographical distribution of target companies. For example, the analysis unit proposes an optimal application strategy considering the location of the target companies. This allows for more appropriate analysis results by considering the geographical distribution of target companies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the geographical distribution of target companies as input and outputs analysis results.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the target company during the analysis process. For example, the analysis unit can use AI to obtain relevant literature on the target company. For example, the analysis unit can perform analysis by referring to the target company's official website and press releases. The analysis unit can also incorporate academic papers and industry reports related to the target company into its analysis. For example, the analysis unit can perform analysis based on information obtained from the target company's official website. The analysis unit can also analyze how the user's strengths are evaluated based on academic papers and industry reports related to the target company. This allows the accuracy of the analysis to be improved by referring to relevant literature on the target company. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes relevant literature on the target company as input and outputs analysis results.

[0088] The generation unit can estimate the user's emotions and adjust the expression of the generated text based on the estimated emotions. For example, the generation unit estimates the user's emotions using an emotion engine or generation AI. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on heart rate fluctuations. The generation unit adjusts the expression of the generated text based on the estimated emotions of the user. For example, if the user is tense, the generation unit uses a simple and intuitive expression. If the user is relaxed, the generation unit can use a detailed and specific expression. If the user is in a hurry, the generation unit can use a concise and to-the-point expression. This allows for the generation of more appropriate text by adjusting the way the text is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 generation unit may be performed using a generative AI, or not. For example, the generation unit can take user emotion data as input and adjust the expression using a generative AI model that adjusts the way the text is expressed.

[0089] The generation unit can generate text by referencing the user's past success stories. For example, the generation unit can use AI to obtain the user's past success stories. For example, the generation unit can retrieve the user's past successful application forms from a database and generate text based on them. The generation unit can also generate text using similar expression methods based on the user's past success stories. For example, the generation unit can generate text by referring to the expression methods and structure of the user's past successful application forms. The generation unit can also analyze the user's past success stories and generate text using the most effective expression methods. For example, the generation unit can extract the most effective expression methods based on the user's past success stories and generate text based on them. This allows for the generation of more effective text by referencing past success stories. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate text using an AI model that takes the user's past success stories as input and outputs text.

[0090] The generation unit can apply different generation algorithms to text generation depending on the skills required by the target company. For example, the generation unit can use AI to apply different generation algorithms depending on the skills required by the target company. For example, if the target company emphasizes leadership, the generation unit can apply an algorithm that generates text related to leadership. Also, if the target company emphasizes teamwork, the generation unit can apply an algorithm that generates text related to teamwork. Also, if the target company emphasizes technical skills, the generation unit can apply an algorithm that generates text related to technical skills. For example, the generation unit can select the optimal generation algorithm according to the skills required by the target company and generate text based on that. This makes it possible to generate text that is optimally suited to the skills required by the target company. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can apply a generation algorithm using an AI model that takes the skills required by the target company as input and outputs text.

[0091] The generation unit can estimate the user's emotions and adjust the length of the generated text based on the estimated emotions. For example, the generation unit estimates the user's emotions using an emotion engine or generation AI. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it could record the user's voice and estimate their emotions using voice analysis technology. For example, it might analyze the tone and speed of the voice to calculate an emotion score. Furthermore, it could collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it might calculate an emotion score based on heart rate fluctuations. The generation unit adjusts the length of the generated text based on the estimated emotions. For example, if the user is tense, it might generate short, concise text. If the user is relaxed, it might generate longer text with more detailed explanations. If the user is in a hurry, it might generate concise, quick-to-read text. This allows for the generation of more appropriate text by adjusting the length of the text according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 generation unit may be performed using a generative AI, or not. For example, the generation unit can take user emotion data as input and adjust the length of the text using a generative AI model that adjusts the length of the text.

[0092] The generation unit can prioritize documents based on the submission deadlines of the target companies when generating documents. For example, the generation unit can use AI to obtain the submission deadlines of the target companies. For example, the generation unit can obtain the submission deadlines from the target companies' official websites or job sites and use that to determine the priority of documents. The generation unit can also prioritize the generation of documents for a company if the submission deadline is approaching. For example, if the submission deadline is approaching, the generation unit will generate documents for that company with the highest priority. The generation unit can also generate documents at the optimal timing based on the submission deadlines of the target companies. For example, the generation unit can efficiently generate documents for multiple companies, taking into account the submission deadlines of the target companies. This allows for efficient creation of application forms by prioritizing documents based on the submission deadlines of the target companies. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the submission deadlines of the target companies as input and determine the priority using an AI model that determines the priority of documents.

[0093] The generation unit can adjust the order of sentences based on the relevance of the target companies when generating text. For example, the generation unit can use AI to analyze the relevance of the target companies. For example, the generation unit can adjust the order of sentences based on the importance of the target companies. The generation unit can also place the most important information first based on the relevance of the target companies. For example, the generation unit can optimize the order of sentences based on the skills and characteristics required by the target companies. The generation unit can also apply algorithms to adjust the order of sentences based on the relevance of the target companies. For example, the generation unit generates sentences in the optimal order based on the relevance of the target companies. This allows for the creation of more effective application forms by adjusting the order of sentences based on the relevance of the target companies. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the relevance of the target companies as input and adjust the order using an AI model that adjusts the order of sentences.

[0094] The batch creation unit can estimate the user's emotions and adjust the batch creation method based on the estimated emotions. For example, the batch creation unit estimates the user's emotions using an emotion engine or generative AI. For instance, it can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. Furthermore, the batch creation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate variability. The batch creation unit adjusts the batch creation method based on the estimated emotions. For example, if the user is tense, it can provide a simple and intuitive batch creation method. If the user is relaxed, it can offer detailed options and suggest a customizable batch creation method. Also, if the user is in a hurry, it can enable the batch creation to be completed quickly. This allows for the creation of more appropriate entry sheets by adjusting the batch creation 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. The 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 batch creation unit may be performed using a generative AI, or not. For example, the batch creation unit can take user emotion data as input and adjust the method using a generative AI model that adjusts the batch creation method.

[0095] The batch creation unit can analyze the user's past application history and select the optimal batch creation method during batch creation. For example, the batch creation unit can use AI to analyze the user's past application history. For instance, the batch creation unit can retrieve information on companies the user has applied to in the past from a database and perform batch creation based on that information. The batch creation unit can also select the most effective batch creation method based on the user's past application history. For example, the batch creation unit can perform batch creation by referring to application forms from successful past applications. Furthermore, the batch creation unit can analyze the user's past application history and propose the optimal batch creation strategy. For example, the batch creation unit proposes the most effective batch creation strategy based on the user's past application history. This allows the optimal batch creation method to be selected by analyzing the user's past application history. Some or all of the above processes in the batch creation unit may be performed using AI, or not. For example, the batch creation unit can use an AI model that selects a batch creation method based on the user's past application history as input.

[0096] The batch creation unit can improve the accuracy of batch creation by referring to the latest job postings of target companies during the batch creation process. For example, the batch creation unit can use AI to obtain the latest job postings of target companies. For example, the batch creation unit can obtain the latest job postings from the official websites or job sites of target companies and perform batch creation based on that information. The batch creation unit can also reflect the skills and characteristics listed in the job postings of target companies in the batch creation. For example, the batch creation unit can register the skills and characteristics listed in the job postings of target companies in a database and use that information to match the user's strengths. The batch creation unit can also adjust the batch creation results based on the latest information, taking into account the update frequency of the job postings of target companies. For example, if the job postings of target companies are frequently updated, the batch creation unit can obtain that information in real time and reflect it in the batch creation results. This improves the accuracy of batch creation by referring to the latest job postings of target companies. Some or all of the above processes in the batch creation unit may be performed using AI, for example, or without using AI. For example, the batch creation unit can perform batch creation using an AI model that takes the latest job postings from target companies as input and outputs the batch creation results.

[0097] The batch creation unit can estimate the user's emotions and determine the priority of batch creation based on the estimated emotions. The batch creation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the batch creation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. Alternatively, the batch creation unit can record the user's voice and estimate emotions using voice analysis technology. For example, the batch creation unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the batch creation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the batch creation unit can calculate an emotion score based on heart rate fluctuations. The batch creation unit determines the priority of batch creation based on the estimated emotions of the user. For example, if the user is stressed, the batch creation unit will prioritize creating application forms for important companies. If the user is relaxed, the batch creation unit can also provide detailed options and suggest a customizable batch creation method. Furthermore, the batch creation unit can quickly create application forms for the most important companies when the user is in a hurry. This allows for more efficient application form creation by determining the priority of batch creation based on 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 batch creation unit may be performed using a generative AI, or not. For example, the batch creation unit can take user emotion data as input and determine the priority of batch creation using a generative AI model that determines the priority of batch creation.

[0098] The batch creation unit can select the optimal batch creation method by considering the geographical distribution of the target companies during batch creation. For example, the batch creation unit can use AI to analyze the geographical distribution of the target companies. For example, the batch creation unit can adjust the batch creation results by considering the location of the target companies. The batch creation unit can also consider in which region the user's strengths will be best valued based on the geographical distribution of the target companies. For example, the batch creation unit considers how the user's strengths will be valued in that region based on the location of the target companies. The batch creation unit can also propose an application strategy for the user based on the geographical distribution of the target companies. For example, the batch creation unit proposes an optimal application strategy by considering the location of the target companies. In this way, the optimal batch creation method can be selected by considering the geographical distribution of the target companies. Some or all of the above processing in the batch creation unit may be performed using AI, for example, or without AI. For example, the batch creation unit can select a method using an AI model that takes the geographical distribution of the target companies as input and selects a batch creation method.

[0099] The batch creation unit can improve the accuracy of batch creation by referring to relevant literature on the target company during the batch creation process. For example, the batch creation unit can use AI to obtain relevant literature on the target company. For example, the batch creation unit can perform batch creation by referring to the target company's official website and press releases. The batch creation unit can also incorporate academic papers and industry reports related to the target company into the batch creation process. For example, the batch creation unit can perform batch creation based on information obtained from the target company's official website. The batch creation unit can also consider how the user's strengths will be evaluated based on academic papers and industry reports related to the target company. This improves the accuracy of batch creation by referring to relevant literature on the target company. Some or all of the above processing in the batch creation unit may be performed using AI, for example, or without AI. For example, the batch creation unit can perform batch creation using an AI model that takes relevant literature on the target company as input and outputs the batch creation results.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The document creation system may also include a success story analysis unit that analyzes successful past application forms submitted by the user. The success story analysis unit retrieves particularly well-received application forms submitted by the user in the past from a database and analyzes their characteristics. For example, the success story analysis unit extracts the expression methods and structure of successful application forms from the past and generates a new application form based on them. The success story analysis unit can also suggest ways to effectively highlight the user's strengths based on past success stories. For example, the success story analysis unit extracts points that were particularly well-received in successful application forms from the past and generates the user's application form based on them. This allows for the generation of more effective application form text by referring to past success stories. Some or all of the above processing in the success story analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the success story analysis unit can generate text using a generative AI model that takes past success stories as input and outputs application form text.

[0102] The document creation system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the content of the application form based on the estimated emotions. The emotion adjustment unit may, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the emotion adjustment unit may record the user's voice and estimate their emotions using voice analysis technology. For example, the emotion adjustment unit may analyze the tone and speed of the voice and calculate an emotion score. The emotion adjustment unit adjusts the content of the application form based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive expression can be used; if the user is relaxed, a detailed and specific expression can be used. If the user is in a hurry, a concise and to-the-point expression can be used. This allows for the generation of more appropriate documents by adjusting the content of the application form according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotion adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion adjustment unit can take user emotion data as input and adjust the content of the entry sheet using a generative AI model that adjusts the content of the entry sheet.

[0103] The document creation system may also include an application history analysis unit that analyzes the user's past application history and proposes the optimal method for creating an application form. The application history analysis unit retrieves information on companies the user has applied to in the past from a database and performs analysis based on that information. The application history analysis unit can also incorporate patterns of successful and unsuccessful applications from the user's past application history into its analysis. For example, the application history analysis unit can refer to application forms from successful applications in the past and perform analysis using similar presentation methods. The application history analysis unit can also analyze the user's past application history and propose the optimal application strategy. For example, the application history analysis unit proposes the most effective application strategy based on the user's past application history. This allows for more appropriate analysis results by considering the user's past application history. Some or all of the above processing in the application history analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the application history analysis unit can perform analysis using a generative AI model that takes the user's past application history as input and outputs analysis results.

[0104] The document creation system may further include an emotion-prioritizing unit that estimates the user's emotions and determines the priority of application forms based on the estimated emotions. The emotion-prioritizing unit may, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it may record the user's voice and estimate their emotions using voice analysis technology. For example, it may analyze the tone and speed of the voice to calculate an emotion score. The emotion-prioritizing unit then determines the priority of application forms based on the estimated user emotions. For example, if the user is stressed, it may prioritize creating application forms for important companies. If the user is relaxed, it may offer detailed options and suggest customizable application forms. If the user is in a hurry, it may quickly create application forms for the most important companies. This allows for more efficient application form creation by prioritizing application forms according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the emotion-prioritizing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion-prioritizing unit can take user emotion data as input and determine the priority using a generative AI model that determines the priority of entry sheets.

[0105] The document creation system may further include a geographic information unit that customizes the application form by taking into account the user's geographic location. The geographic information unit, for example, acquires the user's geographic location and adjusts the content of the application form based on it. For example, if the user lives in a specific region, the geographic information unit may prioritize displaying company information related to that region. The geographic information unit may also prioritize receiving job postings related to a specific city if the user is job hunting in that city. For example, if the geographic information unit is job hunting in a specific city, it may prioritize displaying job postings related to that city. The geographic information unit may also prioritize receiving information related to a country or region if the user is job hunting overseas. For example, if the geographic information unit is job hunting overseas, it may prioritize displaying company information related to that country or region. In this way, by taking into account the user's geographic location, highly relevant information can be prioritized. Some or all of the above processing in the geographic information unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the geographic information unit can receive information using a generative AI model that takes the user's geographic location information as input and outputs highly relevant information.

[0106] The document creation system may further include an emotion composition unit that estimates the user's emotions and adjusts the structure of the application form based on the estimated emotions. The emotion composition unit may, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the emotion composition unit may record the user's voice and estimate their emotions using voice analysis technology. For example, it may analyze the tone and speed of the voice and calculate an emotion score. The emotion composition unit adjusts the structure of the application form based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive structure can be used; if the user is relaxed, a detailed and specific structure can be used. If the user is in a hurry, a concise and to-the-point structure can be used. This allows for the creation of a more appropriate application form by adjusting its structure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the emotion composition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion composition unit can take user emotion data as input and adjust the structure of the entry sheet using a generative AI model that adjusts the structure of the entry sheet.

[0107] The document creation system may further include a social media analysis unit that analyzes the user's social media activity and receives relevant information. The social media analysis unit, for example, analyzes the user's social media activity. For example, the social media analysis unit prioritizes receiving information about companies that the user follows on social media. The social media analysis unit can also receive relevant information based on the interests and concerns that the user has shared on social media. For example, the social media analysis unit prioritizes displaying relevant company information based on the interests and concerns that the user has shared on social media. The social media analysis unit can also receive relevant information based on the groups and communities that the user participates in on social media. For example, the social media analysis unit prioritizes displaying relevant company information based on the groups and communities that the user participates in on social media. This allows for the efficient reception of relevant information by analyzing the user's social media activity. Some or all of the above processing in the social media analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the social media analysis unit can receive information using a generative AI model that takes the user's social media activity as input and outputs relevant information.

[0108] The document creation system may further include an emotion formatting unit that estimates the user's emotions and adjusts the entry sheet format based on the estimated emotions. The emotion formatting unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The emotion formatting unit adjusts the entry sheet format based on the estimated user emotions. For example, if the user is nervous, it uses a simple and intuitive format; if the user is relaxed, it uses a detailed and specific format. Furthermore, if the user is in a hurry, it can use a concise and to-the-point format. This allows for the creation of more appropriate entry sheets by adjusting the format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the emotion formatting section may be performed using a generative AI, or not. For example, the emotion formatting section can take user emotion data as input and adjust the format using a generative AI model that adjusts the format of the entry sheet.

[0109] The document creation system may also include an input history analysis unit that analyzes the user's past input history and proposes the optimal input method. The input history analysis unit, for example, analyzes the user's past input history. For example, the input history analysis unit retrieves information on strengths and desired companies that the user has entered in the past from a database and proposes the optimal input method based on that information. The input history analysis unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the input history analysis unit has used voice input in the past, it will prioritize suggesting voice input. Furthermore, the input history analysis unit can predict and propose strengths and desired companies to be used at specific time periods based on the user's past input history. For example, based on information the user has entered at specific time periods in the past, the input history analysis unit will propose the optimal input method for that time period. In this way, by analyzing past input history, the system can propose the optimal input method for the user. Some or all of the above processing in the input history analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input history analysis unit can take the user's past input history as input and propose an optimal input method using a generative AI model that outputs the optimal input method.

[0110] The document creation system may further include an emotion length adjustment unit that estimates the user's emotions and adjusts the length of the application form text based on the estimated emotions. The emotion length adjustment unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it can record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The emotion length adjustment unit adjusts the length of the application form text based on the estimated user emotions. For example, if the user is nervous, it generates short, concise sentences. If the user is relaxed, it can generate longer sentences with detailed explanations. If the user is in a hurry, it can generate concise, quickly readable sentences. This allows for the generation of more appropriate documents by adjusting the length of the text according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotion length adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion length adjustment unit can take user emotion data as input and adjust the length of a sentence using a generative AI model that adjusts sentence length.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives information about the user's strengths and desired companies. User strengths include leadership, teamwork, and problem-solving skills, while desired company information includes company name, industry, and required skills and characteristics. The reception desk accepts information from users through input forms, as well as through voice and image input. For example, a user can describe their strengths verbally, and this can be converted into text for reception. Alternatively, a user can upload handwritten notes as images, which can then be analyzed and accepted. Step 2: The analysis department analyzes the information received by the reception department and analyzes how the user's strengths match those of the target company. The analysis department uses generative AI to compare the skills and characteristics required by the target company with the user's strengths. For example, if the target company values ​​leadership, it analyzes how the user's leadership experience would be useful. It can also analyze how the user's teamwork experience would be valued if the target company values ​​teamwork. Furthermore, if the target company values ​​technical skills, it can analyze how the user's technical skills match those requirements. Step 3: The generation unit generates text based on the analysis results obtained by the analysis unit. The generation unit uses generation AI to combine the user's strengths with the skills required by the target company to generate the text for the application form. For example, it can generate text such as, "I have experience demonstrating leadership and leading a team to success. This experience matches the leadership skills required by XX Corporation." It can also generate text that effectively appeals to the target company based on the user's teamwork experience. For example, it can generate text such as, "I value teamwork and have experience leading projects to success. This experience matches the teamwork skills required by △△ Company." Step 4: The batch creation unit creates application forms for multiple companies in bulk based on the text generated by the generation unit. The batch creation unit uses generation AI to create application forms that highlight the user's strengths according to the skills and characteristics required by each company. For example, it will generate application forms that emphasize leadership experience for companies that value leadership, and application forms that emphasize teamwork experience for companies that value teamwork. It also allows users to create application forms for multiple companies with a single input. This enables users to create application forms efficiently.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and batch creation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives information about the user's strengths and desired companies using the reception device 38 of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and uses generation AI to compare the user's strengths with the skills and characteristics required by the desired companies. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates the text of the application form based on the analysis results. The batch creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates customized application forms for multiple companies. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and batch creation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the smart glasses 214 to receive information about the user's strengths and desired companies. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and uses generation AI to compare the user's strengths with the skills and characteristics required by the desired companies. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates the text of the application form based on the analysis results. The batch creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates customized application forms for multiple companies. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and batch creation 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 receives information about the user's strengths and desired companies using the microphone 238 of the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses generation AI to compare the user's strengths with the skills and characteristics required by the desired companies. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates the text of the application form based on the analysis results. The batch creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates customized application forms for multiple companies. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and batch creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the robot 414 to receive information about the user's strengths and desired companies. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses generation AI to compare the user's strengths with the skills and characteristics required by the desired companies. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates the text of the application form based on the analysis results. The batch creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates customized application forms for multiple companies. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The reception desk accepts information about the user's strengths and desired companies, The analysis unit analyzes the information received by the reception unit and analyzes how the user's strengths match those of the company they wish to work for. A generation unit that generates text based on the analysis results obtained by the analysis unit, The system includes a batch creation unit that creates entry sheets for multiple companies at once based on the text generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We compare the skills and characteristics sought by the target company with the user's strengths to determine how well they match. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is This tool combines the user's strengths with the skills required by their target company to generate application form text. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned batch creation unit, We generate application forms that highlight the user's strengths, tailored to the skills and characteristics sought by each company. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned batch creation unit, Users can create application forms for multiple companies with a single input. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate text by referring to past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Allow users to review and edit the generated entry sheet. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, the input fields are customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and accepts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we refer to the latest job postings from target companies to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the user's past application history will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the geographical distribution of the target companies will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, refer to relevant literature on your target company to improve the accuracy of your analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the way the generated text is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating text, the system generates text by referencing the user's past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating text, different generation algorithms are applied depending on the skills required by the target company. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the generated text based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating the application, the priority of the text is determined based on the submission deadlines of the target companies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating the text, the order of sentences is adjusted based on their relevance to the target company. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned batch creation unit, It estimates the user's emotions and adjusts the batch creation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned batch creation unit, During bulk creation, the system analyzes the user's past application history to select the most suitable bulk creation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned batch creation unit, When creating applications in bulk, we improve the accuracy of the bulk creation process by referencing the latest job postings from target companies. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned batch creation unit, The system estimates user sentiment and determines the priority of batch creation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned batch creation unit, When creating applications in bulk, select the most suitable method considering the geographical distribution of your target companies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned batch creation unit, When creating applications in bulk, refer to relevant literature for your target companies to improve the accuracy of the bulk creation process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 desk accepts information about the user's strengths and desired companies, The analysis unit analyzes the information received by the reception unit and analyzes how the user's strengths match those of the company they wish to work for. A generation unit that generates text based on the analysis results obtained by the analysis unit, The system includes a batch creation unit that creates entry sheets for multiple companies at once based on the text generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, We compare the skills and characteristics sought by the target company with the user's strengths to determine how well they match. The system according to feature 1.

3. The generating unit is This tool combines the user's strengths with the skills required by their target company to generate application form text. The system according to feature 1.

4. The aforementioned batch creation unit, We generate application forms that highlight the user's strengths, tailored to the skills and characteristics sought by each company. The system according to feature 1.

5. The aforementioned batch creation unit, Users can create application forms for multiple companies with a single input. The system according to feature 1.

6. The generating unit is Generate text by referring to past success stories. The system according to feature 1.

7. The generating unit is Allow users to review and edit the generated entry sheet. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user emotions. The system according to feature 1.

Images