system

Abstract

The system according to this embodiment aims to enable job seekers to efficiently create resumes that best showcase their work experience and skills. [Solution] The system according to the embodiment comprises a learning unit, a generation unit, and an editing unit. The learning unit learns the user's work history, skills, and preferences. The generation unit generates a resume optimized for job postings based on the information learned by the learning unit. The editing unit allows the user to review and edit the resume 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 conventional technology, there was a problem that it took time and effort for job seekers to create a resume that optimally showcases their work experience and skills.

[0005] The system according to the embodiment aims to efficiently create a resume that optimally showcases a job seeker's work experience and skills.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a generation unit, and an editing unit. The learning unit learns the user's work history, skills, and preferences. The generation unit generates a resume optimized for job postings based on the information learned by the learning unit. The editing unit allows the user to review and edit the resume generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows job seekers to efficiently create resumes that best showcase their work experience and skills. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between 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 three or more matters are expressed by connecting them with "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) An automated resume generation system according to an embodiment of the present invention is a system that learns a user's work history, skills, and aspirations, and automatically generates a resume optimized for job applications. This automated resume generation system allows users to input their work history, skills, and aspirations into a dedicated platform, where the AI ​​learns this information and generates an optimal resume that reflects the latest trends in the job market. The generated resume allows users to review and edit their self-introduction and other sections, enabling them to efficiently apply for jobs. This increases the chances for job seekers to showcase their abilities to the fullest and find a suitable position. First, the user accesses a dedicated platform and inputs their work history, skills, and career aspirations. For example, they input information such as past work experience, acquired qualifications, and desired job type and industry. Next, the AI ​​learns this information and understands the user's characteristics and tendencies. Based on the user's input, the AI ​​generates an optimal resume that reflects the latest trends in the job market. The generated resume can be reviewed and edited by the user. For example, the user can review their self-introduction and work history details and make corrections as needed. This allows the user to showcase their strengths to the fullest. Furthermore, because the AI ​​constantly keeps up with the latest trends in the job market, the generated resumes always meet the latest needs. This service allows job seekers to showcase their abilities to the fullest and increases their chances of finding a suitable job. For example, even job seekers who struggle to write self-introduction statements can create effective resumes because the AI ​​suggests optimal content. Similarly, job seekers who find it difficult to create appropriate resumes tailored to specific job postings can efficiently apply for jobs because the AI ​​automatically generates the best possible resume. In short, this automated resume generation system increases job seekers' opportunities to showcase their abilities to the fullest and find a suitable job.

[0029] The automated resume generation system according to the embodiment comprises a learning unit, a generation unit, and an editing unit. The learning unit learns the user's work history, skills, and preferences. The learning unit learns this information, for example, when the user inputs their work history, skills, and preferences into a dedicated platform. The learning unit understands the user's characteristics and tendencies based on the user's input information. For example, the learning unit learns information such as the user's past work experience, acquired qualifications, and desired job type and industry. The learning unit can use AI to analyze the user's input information and grasp the user's characteristics and tendencies. The generation unit generates a resume optimized for job postings based on the information learned by the learning unit. For example, the generation unit generates an optimal resume that reflects the latest trends in the job market. The generation unit can use AI to understand the user's characteristics and tendencies and generate an optimal resume. The generation unit generates a resume that reflects the latest trends in the job market based on the user's input information. For example, the generation unit considers the user's work history, skills, and preferences and generates a resume that reflects the latest trends in the job market. The generation unit can use AI to understand the user's characteristics and tendencies and generate an optimal resume. The editing unit allows the user to review and edit the resume generated by the generation unit. For example, the editing unit allows the user to review the self-introduction statement and details of their work history and make revisions as needed. The editing unit can use AI to allow the user to review and edit the generated resume. The editing unit allows the user to review the self-introduction statement and details of their work history and make revisions as needed. For example, the editing unit allows the user to review the self-introduction statement and details of their work history and make revisions as needed. As a result, the automated resume generation system according to the embodiment learns the user's work history, skills, and aspirations, generates a resume optimized for job postings, and allows the user to review and edit it. Some or all of the above-described processes in the learning unit, generation unit, and editing unit may be performed using AI, for example, or not using AI. For example, the learning unit inputs user input information into the AI ​​and allows the AI ​​to understand the user's characteristics and tendencies. The generation unit inputs the information learned by the learning unit into the AI ​​and allows the AI ​​to generate a resume optimized for job postings.The editorial department can input resumes generated by the generation department into the AI, and have the AI ​​assist users in reviewing and editing them.

[0030] The learning unit learns the user's work history, skills, and aspirations. For example, the learning unit learns this information when the user inputs their work history, skills, and aspirations into a dedicated platform. Specifically, the user inputs detailed information such as what kind of work they have done in the past, what skills they possess, and what types of jobs and industries they are interested in. This information is stored as text data and analyzed by AI. The AI ​​uses natural language processing technology to analyze the user's input information and extract relationships between work history and skills, as well as patterns of aspirations. For example, if a user has experience as a "project manager" and possesses skills such as "leadership" and "team management," the AI ​​will understand the user's characteristics based on this information. It will also learn the user's aspirations, such as their interest in the "IT industry" or "startup companies." Furthermore, the learning unit can periodically update the user's input information to reflect the latest information. For example, by adding newly acquired qualifications or new work experience, the AI ​​will relearn this information, keeping the user's characteristics and tendencies up to date. This allows the learning department to accurately and thoroughly understand the user's work history, skills, and preferences, and to provide optimal information to the generation and editing departments.

[0031] The generation unit generates resumes optimized for job postings based on information learned by the learning unit. For example, the generation unit generates optimal resumes that reflect the latest trends in the job market. Specifically, the AI ​​analyzes the user's work history, skills, and aspirations, and automatically creates resumes that match job market trends and the skill sets required by companies. For example, if "digital marketing" and "data analysis" skills are highly valued in the current job market, the generation unit will generate a resume that emphasizes these skills. It can also create resumes specialized for specific industries or job types based on the user's aspirations. For example, if the user is interested in the "IT industry," the generation unit will generate a resume that emphasizes work experience and skills related to the IT industry. Furthermore, the generation unit automatically adjusts the layout and design to effectively showcase the user's work history and skills. For example, it selects appropriate fonts and colors to highlight important skills and experience, creating a visually appealing resume. The generation unit automates these processes using AI, allowing users to obtain optimal resumes without any effort. This enables the generation unit to understand the user's characteristics and tendencies and quickly and accurately generate optimal resumes that reflect the latest trends in the job market.

[0032] The editorial team allows users to review and edit resumes generated by the generation team. For example, the editorial team allows users to review their self-introduction and work history details and make revisions as needed. Specifically, it provides a dedicated interface through which users can review the generated resume and freely edit each section. For instance, users can write a more detailed self-introduction or add more details about their work history. The editorial team also uses AI to assist users with their editing. For example, when a user is editing their self-introduction, the AI ​​suggests appropriate phrasing and grammatical corrections. Furthermore, the editorial team reflects user edits in real time and provides a preview of the final resume. This allows users to immediately review their edits and make further revisions as needed. The editorial team also collects user feedback and strives to improve editing functions. For example, if a user is dissatisfied with a particular editing function, the editorial team will use that feedback to improve the function. The editorial team also provides an intuitive and user-friendly interface to enable users to edit efficiently. This allows the editorial team to help users review and edit their generated resumes and apply for jobs in the most optimal way.

[0033] The learning unit can analyze a user's past work experience and skill changes to optimize the learning algorithm. For example, the learning unit can analyze a user's past work experience, identify skill growth patterns, and adjust the learning algorithm accordingly. The learning unit can also track a user's skill changes and build a learning algorithm tailored to their current skill level. The learning unit can also consider changes in a user's work experience and design a learning algorithm suitable for their future career path. This allows the learning algorithm to be optimized by analyzing a user's past work experience and skill changes. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input data on a user's past work experience and skill changes into an AI and have the AI ​​perform the optimization of the learning algorithm.

[0034] The learning unit can track changes in the user's career aspirations in real time during learning and update the learning content accordingly. For example, if the user's aspirations change, the learning unit updates the learning content in real time to reflect the latest aspirations. The learning unit can also add relevant new skills and knowledge to the learning content when the user's career aspirations change. The learning unit can also detect changes in the user's aspirations and restructure the learning content at the appropriate time. This allows the learning unit to track changes in the user's career aspirations in real time and update the learning content to reflect the latest aspirations. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input user aspiration data into AI and have the AI ​​perform the updating of the learning content.

[0035] The learning unit can learn region-specific occupational trends by considering the user's geographical location information during the learning process. For example, the learning unit can learn region-specific occupational trends based on the user's geographical location information and provide optimal skills. The learning unit can also provide learning content that reflects the trends of the local job market based on the user's location. The learning unit can also learn skills and knowledge related to region-specific occupations by considering the user's geographical location information. This allows the learning unit to provide skills appropriate for the region by learning region-specific occupational trends by considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into AI and have the AI ​​perform the learning of region-specific occupational trends.

[0036] The learning unit can analyze the user's social media activity during learning and reflect relevant skills and work experience in the learning process. For example, the learning unit can analyze the user's social media activity and reflect relevant skills and work experience in the learning content. The learning unit can also identify the user's interests and passions from their social media activity and reflect them in the learning content. The learning unit can also analyze the user's social media network and incorporate relevant work experience and skills into the learning process. This allows the learning unit to provide more appropriate learning content by analyzing the user's social media activity and reflecting relevant skills and work experience in the learning process. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the user's social media activity data into an AI and have the AI ​​perform the learning of relevant skills and work experience.

[0037] The generation unit can update the content of resumes in real time, reflecting the latest trends in the job market. For example, the generation unit reflects the required skills and experience in the resume based on the latest trends in the job market. The generation unit can also update the content of resumes in real time, reflecting changes in the job market. The generation unit can also generate resumes that incorporate the latest trends in the job market. This allows for the generation of resumes that meet the latest needs by reflecting the latest trends in the job market in real time and updating the content of resumes. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the latest job market trend data into AI and have the AI ​​perform the update of the resume content.

[0038] The generation unit can adjust the level of detail in the resume based on the importance of the user's work experience and skills during generation. For example, the generation unit can generate a resume that highlights particularly important parts of the user's work experience. The generation unit can also generate a resume that details particularly important skills among the user's skills. The generation unit can also adjust the level of detail in the resume according to the importance of the user's work experience and skills. This allows for the generation of a resume that highlights important information by adjusting the level of detail based on the importance of the user's work experience and skills. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on the importance of the user's work experience and skills into AI and have the AI ​​perform the adjustment of the level of detail in the resume.

[0039] The generation unit can determine the priority of the resume based on the timing of the user's work history submission during the generation process. For example, the generation unit may prioritize the most recent work experience based on the timing of the user's work history submission. The generation unit can also consider the timing of the user's work history submission and place important work experience higher in the rankings. The generation unit can also optimize the content of the resume based on the timing of the user's work history submission. This allows the generation of a resume that prioritizes the most recent work experience by determining the priority of the resume based on the timing of the user's work history submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's work history submission timing data into AI and have the AI ​​perform the task of determining the priority of the resume.

[0040] The generation unit can adjust the order of the resumes based on the relevance of the user's work experience during generation. For example, the generation unit can place the most relevant experience at the top based on the relevance of the user's work experience. The generation unit can also optimize the order of the resumes, taking into account the relevance of the user's work experience. The generation unit can also highlight important experience based on the relevance of the user's work experience. This allows the generation of a resume that highlights highly relevant experience by adjusting the order of the resumes based on the relevance of the user's work experience. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the relevance data of the user's work experience into AI and have the AI ​​perform the adjustment of the order of the resumes.

[0041] The editorial department can suggest the optimal editing method by referring to the user's past editing history during editing. For example, the editorial department can refer to the user's past editing history and suggest the optimal editing method. The editorial department can also learn and suggest effective editing methods from the user's past editing history. The editorial department can also analyze the user's past editing history and provide the optimal editing method. This makes effective editing possible by suggesting the optimal editing method by referring to the user's past editing history. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input the user's past editing history data into AI and have the AI ​​suggest the optimal editing method.

[0042] The editorial department can provide a function that automatically completes the user's work history and skills details during editing. For example, the editorial department can automatically complete the user's work history and skills details to assist in editing. The editorial department can also automatically complete any missing parts of the user's work history and skills. The editorial department can also automatically complete the user's work history and skills details to reduce the effort required for editing. This reduces the effort required for editing by automatically completing the user's work history and skills details. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input the user's work history and skills data into AI and have the AI ​​complete the details.

[0043] The editorial team can provide an optimal editing interface during editing, taking into account the user's device information. For example, if the user is using a smartphone, the editorial team can provide an editing interface that matches the screen size. If the user is using a tablet, the editorial team can also provide an editing interface optimized for a larger screen. If the user is using a desktop, the editorial team can also provide an interface that allows for detailed editing. By providing an optimal editing interface that takes the user's device information into account, editing tailored to the user becomes possible. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input the user's device information into AI and have the AI ​​perform the task of providing the optimal editing interface.

[0044] The editorial team can analyze users' social media activity during the editing process and incorporate relevant information into the edits. For example, the editorial team can analyze users' social media activity and incorporate relevant information into the edits. The editorial team can also identify users' interests and preferences from their social media activity and incorporate them into the edits. The editorial team can also analyze users' social media networks and incorporate relevant information into the edits. This allows for more appropriate editing by analyzing users' social media activity and incorporating relevant information into the edits. Some or all of the above processes performed by the editorial team may be carried out using AI, for example, or not. For example, the editorial team can input user social media activity data into AI and have the AI ​​perform the task of incorporating relevant information into the edits.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The automated resume generation system can further analyze the user's past application history and customize resumes based on the trends of the companies they are applying to. For example, it can generate resumes that emphasize specific skills and experience based on the industries and job roles of companies the user has applied to in the past. It can also consider past application results, learn successful application patterns, and generate resumes with similar patterns. Furthermore, it can improve suitability for the company by using language that matches the culture and values ​​of the company the user is applying to. This allows users to create more effective resumes by leveraging their past application history.

[0047] An automated resume generation system can predict a user's future career path based on their work history and skills, and reflect this in their resume. For example, it can analyze the user's current skill set and market demand, and add the skills and experience necessary for their future career path to their resume. It can also analyze trends in the user's work history and suggest appropriate job roles and industries as the next step. Furthermore, it can set long-term career goals based on the user's aspirations and create a resume tailored to those goals. This allows users to create resumes that look ahead to their future career paths.

[0048] An automated resume generation system can create resumes that reflect relevant industry trends based on the user's work history and skills. For example, if a user has experience in the IT industry, the system can generate a resume that reflects the latest technology trends and industry developments. Similarly, if a user has experience in the marketing industry, the system can generate a resume that reflects the latest marketing methods and tools. Furthermore, if a user has experience in the healthcare industry, the system can generate a resume that reflects the latest medical technologies and regulations. This allows users to create resumes that reflect the trends of their respective industries.

[0049] The automated resume generation system can create resumes that support career changes to different industries, based on the user's work history and skills. For example, if a user wishes to change careers from the IT industry to the marketing industry, the system can create a resume that demonstrates how IT skills can be applied to marketing. Similarly, if a user wishes to change careers from manufacturing to the service industry, the system can create a resume that emphasizes the project management skills cultivated in manufacturing. Furthermore, if a user wishes to change careers from the education industry to the healthcare industry, the system can create a resume that emphasizes the communication skills acquired in the education industry. In this way, users can create resumes that support career changes to different industries.

[0050] The automated resume generation system can create resumes tailored to the international job market based on the user's work history and skills. For example, if a user wishes to work abroad, the system can generate a resume that reflects the trends in the international job market. Similarly, if a user wishes to apply to a multinational corporation, the system can generate a resume that reflects international business etiquette and culture. Furthermore, if a user wishes to work in a specific country or region, the system can generate a resume that reflects the needs of the job market in that country or region. This allows users to create resumes that are relevant to the international job market.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The learning unit learns the user's work history, skills, and preferences. By having the user input their work history, skills, and preferences into a dedicated platform, the learning unit learns this information and understands the user's characteristics and tendencies. For example, it learns information such as past work experience, acquired qualifications, and desired job types and industries. The learning unit can use AI to analyze the user's input information and grasp the user's characteristics and tendencies. Step 2: The generation unit generates a resume optimized for the job based on the information learned by the learning unit. The generation unit generates an optimal resume that reflects the latest trends in the job market. The generation unit can use AI to understand the user's characteristics and tendencies and generate an optimal resume. For example, it can consider the user's work history, skills, and aspirations and generate a resume that reflects the latest trends in the job market. Step 3: The editorial team reviews and edits the resumes generated by the generation team. Users can review their self-introduction and work history details and make revisions as needed. The editorial team can review and edit user-generated resumes using AI.

[0053] (Example of form 2) An automated resume generation system according to an embodiment of the present invention is a system that learns a user's work history, skills, and aspirations, and automatically generates a resume optimized for job applications. This automated resume generation system allows users to input their work history, skills, and aspirations into a dedicated platform, where the AI ​​learns this information and generates an optimal resume that reflects the latest trends in the job market. The generated resume allows users to review and edit their self-introduction and other sections, enabling them to efficiently apply for jobs. This increases the chances for job seekers to showcase their abilities to the fullest and find a suitable position. First, the user accesses a dedicated platform and inputs their work history, skills, and career aspirations. For example, they input information such as past work experience, acquired qualifications, and desired job type and industry. Next, the AI ​​learns this information and understands the user's characteristics and tendencies. Based on the user's input, the AI ​​generates an optimal resume that reflects the latest trends in the job market. The generated resume can be reviewed and edited by the user. For example, the user can review their self-introduction and work history details and make corrections as needed. This allows the user to showcase their strengths to the fullest. Furthermore, because the AI ​​constantly keeps up with the latest trends in the job market, the generated resumes always meet the latest needs. This service allows job seekers to showcase their abilities to the fullest and increases their chances of finding a suitable job. For example, even job seekers who struggle to write self-introduction statements can create effective resumes because the AI ​​suggests optimal content. Similarly, job seekers who find it difficult to create appropriate resumes tailored to specific job postings can efficiently apply for jobs because the AI ​​automatically generates the best possible resume. In short, this automated resume generation system increases job seekers' opportunities to showcase their abilities to the fullest and find a suitable job.

[0054] The automated resume generation system according to the embodiment comprises a learning unit, a generation unit, and an editing unit. The learning unit learns the user's work history, skills, and preferences. The learning unit learns this information, for example, when the user inputs their work history, skills, and preferences into a dedicated platform. The learning unit understands the user's characteristics and tendencies based on the user's input information. For example, the learning unit learns information such as the user's past work experience, acquired qualifications, and desired job type and industry. The learning unit can use AI to analyze the user's input information and grasp the user's characteristics and tendencies. The generation unit generates a resume optimized for job postings based on the information learned by the learning unit. For example, the generation unit generates an optimal resume that reflects the latest trends in the job market. The generation unit can use AI to understand the user's characteristics and tendencies and generate an optimal resume. The generation unit generates a resume that reflects the latest trends in the job market based on the user's input information. For example, the generation unit considers the user's work history, skills, and preferences and generates a resume that reflects the latest trends in the job market. The generation unit can use AI to understand the user's characteristics and tendencies and generate an optimal resume. The editing unit allows the user to review and edit the resume generated by the generation unit. For example, the editing unit allows the user to review the self-introduction statement and details of their work history and make revisions as needed. The editing unit can use AI to allow the user to review and edit the generated resume. The editing unit allows the user to review the self-introduction statement and details of their work history and make revisions as needed. For example, the editing unit allows the user to review the self-introduction statement and details of their work history and make revisions as needed. As a result, the automated resume generation system according to the embodiment learns the user's work history, skills, and aspirations, generates a resume optimized for job postings, and allows the user to review and edit it. Some or all of the above-described processes in the learning unit, generation unit, and editing unit may be performed using AI, for example, or not using AI. For example, the learning unit inputs user input information into the AI ​​and allows the AI ​​to understand the user's characteristics and tendencies. The generation unit inputs the information learned by the learning unit into the AI ​​and allows the AI ​​to generate a resume optimized for job postings.The editorial department can input resumes generated by the generation department into the AI, and have the AI ​​assist users in reviewing and editing them.

[0055] The learning unit learns the user's work history, skills, and aspirations. For example, the learning unit learns this information when the user inputs their work history, skills, and aspirations into a dedicated platform. Specifically, the user inputs detailed information such as what kind of work they have done in the past, what skills they possess, and what types of jobs and industries they are interested in. This information is stored as text data and analyzed by AI. The AI ​​uses natural language processing technology to analyze the user's input information and extract relationships between work history and skills, as well as patterns of aspirations. For example, if a user has experience as a "project manager" and possesses skills such as "leadership" and "team management," the AI ​​will understand the user's characteristics based on this information. It will also learn the user's aspirations, such as their interest in the "IT industry" or "startup companies." Furthermore, the learning unit can periodically update the user's input information to reflect the latest information. For example, by adding newly acquired qualifications or new work experience, the AI ​​will relearn this information, keeping the user's characteristics and tendencies up to date. This allows the learning department to accurately and thoroughly understand the user's work history, skills, and preferences, and to provide optimal information to the generation and editing departments.

[0056] The generation unit generates resumes optimized for job postings based on information learned by the learning unit. For example, the generation unit generates optimal resumes that reflect the latest trends in the job market. Specifically, the AI ​​analyzes the user's work history, skills, and aspirations, and automatically creates resumes that match job market trends and the skill sets required by companies. For example, if "digital marketing" and "data analysis" skills are highly valued in the current job market, the generation unit will generate a resume that emphasizes these skills. It can also create resumes specialized for specific industries or job types based on the user's aspirations. For example, if the user is interested in the "IT industry," the generation unit will generate a resume that emphasizes work experience and skills related to the IT industry. Furthermore, the generation unit automatically adjusts the layout and design to effectively showcase the user's work history and skills. For example, it selects appropriate fonts and colors to highlight important skills and experience, creating a visually appealing resume. The generation unit automates these processes using AI, allowing users to obtain optimal resumes without any effort. This enables the generation unit to understand the user's characteristics and tendencies and quickly and accurately generate optimal resumes that reflect the latest trends in the job market.

[0057] The editorial team allows users to review and edit resumes generated by the generation team. For example, the editorial team allows users to review their self-introduction and work history details and make revisions as needed. Specifically, it provides a dedicated interface through which users can review the generated resume and freely edit each section. For instance, users can write a more detailed self-introduction or add more details about their work history. The editorial team also uses AI to assist users with their editing. For example, when a user is editing their self-introduction, the AI ​​suggests appropriate phrasing and grammatical corrections. Furthermore, the editorial team reflects user edits in real time and provides a preview of the final resume. This allows users to immediately review their edits and make further revisions as needed. The editorial team also collects user feedback and strives to improve editing functions. For example, if a user is dissatisfied with a particular editing function, the editorial team will use that feedback to improve the function. The editorial team also provides an intuitive and user-friendly interface to enable users to edit efficiently. This allows the editorial team to help users review and edit their generated resumes and apply for jobs in the most optimal way.

[0058] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize selecting training data that promotes relaxation. If the user is highly motivated, the learning unit can also select challenging training data. If the user is tired, the learning unit can also select simple and easy-to-understand training data. This allows for learning tailored to the user by selecting training data based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processes in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform the selection of training data.

[0059] The learning unit can analyze a user's past work experience and skill changes to optimize the learning algorithm. For example, the learning unit can analyze a user's past work experience, identify skill growth patterns, and adjust the learning algorithm accordingly. The learning unit can also track a user's skill changes and build a learning algorithm tailored to their current skill level. The learning unit can also consider changes in a user's work experience and design a learning algorithm suitable for their future career path. This allows the learning algorithm to be optimized by analyzing a user's past work experience and skill changes. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input data on a user's past work experience and skill changes into an AI and have the AI ​​perform the optimization of the learning algorithm.

[0060] The learning unit can track changes in the user's career aspirations in real time during learning and update the learning content accordingly. For example, if the user's aspirations change, the learning unit updates the learning content in real time to reflect the latest aspirations. The learning unit can also add relevant new skills and knowledge to the learning content when the user's career aspirations change. The learning unit can also detect changes in the user's aspirations and restructure the learning content at the appropriate time. This allows the learning unit to track changes in the user's career aspirations in real time and update the learning content to reflect the latest aspirations. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input user aspiration data into AI and have the AI ​​perform the updating of the learning content.

[0061] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. If the user is highly motivated, the learning unit can also increase the learning frequency to efficiently improve skills. If the user is tired, the learning unit can adjust the learning frequency to prioritize rest. In this way, by adjusting the learning frequency based on the user's emotions, it is possible to provide a learning frequency that is appropriate for the user. 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 learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into AI and have the AI ​​adjust the learning frequency.

[0062] The learning unit can learn region-specific occupational trends by considering the user's geographical location information during the learning process. For example, the learning unit can learn region-specific occupational trends based on the user's geographical location information and provide optimal skills. The learning unit can also provide learning content that reflects the trends of the local job market based on the user's location. The learning unit can also learn skills and knowledge related to region-specific occupations by considering the user's geographical location information. This allows the learning unit to provide skills appropriate for the region by learning region-specific occupational trends by considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into AI and have the AI ​​perform the learning of region-specific occupational trends.

[0063] The learning unit can analyze the user's social media activity during learning and reflect relevant skills and work experience in the learning process. For example, the learning unit can analyze the user's social media activity and reflect relevant skills and work experience in the learning content. The learning unit can also identify the user's interests and passions from their social media activity and reflect them in the learning content. The learning unit can also analyze the user's social media network and incorporate relevant work experience and skills into the learning process. This allows the learning unit to provide more appropriate learning content by analyzing the user's social media activity and reflecting relevant skills and work experience in the learning process. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the user's social media activity data into an AI and have the AI ​​perform the learning of relevant skills and work experience.

[0064] The generation unit can estimate the user's emotions and adjust the way the resume is written based on those emotions. For example, if the user is relaxed, the generation unit will generate a resume using soft language. If the user is nervous, the generation unit can also generate a resume using concise and clear language. If the user is confident, the generation unit can also generate a resume using positive language. In this way, by adjusting the way the resume is written based on the user's emotions, a resume suitable for the user can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the way the resume is written.

[0065] The generation unit can update the content of resumes in real time, reflecting the latest trends in the job market. For example, the generation unit reflects the required skills and experience in the resume based on the latest trends in the job market. The generation unit can also update the content of resumes in real time, reflecting changes in the job market. The generation unit can also generate resumes that incorporate the latest trends in the job market. This allows for the generation of resumes that meet the latest needs by reflecting the latest trends in the job market in real time and updating the content of resumes. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the latest job market trend data into AI and have the AI ​​perform the update of the resume content.

[0066] The generation unit can adjust the level of detail in the resume based on the importance of the user's work experience and skills during generation. For example, the generation unit can generate a resume that highlights particularly important parts of the user's work experience. The generation unit can also generate a resume that details particularly important skills among the user's skills. The generation unit can also adjust the level of detail in the resume according to the importance of the user's work experience and skills. This allows for the generation of a resume that highlights important information by adjusting the level of detail based on the importance of the user's work experience and skills. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on the importance of the user's work experience and skills into AI and have the AI ​​perform the adjustment of the level of detail in the resume.

[0067] The generation unit can estimate the user's emotions and adjust the length of the resume based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise resume. If the user is relaxed, the generation unit can also generate a longer resume with more detailed information. If the user is confident, the generation unit can also generate a resume using positive language. This allows for the generation of a resume tailored to the user by adjusting its length based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the resume.

[0068] The generation unit can determine the priority of the resume based on the timing of the user's work history submission during the generation process. For example, the generation unit may prioritize the most recent work experience based on the timing of the user's work history submission. The generation unit can also consider the timing of the user's work history submission and place important work experience higher in the rankings. The generation unit can also optimize the content of the resume based on the timing of the user's work history submission. This allows the generation of a resume that prioritizes the most recent work experience by determining the priority of the resume based on the timing of the user's work history submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's work history submission timing data into AI and have the AI ​​perform the task of determining the priority of the resume.

[0069] The generation unit can adjust the order of the resumes based on the relevance of the user's work experience during generation. For example, the generation unit can place the most relevant experience at the top based on the relevance of the user's work experience. The generation unit can also optimize the order of the resumes, taking into account the relevance of the user's work experience. The generation unit can also highlight important experience based on the relevance of the user's work experience. This allows the generation of a resume that highlights highly relevant experience by adjusting the order of the resumes based on the relevance of the user's work experience. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the relevance data of the user's work experience into AI and have the AI ​​perform the adjustment of the order of the resumes.

[0070] The editorial team can estimate the user's emotions and provide editorial advice based on those emotions. For example, if the user is nervous, the editorial team can provide concise and clear editorial advice. If the user is relaxed, the editorial team can also provide detailed editorial advice. If the user is confident, the editorial team can also provide proactive editorial advice. By providing editorial advice based on the user's emotions, the editorial team can provide editorial advice that is appropriate for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, or not. For example, the editorial team can input user emotion data into an AI and have the AI ​​provide editorial advice.

[0071] The editorial department can suggest the optimal editing method by referring to the user's past editing history during editing. For example, the editorial department can refer to the user's past editing history and suggest the optimal editing method. The editorial department can also learn and suggest effective editing methods from the user's past editing history. The editorial department can also analyze the user's past editing history and provide the optimal editing method. This makes effective editing possible by suggesting the optimal editing method by referring to the user's past editing history. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input the user's past editing history data into AI and have the AI ​​suggest the optimal editing method.

[0072] The editorial department can provide a function that automatically completes the user's work history and skills details during editing. For example, the editorial department can automatically complete the user's work history and skills details to assist in editing. The editorial department can also automatically complete any missing parts of the user's work history and skills. The editorial department can also automatically complete the user's work history and skills details to reduce the effort required for editing. This reduces the effort required for editing by automatically completing the user's work history and skills details. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input the user's work history and skills data into AI and have the AI ​​complete the details.

[0073] The editorial team can estimate the user's emotions and determine editing priorities based on those emotions. For example, if the user is stressed, the editorial team might prioritize editing important parts. If the user is relaxed, the editorial team might prioritize editing detailed parts. If the user is in a hurry, the editorial team might prioritize editing to the main points. This allows for editing tailored to the user by determining editing priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, or not. For example, the editorial team can input user emotion data into an AI and have the AI ​​determine editing priorities.

[0074] The editorial team can provide an optimal editing interface during editing, taking into account the user's device information. For example, if the user is using a smartphone, the editorial team can provide an editing interface that matches the screen size. If the user is using a tablet, the editorial team can also provide an editing interface optimized for a larger screen. If the user is using a desktop, the editorial team can also provide an interface that allows for detailed editing. By providing an optimal editing interface that takes the user's device information into account, editing tailored to the user becomes possible. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input the user's device information into AI and have the AI ​​perform the task of providing the optimal editing interface.

[0075] The editorial team can analyze users' social media activity during the editing process and incorporate relevant information into the edits. For example, the editorial team can analyze users' social media activity and incorporate relevant information into the edits. The editorial team can also identify users' interests and preferences from their social media activity and incorporate them into the edits. The editorial team can also analyze users' social media networks and incorporate relevant information into the edits. This allows for more appropriate editing by analyzing users' social media activity and incorporating relevant information into the edits. Some or all of the above processes performed by the editorial team may be carried out using AI, for example, or not. For example, the editorial team can input user social media activity data into AI and have the AI ​​perform the task of incorporating relevant information into the edits.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The automated resume generation system can further analyze the user's past application history and customize resumes based on the trends of the companies they are applying to. For example, it can generate resumes that emphasize specific skills and experience based on the industries and job roles of companies the user has applied to in the past. It can also consider past application results, learn successful application patterns, and generate resumes with similar patterns. Furthermore, it can improve suitability for the company by using language that matches the culture and values ​​of the company the user is applying to. This allows users to create more effective resumes by leveraging their past application history.

[0078] An automated resume generation system can estimate a user's emotions and help them select potential employers based on those emotions. For example, if a user is feeling stressed, the system can prioritize suggesting companies that offer a relaxing work environment. If a user is highly motivated, it can suggest companies that offer challenging projects. Furthermore, if a user is feeling tired, it can suggest companies that prioritize work-life balance. By selecting appropriate employers based on the user's emotions, the system can help users find the optimal work environment.

[0079] An automated resume generation system can predict a user's future career path based on their work history and skills, and reflect this in their resume. For example, it can analyze the user's current skill set and market demand, and add the skills and experience necessary for their future career path to their resume. It can also analyze trends in the user's work history and suggest appropriate job roles and industries as the next step. Furthermore, it can set long-term career goals based on the user's aspirations and create a resume tailored to those goals. This allows users to create resumes that look ahead to their future career paths.

[0080] An automated resume generation system can estimate the user's emotions and adjust the resume design based on those emotions. For example, if the user is relaxed, it can suggest a design using soft colors and fonts. If the user is nervous, it can suggest a simple and clear design. Furthermore, if the user is confident, it can suggest a powerful design. In this way, by adjusting the resume design based on the user's emotions, a more effective resume can be created.

[0081] An automated resume generation system can create resumes that reflect relevant industry trends based on the user's work history and skills. For example, if a user has experience in the IT industry, the system can generate a resume that reflects the latest technology trends and industry developments. Similarly, if a user has experience in the marketing industry, the system can generate a resume that reflects the latest marketing methods and tools. Furthermore, if a user has experience in the healthcare industry, the system can generate a resume that reflects the latest medical technologies and regulations. This allows users to create resumes that reflect the trends of their respective industries.

[0082] An automated resume generation system can estimate a user's emotions and suggest the best time to submit their resume based on those emotions. For example, if a user is feeling stressed, it can suggest submitting their resume during a time when they can relax. If a user is highly motivated, it can suggest submitting their resume during a time when they can concentrate, such as early in the morning or late at night. Furthermore, if a user is tired, it can suggest submitting their resume after resting. By suggesting the optimal time to submit a resume based on the user's emotions, the system can support effective job applications.

[0083] The automated resume generation system can create resumes that support career changes to different industries, based on the user's work history and skills. For example, if a user wishes to change careers from the IT industry to the marketing industry, the system can create a resume that demonstrates how IT skills can be applied to marketing. Similarly, if a user wishes to change careers from manufacturing to the service industry, the system can create a resume that emphasizes the project management skills cultivated in manufacturing. Furthermore, if a user wishes to change careers from the education industry to the healthcare industry, the system can create a resume that emphasizes the communication skills acquired in the education industry. In this way, users can create resumes that support career changes to different industries.

[0084] An automated resume generation system can estimate the user's emotions and adjust the resume content based on those emotions. For example, if the user is relaxed, it can generate a resume with detailed information. If the user is nervous, it can generate a concise and to-the-point resume. Furthermore, if the user is confident, it can generate a resume using positive language. In this way, by adjusting the resume content based on the user's emotions, a more effective resume can be created.

[0085] The automated resume generation system can create resumes tailored to the international job market based on the user's work history and skills. For example, if a user wishes to work abroad, the system can generate a resume that reflects the trends in the international job market. Similarly, if a user wishes to apply to a multinational corporation, the system can generate a resume that reflects international business etiquette and culture. Furthermore, if a user wishes to work in a specific country or region, the system can generate a resume that reflects the needs of the job market in that country or region. This allows users to create resumes that are relevant to the international job market.

[0086] An automated resume generation system can estimate the user's emotions and adjust the resume format based on those emotions. For example, if the user is relaxed, it can suggest a format with soft colors and fonts. If the user is nervous, it can suggest a simple and clear format. Furthermore, if the user is confident, it can suggest a strong design. This allows for the creation of a more effective resume by adjusting the format based on the user's emotions.

[0087] The following briefly describes the processing flow for example form 2.

[0088] Step 1: The learning unit learns the user's work history, skills, and preferences. By having the user input their work history, skills, and preferences into a dedicated platform, the learning unit learns this information and understands the user's characteristics and tendencies. For example, it learns information such as past work experience, acquired qualifications, and desired job types and industries. The learning unit can use AI to analyze the user's input information and grasp the user's characteristics and tendencies. Step 2: The generation unit generates a resume optimized for the job based on the information learned by the learning unit. The generation unit generates an optimal resume that reflects the latest trends in the job market. The generation unit can use AI to understand the user's characteristics and tendencies and generate an optimal resume. For example, it can consider the user's work history, skills, and aspirations and generate a resume that reflects the latest trends in the job market. Step 3: The editorial team reviews and edits the resumes generated by the generation team. Users can review their self-introduction and work history details and make revisions as needed. The editorial team can review and edit user-generated resumes using AI.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] Each of the multiple elements described above, including the learning unit, generation unit, and editing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns information when the user inputs their work history, skills, and preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal resume that reflects the latest trends in the job market. The editing unit is implemented by the control unit 46A of the smart device 14 and allows the user to review and edit the generated resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0093] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the learning unit, generation unit, and editing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns information such as the user's work history, skills, and preferences. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal resume that reflects the latest trends in the job market. The editing unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to review and edit the generated resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the learning unit, generation unit, and editing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns information such as the user's work history, skills, and preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal resume that reflects the latest trends in the job market. The editing unit is implemented by the control unit 46A of the headset terminal 314 and allows the user to review and edit the generated resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the learning unit, generation unit, and editing unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns information such as the user's work history, skills, and preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal resume that reflects the latest trends in the job market. The editing unit is implemented by the control unit 46A of the robot 414 and allows the user to review and edit the generated resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] (Note 1) A learning unit that learns the user's work history, skills, and preferences, A generation unit generates a resume optimized for job applications based on the information learned by the learning unit, The system comprises an editing unit in which the user reviews and edits the resume generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, We analyze the user's past work experience and skill changes to optimize the learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, During the learning process, the system tracks changes in the user's career aspirations in real time and updates the learning content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, During training, the system learns region-specific occupational trends by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity and incorporates relevant skills and work experience into the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts the way the resume is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During generation, the system reflects the latest trends in the job market in real time and updates the content of the resume accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, the level of detail in the resume is adjusted based on the importance of the user's work experience and skills. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the length of the resume based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the system prioritizes resumes based on when the user submitted their work history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the order of resumes is adjusted based on the relevance of the user's work experience. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editorial department, It estimates user sentiment and provides editorial advice based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editorial department, During editing, the system will refer to the user's past editing history to suggest the optimal editing method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editorial department, This feature automatically completes the user's work history and skill details during editing. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editorial department, It estimates the user's emotions and determines editing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editorial department, During editing, the system provides an optimal editing interface that takes into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editorial department, During editing, analyze users' social media activity and incorporate relevant information into the edit. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0161] 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. A learning unit that learns the user's work history, skills, and preferences, A generation unit generates a resume optimized for job applications based on the information learned by the learning unit, The system comprises an editing unit in which the user reviews and edits the resume generated by the generation unit. A system characterized by the following features.

2. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

3. The aforementioned learning unit, We analyze the user's past work experience and skill changes to optimize the learning algorithm. The system according to feature 1.

4. The aforementioned learning unit, During the learning process, the system tracks changes in the user's career aspirations in real time and updates the learning content accordingly. The system according to feature 1.

5. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system according to feature 1.

6. The aforementioned learning unit, During training, the system learns region-specific occupational trends by taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity and incorporates relevant skills and work experience into the learning process. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the way the resume is presented based on those estimated emotions. The system according to feature 1.

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