system

Abstract

The system according to this embodiment aims to enable users to easily create high-quality resumes. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a calculation unit. The reception unit receives information entered by the user. The generation unit generates a resume based on the information received by the reception unit. The provision unit provides the resume generated by the generation unit. The calculation unit calculates the market value based on 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, 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 is a problem that it takes a lot of time and labor for a user to create a resume.

[0005] The system according to the embodiment aims to enable a user to easily create a high-quality resume.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a calculation unit. The reception unit receives information entered by the user. The generation unit generates a resume based on the information received by the reception unit. The provision unit provides the resume generated by the generation unit. The calculation unit calculates the market value based on the resume generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily create high-quality resumes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The resume creation system according to an embodiment of the present invention is a system that utilizes a generation AI to create a high-quality resume in a short time by allowing the user to simply input a few items. This system operates when the user inputs a few items. For example, the user inputs basic information such as name, work history, skills, and qualifications. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a high-quality resume. The generation AI automatically creates text that effectively highlights the user's experience. The generated resume is provided to the user, who can make revisions as needed. Furthermore, this system also has a function to calculate market value from the resume. The generation AI analyzes the user's resume and calculates the current market value. This market value calculation function allows the user to determine the timing of a job change and whether their current compensation is fair. In addition, companies can use this system as an employee evaluation indicator by introducing it. This allows for fair evaluation and reduces the risk of talented employees changing jobs. As a result, the resume creation system allows the user to create a high-quality resume in a short time by simply inputting a few items.

[0029] The resume creation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a calculation unit. The reception unit receives information entered by the user. The information entered by the user includes, but is not limited to, basic information such as name, work history, skills, and qualifications. The reception unit stores the information entered by the user in a database, for example. The reception unit can also send the entered information to a generation AI. The generation unit uses the generation AI to generate a resume based on the information received by the reception unit. The generation unit automatically creates text to effectively highlight the user's career, for example. The generation unit uses the generation AI to analyze the entered information and generates a high-quality resume. The generation unit generates a resume, for example, when the generation AI receives a prompt such as "Please create a resume based on this information." The provision unit provides the resume generated by the generation unit to the user. The provision unit displays the generated resume to the user, for example. The provision unit also allows the user to make modifications as needed. The provision unit provides the generated resume to the user, for example, through a web application or mobile application. The calculation unit calculates the market value based on the resume generated by the generation unit. The calculation unit analyzes the resume and calculates the current market value, for example. The calculation unit uses the generation AI to analyze the content of the resume and calculate the market value. The calculation unit receives a prompt from the generation AI, such as "Calculate the market value of this resume," and calculates the market value. As a result, the resume creation system according to this embodiment allows the user to create a high-quality resume in a short time by simply entering a few items.

[0030] The reception desk receives information entered by users. This information includes, but is not limited to, basic information such as name, work history, skills, and qualifications. The reception desk stores the information entered by users in a database. Specifically, information entered by users through web forms or mobile applications is sent to the server in real time and stored in the database. The database is secure and protected to protect users' personal information. The reception desk can also send the entered information to a generating AI. The information sent to the generating AI is pre-formatted and converted into a form that is easy to analyze. For example, information such as name, work history, and skills is tagged in a specific way so that the generating AI can analyze it efficiently. Furthermore, the reception desk has a function to detect deficiencies and errors in the information entered by users and provide appropriate feedback. For example, if there are typos or missing required fields in the entered information, the user is notified in real time and prompted to make corrections. This allows the reception desk to collect accurate and complete information and smoothly proceed with the resume generation process by the generating AI.

[0031] The generation unit uses a generation AI to generate a resume based on information received by the reception unit. For example, the generation unit automatically creates text that effectively highlights the user's experience. The generation unit analyzes the input information using the generation AI and generates a high-quality resume. Specifically, the generation AI utilizes natural language processing technology to express the user's work history and skills in the most optimal way. For example, the generation AI receives a prompt such as "Please create a resume based on this information" and generates a resume. The generation AI analyzes the user's work history and skills and selects appropriate expressions for each item. For example, if the user has experience as a "project manager," the generation AI will emphasize that experience and describe specific achievements and skills in detail. The generation AI also checks whether the user's resume conforms to industry standards and makes corrections as needed. Furthermore, the generation unit has a function to evaluate the quality of the generated resume and regenerate it if necessary. This allows the generation unit to provide users with high-quality resumes and support their job search and career advancement.

[0032] The service provider provides users with resumes generated by the generation service provider. For example, the service provider displays the generated resume to the user. Specifically, the generated resume is provided to the user through a web application or mobile application. Users can review the generated resume and make modifications as needed. The service provider provides an interface for users to edit their resumes, making it easy to modify and add information. For example, users can freely edit the content of their resume using drag-and-drop operations or a text editor. The service provider also includes a function to download the generated resume in PDF or Word format. This allows users to print or email the generated resume. Furthermore, the service provider provides a function that allows users to directly upload their resumes to multiple job sites and company recruitment systems. This enables users to efficiently utilize the generated resume and smoothly proceed with their job search.

[0033] The calculation unit calculates market value based on the resume generated by the generation unit. For example, the calculation unit analyzes the resume and calculates the current market value. Specifically, it uses a generation AI to analyze the content of the resume and calculate the market value. The generation AI receives a prompt such as "Calculate the market value of this resume" and calculates the market value. Based on information such as work history, skills, and qualifications listed in the resume, the generation AI evaluates the market value considering the balance of supply and demand in the current labor market. For example, in industries or regions where a particular skill set is highly valued, the market value will be calculated to be high. In addition, the calculation unit uses historical data and statistical information to calculate a more accurate market value. For example, based on past job postings and salary data, it compares the market value of people with similar work experience and calculates the user's market value. Furthermore, the calculation unit updates the user's market value in real time, providing an evaluation based on the latest information. This allows the calculation unit to accurately communicate the user's current market value, which can be used as a reference for career planning and salary negotiations.

[0034] The generation unit can automatically create text that effectively highlights the user's background. For example, the generation unit generates text that emphasizes the user's work history and skills. The generation unit uses a generation AI to create text that effectively highlights the user's background. For example, the generation unit can generate an appeal text when the generation AI receives a prompt such as "Please create an effective appeal text based on this background." By generating text that effectively highlights the user's background, the generation unit can improve the quality of the resume. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's background information into the generation AI and have the generation AI generate the appeal text.

[0035] The calculation unit can analyze a resume and calculate its current market value. For example, the calculation unit can analyze the contents of a resume and calculate its current market value. The calculation unit can use a generating AI to analyze the contents of a resume and calculate its market value. For example, the calculation unit can use a generating AI to calculate the market value when it receives a prompt such as "Calculate the market value of this resume." By analyzing a resume and calculating its market value, the calculation unit allows the user to understand their own market value. Some or all of the above-described processes in the calculation unit may be performed using a generating AI or not. For example, the calculation unit can input the contents of a resume into a generating AI and have the generating AI perform the calculation of the market value.

[0036] The service provider provides the generated resume to the user, allowing the user to make revisions as needed. For example, the service provider displays the generated resume to the user. The service provider also allows the user to make revisions as needed. For example, the service provider provides the generated resume to the user through a web application or mobile application. The service provider enables the user to create a more accurate resume by allowing them to revise the generated resume. Some or all of the above processes in the service provider may be performed using a generation AI, or not. For example, the service provider can input the generated resume into a generation AI and have the generation AI execute revision suggestions.

[0037] The calculation unit can calculate market value for use by companies as an employee evaluation metric. For example, the calculation unit analyzes a resume and calculates its market value for use by companies as an employee evaluation metric. The calculation unit uses a generating AI to analyze the content of the resume and calculate the market value. For example, the calculation unit receives a prompt from the generating AI saying, "Calculate the market value of this resume," and calculates the market value. By using market value as an employee evaluation metric, companies can make reasonable evaluations. Some or all of the above-described processes in the calculation unit may be performed using the generating AI, or they may not be performed using the generating AI. For example, the calculation unit can input the content of the resume into the generating AI and have the generating AI perform the calculation of the market value.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display items that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest items that the user will use at a specific time of day based on their past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's past input data into a generative AI and have the generative AI suggest the optimal input method.

[0039] The reception desk can automatically suggest additional information related to the user's work history as they input it. For example, the reception desk can suggest relevant skills and qualifications based on the work history entered by the user. It can also automatically suggest projects and achievements related to the user's work history. Furthermore, the reception desk can provide relevant industry trend information based on the user's work history. This enriches the content of the resume by automatically suggesting additional information related to the user's work history. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's work history data into a generative AI and have the generative AI suggest additional information.

[0040] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location during input. For example, if the user works in a specific region, the reception system will prioritize displaying work experience related to that region. The reception system can also prioritize displaying qualifications and skills acquired by the user in a specific region. Furthermore, the reception system can prioritize displaying projects and achievements the user has undertaken in a specific region. This improves the efficiency of input by prioritizing the display of highly relevant input fields based on the user's geographical location. Some or all of the above processing in the reception system may be performed using or without a generative AI. For example, the reception system can input the user's geographical location into a generative AI and have the generative AI suggest highly relevant input fields.

[0041] The reception desk can analyze the user's social media activity during input and suggest relevant input fields. For example, the reception desk can suggest relevant fields based on the user's work history shared on social media. It can also suggest relevant skills and qualifications based on the user's social media activity. Furthermore, it can suggest relevant projects and achievements based on the user's social media activity. In this way, by analyzing the user's social media activity, relevant input fields can be suggested, enriching the content of the resume. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's social media data into a generative AI and have the generative AI suggest relevant input fields.

[0042] The generation unit can adjust the level of detail based on the importance of the user's work experience when generating a resume. For example, the generation unit can add detailed explanations to important work experience. It can also keep explanations brief for less important work experience. Furthermore, the generation unit can describe in detail points that should be particularly emphasized in the user's work experience. In this way, the quality of the resume can be improved by adjusting the level of detail based on the importance of the user's work experience. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's work experience data into a generation AI and have the generation AI perform the level of detail adjustment.

[0043] The generation unit can apply different generation algorithms depending on the user's occupation and industry when generating a resume. For example, the generation unit can apply an algorithm that emphasizes technical skills to a resume in the IT industry. It can also apply an algorithm that emphasizes professional qualifications and experience to a resume in the medical industry. Furthermore, it can apply an algorithm that emphasizes teaching history and experience to a resume in the education industry. By applying different generation algorithms depending on the user's occupation and industry, a more appropriate resume can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's occupation and industry information into a generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine priorities based on the submission dates of the user's work history when generating a resume. For example, the generation unit may prioritize recent work history. It can also briefly describe older work history. Furthermore, the generation unit can highlight important work history based on the submission date. This improves the quality of the resume by determining priorities based on the submission date of the user's work history. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the priority determination.

[0045] The generation unit can adjust the order of the user's work experience based on its relevance when generating a resume. For example, the generation unit may list highly relevant work experience first. It can also list less relevant work experience later. Furthermore, the generation unit can highlight particularly relevant parts of the user's work experience. This improves the quality of the resume by adjusting the order 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 a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's work experience data into a generation AI and have the generation AI perform the order adjustment.

[0046] The service provider can select the optimal display method when a resume is submitted, by referring to the user's past revision history. For example, the service provider can highlight sections that the user has previously revised. The service provider can also suggest the optimal display method based on the user's past revision history. Furthermore, the service provider can provide a display method that reflects the content the user has previously revised. This allows the service provider to select the optimal display method by referring to the user's past revision history, thereby improving the quality of the resume. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's revision history data into a generation AI and have the generation AI select the display method.

[0047] The service provider can select the optimal display method based on the user's device information when providing a resume. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This improves the readability of the resume by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI and have the generation AI select the display method.

[0048] The service provider can select the optimal display method based on the user's device information when providing a resume. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This improves the readability of the resume by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI and have the generation AI select the display method.

[0049] The service provider can provide a multilingual display method for resumes when they are provided, depending on the user's language settings. For example, the service provider can automatically set the language of the resume based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can display the resume in a specific language if the user selects one. By providing a multilingual display method according to the user's language settings, the range of uses for the resume can be expanded. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's language setting information into a generation AI and have the generation AI select the display method.

[0050] The calculation unit can analyze the user's past work experience to select the optimal calculation method when calculating market value. For example, the calculation unit calculates market value based on the user's past work experience. The calculation unit can also calculate market value by considering specific skills and qualifications from the user's past work experience. Furthermore, the calculation unit can analyze the user's past work experience and select the most appropriate method for calculating market value. This allows for the selection of the optimal method for calculating market value by analyzing the user's past work experience, thereby improving accuracy. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the calculation unit can input the user's past work experience data into a generating AI and have the generating AI select the calculation method.

[0051] The calculation unit can customize the calculation method based on the user's current job situation when calculating market value. For example, the calculation unit calculates market value based on the user's current job situation. The calculation unit can also calculate market value by considering specific skills and qualifications from the user's current job situation. Furthermore, the calculation unit can analyze the user's current job situation and customize the most appropriate method for calculating market value. This allows for the provision of a more accurate market value by customizing the calculation method based on the user's current job situation. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the calculation unit can input the user's current job situation data into a generating AI and have the generating AI perform the customization of the calculation method.

[0052] The calculation unit can select the optimal calculation method based on the user's geographical location information when calculating market value. For example, the calculation unit calculates market value based on the user's geographical location information. The calculation unit can also calculate market value considering the market value of a specific region based on the user's geographical location information. Furthermore, the calculation unit can analyze the user's geographical location information and select the most appropriate method for calculating market value. By selecting the optimal calculation method based on the user's geographical location information, a more accurate market value can be provided. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input the user's geographical location information into a generation AI and have the generation AI perform the selection of the calculation method.

[0053] The calculation unit can analyze a user's social media activity and propose a method for calculating market value when calculating market value. For example, the calculation unit calculates market value based on the user's social media activity. The calculation unit can also calculate market value by considering specific skills and qualifications from the user's social media activity. Furthermore, the calculation unit can analyze the user's social media activity and propose the most appropriate method for calculating market value. This allows for the proposal of the optimal method for calculating market value by analyzing the user's social media activity, thereby improving accuracy. Some or all of the above processing in the calculation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the calculation unit can input the user's social media data into a generative AI and have the generative AI execute the proposal of a calculation method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can automatically select a resume template based on the user's input. For example, if a user enters work experience in the IT industry, it can provide a template specifically for the IT industry. Similarly, if a user enters work experience in the medical industry, it can provide a template specifically for the medical industry. Furthermore, if a user enters work experience in the education industry, it can provide a template specifically for the education industry. This improves the quality of the resume by providing the most suitable template for the user's work experience. Some or all of the above processing in the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk can input the user's input data into a generation AI and have the generation AI select a template.

[0056] The generation unit can automatically add relevant industry trend information based on the user's work history. For example, if the user enters work history in the IT industry, the latest technology trends and market developments can be added to the resume. Similarly, if the user enters work history in the medical industry, the latest medical technologies and research findings can be added. Furthermore, if the user enters work history in the education industry, the latest teaching methods and policies can be added. This enriches the content of the user's resume by adding the latest industry information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the addition of trend information.

[0057] The calculation unit can predict a user's future career path based on their work history. For example, if a user inputs work history in the IT industry, it can suggest positions such as project manager or CTO as a future career path. Similarly, if a user inputs work history in the medical industry, it can suggest positions such as medical administrator or researcher. Furthermore, if a user inputs work history in the education industry, it can suggest positions such as education administrator or curriculum developer. This allows the system to support the user's career planning by predicting their future career path based on their work history. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input the user's work history data into a generation AI and have the generation AI perform the career path prediction.

[0058] The service provider can provide users' resumes in multiple formats. For example, in addition to PDF format, it can also provide them in Word and HTML formats. Furthermore, if the user requests it, the resume can be provided in a printable format. It can also generate a link for users to share their resumes online. This improves convenience by providing resumes in various formats according to user needs. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the generated resume into a generation AI and have the generation AI perform the format conversion.

[0059] The calculation unit can provide an estimated salary based on the user's work history. For example, if the user enters work history in the IT industry, the calculation unit can provide an estimated salary based on the average salary in that industry. Similarly, if the user enters work history in the medical industry, the calculation unit can provide an estimated salary based on the average salary in that industry. Furthermore, if the user enters work history in the education industry, the calculation unit can provide an estimated salary based on the average salary in that industry. This allows users to use the estimated salary based on their work history as a reference for job hunting and salary negotiations. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the calculation unit can input the user's work history data into a generating AI and have the generating AI perform the salary estimation.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives information entered by the user. This information includes basic information such as name, work history, skills, and qualifications. The reception desk can save the information entered by the user to a database and can also send the entered information to a generating AI. Step 2: The generation unit uses generation AI to generate a resume based on the information received by the reception unit. The generation unit automatically creates text that effectively highlights the user's experience and generates a high-quality resume. Step 3: The provider unit provides the user with the resume generated by the generator unit. The provider unit displays the generated resume to the user and allows the user to make modifications as needed. The provider unit provides the generated resume to the user through a web application or mobile application. Step 4: The calculation unit calculates the market value based on the resume generated by the generation unit. The calculation unit uses generation AI to analyze the resume and calculate the current market value.

[0062] (Example of form 2) The resume creation system according to an embodiment of the present invention is a system that utilizes a generation AI to create a high-quality resume in a short time by allowing the user to simply input a few items. This system operates when the user inputs a few items. For example, the user inputs basic information such as name, work history, skills, and qualifications. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a high-quality resume. The generation AI automatically creates text that effectively highlights the user's experience. The generated resume is provided to the user, who can make revisions as needed. Furthermore, this system also has a function to calculate market value from the resume. The generation AI analyzes the user's resume and calculates the current market value. This market value calculation function allows the user to determine the timing of a job change and whether their current compensation is fair. In addition, companies can use this system as an employee evaluation indicator by introducing it. This allows for fair evaluation and reduces the risk of talented employees changing jobs. As a result, the resume creation system allows the user to create a high-quality resume in a short time by simply inputting a few items.

[0063] The resume creation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a calculation unit. The reception unit receives information entered by the user. The information entered by the user includes, but is not limited to, basic information such as name, work history, skills, and qualifications. The reception unit stores the information entered by the user in a database, for example. The reception unit can also send the entered information to a generation AI. The generation unit uses the generation AI to generate a resume based on the information received by the reception unit. The generation unit automatically creates text to effectively highlight the user's career, for example. The generation unit uses the generation AI to analyze the entered information and generates a high-quality resume. The generation unit generates a resume, for example, when the generation AI receives a prompt such as "Please create a resume based on this information." The provision unit provides the resume generated by the generation unit to the user. The provision unit displays the generated resume to the user, for example. The provision unit also allows the user to make modifications as needed. The provision unit provides the generated resume to the user, for example, through a web application or mobile application. The calculation unit calculates the market value based on the resume generated by the generation unit. The calculation unit analyzes the resume and calculates the current market value, for example. The calculation unit uses the generation AI to analyze the content of the resume and calculate the market value. The calculation unit receives a prompt from the generation AI, such as "Calculate the market value of this resume," and calculates the market value. As a result, the resume creation system according to this embodiment allows the user to create a high-quality resume in a short time by simply entering a few items.

[0064] The reception desk receives information entered by users. This information includes, but is not limited to, basic information such as name, work history, skills, and qualifications. The reception desk stores the information entered by users in a database. Specifically, information entered by users through web forms or mobile applications is sent to the server in real time and stored in the database. The database is secure and protected to protect users' personal information. The reception desk can also send the entered information to a generating AI. The information sent to the generating AI is pre-formatted and converted into a form that is easy to analyze. For example, information such as name, work history, and skills is tagged in a specific way so that the generating AI can analyze it efficiently. Furthermore, the reception desk has a function to detect deficiencies and errors in the information entered by users and provide appropriate feedback. For example, if there are typos or missing required fields in the entered information, the user is notified in real time and prompted to make corrections. This allows the reception desk to collect accurate and complete information and smoothly proceed with the resume generation process by the generating AI.

[0065] The generation unit uses a generation AI to generate a resume based on information received by the reception unit. For example, the generation unit automatically creates text that effectively highlights the user's experience. The generation unit analyzes the input information using the generation AI and generates a high-quality resume. Specifically, the generation AI utilizes natural language processing technology to express the user's work history and skills in the most optimal way. For example, the generation AI receives a prompt such as "Please create a resume based on this information" and generates a resume. The generation AI analyzes the user's work history and skills and selects appropriate expressions for each item. For example, if the user has experience as a "project manager," the generation AI will emphasize that experience and describe specific achievements and skills in detail. The generation AI also checks whether the user's resume conforms to industry standards and makes corrections as needed. Furthermore, the generation unit has a function to evaluate the quality of the generated resume and regenerate it if necessary. This allows the generation unit to provide users with high-quality resumes and support their job search and career advancement.

[0066] The service provider provides users with resumes generated by the generation service provider. For example, the service provider displays the generated resume to the user. Specifically, the generated resume is provided to the user through a web application or mobile application. Users can review the generated resume and make modifications as needed. The service provider provides an interface for users to edit their resumes, making it easy to modify and add information. For example, users can freely edit the content of their resume using drag-and-drop operations or a text editor. The service provider also includes a function to download the generated resume in PDF or Word format. This allows users to print or email the generated resume. Furthermore, the service provider provides a function that allows users to directly upload their resumes to multiple job sites and company recruitment systems. This enables users to efficiently utilize the generated resume and smoothly proceed with their job search.

[0067] The calculation unit calculates market value based on the resume generated by the generation unit. For example, the calculation unit analyzes the resume and calculates the current market value. Specifically, it uses a generation AI to analyze the content of the resume and calculate the market value. The generation AI receives a prompt such as "Calculate the market value of this resume" and calculates the market value. Based on information such as work history, skills, and qualifications listed in the resume, the generation AI evaluates the market value considering the balance of supply and demand in the current labor market. For example, in industries or regions where a particular skill set is highly valued, the market value will be calculated to be high. In addition, the calculation unit uses historical data and statistical information to calculate a more accurate market value. For example, based on past job postings and salary data, it compares the market value of people with similar work experience and calculates the user's market value. Furthermore, the calculation unit updates the user's market value in real time, providing an evaluation based on the latest information. This allows the calculation unit to accurately communicate the user's current market value, which can be used as a reference for career planning and salary negotiations.

[0068] The generation unit can automatically create text that effectively highlights the user's background. For example, the generation unit generates text that emphasizes the user's work history and skills. The generation unit uses a generation AI to create text that effectively highlights the user's background. For example, the generation unit can generate an appeal text when the generation AI receives a prompt such as "Please create an effective appeal text based on this background." By generating text that effectively highlights the user's background, the generation unit can improve the quality of the resume. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's background information into the generation AI and have the generation AI generate the appeal text.

[0069] The calculation unit can analyze a resume and calculate its current market value. For example, the calculation unit can analyze the contents of a resume and calculate its current market value. The calculation unit can use a generating AI to analyze the contents of a resume and calculate its market value. For example, the calculation unit can use a generating AI to calculate the market value when it receives a prompt such as "Calculate the market value of this resume." By analyzing a resume and calculating its market value, the calculation unit allows the user to understand their own market value. Some or all of the above-described processes in the calculation unit may be performed using a generating AI or not. For example, the calculation unit can input the contents of a resume into a generating AI and have the generating AI perform the calculation of the market value.

[0070] The service provider provides the generated resume to the user, allowing the user to make revisions as needed. For example, the service provider displays the generated resume to the user. The service provider also allows the user to make revisions as needed. For example, the service provider provides the generated resume to the user through a web application or mobile application. The service provider enables the user to create a more accurate resume by allowing them to revise the generated resume. Some or all of the above processes in the service provider may be performed using a generation AI, or not. For example, the service provider can input the generated resume into a generation AI and have the generation AI execute revision suggestions.

[0071] The calculation unit can calculate market value for use by companies as an employee evaluation metric. For example, the calculation unit analyzes a resume and calculates its market value for use by companies as an employee evaluation metric. The calculation unit uses a generating AI to analyze the content of the resume and calculate the market value. For example, the calculation unit receives a prompt from the generating AI saying, "Calculate the market value of this resume," and calculates the market value. By using market value as an employee evaluation metric, companies can make reasonable evaluations. Some or all of the above-described processes in the calculation unit may be performed using the generating AI, or they may not be performed using the generating AI. For example, the calculation unit can input the content of the resume into the generating AI and have the generating AI perform the calculation of the market value.

[0072] The reception desk can estimate the user's emotions and adjust the order of input items based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt them to input the simplest items first. If the user is relaxed, the reception desk may also prompt them to input the more detailed items first. Furthermore, if the user is in a hurry, the reception desk may prioritize the input of the most important items. By adjusting the order of input items according to the user's emotions, it is possible to reduce user stress and promote efficient input. 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 processing in the reception desk may be performed using generative AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display items that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest items that the user will use at a specific time of day based on their past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's past input data into a generative AI and have the generative AI suggest the optimal input method.

[0074] The reception desk can automatically suggest additional information related to the user's work history as they input it. For example, the reception desk can suggest relevant skills and qualifications based on the work history entered by the user. It can also automatically suggest projects and achievements related to the user's work history. Furthermore, the reception desk can provide relevant industry trend information based on the user's work history. This enriches the content of the resume by automatically suggesting additional information related to the user's work history. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's work history data into a generative AI and have the generative AI suggest additional information.

[0075] The reception desk can estimate the user's emotions and prioritize input items based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt them to input the simplest items first. If the user is relaxed, the reception desk may also prompt them to input the more detailed items first. Furthermore, if the user is in a hurry, the reception desk may prioritize the most important items. By prioritizing input items according to the user's emotions, it is possible to reduce user stress and promote efficient input. 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 processing in the reception desk may be performed using generative AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location during input. For example, if the user works in a specific region, the reception system will prioritize displaying work experience related to that region. The reception system can also prioritize displaying qualifications and skills acquired by the user in a specific region. Furthermore, the reception system can prioritize displaying projects and achievements the user has undertaken in a specific region. This improves the efficiency of input by prioritizing the display of highly relevant input fields based on the user's geographical location. Some or all of the above processing in the reception system may be performed using or without a generative AI. For example, the reception system can input the user's geographical location into a generative AI and have the generative AI suggest highly relevant input fields.

[0077] The reception desk can analyze the user's social media activity during input and suggest relevant input fields. For example, the reception desk can suggest relevant fields based on the user's work history shared on social media. It can also suggest relevant skills and qualifications based on the user's social media activity. Furthermore, it can suggest relevant projects and achievements based on the user's social media activity. In this way, by analyzing the user's social media activity, relevant input fields can be suggested, enriching the content of the resume. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's social media data into a generative AI and have the generative AI suggest relevant input fields.

[0078] The generation unit can estimate the user's emotions and adjust the way the resume is written based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a resume using detailed language. If the user is in a hurry, the generation unit can generate a resume using concise language. Furthermore, if the user is stressed, the generation unit can generate a resume using easy-to-read language. In this way, by adjusting the way the resume is written according to the user's emotions, a more appropriate resume can be generated. Emotion estimation is achieved using an emotion estimation function, such as 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 the generation AI or not. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform the adjustment of the writing style.

[0079] The generation unit can adjust the level of detail based on the importance of the user's work experience when generating a resume. For example, the generation unit can add detailed explanations to important work experience. It can also keep explanations brief for less important work experience. Furthermore, the generation unit can describe in detail points that should be particularly emphasized in the user's work experience. In this way, the quality of the resume can be improved by adjusting the level of detail based on the importance of the user's work experience. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's work experience data into a generation AI and have the generation AI perform the level of detail adjustment.

[0080] The generation unit can apply different generation algorithms depending on the user's occupation and industry when generating a resume. For example, the generation unit can apply an algorithm that emphasizes technical skills to a resume in the IT industry. It can also apply an algorithm that emphasizes professional qualifications and experience to a resume in the medical industry. Furthermore, it can apply an algorithm that emphasizes teaching history and experience to a resume in the education industry. By applying different generation algorithms depending on the user's occupation and industry, a more appropriate resume can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's occupation and industry information into a generation AI and have the generation AI execute the application of the generation algorithm.

[0081] 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 detailed explanations. Furthermore, if the user is stressed, the generation unit can generate a resume of an easily readable length. In this way, by adjusting the length of the resume according to the user's emotions, a more appropriate resume can be generated. 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 processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the length adjustment.

[0082] The generation unit can determine priorities based on the submission dates of the user's work history when generating a resume. For example, the generation unit may prioritize recent work history. It can also briefly describe older work history. Furthermore, the generation unit can highlight important work history based on the submission date. This improves the quality of the resume by determining priorities based on the submission date of the user's work history. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the priority determination.

[0083] The generation unit can adjust the order of the user's work experience based on its relevance when generating a resume. For example, the generation unit may list highly relevant work experience first. It can also list less relevant work experience later. Furthermore, the generation unit can highlight particularly relevant parts of the user's work experience. This improves the quality of the resume by adjusting the order 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 a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's work experience data into a generation AI and have the generation AI perform the order adjustment.

[0084] The service provider can estimate the user's emotions and adjust how the resume is displayed based on those emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting how the resume is displayed according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI adjust the display method.

[0085] The service provider can select the optimal display method when a resume is submitted, by referring to the user's past revision history. For example, the service provider can highlight sections that the user has previously revised. The service provider can also suggest the optimal display method based on the user's past revision history. Furthermore, the service provider can provide a display method that reflects the content the user has previously revised. This allows the service provider to select the optimal display method by referring to the user's past revision history, thereby improving the quality of the resume. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's revision history data into a generation AI and have the generation AI select the display method.

[0086] The service provider can select the optimal display method based on the user's device information when providing a resume. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This improves the readability of the resume by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI and have the generation AI select the display method.

[0087] The service provider can estimate the user's emotions and adjust the operation procedures for the resume based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and intuitive operation procedures. If the user is relaxed, the service provider can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. By adjusting the operation procedures according to the user's emotions, a more intuitive and user-friendly operation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as 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 service provider may be performed using generative AI or not. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform the adjustment of the operation procedures.

[0088] The service provider can select the optimal display method based on the user's device information when providing a resume. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This improves the readability of the resume by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI and have the generation AI select the display method.

[0089] The service provider can provide a multilingual display method for resumes when they are provided, depending on the user's language settings. For example, the service provider can automatically set the language of the resume based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can display the resume in a specific language if the user selects one. By providing a multilingual display method according to the user's language settings, the range of uses for the resume can be expanded. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's language setting information into a generation AI and have the generation AI select the display method.

[0090] The calculation unit can estimate the user's emotions and adjust the market value calculation method based on the estimated user emotions. For example, if the user is relaxed, the calculation unit can provide a detailed market value calculation method. If the user is in a hurry, the calculation unit can also provide a concise market value calculation method. Furthermore, if the user is stressed, the calculation unit can provide an easy-to-understand market value calculation method. This allows for the provision of a more appropriate market value by adjusting the market value calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using the generative AI or not. For example, the calculation unit can input user facial expression data into the generative AI and have the generative AI perform the adjustment of the calculation method.

[0091] The calculation unit can analyze the user's past work experience to select the optimal calculation method when calculating market value. For example, the calculation unit calculates market value based on the user's past work experience. The calculation unit can also calculate market value by considering specific skills and qualifications from the user's past work experience. Furthermore, the calculation unit can analyze the user's past work experience and select the most appropriate method for calculating market value. This allows for the selection of the optimal method for calculating market value by analyzing the user's past work experience, thereby improving accuracy. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the calculation unit can input the user's past work experience data into a generating AI and have the generating AI select the calculation method.

[0092] The calculation unit can customize the calculation method based on the user's current job situation when calculating market value. For example, the calculation unit calculates market value based on the user's current job situation. The calculation unit can also calculate market value by considering specific skills and qualifications from the user's current job situation. Furthermore, the calculation unit can analyze the user's current job situation and customize the most appropriate method for calculating market value. This allows for the provision of a more accurate market value by customizing the calculation method based on the user's current job situation. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the calculation unit can input the user's current job situation data into a generating AI and have the generating AI perform the customization of the calculation method.

[0093] The calculation unit can estimate the user's emotions and determine the priority of market values ​​based on the estimated user emotions. For example, if the user is relaxed, the calculation unit can provide a detailed priority of market values. If the user is in a hurry, the calculation unit can also provide a concise priority of market values. Furthermore, if the user is stressed, the calculation unit can provide an easy-to-understand priority of market values. This allows for the provision of more appropriate market values ​​by determining the priority of market values ​​according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using or without a generative AI. For example, the calculation unit can input user facial expression data into a generative AI and have the generative AI perform the priority determination.

[0094] The calculation unit can select the optimal calculation method based on the user's geographical location information when calculating market value. For example, the calculation unit calculates market value based on the user's geographical location information. The calculation unit can also calculate market value considering the market value of a specific region based on the user's geographical location information. Furthermore, the calculation unit can analyze the user's geographical location information and select the most appropriate method for calculating market value. By selecting the optimal calculation method based on the user's geographical location information, a more accurate market value can be provided. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input the user's geographical location information into a generation AI and have the generation AI perform the selection of the calculation method.

[0095] The calculation unit can analyze a user's social media activity and propose a method for calculating market value when calculating market value. For example, the calculation unit calculates market value based on the user's social media activity. The calculation unit can also calculate market value by considering specific skills and qualifications from the user's social media activity. Furthermore, the calculation unit can analyze the user's social media activity and propose the most appropriate method for calculating market value. This allows for the proposal of the optimal method for calculating market value by analyzing the user's social media activity, thereby improving accuracy. Some or all of the above processing in the calculation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the calculation unit can input the user's social media data into a generative AI and have the generative AI execute the proposal of a calculation method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can automatically select a resume template based on the user's input. For example, if a user enters work experience in the IT industry, it can provide a template specifically for the IT industry. Similarly, if a user enters work experience in the medical industry, it can provide a template specifically for the medical industry. Furthermore, if a user enters work experience in the education industry, it can provide a template specifically for the education industry. This improves the quality of the resume by providing the most suitable template for the user's work experience. Some or all of the above processing in the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk can input the user's input data into a generation AI and have the generation AI select a template.

[0098] The generation unit can automatically add relevant industry trend information based on the user's work history. For example, if the user enters work history in the IT industry, the latest technology trends and market developments can be added to the resume. Similarly, if the user enters work history in the medical industry, the latest medical technologies and research findings can be added. Furthermore, if the user enters work history in the education industry, the latest teaching methods and policies can be added. This enriches the content of the user's resume by adding the latest industry information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's work history data into a generation AI and have the generation AI perform the addition of trend information.

[0099] The calculation unit can predict a user's future career path based on their work history. For example, if a user inputs work history in the IT industry, it can suggest positions such as project manager or CTO as a future career path. Similarly, if a user inputs work history in the medical industry, it can suggest positions such as medical administrator or researcher. Furthermore, if a user inputs work history in the education industry, it can suggest positions such as education administrator or curriculum developer. This allows the system to support the user's career planning by predicting their future career path based on their work history. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input the user's work history data into a generation AI and have the generation AI perform the career path prediction.

[0100] The service provider can provide users' resumes in multiple formats. For example, in addition to PDF format, it can also provide them in Word and HTML formats. Furthermore, if the user requests it, the resume can be provided in a printable format. It can also generate a link for users to share their resumes online. This improves convenience by providing resumes in various formats according to user needs. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the generated resume into a generation AI and have the generation AI perform the format conversion.

[0101] The calculation unit can provide an estimated salary based on the user's work history. For example, if the user enters work history in the IT industry, the calculation unit can provide an estimated salary based on the average salary in that industry. Similarly, if the user enters work history in the medical industry, the calculation unit can provide an estimated salary based on the average salary in that industry. Furthermore, if the user enters work history in the education industry, the calculation unit can provide an estimated salary based on the average salary in that industry. This allows users to use the estimated salary based on their work history as a reference for job hunting and salary negotiations. Some or all of the above processing in the calculation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the calculation unit can input the user's work history data into a generating AI and have the generating AI perform the salary estimation.

[0102] The reception unit can estimate the user's emotions and provide input assistance based on the estimated emotions. For example, if the user is stressed, it can provide assistance to simplify the input process. If the user is relaxed, it can provide assistance to encourage more detailed input. Furthermore, if the user is in a hurry, it can provide assistance to prioritize inputting the most important items. This improves input efficiency by providing input assistance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The generation unit can estimate the user's emotions and adjust the design of the resume based on those emotions. For example, if the user is relaxed, it can provide a colorful and visually appealing design. If the user is stressed, it can provide a simple and calming design. Furthermore, if the user is in a hurry, it can provide a design that emphasizes the key points. By adjusting the resume design according to the user's emotions, a more appropriate resume can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform the design adjustments.

[0104] The service provider can estimate the user's emotions and provide feedback on their resume based on those emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, concise and positive feedback can be provided. Furthermore, if the user is in a hurry, to-the-point feedback can be provided. This improves user satisfaction by providing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using or without a generative AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI provide the feedback.

[0105] The calculation unit can estimate the user's emotions and adjust the market value calculation result based on the estimated user emotions. For example, if the user is relaxed, it can provide a detailed market value calculation result. If the user is stressed, it can provide a concise and easy-to-understand market value calculation result. Furthermore, if the user is in a hurry, it can provide a market value calculation result that gets straight to the point. In this way, by adjusting the market value calculation result according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 calculation unit may be performed using the generative AI or not. For example, the calculation unit can input the user's facial expression data into the generative AI and have the generative AI perform the adjustment of the calculation result.

[0106] The service provider can estimate the user's emotions and, based on those emotions, suggest revisions to the resume. For example, if the user is relaxed, it can provide detailed revision suggestions. If the user is stressed, it can provide concise and positive revision suggestions. Furthermore, if the user is in a hurry, it can provide concise revision suggestions. By providing revision suggestions according to the user's emotions, user satisfaction can be improved. 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 processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI provide revision suggestions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives information entered by the user. This information includes basic information such as name, work history, skills, and qualifications. The reception desk can save the information entered by the user to a database and can also send the entered information to a generating AI. Step 2: The generation unit uses generation AI to generate a resume based on the information received by the reception unit. The generation unit automatically creates text that effectively highlights the user's experience and generates a high-quality resume. Step 3: The provider unit provides the user with the resume generated by the generator unit. The provider unit displays the generated resume to the user and allows the user to make modifications as needed. The provider unit provides the generated resume to the user through a web application or mobile application. Step 4: The calculation unit calculates the market value based on the resume generated by the generation unit. The calculation unit uses generation AI to analyze the resume and calculate the current market value.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and calculation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a resume using a generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated resume to the user. The calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and calculates the market value based on the resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and calculation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information entered by the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a resume using generation AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated resume to the user. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and calculates the market value based on the resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and calculation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a resume using a generation AI. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated resume to the user. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the market value based on the resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and calculation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information entered by the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a resume using a generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated resume to the user. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the market value based on the resume. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk that receives information entered by the user, A generation unit that generates a resume based on the information received by the reception unit, A providing unit that provides the resume generated by the generation unit, The system includes a calculation unit that calculates market value based on the resume generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Automatically generates text to effectively showcase the user's background. The system described in Appendix 1, characterized by the features described herein. (Note 3) The calculation unit described above, Analyze your resume and calculate your current market value. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated resume is provided to the user, who can then make revisions as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The calculation unit described above, Calculate the market value that companies can use as an employee evaluation metric. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the order of input fields based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is As users enter information, the system automatically suggests additional information related to their work history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter data, the system prioritizes displaying the most relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the way the resume is written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a resume, adjust the level of detail based on the importance of the user's work experience. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a resume, different generation algorithms are applied depending on the user's job title and industry. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system 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 16) The generating unit is When generating a resume, priority is determined based on when the user submitted their work history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a resume, the order of the user's work experience is adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the resume is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When a user submits their resume, the system will refer to their past revision history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When submitting a resume, the system selects the optimal display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the resume creation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When submitting a resume, the system selects the optimal display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When submitting a resume, the system provides a multilingual display method according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The calculation unit described above, We estimate user sentiment and adjust the market value calculation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The calculation unit described above, When calculating market value, the system analyzes the user's past work experience to select the most suitable calculation method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The calculation unit described above, When calculating market value, the calculation method is customized based on the user's current job situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The calculation unit described above, It estimates user sentiment and determines market value priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The calculation unit described above, When calculating market value, the optimal calculation method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The calculation unit described above, When calculating market value, we propose a method for calculating market value by analyzing users' social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 reception desk that receives information entered by the user, A generation unit that generates a resume based on the information received by the reception unit, A providing unit that provides the resume generated by the generation unit, The system includes a calculation unit that calculates market value based on the resume generated by the generation unit. A system characterized by the following features.

2. The generating unit is Automatically generates text to effectively showcase the user's background. The system according to feature 1.

3. The calculation unit described above, Analyze your resume and calculate your current market value. The system according to feature 1.

4. The aforementioned supply unit is, The generated resume is provided to the user, who can then make revisions as needed. The system according to feature 1.

5. The calculation unit described above, Calculate the market value that companies can use as an employee evaluation metric. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the order of input fields based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is As users enter information, the system automatically suggests additional information related to their work history. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

10. The aforementioned reception unit is When users enter data, the system prioritizes displaying the most relevant input fields based on their geographical location. The system according to feature 1.

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