System

Abstract

An object of the system according to the embodiment is to automatically generate the latest resumes and curriculum vitae only by the user inputting daily work reports.SOLUTION: A system according to an embodiment includes a daily report input unit, a learning analysis unit, and an automatic generation unit. The daily report input unit inputs a daily work report of the user. The learning analysis unit learns and analyzes the information input by the daily report input unit. An automatic generation part automatically generates the latest resumes or curriculum vitae based on the information learned by the learning analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating a resume or curriculum vitae is complicated and places a heavy burden on users.

[0005] The system according to the embodiment aims to automatically generate an up-to-date resume or curriculum vitae simply by inputting a daily work report by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a daily report input unit, a learning analysis unit, and an automatic generation unit. The daily report input unit inputs a user's daily work report. The learning analysis unit learns and analyzes the information input by the daily report input unit. The automatic generation unit automatically generates an updated resume or curriculum vitae based on the information learned by the learning analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate the latest resumes and curriculum vitae simply by the user inputting their daily work reports. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The resume creation support system according to an embodiment of the present invention is a system in which a user simply inputs their daily work report, and the generation AI learns that information and automatically generates the latest resumes and curriculum vitae. This allows the user to immediately start job hunting and reduces the burden of creating self-introduction materials.

[0029] A resume creation support system according to an embodiment includes a daily report input unit, a learning analysis unit, and an automatic generation unit. The daily report input unit inputs a user's daily work report. For example, the user inputs daily work content and achievements as a daily work report. The learning analysis unit learns and analyzes the information input by the daily report input unit. For example, the generation AI learns and analyzes the information in the daily work report input by the user. The automatic generation unit automatically generates the latest resume or curriculum vitae based on the information learned by the learning analysis unit. For example, the generation AI automatically generates the latest resume or curriculum vitae based on the learned information. As a result, the resume creation support system according to an embodiment can automatically generate the latest resume or curriculum vitae simply by the user inputting their daily work report.

[0030] The daily report input unit has a voice input function, allowing the user to input the daily work report by voice. For example, the user can use the microphone on their smartphone or computer to input the daily work report by voice. For example, by simply saying, "Today I managed the progress of Project X and held a meeting with team members," the voice recognition technology converts the content into text. This allows the user to input the daily work report by voice.

[0031] The daily report input unit is equipped with a template or suggestion function, allowing users to input daily work reports simply by selecting from options. For example, the daily report input unit provides templates on the input screen for daily work reports, and the necessary fields are automatically displayed when the user simply selects a template that suits their work content. For example, if the "Project Management" template is selected, fields for progress status and tools used are automatically displayed. This allows users to easily input daily work reports using the template or suggestion function.

[0032] The daily report input unit is equipped with a mobile app, which allows the user to input the daily work report anywhere. For example, the daily report input unit develops a mobile app so that the user can input the daily work report anywhere using a smartphone or tablet. For example, the user can easily input the daily work report while commuting or while out and about. This allows the user to input the daily work report anywhere.

[0033] The daily report input unit can work in conjunction with other work management tools to automate the input of daily work reports. The daily report input unit can work in conjunction with, for example, project management software or task management apps, and automatically reflect information entered by the user into these tools in the daily work report. For example, the progress status and task completion status entered into the project management software can be automatically incorporated into the daily work report. This allows the input of daily work reports to be automated.

[0034] The learning analysis unit can also refer to the user's past resumes and work history to perform more accurate analysis. For example, the generation AI can refer to the user's past resumes and work history to perform more accurate analysis based on past achievements and career history. For example, it can refer to past projects and skills used and analyze them in comparison with current daily work reports. This allows for more accurate analysis by referring to the user's past resumes and work history.

[0035] The learning analysis unit can also take industry trends or the latest technical information into consideration when analyzing the daily work reports. For example, when the generation AI analyzes the daily work reports, the learning analysis unit automatically collects industry trends and the latest technical information and reflects this in the analysis. For example, the learning analysis unit analyzes the contents of the daily work reports taking into consideration the latest technological trends and market needs. This makes it possible to analyze the daily work reports taking into consideration industry trends and the latest technical information.

[0036] The learning analysis unit can also anonymously learn from other users' daily work report data and extract common patterns and trends. For example, the generation AI in the learning analysis unit anonymously learns from other users' daily work report data and extracts common patterns and trends. For example, it learns from the daily work reports of users in the same industry or occupation and extracts common skills and tools. This makes it possible to anonymously learn from other users' daily work report data and extract common patterns and trends.

[0037] When analyzing daily work reports, the learning analysis unit can handle data in different languages ​​and perform analysis from a global perspective. For example, the learning analysis unit enables the generation AI to analyze daily work report data in different languages ​​and performs analysis from a global perspective. For example, it analyzes daily work reports entered in multiple languages, such as English, Japanese, and French, and extracts common patterns and trends. This makes it possible to handle data in different languages ​​and perform analysis from a global perspective.

[0038] The automatic generation unit can customize the resume or CV to suit the user's career goals or desired job type. For example, the generation AI customizes the resume or CV based on the user's career goals or desired job type. For example, it highlights the skills and experience related to the job type the user desires. This allows customization to suit the user's career goals or desired job type.

[0039] The automatic generation unit can automatically apply a visually appealing design or layout to the resume or job history to be generated. For example, the automatic generation unit automatically applies a visually appealing design or layout when the generation AI generates a resume or job history. For example, a professional design template is used to create a layout that is easy to read and understand. This makes it possible to automatically apply a visually appealing design or layout.

[0040] The automatic generation unit can make it possible to output the generated resumes and work histories in different formats. For example, when the generation AI generates a resume or work history, the automatic generation unit makes it possible to output it in different formats. For example, it can output it in a format that suits the user's needs, such as PDF, Word, or HTML. This allows the resume or work history to be output in different formats.

[0041] The automatic generation unit can automatically incorporate a user's social media profile information into the resume or work history that it generates. For example, when the generation AI generates a resume or work history, the automatic generation unit automatically incorporates a user's social media profile information. For example, LinkedIn profile information is automatically incorporated and reflected in the resume or work history. This makes it possible to automatically incorporate a user's social media profile information.

[0042] In the automatic generation unit, when a user customizes the resume or work history to be generated, the generation AI can provide feedback in real time and suggest the optimal expression or layout. In the automatic generation unit, for example, when a user customizes a resume or work history, the generation AI can provide feedback in real time and suggest the optimal expression or layout. For example, if a user wants to emphasize a particular skill, the generation AI can suggest the optimal expression or layout related to that skill. In this way, when a user customizes, the generation AI can provide feedback in real time and suggest the optimal expression or layout.

[0043] The automatic generation unit can allow a user to refer to previously created versions of a resume or curriculum vitae when customizing the resume or curriculum vitae to be generated. For example, the automatic generation unit allows a user to refer to previously created versions when customizing a resume or curriculum vitae. For example, the current resume or curriculum vitae is customized while comparing it with previous versions. This allows a user to refer to previously created versions when customizing.

[0044] The automatic generation unit can provide a function that allows a user to refer to samples created by other users when customizing the resume or curriculum vitae to be generated. The automatic generation unit provides a function that allows a user to refer to samples created by other users when customizing a resume or curriculum vitae, for example. For example, samples from the same industry or job type can be displayed and used as reference. This allows a user to refer to samples created by other users when customizing.

[0045] The automatic generation unit can link with the user's cloud storage and automatically back up data when saving the generated resume or curriculum vitae. For example, when the generation AI saves a resume or curriculum vitae, the automatic generation unit can link with the user's cloud storage and automatically back up data. For example, it can automatically save it to cloud storage such as Google Drive or Dropbox. This allows for automatic data backup in link with the user's cloud storage.

[0046] The automatic generation unit can retain past versions when updating the resume or job history it generates, allowing the user to revert to a past version at any time. For example, when the generation AI updates a resume or job history, the automatic generation unit can retain past versions, allowing the user to revert to a past version at any time. For example, it can display a list of past versions so that the user can select and revert to one. This allows the user to revert to a past version at any time.

[0047] The automatic generation unit can make it possible to synchronize the generated resumes and job history documents between different devices. For example, when the generation AI saves a resume or job history document, the automatic generation unit makes it possible to synchronize the resume or job history document between different devices. For example, it makes it possible to view and edit the same resume or job history document on multiple devices, such as smartphones, tablets, and PCs. This allows the resume or job history document to be synchronized between different devices.

[0048] The automatic generation unit can automatically update the user's social media profile information when updating the resume or job history it generates. For example, when the generation AI updates the resume or job history, the automatic generation unit also automatically updates the user's social media profile information. For example, it automatically updates LinkedIn profile information to reflect the latest information. This allows the user's social media profile information to be automatically updated.

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

[0050] The resume creation support system can also be equipped with a skill matching function based on the user's career goals. For example, it can automatically extract the skills required for the user's desired job or industry and compare them with the user's current skill set to show any skills that are lacking. This allows the user to clearly understand the skills needed to achieve their career goals and create a plan for skill development. The skill matching function can also take into account the user's past qualifications and training. For example, it can evaluate the user's skill progress based on the content of the training they have taken in the past and compare it with their current daily work report. Furthermore, the skill matching function can also reflect industry trends and the latest technological information. For example, it can predict the skills the user will need in the future based on the latest technological trends and suggest a learning plan.

[0051] The resume creation support system can also be equipped with a function to analyze a user's daily work report and provide feedback for health management. For example, if a user enters "I worked long hours today" in their daily work report, the system can use that information to emphasize the importance of rest and provide advice on how to take appropriate rest. Also, if a user enters "I wasn't feeling well today," the system can make specific suggestions for health management. Furthermore, by analyzing a user's daily work report over the long term and identifying trends in stress and fatigue, the system can provide preventative health management advice. This allows users to receive health management support through their daily work reports.

[0052] The resume creation support system can also be equipped with a function to analyze a user's daily work log and provide career advice. For example, if a user enters in their daily work log that they "took on a new project," the system can provide advice on career advancement based on that information. Also, if a user enters that they "received training to improve their skills," the system can suggest the next skills or qualifications they should learn based on the training content. Furthermore, by analyzing the user's daily work log over the long term and evaluating their career direction and progress toward their goals, the system can also suggest specific career plans. This allows users to receive career advice through their daily work log.

[0053] The resume creation support system can also be equipped with a function that analyzes a user's daily work report and provides feedback to promote communication with team members. For example, if a user enters in their daily work report that they "held a meeting with team members," the system can use that information to provide feedback on areas for improvement and success stories in communication. Similarly, if a user enters that they "exchanged opinions with team members," the system can suggest effective communication methods based on the content of that exchange. Furthermore, by analyzing a user's daily work report over the long term and understanding communication trends across the team, the system can provide specific advice for team building. This allows users to receive feedback on how to improve communication with team members through their daily work reports.

[0054] The resume creation support system can also be equipped with a function to analyze a user's daily work report and provide feedback for project management. For example, if a user enters "I performed project progress management" in their daily work report, the system will use that information to evaluate the project's progress and provide feedback on areas for improvement and success stories. Also, if a user enters "I performed project risk management," the system can suggest effective risk management methods based on the details of that risk management. Furthermore, by analyzing a user's daily work report over the long term and understanding the overall project trend, the system can provide specific advice for project management. This allows users to receive feedback for project management through their daily work reports.

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

[0056] Step 1: The daily report input unit inputs the user's daily work report. For example, the user inputs the details and results of daily work as the daily work report. Step 2: The learning and analysis unit learns and analyzes the information entered by the daily report input unit. For example, the generation AI learns and analyzes the information in the daily work report entered by the user. Step 3: The automatic generation unit automatically generates the latest resumes and curriculum vitae based on the information learned by the learning and analysis unit. For example, the generation AI automatically generates the latest resumes and curriculum vitae based on the learned information.

[0057] (Example 2) The resume creation support system according to an embodiment of the present invention is a system in which a user simply inputs their daily work report, and the generation AI learns that information and automatically generates the latest resumes and curriculum vitae. This allows the user to immediately start job hunting and reduces the burden of creating self-introduction materials.

[0058] A resume creation support system according to an embodiment includes a daily report input unit, a learning analysis unit, and an automatic generation unit. The daily report input unit inputs a user's daily work report. For example, the user inputs daily work content and achievements as a daily work report. The learning analysis unit learns and analyzes the information input by the daily report input unit. For example, the generation AI learns and analyzes the information in the daily work report input by the user. The automatic generation unit automatically generates the latest resume or curriculum vitae based on the information learned by the learning analysis unit. For example, the generation AI automatically generates the latest resume or curriculum vitae based on the learned information. As a result, the resume creation support system according to an embodiment can automatically generate the latest resume or curriculum vitae simply by the user inputting their daily work report.

[0059] The daily report input unit has a voice input function, allowing the user to input the daily work report by voice. For example, the user can use the microphone on their smartphone or computer to input the daily work report by voice. For example, by simply saying, "Today I managed the progress of Project X and held a meeting with team members," the voice recognition technology converts the content into text. This allows the user to input the daily work report by voice.

[0060] The daily report input unit is equipped with a template or suggestion function, allowing users to input daily work reports simply by selecting from options. For example, the daily report input unit provides templates on the input screen for daily work reports, and the necessary fields are automatically displayed when the user simply selects a template that suits their work content. For example, if the "Project Management" template is selected, fields for progress status and tools used are automatically displayed. This allows users to easily input daily work reports using the template or suggestion function.

[0061] The daily report input unit has an emotion estimation function, and can analyze the emotion a user expresses when entering data and provide feedback to elicit positive emotions. For example, when a user enters a daily work report, the daily report input unit uses the emotion estimation function to analyze the user's emotion from the input content. For example, if the user enters "I was busy today," the emotion estimation function detects stress and provides advice on how to relax. This makes it possible to analyze the user's emotion and provide feedback to elicit positive emotions.

[0062] The daily report input unit is equipped with a mobile app, which allows the user to input the daily work report anywhere. For example, the daily report input unit develops a mobile app so that the user can input the daily work report anywhere using a smartphone or tablet. For example, the user can easily input the daily work report while commuting or while out and about. This allows the user to input the daily work report anywhere.

[0063] The daily report input unit can work in conjunction with other work management tools to automate the input of daily work reports. The daily report input unit can work in conjunction with, for example, project management software or task management apps, and automatically reflect information entered by the user into these tools in the daily work report. For example, the progress status and task completion status entered into the project management software can be automatically incorporated into the daily work report. This allows the input of daily work reports to be automated.

[0064] The daily report input unit has an emotion estimation function, and can display the user's emotion in real time when inputting data, and provide advice according to the input content. For example, when a user inputs a daily work report, the daily report input unit uses the emotion estimation function to display the user's emotion in real time from the input content. For example, if the user inputs "I was busy today," the emotion estimation function detects stress and displays advice on how to relax. This makes it possible to display the user's emotion in real time and provide advice according to the input content.

[0065] The learning analysis unit can also refer to the user's past resumes and work history to perform more accurate analysis. For example, the generation AI can refer to the user's past resumes and work history to perform more accurate analysis based on past achievements and career history. For example, it can refer to past projects and skills used and analyze them in comparison with current daily work reports. This allows for more accurate analysis by referring to the user's past resumes and work history.

[0066] The learning analysis unit can also take industry trends or the latest technical information into consideration when analyzing the daily work reports. For example, when the generation AI analyzes the daily work reports, the learning analysis unit automatically collects industry trends and the latest technical information and reflects this in the analysis. For example, the learning analysis unit analyzes the contents of the daily work reports taking into consideration the latest technological trends and market needs. This makes it possible to analyze the daily work reports taking into consideration industry trends and the latest technical information.

[0067] The learning analysis unit uses the emotion estimation function to analyze the emotions contained in the user's daily work report and can emphasize emotionally positive elements. For example, when the generation AI analyzes the daily work report, the learning analysis unit uses the emotion estimation function to analyze the user's emotions and emphasize positive elements. For example, if the user enters "The project went smoothly today," the generation AI will analyze the user's daily work report and emphasize the positive elements. This allows the emotions contained in the user's daily work report to be analyzed and the positive elements to be emphasized.

[0068] The learning analysis unit can also anonymously learn from other users' daily work report data and extract common patterns and trends. For example, the generation AI in the learning analysis unit anonymously learns from other users' daily work report data and extracts common patterns and trends. For example, it learns from the daily work reports of users in the same industry or occupation and extracts common skills and tools. This makes it possible to anonymously learn from other users' daily work report data and extract common patterns and trends.

[0069] When analyzing daily work reports, the learning analysis unit can handle data in different languages ​​and perform analysis from a global perspective. For example, the learning analysis unit enables the generation AI to analyze daily work report data in different languages ​​and performs analysis from a global perspective. For example, it analyzes daily work reports entered in multiple languages, such as English, Japanese, and French, and extracts common patterns and trends. This makes it possible to handle data in different languages ​​and perform analysis from a global perspective.

[0070] The learning analysis unit can use the emotion estimation function to analyze the user's emotional response to the daily work report in real time and provide feedback to elicit positive emotions. For example, when the generation AI analyzes the daily work report, the learning analysis unit can use the emotion estimation function to analyze the user's emotional response in real time and provide feedback to elicit positive emotions. For example, if the user enters, "The project went smoothly today," the generation AI will analyze the positive elements and display positive feedback. This allows the user's emotional response to the daily work report to be analyzed in real time and feedback to elicit positive emotions to be provided.

[0071] The automatic generation unit can customize the resume or CV to suit the user's career goals or desired job type. For example, the generation AI customizes the resume or CV based on the user's career goals or desired job type. For example, it highlights the skills and experience related to the job type the user desires. This allows customization to suit the user's career goals or desired job type.

[0072] The automatic generation unit can automatically apply a visually appealing design or layout to the resume or job history to be generated. For example, the automatic generation unit automatically applies a visually appealing design or layout when the generation AI generates a resume or job history. For example, a professional design template is used to create a layout that is easy to read and understand. This makes it possible to automatically apply a visually appealing design or layout.

[0073] The automatic generation unit can make it possible to output the generated resumes and work histories in different formats. For example, when the generation AI generates a resume or work history, the automatic generation unit makes it possible to output it in different formats. For example, it can output it in a format that suits the user's needs, such as PDF, Word, or HTML. This allows the resume or work history to be output in different formats.

[0074] The automatic generation unit can automatically incorporate a user's social media profile information into the resume or work history that it generates. For example, when the generation AI generates a resume or work history, the automatic generation unit automatically incorporates a user's social media profile information. For example, LinkedIn profile information is automatically incorporated and reflected in the resume or work history. This makes it possible to automatically incorporate a user's social media profile information.

[0075] The automatic generation unit uses the emotion estimation function to provide customization options based on the user's emotions, making it possible to create content that elicits positive emotions. For example, when the generation AI generates a resume or a job history, the automatic generation unit uses the emotion estimation function to provide customization options based on the user's emotions. For example, the automatic generation unit can highlight projects that the user felt a sense of accomplishment in order to create content that elicits positive emotions. This allows the automatic generation unit to provide customization options based on the user's emotions, making it possible to create content that elicits positive emotions.

[0076] In the automatic generation unit, when a user customizes the resume or work history to be generated, the generation AI can provide feedback in real time and suggest the optimal expression or layout. In the automatic generation unit, for example, when a user customizes a resume or work history, the generation AI can provide feedback in real time and suggest the optimal expression or layout. For example, if a user wants to emphasize a particular skill, the generation AI can suggest the optimal expression or layout related to that skill. In this way, when a user customizes, the generation AI can provide feedback in real time and suggest the optimal expression or layout.

[0077] The automatic generation unit can allow a user to refer to previously created versions of a resume or curriculum vitae when customizing the resume or curriculum vitae to be generated. For example, the automatic generation unit allows a user to refer to previously created versions when customizing a resume or curriculum vitae. For example, the current resume or curriculum vitae is customized while comparing it with previous versions. This allows a user to refer to previously created versions when customizing.

[0078] The automatic generation unit can use the emotion estimation function to analyze the emotions of the user when customizing the resume or curriculum vitae and provide advice to draw out positive emotions. For example, when a user customizes a resume or curriculum vitae, the automatic generation unit can use the emotion estimation function to analyze the emotions and provide advice to draw out positive emotions. For example, if the user is feeling stressed, advice to relax is displayed. This makes it possible to analyze the emotions of the user when customizing the resume and provide advice to draw out positive emotions.

[0079] The automatic generation unit can provide a function that allows a user to refer to samples created by other users when customizing the resume or curriculum vitae to be generated. The automatic generation unit provides a function that allows a user to refer to samples created by other users when customizing a resume or curriculum vitae, for example. For example, samples from the same industry or job type can be displayed and used as reference. This allows a user to refer to samples created by other users when customizing.

[0080] The automatic generation unit can use the emotion estimation function to display the emotions of the user when customizing in real time and provide customization options based on the emotions. For example, when a user customizes a resume or a curriculum vitae, the automatic generation unit can use the emotion estimation function to display the emotions in real time and provide customization options based on the emotions. For example, if the user is feeling stressed, a customization option for relaxation is displayed. This makes it possible to display the emotions of the user when customizing in real time and provide customization options based on the emotions.

[0081] The automatic generation unit can link with the user's cloud storage and automatically back up data when saving the generated resume or curriculum vitae. For example, when the generation AI saves a resume or curriculum vitae, the automatic generation unit can link with the user's cloud storage and automatically back up data. For example, it can automatically save it to cloud storage such as Google Drive or Dropbox. This allows for automatic data backup in link with the user's cloud storage.

[0082] The automatic generation unit can retain past versions when updating the resume or job history it generates, allowing the user to revert to a past version at any time. For example, when the generation AI updates a resume or job history, the automatic generation unit can retain past versions, allowing the user to revert to a past version at any time. For example, it can display a list of past versions so that the user can select and revert to one. This allows the user to revert to a past version at any time.

[0083] The automatic generation unit can use the emotion estimation function to analyze the emotions of the user when saving or updating, and provide feedback to elicit positive emotions. For example, when the generation AI saves or updates a resume or job history, the automatic generation unit can use the emotion estimation function to analyze the user's emotions and provide feedback to elicit positive emotions. For example, if the user is feeling stressed, feedback to help them relax is displayed. This makes it possible to analyze the emotions of the user when saving or updating, and provide feedback to elicit positive emotions.

[0084] The automatic generation unit can make it possible to synchronize the generated resumes and job history documents between different devices. For example, when the generation AI saves a resume or job history document, the automatic generation unit makes it possible to synchronize the resume or job history document between different devices. For example, it makes it possible to view and edit the same resume or job history document on multiple devices, such as smartphones, tablets, and PCs. This allows the resume or job history document to be synchronized between different devices.

[0085] The automatic generation unit can automatically update the user's social media profile information when updating the resume or job history it generates. For example, when the generation AI updates the resume or job history, the automatic generation unit also automatically updates the user's social media profile information. For example, it automatically updates LinkedIn profile information to reflect the latest information. This allows the user's social media profile information to be automatically updated.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The resume creation support system can also be equipped with a skill matching function based on the user's career goals. For example, it can automatically extract the skills required for the user's desired job or industry and compare them with the user's current skill set to show any skills that are lacking. This allows the user to clearly understand the skills needed to achieve their career goals and create a plan for skill development. The skill matching function can also take into account the user's past qualifications and training. For example, it can evaluate the user's skill progress based on the content of the training they have taken in the past and compare it with their current daily work report. Furthermore, the skill matching function can also reflect industry trends and the latest technological information. For example, it can predict the skills the user will need in the future based on the latest technological trends and suggest a learning plan.

[0088] The resume creation support system can further include a function to estimate a user's emotions and support the input of a daily work report based on the estimated emotions. For example, when a user inputs a daily work report, the emotion estimation function can analyze the user's emotions in real time and provide advice to bring out positive emotions. For example, if the user inputs "I was busy today," the emotion estimation function can detect stress and display advice to help them relax. In addition, for work that the user felt a sense of accomplishment from, feedback emphasizing the positive emotions can be provided. This allows the user to receive emotion-based support when inputting a daily work report.

[0089] The resume creation support system can also be equipped with a function to analyze a user's daily work report and provide feedback for health management. For example, if a user enters "I worked long hours today" in their daily work report, the system can use that information to emphasize the importance of rest and provide advice on how to take appropriate rest. Also, if a user enters "I wasn't feeling well today," the system can make specific suggestions for health management. Furthermore, by analyzing a user's daily work report over the long term and identifying trends in stress and fatigue, the system can provide preventative health management advice. This allows users to receive health management support through their daily work reports.

[0090] The resume creation support system can further include a function for estimating a user's emotions and providing incentives to encourage the user to enter their daily work report based on the estimated emotions. For example, when a user enters their daily work report, the emotion estimation function can be used to analyze the user's emotions and provide rewards to elicit positive emotions. For example, if a user enters their daily work report with positive emotions, the system can award points or badges to increase the user's motivation. Also, if the user is feeling stressed, the system can provide content that helps them relax along with advice on how to relax. This allows the user to continue entering their daily work report while receiving incentives based on their emotions.

[0091] The resume creation support system can also be equipped with a function to analyze a user's daily work log and provide career advice. For example, if a user enters in their daily work log that they "took on a new project," the system can provide advice on career advancement based on that information. Also, if a user enters that they "received training to improve their skills," the system can suggest the next skills or qualifications they should learn based on the training content. Furthermore, by analyzing the user's daily work log over the long term and evaluating their career direction and progress toward their goals, the system can also suggest specific career plans. This allows users to receive career advice through their daily work log.

[0092] The resume creation support system can further include a reminder function that estimates the user's emotions and supports the input of a daily work report based on the estimated emotions. For example, when a user inputs a daily work report, the emotion estimation function can be used to analyze the user's emotions and provide reminders to elicit positive emotions. For example, if the user is feeling stressed, the system can display a reminder to relax and encourage the user to take appropriate rest. Also, if the user is feeling positive emotions, the system can provide reminders to maintain those emotions. This allows the user to continue inputting their daily work report while receiving reminders based on their emotions.

[0093] The resume creation support system can also be equipped with a function that analyzes a user's daily work report and provides feedback to promote communication with team members. For example, if a user enters in their daily work report that they "held a meeting with team members," the system can use that information to provide feedback on areas for improvement and success stories in communication. Similarly, if a user enters that they "exchanged opinions with team members," the system can suggest effective communication methods based on the content of that exchange. Furthermore, by analyzing a user's daily work report over the long term and understanding communication trends across the team, the system can provide specific advice for team building. This allows users to receive feedback on how to improve communication with team members through their daily work reports.

[0094] The resume creation support system can further include a function for estimating a user's emotions and providing customization options to support the input of a daily work report based on the estimated emotions. For example, when a user inputs a daily work report, the emotion estimation function can be used to analyze the user's emotions and provide customization options to elicit positive emotions. For example, for work that the user felt a sense of accomplishment, a customization option to emphasize the positive emotions can be displayed. Also, if the user is feeling stressed, a customization option to relax can be provided. This allows the user to continue inputting their daily work report while receiving customization options based on their emotions.

[0095] The resume creation support system can also be equipped with a function to analyze a user's daily work report and provide feedback for project management. For example, if a user enters "I performed project progress management" in their daily work report, the system will use that information to evaluate the project's progress and provide feedback on areas for improvement and success stories. Also, if a user enters "I performed project risk management," the system can suggest effective risk management methods based on the details of that risk management. Furthermore, by analyzing a user's daily work report over the long term and understanding the overall project trend, the system can provide specific advice for project management. This allows users to receive feedback for project management through their daily work reports.

[0096] The resume creation support system can further include a guide function that estimates the user's emotions and supports the input of a daily work report based on the estimated emotions. For example, when a user inputs a daily work report, the emotion estimation function can be used to analyze the user's emotions and provide guidance to elicit positive emotions. For example, if the user is feeling stressed, a guide to help them relax can be displayed, encouraging them to take appropriate rest. Also, if the user is feeling positive, a guide to help them maintain those emotions can be provided. This allows the user to continue inputting their daily work report while receiving guidance based on their emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The daily report input unit inputs the user's daily work report. For example, the user inputs the details and results of daily work as the daily work report. Step 2: The learning and analysis unit learns and analyzes the information entered by the daily report input unit. For example, the generation AI learns and analyzes the information in the daily work report entered by the user. Step 3: The automatic generation unit automatically generates the latest resumes and curriculum vitae based on the information learned by the learning and analysis unit. For example, the generation AI automatically generates the latest resumes and curriculum vitae based on the learned information.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a daily report input section for inputting a user's daily business report; a learning and analysis unit that learns and analyzes the information input by the daily report input unit; an automatic generation unit that automatically generates the latest resume or curriculum vitae based on the information learned by the learning analysis unit; A system characterized by:

2. The daily report input unit Emotion estimation function analyzes the emotions of users as they type and provides feedback to elicit positive emotions 2. The system of claim 1.

3. The learning analysis unit It also references the user's past resume and work history to perform a more accurate analysis.

2. The system of claim 1.

4. The automatic generation unit Customize to fit your career goals or desired job type 2. The system of claim 1.

5. The automatic generation unit As users customize their resume or CV, the AI ​​provides real-time feedback and suggests optimal wording or layout.

2. The system of claim 1.

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