System

Abstract

An object of the system according to the embodiment is to allow a job seeker to sufficiently appeal his / her strength and to improve access to an appropriate job.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information on the work experience, skill, and orientation of the user. The analysis unit learns and analyzes the information received by the reception unit. The generation unit generates an appropriate resume based on the information analyzed by the analysis unit. A provision part provides the resume generated by the generation part to the user to allow the user to confirm and edit it.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 technology makes it difficult for job seekers to create resumes that fully highlight their strengths, which can limit their access to suitable jobs.

[0005] The system according to the embodiment aims to enable job seekers to fully showcase their strengths and improve their access to suitable jobs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information about a user's work history, skills, and aspirations. The analysis unit learns and analyzes the information received by the reception unit. The generation unit generates an appropriate resume based on the information analyzed by the analysis unit. The provision unit provides the resume generated by the generation unit to the user, allowing the user to review and edit it. [Effects of the Invention]

[0007] The system according to the embodiment allows job seekers to fully showcase their strengths and improve their access to suitable jobs. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) An automatic resume generation system according to an embodiment of the present invention learns a user's work history, skills, and aspirations and automatically generates a resume optimal for a job. In this automatic resume generation system, a user inputs their work history, skills, and aspirations into a dedicated platform, and AI learns this information and automatically generates an optimal resume based on the latest trends and demands of the job market. The generated resume is provided to the user on the dedicated platform for review and editing. For example, the automatic resume generation system allows a user to input detailed information such as their past work experience, acquired qualifications, specialized skills, and future career aspirations. The AI ​​then learns the input information and generates an optimal resume based on the latest trends and demands of the job market. The generated resume is provided to the user on the dedicated platform for review and editing. This allows job seekers to fully showcase their strengths and improve their access to suitable jobs. This allows the automatic resume generation system to learn a user's work history, skills, and aspirations and automatically generate a resume optimal for a job. For example, a user can easily create a resume that meets the latest trends and demands of the job market, smoothly progressing through job applications. This allows job seekers to fully showcase their strengths and improve their access to suitable jobs.

[0029] An automatic resume generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information about a user's work history, skills, and aspirations. For example, a user inputs their work history, skills, and aspirations into a dedicated platform. The user can input detailed information such as their past work experience, acquired qualifications, preferred skills, and future career aspirations. The analysis unit learns and analyzes the information received by the reception unit. For example, an AI analyzes the user's work history, skills, and aspirations and generates an optimal resume based on the latest trends and demands in the job market. The generation unit generates an optimal resume based on the information analyzed by the analysis unit. For example, the AI ​​considers the skills and experience required in the current job market and generates a resume that maximizes the user's strengths. The provision unit provides the resume generated by the generation unit to the user, allowing them to review and edit it. For example, the user can review the generated resume and enter corrections or additional information as necessary. This allows the automatic resume generation system according to an embodiment to learn the user's work history, skills, and aspirations and automatically generate a resume optimal for the job.

[0030] The reception unit can receive detailed information about the user's work history, skills, and aspirations. Examples of detailed information include, but are not limited to, details of job duties, skill levels, and specific aspirational goals. The reception unit can receive information such as what projects the user has participated in in the past, what roles the user played, and what results the user achieved. The reception unit can also receive detailed information such as the user's acquired qualifications, special skills, and future career aspirations. This allows the reception of detailed information about the user's work history, skills, and aspirations.

[0031] The analytics unit may use online job databases and industry reports to understand trends in the job market. Examples of job market trends include, but are not limited to, in-demand occupations, industry trends, and salary trends. For example, the analytics unit may use online job databases to understand the skills and experience that are in demand in the current job market. The analytics unit may also use industry reports to understand trends in specific industries and salary trends. This allows the analytics unit to understand trends in the job market.

[0032] The generation unit can generate a resume that fully brings out the user's strengths. The user's strengths include, but are not limited to, specific skills, experience, and achievements. The generation unit can, for example, use AI to analyze the user's work history, skills, and inclinations, and generate a resume that maximizes the user's strengths. For example, the generation unit can generate a resume that maximizes the user's strengths by taking into account the skills and experience required in the current job market. The generation unit can also select specific expressions and formats to highlight the user's strengths. This makes it possible to generate a resume that maximizes the user's strengths.

[0033] The providing unit may provide the generated resume through a user interface, allowing the user to review and edit it. Specific methods for reviewing and editing include, but are not limited to, the design of the user interface and details of the editing function. For example, the providing unit may provide the generated resume on a dedicated platform, allowing the user to review and edit it. The user may review the generated resume and make corrections or enter additional information as necessary. The providing unit may also customize the resume to suit the company to which the user is applying when applying for a specific job. This allows the user to review and edit the generated resume.

[0034] Furthermore, the automatic resume generation system includes an application unit that sends the generated resume to a job site or a company. The application unit, for example, has a function for sending the generated resume to a job site or a company. The application unit automatically sends the generated resume to a job site or a company selected by the user. The application unit can also suggest the optimal application method when the user selects where to apply. For example, the application unit may refer to the user's past application history and select the optimal application method. This allows the generated resume to be sent to a job site or a company.

[0035] The reception unit can analyze the user's past work history and skill input history and select the optimal input method. The optimal input method includes, but is not limited to, input efficiency and user convenience, for example. The reception unit, for example, preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also automatically complete information previously input by the user, simplifying the input process. This allows the optimal input method to be selected based on the user's past input history.

[0036] The reception unit can filter the work history and skills based on the user's current occupation and areas of interest when the work history and skills are input. Examples of filtering include, but are not limited to, occupation and area of ​​interest categories, filtering algorithms, and the like. For example, the reception unit allows the user to input skills and experience related to the user's current occupation with priority. The reception unit can also suggest related work history and skills based on the user's areas of interest. The reception unit can also filter unnecessary information based on the user's current occupation and areas of interest, simplifying the input process. This allows filtering to be performed based on the user's current occupation and areas of interest.

[0037] When inputting work history and skills, the reception unit can select an appropriate input means according to the user's input method. Optimal input means include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the reception unit can support the input using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also provide a text input interface. Furthermore, if the user desires image input, the reception unit can also support the input using image recognition technology. This allows the optimal input means to be selected according to the user's input method.

[0038] When inputting work history and skills, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. Highly relevant information includes, but is not limited to, job content and skills based on the geographical location. For example, the reception unit allows the user to prioritize input of work history and skills related to the user's current location. The reception unit can also suggest related work history and skills based on the user's geographical location information. The reception unit can also filter unnecessary information based on the user's geographical location information to simplify the input work. This allows highly relevant information to be prioritized input based on the user's geographical location information.

[0039] When entering work history and skills, the reception unit can analyze the user's social media activity and input related information. Social media activity includes, but is not limited to, for example, the content of posts and reactions from followers. For example, the reception unit can analyze the user's social media activity and suggest related work history and skills. The reception unit can also suggest related work history and skills by referring to the activities of the user's friends on social media. The reception unit can also analyze the content of the user's social media posts and suggest related work history and skills. This makes it possible to input related information based on the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting work history and skills. Examples of customizing the input method include, but are not limited to, an adjustment method based on the user's feedback. For example, the reception unit can suggest an optimal input method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. This allows the input method to be customized based on the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of work history and skills. The level of detail of the analysis includes, for example, the depth of analysis based on importance and the provision of detailed data, but is not limited to these examples. For example, the analysis unit performs a detailed analysis on important work history and skills. The analysis unit can also perform a simplified analysis on less important work history and skills. The analysis unit can also determine the priority of the analysis based on the importance of work history and skills. This makes it possible to adjust the level of detail of the analysis based on the importance of work history and skills.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of work history and skills. Examples of different analysis algorithms include, but are not limited to, clustering algorithms and regression analysis. For example, the analysis unit can apply a technical analysis algorithm to technical work history and skills. Furthermore, the analysis unit can also apply a managerial analysis algorithm to managerial work history and skills. Furthermore, the analysis unit can also apply a creative analysis algorithm to creative work history and skills. This allows different analysis algorithms to be applied depending on the category of work history and skills.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Examples of analysis accuracy include, but are not limited to, how past data is used and algorithm improvements. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of work history and skills. The analysis priority includes, but is not limited to, a method of determining priority based on the time of submission, for example. For example, the analysis unit prioritizes analysis of the most recently submitted work history and skills. The analysis unit can also postpone work history and skills that have been submitted older. The analysis unit can also determine the priority of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of work history and skills.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of work history and skills. The order of analysis includes, for example, a method of determining the order based on the relevance, but is not limited to such an example. For example, the analysis unit prioritizes the analysis of highly relevant work history and skills. The analysis unit can also postpone the analysis of less relevant work history and skills. The analysis unit can also determine the order of analysis based on the relevance of work history and skills. This makes it possible to adjust the order of analysis based on the relevance of work history and skills.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The use of technical terms includes, but is not limited to, an adjustment method according to the user's level of expertise. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid using technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] When generating a resume, the generation unit can adjust the level of detail of the generation based on the importance of work history and skills. Examples of the level of detail of the generation include, but are not limited to, adjustment methods based on the importance of work history and skills. For example, the generation unit generates a resume that includes detailed information for important work history and skills. The generation unit can also generate a resume that includes simplified information for less important work history and skills. The generation unit can also adjust the level of detail of the resume based on the importance of work history and skills. This makes it possible to adjust the level of detail of the generation based on the importance of work history and skills.

[0048] When generating a resume, the generation unit can apply different generation algorithms depending on the category of work history and skills. Examples of different generation algorithms include, but are not limited to, template-based generation and machine learning-based generation. For example, the generation unit can apply a technical generation algorithm to technical work history and skills. The generation unit can also apply a managerial generation algorithm to managerial work history and skills. The generation unit can also apply a creative generation algorithm to creative work history and skills. This allows different generation algorithms to be applied depending on the category of work history and skills.

[0049] When generating a resume, the generation unit can improve the accuracy of generation by referring to the user's past generation results. Generation accuracy includes, but is not limited to, for example, how to use the past generation results and improving the algorithm. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also extract specific patterns from the user's past generation results to improve the accuracy of generation. The generation unit can also determine the priority of generation by referring to the user's past generation results. This makes it possible to improve the accuracy of generation by referring to the user's past generation results.

[0050] When generating a resume, the generation unit can determine the generation priority based on the time of submission of the work history and skills. The generation priority includes, but is not limited to, a method of determining the priority based on the time of submission, for example. The generation unit, for example, preferentially reflects the most recently submitted work history and skills in the resume. The generation unit can also postpone the work history and skills that were submitted earlier. The generation unit can also determine the generation priority of the resume based on the time of submission. This makes it possible to determine the generation priority based on the time of submission of the work history and skills.

[0051] When generating a resume, the generation unit can adjust the generation order based on the relevance of work history and skills. The generation order includes, for example, but is not limited to, a method of determining the order based on relevance. For example, the generation unit preferentially reflects highly relevant work history and skills in the resume. The generation unit can also postpone less relevant work history and skills. The generation unit can also determine the order of generating the resume based on the relevance of work history and skills. This makes it possible to adjust the generation order based on the relevance of work history and skills.

[0052] When generating a resume, the generation unit can adjust the use of technical terms in the generation according to the user's level of expertise. The use of technical terms includes, but is not limited to, an adjustment method according to the user's level of expertise. For example, the generation unit uses a lot of technical terms when the user's level of expertise is high. The generation unit can also avoid technical terms when the user's level of expertise is low. The generation unit can also adjust the use of technical terms in the generation according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the generation according to the user's level of expertise.

[0053] When displaying a resume, the providing unit can select an appropriate display method by referring to the user's past operation history. The optimal display method includes, but is not limited to, a selection method based on the user's past operation history, for example. The providing unit, for example, preferentially provides a display method that the user has used favorably in the past. The providing unit can also suggest a specific display method based on the user's past operation history. The providing unit can also customize the display interface based on the user's past operation history. This makes it possible to select an optimal display method based on the user's past operation history.

[0054] The providing unit can customize the display content according to the user's current task when displaying the resume. Examples of customizing the display content include, but are not limited to, an adjustment method based on the current task. For example, when the user is applying for a job, the providing unit can prioritize displaying information necessary for the application. Furthermore, when the user is editing a resume, the providing unit can also prioritize displaying information necessary for editing. Furthermore, the providing unit can also customize the display content according to the user's current task. This allows the display content to be customized according to the user's current task.

[0055] When displaying a resume, the providing unit can select an appropriate display method taking into account the user's device information. The optimal display method includes, but is not limited to, a selection method based on the device information, for example. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a display method that includes detailed information. This makes it possible to select the optimal display method based on the user's device information.

[0056] The providing unit can make the displayed content multilingual in accordance with the user's language setting when displaying the resume. Multilingual support includes, but is not limited to, a method of changing the displayed content based on the language setting, for example. The providing unit can automatically set the language of the resume based on the language setting of the user's device, for example. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also display the resume in a specific language when the user selects that language. This allows the displayed content to be multilingual based on the user's language setting.

[0057] The providing unit may analyze the user's social media activity and provide related information when displaying the resume. Social media activity may include, but is not limited to, for example, the content of posts and the reactions of followers. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the content of the user's social media posts and suggest related work history and skills. The providing unit may also suggest related work history and skills based on the activities of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity.

[0058] When displaying a resume, the providing unit can make suggestions based on the schedule by referring to the user's calendar information. Calendar information includes, for example, the type of schedule and the criteria for suggestions, but is not limited to these examples. For example, the providing unit can refer to the schedule registered in the user's calendar and suggest related work history and skills. The providing unit can also suggest work history and skills related to a specific event from the user's calendar information. The providing unit can also suggest an optimal resume that matches the schedule based on the user's calendar information. This allows suggestions based on the schedule to be made based on the user's calendar information.

[0059] When applying, the application unit can select the optimal application method by referring to the user's past application history. The optimal application method includes, but is not limited to, for example, a selection method based on the user's past application history. For example, the application unit can preferentially suggest application methods that the user has been successful with in the past. The application unit can also suggest specific application methods based on the user's past application history. The application unit can also customize the application interface based on the user's past application history. This makes it possible to select the optimal application method based on the user's past application history.

[0060] When applying, the application unit can filter application destinations based on the user's current occupation or area of ​​interest. Examples of methods for filtering application destinations include, but are not limited to, filtering methods based on occupation or area of ​​interest. For example, the application unit preferentially suggests application destinations related to the user's current occupation. The application unit can also suggest related application destinations based on the user's area of ​​interest. The application unit can also filter unnecessary application destinations based on the user's current occupation or area of ​​interest. This makes it possible to filter application destinations based on the user's current occupation or area of ​​interest.

[0061] When applying, the application unit can prioritize selecting highly relevant application destinations taking into account the user's geographical location information. Highly relevant application destinations include, but are not limited to, selection methods based on geographical location information, for example. The application unit, for example, prioritizes suggesting application destinations related to the user's current location. The application unit can also suggest related application destinations based on the user's geographical location information. The application unit can also filter unnecessary application destinations based on the user's geographical location information. This allows highly relevant application destinations to be prioritized based on the user's geographical location information.

[0062] At the time of application, the application unit can analyze the user's social media activity and select relevant application destinations. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, the application unit can analyze the user's social media activity and suggest relevant application destinations. The application unit can also suggest relevant application destinations based on the activity of the user's friends on social media. The application unit can also analyze the content of the user's social media posts and suggest relevant application destinations. This makes it possible to select relevant application destinations based on the user's social media activity.

[0063] When applying, the application unit can make suggestions based on the user's schedule by referring to the user's calendar information. Calendar information includes, for example, the type of schedule and the criteria for suggestions, but is not limited to these examples. For example, the application unit can refer to the schedule registered in the user's calendar and suggest related destinations for application. The application unit can also suggest destinations for application related to a specific event from the user's calendar information. The application unit can also suggest the optimal destination for application based on the user's calendar information to match the schedule. This allows suggestions based on the user's calendar information.

[0064] The application unit can suggest appropriate places to apply to, taking into consideration the user's health condition when applying. Examples of health conditions include, but are not limited to, health checkup results, self-reported health conditions, etc. For example, if the user is tired, the application unit can prioritize suggesting nearby places to apply to. Furthermore, if the user is in good health, the application unit can also suggest places to apply to that are far away. Furthermore, the application unit can adjust the priority of places to apply to based on the user's health condition. This makes it possible to suggest the most appropriate place to apply to based on the user's health condition.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The reception unit not only receives information about the user's work history, skills, and aspirations, but also collects the user's past application history and provides it to the analysis unit. For example, by collecting information such as the companies and job types to which the user has applied in the past, and the results of the applications, and providing this information to the analysis unit, the analysis unit can understand the user's application trends and generate a more accurate resume. The reception unit can also collect feedback from companies to which the user has applied in the past and provide this to the analysis unit. This allows a more appropriate resume to be generated based on the user's application history.

[0067] The analysis unit can also take into account the user's social media activity when analyzing the user's work history, skills, and aspirations. For example, the analysis unit can analyze the content the user posts on social media and the reactions of their followers to understand the user's interests. The analysis unit can also suggest suitable jobs and companies for the user based on the activities of the user's friends on social media. Furthermore, the analysis unit can analyze the content of the user's social media posts to extract the user's strengths and selling points. This makes it possible to generate a more appropriate resume based on the user's social media activity.

[0068] When providing the generated resume to the user, the providing unit can select an appropriate display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. This makes it possible to select the optimal display method based on the user's device information.

[0069] When accepting information on the user's work history, skills, and preferences, the accepting unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, the accepting unit can prioritize input of work history and skills related to the user's current location. The accepting unit can also suggest related work history and skills based on the user's geographical location information. Furthermore, the accepting unit can filter unnecessary information based on the user's geographical location information to simplify the input work. This allows highly relevant information to be prioritized input based on the user's geographical location information.

[0070] The analysis unit can take the user's health condition into consideration when analyzing the user's work history, skills, and inclinations. For example, if the user is tired, the level of detail of the analysis can be simplified. Alternatively, if the user is in good health, a detailed analysis can be performed. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's health condition. This allows the level of detail and priority of the analysis to be adjusted based on the user's health condition.

[0071] When providing the generated resume to the user, the providing unit can select an appropriate display method by referring to the user's past operation history. For example, the providing unit can provide a display method that the user has used favorably in the past with priority. The providing unit can also suggest a specific display method based on the user's past operation history. Furthermore, the providing unit can customize the display interface based on the user's past operation history. This makes it possible to select the optimal display method based on the user's past operation history.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit accepts information about the user's work history, skills, and aspirations. For example, the user enters their work history, skills, and aspirations into a dedicated platform. The user can enter detailed information such as past work experience, acquired qualifications, preferred skills, and future career aspirations. Step 2: The analysis unit learns and analyzes the information received by the reception unit. For example, the AI ​​analyzes the user's work history, skills, and aspirations, and generates an optimal resume based on the latest trends and demands of the job market. Step 3: The generator generates an optimal resume based on the information analyzed by the analyzer. For example, the AI ​​considers the skills and experience required in the current job market and generates a resume that maximizes the user's strengths. Step 4: The providing unit provides the resume generated by the generating unit to the user and allows the user to check and edit the resume. For example, the user can check the generated resume and enter corrections or additional information as necessary.

[0074] (Example 2) An automatic resume generation system according to an embodiment of the present invention learns a user's work history, skills, and aspirations and automatically generates a resume optimal for a job. In this automatic resume generation system, a user inputs their work history, skills, and aspirations into a dedicated platform, and AI learns this information and automatically generates an optimal resume based on the latest trends and demands of the job market. The generated resume is provided to the user on the dedicated platform for review and editing. For example, the automatic resume generation system allows a user to input detailed information such as their past work experience, acquired qualifications, specialized skills, and future career aspirations. The AI ​​then learns the input information and generates an optimal resume based on the latest trends and demands of the job market. The generated resume is provided to the user on the dedicated platform for review and editing. This allows job seekers to fully showcase their strengths and improve their access to suitable jobs. This allows the automatic resume generation system to learn a user's work history, skills, and aspirations and automatically generate a resume optimal for a job. For example, a user can easily create a resume that meets the latest trends and demands of the job market, smoothly progressing through job applications. This allows job seekers to fully showcase their strengths and improve their access to suitable jobs.

[0075] An automatic resume generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information about a user's work history, skills, and aspirations. For example, a user inputs their work history, skills, and aspirations into a dedicated platform. The user can input detailed information such as their past work experience, acquired qualifications, preferred skills, and future career aspirations. The analysis unit learns and analyzes the information received by the reception unit. For example, an AI analyzes the user's work history, skills, and aspirations and generates an optimal resume based on the latest trends and demands in the job market. The generation unit generates an optimal resume based on the information analyzed by the analysis unit. For example, the AI ​​considers the skills and experience required in the current job market and generates a resume that maximizes the user's strengths. The provision unit provides the resume generated by the generation unit to the user, allowing them to review and edit it. For example, the user can review the generated resume and enter corrections or additional information as necessary. This allows the automatic resume generation system according to an embodiment to learn the user's work history, skills, and aspirations and automatically generate a resume optimal for the job.

[0076] The reception unit can receive detailed information about the user's work history, skills, and aspirations. Examples of detailed information include, but are not limited to, details of job duties, skill levels, and specific aspirational goals. The reception unit can receive information such as what projects the user has participated in in the past, what roles the user played, and what results the user achieved. The reception unit can also receive detailed information such as the user's acquired qualifications, special skills, and future career aspirations. This allows the reception of detailed information about the user's work history, skills, and aspirations.

[0077] The analytics unit may use online job databases and industry reports to understand trends in the job market. Examples of job market trends include, but are not limited to, in-demand occupations, industry trends, and salary trends. For example, the analytics unit may use online job databases to understand the skills and experience that are in demand in the current job market. The analytics unit may also use industry reports to understand trends in specific industries and salary trends. This allows the analytics unit to understand trends in the job market.

[0078] The generation unit can generate a resume that fully brings out the user's strengths. The user's strengths include, but are not limited to, specific skills, experience, and achievements. The generation unit can, for example, use AI to analyze the user's work history, skills, and inclinations, and generate a resume that maximizes the user's strengths. For example, the generation unit can generate a resume that maximizes the user's strengths by taking into account the skills and experience required in the current job market. The generation unit can also select specific expressions and formats to highlight the user's strengths. This makes it possible to generate a resume that maximizes the user's strengths.

[0079] The providing unit may provide the generated resume through a user interface, allowing the user to review and edit it. Specific methods for reviewing and editing include, but are not limited to, the design of the user interface and details of the editing function. For example, the providing unit may provide the generated resume on a dedicated platform, allowing the user to review and edit it. The user may review the generated resume and make corrections or enter additional information as necessary. The providing unit may also customize the resume to suit the company to which the user is applying when applying for a specific job. This allows the user to review and edit the generated resume.

[0080] Furthermore, the automatic resume generation system includes an application unit that sends the generated resume to a job site or a company. The application unit, for example, has a function for sending the generated resume to a job site or a company. The application unit automatically sends the generated resume to a job site or a company selected by the user. The application unit can also suggest the optimal application method when the user selects where to apply. For example, the application unit may refer to the user's past application history and select the optimal application method. This allows the generated resume to be sent to a job site or a company.

[0081] The reception unit can estimate the user's emotions and adjust the timing of inputting work history and skills based on the estimated user emotions. Emotion estimation includes, but is not limited to, for example, an emotion recognition algorithm, data to be used, and the like. For example, if the user is feeling stressed, the reception unit can divide the input so that it can be completed in a short time. Furthermore, if the user is relaxed, the reception unit can prompt the user to enter more detailed information and collect more information. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting only the most important information. This makes it possible to adjust the timing of inputting work history and skills according to the user's emotions.

[0082] The reception unit can analyze the user's past work history and skill input history and select the optimal input method. The optimal input method includes, but is not limited to, input efficiency and user convenience, for example. The reception unit, for example, preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also automatically complete information previously input by the user, simplifying the input process. This allows the optimal input method to be selected based on the user's past input history.

[0083] The reception unit can filter the work history and skills based on the user's current occupation and areas of interest when the work history and skills are input. Examples of filtering include, but are not limited to, occupation and area of ​​interest categories, filtering algorithms, and the like. For example, the reception unit allows the user to input skills and experience related to the user's current occupation with priority. The reception unit can also suggest related work history and skills based on the user's areas of interest. The reception unit can also filter unnecessary information based on the user's current occupation and areas of interest, simplifying the input process. This allows filtering to be performed based on the user's current occupation and areas of interest.

[0084] When inputting work history and skills, the reception unit can select an appropriate input means according to the user's input method. Optimal input means include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the reception unit can support the input using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also provide a text input interface. Furthermore, if the user desires image input, the reception unit can also support the input using image recognition technology. This allows the optimal input means to be selected according to the user's input method.

[0085] The reception unit can estimate the user's emotions and determine the priority of the work history and skills to be input based on the estimated user's emotions. The priority of the work history and skills to be input includes, but is not limited to, for example, importance, relevance, and the user's intentions. For example, when the user is feeling stressed, the reception unit can cause the user to input only the most important work history and skills with priority. Furthermore, when the user is relaxed, the reception unit can cause the user to input detailed work history and skills with priority. Furthermore, when the user is in a hurry, the reception unit can cause the user to input only the most important information with priority. This makes it possible to determine the input priority of the work history and skills based on the user's emotions.

[0086] When inputting work history and skills, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. Highly relevant information includes, but is not limited to, job content and skills based on the geographical location. For example, the reception unit allows the user to prioritize input of work history and skills related to the user's current location. The reception unit can also suggest related work history and skills based on the user's geographical location information. The reception unit can also filter unnecessary information based on the user's geographical location information to simplify the input work. This allows highly relevant information to be prioritized input based on the user's geographical location information.

[0087] When entering work history and skills, the reception unit can analyze the user's social media activity and input related information. Social media activity includes, but is not limited to, for example, the content of posts and reactions from followers. For example, the reception unit can analyze the user's social media activity and suggest related work history and skills. The reception unit can also suggest related work history and skills by referring to the activities of the user's friends on social media. The reception unit can also analyze the content of the user's social media posts and suggest related work history and skills. This makes it possible to input related information based on the user's social media activity.

[0088] The reception unit can customize the input method by reflecting the user's past feedback when inputting work history and skills. Examples of customizing the input method include, but are not limited to, an adjustment method based on the user's feedback. For example, the reception unit can suggest an optimal input method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. This allows the input method to be customized based on the user's past feedback.

[0089] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. Examples of ways to present the analysis include, but are not limited to, graph display, text display, and interactive display. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the way the analysis is presented to be adjusted based on the user's emotions.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of work history and skills. The level of detail of the analysis includes, for example, the depth of analysis based on importance and the provision of detailed data, but is not limited to these examples. For example, the analysis unit performs a detailed analysis on important work history and skills. The analysis unit can also perform a simplified analysis on less important work history and skills. The analysis unit can also determine the priority of the analysis based on the importance of work history and skills. This makes it possible to adjust the level of detail of the analysis based on the importance of work history and skills.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the category of work history and skills. Examples of different analysis algorithms include, but are not limited to, clustering algorithms and regression analysis. For example, the analysis unit can apply a technical analysis algorithm to technical work history and skills. Furthermore, the analysis unit can also apply a managerial analysis algorithm to managerial work history and skills. Furthermore, the analysis unit can also apply a creative analysis algorithm to creative work history and skills. This allows different analysis algorithms to be applied depending on the category of work history and skills.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Examples of analysis accuracy include, but are not limited to, how past data is used and algorithm improvements. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. Examples of the length of the analysis include, but are not limited to, an adjustment method based on the user's emotions. For example, the analysis unit can provide a short and concise analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a short and concise analysis result when the user is in a hurry. This allows the length of the analysis to be adjusted based on the user's emotions.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of work history and skills. The analysis priority includes, but is not limited to, a method of determining priority based on the time of submission, for example. For example, the analysis unit prioritizes analysis of the most recently submitted work history and skills. The analysis unit can also postpone work history and skills that have been submitted older. The analysis unit can also determine the priority of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of work history and skills.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of work history and skills. The order of analysis includes, for example, a method of determining the order based on the relevance, but is not limited to such an example. For example, the analysis unit prioritizes the analysis of highly relevant work history and skills. The analysis unit can also postpone the analysis of less relevant work history and skills. The analysis unit can also determine the order of analysis based on the relevance of work history and skills. This makes it possible to adjust the order of analysis based on the relevance of work history and skills.

[0096] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The use of technical terms includes, but is not limited to, an adjustment method according to the user's level of expertise. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid using technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0097] The generation unit can estimate the user's emotions and adjust the resume generation method based on the estimated user's emotions. Examples of the resume generation method include, but are not limited to, an adjustment method based on the user's emotions. For example, the generation unit can generate a detailed resume when the user is relaxed. Also, the generation unit can generate a concise resume when the user is in a hurry. Also, the generation unit can generate a visually appealing resume when the user is excited. In this way, the resume generation method can be adjusted based on the user's emotions.

[0098] When generating a resume, the generation unit can adjust the level of detail of the generation based on the importance of work history and skills. Examples of the level of detail of the generation include, but are not limited to, adjustment methods based on the importance of work history and skills. For example, the generation unit generates a resume that includes detailed information for important work history and skills. The generation unit can also generate a resume that includes simplified information for less important work history and skills. The generation unit can also adjust the level of detail of the resume based on the importance of work history and skills. This makes it possible to adjust the level of detail of the generation based on the importance of work history and skills.

[0099] When generating a resume, the generation unit can apply different generation algorithms depending on the category of work history and skills. Examples of different generation algorithms include, but are not limited to, template-based generation and machine learning-based generation. For example, the generation unit can apply a technical generation algorithm to technical work history and skills. The generation unit can also apply a managerial generation algorithm to managerial work history and skills. The generation unit can also apply a creative generation algorithm to creative work history and skills. This allows different generation algorithms to be applied depending on the category of work history and skills.

[0100] When generating a resume, the generation unit can improve the accuracy of generation by referring to the user's past generation results. Generation accuracy includes, but is not limited to, for example, how to use the past generation results and improving the algorithm. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also extract specific patterns from the user's past generation results to improve the accuracy of generation. The generation unit can also determine the priority of generation by referring to the user's past generation results. This makes it possible to improve the accuracy of generation by referring to the user's past generation results.

[0101] The generation unit can estimate the user's emotions and adjust the length of the resume based on the estimated user emotions. Examples of the length of the resume include, but are not limited to, adjustment methods based on the user's emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point resume. If the user is relaxed, the generation unit can generate a longer resume with detailed information. If the user is excited, the generation unit can generate a resume with visually appealing effects. This allows the length of the resume to be adjusted based on the user's emotions.

[0102] When generating a resume, the generation unit can determine the generation priority based on the time of submission of the work history and skills. The generation priority includes, but is not limited to, a method of determining the priority based on the time of submission, for example. The generation unit, for example, preferentially reflects the most recently submitted work history and skills in the resume. The generation unit can also postpone the work history and skills that were submitted earlier. The generation unit can also determine the generation priority of the resume based on the time of submission. This makes it possible to determine the generation priority based on the time of submission of the work history and skills.

[0103] When generating a resume, the generation unit can adjust the generation order based on the relevance of work history and skills. The generation order includes, for example, but is not limited to, a method of determining the order based on relevance. For example, the generation unit preferentially reflects highly relevant work history and skills in the resume. The generation unit can also postpone less relevant work history and skills. The generation unit can also determine the order of generating the resume based on the relevance of work history and skills. This makes it possible to adjust the generation order based on the relevance of work history and skills.

[0104] When generating a resume, the generation unit can adjust the use of technical terms in the generation according to the user's level of expertise. The use of technical terms includes, but is not limited to, an adjustment method according to the user's level of expertise. For example, the generation unit uses a lot of technical terms when the user's level of expertise is high. The generation unit can also avoid technical terms when the user's level of expertise is low. The generation unit can also adjust the use of technical terms in the generation according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the generation according to the user's level of expertise.

[0105] The providing unit can estimate the user's emotions and adjust the resume display method based on the estimated user's emotions. Examples of the resume display method include, but are not limited to, an adjustment method based on the user's emotions. For example, when the user is nervous, the providing unit can provide a simple, highly visible display method. When the user is relaxed, the providing unit can also provide a display method including detailed information. When the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the resume display method based on the user's emotions.

[0106] When displaying a resume, the providing unit can select an appropriate display method by referring to the user's past operation history. The optimal display method includes, but is not limited to, a selection method based on the user's past operation history, for example. The providing unit, for example, preferentially provides a display method that the user has used favorably in the past. The providing unit can also suggest a specific display method based on the user's past operation history. The providing unit can also customize the display interface based on the user's past operation history. This makes it possible to select an optimal display method based on the user's past operation history.

[0107] The providing unit can customize the display content according to the user's current task when displaying the resume. Examples of customizing the display content include, but are not limited to, an adjustment method based on the current task. For example, when the user is applying for a job, the providing unit can prioritize displaying information necessary for the application. Furthermore, when the user is editing a resume, the providing unit can also prioritize displaying information necessary for editing. Furthermore, the providing unit can also customize the display content according to the user's current task. This allows the display content to be customized according to the user's current task.

[0108] When displaying a resume, the providing unit can select an appropriate display method taking into account the user's device information. The optimal display method includes, but is not limited to, a selection method based on the device information, for example. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a display method that includes detailed information. This makes it possible to select the optimal display method based on the user's device information.

[0109] The providing unit can estimate the user's emotions and adjust the resume operation procedures based on the estimated user's emotions. Examples of adjusting the operation procedures include, but are not limited to, an adjustment method based on the user's emotions. For example, the providing unit can provide simple and intuitive operation procedures when the user is nervous. Furthermore, the providing unit can provide detailed operation procedures when the user is relaxed. Furthermore, the providing unit can provide procedures that can be operated quickly when the user is in a hurry. In this way, the resume operation procedures can be adjusted based on the user's emotions.

[0110] The providing unit can make the displayed content multilingual in accordance with the user's language setting when displaying the resume. Multilingual support includes, but is not limited to, a method of changing the displayed content based on the language setting, for example. The providing unit can automatically set the language of the resume based on the language setting of the user's device, for example. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also display the resume in a specific language when the user selects that language. This allows the displayed content to be multilingual based on the user's language setting.

[0111] The providing unit may analyze the user's social media activity and provide related information when displaying the resume. Social media activity may include, but is not limited to, for example, the content of posts and the reactions of followers. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the content of the user's social media posts and suggest related work history and skills. The providing unit may also suggest related work history and skills based on the activities of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity.

[0112] When displaying a resume, the providing unit can make suggestions based on the schedule by referring to the user's calendar information. Calendar information includes, for example, the type of schedule and the criteria for suggestions, but is not limited to these examples. For example, the providing unit can refer to the schedule registered in the user's calendar and suggest related work history and skills. The providing unit can also suggest work history and skills related to a specific event from the user's calendar information. The providing unit can also suggest an optimal resume that matches the schedule based on the user's calendar information. This allows suggestions based on the schedule to be made based on the user's calendar information.

[0113] The application unit can estimate the user's emotions and adjust the timing of application based on the estimated user emotions. Examples of application timing include, but are not limited to, methods of adjustment based on the user's emotions. For example, if the user is nervous, the application unit can encourage the user to apply at a time when the user is relaxed. Furthermore, if the user is relaxed, the application unit can encourage the user to apply immediately. Furthermore, if the user is in a hurry, the application unit can enable the user to apply quickly. This makes it possible to adjust the timing of application based on the user's emotions.

[0114] When applying, the application unit can select the optimal application method by referring to the user's past application history. The optimal application method includes, but is not limited to, for example, a selection method based on the user's past application history. For example, the application unit can preferentially suggest application methods that the user has been successful with in the past. The application unit can also suggest specific application methods based on the user's past application history. The application unit can also customize the application interface based on the user's past application history. This makes it possible to select the optimal application method based on the user's past application history.

[0115] When applying, the application unit can filter application destinations based on the user's current occupation or area of ​​interest. Examples of methods for filtering application destinations include, but are not limited to, filtering methods based on occupation or area of ​​interest. For example, the application unit preferentially suggests application destinations related to the user's current occupation. The application unit can also suggest related application destinations based on the user's area of ​​interest. The application unit can also filter unnecessary application destinations based on the user's current occupation or area of ​​interest. This makes it possible to filter application destinations based on the user's current occupation or area of ​​interest.

[0116] When applying, the application unit can prioritize selecting highly relevant application destinations taking into account the user's geographical location information. Highly relevant application destinations include, but are not limited to, selection methods based on geographical location information, for example. The application unit, for example, prioritizes suggesting application destinations related to the user's current location. The application unit can also suggest related application destinations based on the user's geographical location information. The application unit can also filter unnecessary application destinations based on the user's geographical location information. This allows highly relevant application destinations to be prioritized based on the user's geographical location information.

[0117] The application unit can estimate the user's emotions and determine the priority of applications based on the estimated user's emotions. The application priority can be determined, for example, by a method based on the user's emotions, but is not limited to such examples. For example, if the user is nervous, the application unit can preferentially suggest only the most important application destinations. Furthermore, if the user is relaxed, the application unit can also preferentially suggest detailed application destinations. Furthermore, if the user is in a hurry, the application unit can also preferentially suggest only the most important application destinations. This allows the application priority to be determined based on the user's emotions.

[0118] At the time of application, the application unit can analyze the user's social media activity and select relevant application destinations. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, the application unit can analyze the user's social media activity and suggest relevant application destinations. The application unit can also suggest relevant application destinations based on the activity of the user's friends on social media. The application unit can also analyze the content of the user's social media posts and suggest relevant application destinations. This makes it possible to select relevant application destinations based on the user's social media activity.

[0119] When applying, the application unit can make suggestions based on the user's schedule by referring to the user's calendar information. Calendar information includes, for example, the type of schedule and the criteria for suggestions, but is not limited to these examples. For example, the application unit can refer to the schedule registered in the user's calendar and suggest related destinations for application. The application unit can also suggest destinations for application related to a specific event from the user's calendar information. The application unit can also suggest the optimal destination for application based on the user's calendar information to match the schedule. This allows suggestions based on the user's calendar information.

[0120] The application unit can suggest appropriate places to apply to, taking into consideration the user's health condition when applying. Examples of health conditions include, but are not limited to, health checkup results, self-reported health conditions, etc. For example, if the user is tired, the application unit can prioritize suggesting nearby places to apply to. Furthermore, if the user is in good health, the application unit can also suggest places to apply to that are far away. Furthermore, the application unit can adjust the priority of places to apply to based on the user's health condition. This makes it possible to suggest the most appropriate place to apply to based on the user's health condition. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and application unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives information about the user's work history, skills, and preferences via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, learns and analyzes the user's work history, skills, and preferences. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates an optimal resume based on the analyzed information. The provision unit, for example, provides the generated resume to the user via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12, allowing the user to review and edit it. The application unit, for example, transmits the generated resume to a job site or a company via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and application unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives information about the user's work history, skills, and inclinations via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and learns and analyzes the user's work history, skills, and inclinations. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal resume based on the analyzed information. The provision unit provides the generated resume to the user via, for example, the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12, allowing the user to review and edit it. The application unit transmits the generated resume to a job site or a company via, for example, the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and application unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives information about the user's work history, skills, and preferences via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, learns and analyzes the user's work history, skills, and preferences. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates an optimal resume based on the analyzed information. The provision unit, for example, provides the generated resume to the user via the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing device 12, allowing the user to review and edit it. The application unit, for example, transmits the generated resume to a job site or a company via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and application unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives information about the user's work history, skills, and inclinations via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and learns and analyzes the user's work history, skills, and inclinations. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal resume based on the analyzed information. The provision unit provides the generated resume to the user via, for example, the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12, allowing the user to review and edit it. The application unit transmits the generated resume to a job site or a company via, for example, the communication I / F 26 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The reception unit not only receives information about the user's work history, skills, and aspirations, but also collects the user's past application history and provides it to the analysis unit. For example, by collecting information such as the companies and job types to which the user has applied in the past, and the results of the applications, and providing this information to the analysis unit, the analysis unit can understand the user's application trends and generate a more accurate resume. The reception unit can also collect feedback from companies to which the user has applied in the past and provide this to the analysis unit. This allows a more appropriate resume to be generated based on the user's application history.

[0123] The analysis unit can also take into account the user's social media activity when analyzing the user's work history, skills, and aspirations. For example, the analysis unit can analyze the content the user posts on social media and the reactions of their followers to understand the user's interests. The analysis unit can also suggest suitable jobs and companies for the user based on the activities of the user's friends on social media. Furthermore, the analysis unit can analyze the content of the user's social media posts to extract the user's strengths and selling points. This makes it possible to generate a more appropriate resume based on the user's social media activity.

[0124] When generating a resume based on the user's work history, skills, and aspirations, the generation unit can estimate the user's emotions and adjust the resume design based on the estimated user's emotions. For example, if the user is relaxed, a visually appealing design can be adopted. If the user is nervous, a simple, highly visible design can be adopted. Furthermore, if the user is in a hurry, a concise design that focuses on the main points can be adopted. In this way, the resume design can be adjusted based on the user's emotions.

[0125] When providing the generated resume to the user, the providing unit can select an appropriate display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. This makes it possible to select the optimal display method based on the user's device information.

[0126] The application unit can estimate the user's emotions and adjust the timing of application based on the estimated user emotions. For example, if the user is nervous, the application unit can encourage the user to apply at a time when the user is relaxed. Also, if the user is relaxed, the application unit can encourage the user to apply immediately. Furthermore, if the user is in a hurry, the application unit can be made to be quick. In this way, the application timing can be adjusted based on the user's emotions.

[0127] When accepting information on the user's work history, skills, and preferences, the accepting unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, the accepting unit can prioritize input of work history and skills related to the user's current location. The accepting unit can also suggest related work history and skills based on the user's geographical location information. Furthermore, the accepting unit can filter unnecessary information based on the user's geographical location information to simplify the input work. This allows highly relevant information to be prioritized input based on the user's geographical location information.

[0128] The analysis unit can take the user's health condition into consideration when analyzing the user's work history, skills, and inclinations. For example, if the user is tired, the level of detail of the analysis can be simplified. Alternatively, if the user is in good health, a detailed analysis can be performed. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's health condition. This allows the level of detail and priority of the analysis to be adjusted based on the user's health condition.

[0129] When generating a resume based on the user's work history, skills, and aspirations, the generator can estimate the user's emotions and adjust the length of the resume based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point resume can be generated. If the user is relaxed, a longer resume containing detailed information can be generated. Furthermore, if the user is excited, a resume with visually appealing effects can be generated. In this way, the length of the resume can be adjusted based on the user's emotions.

[0130] When providing the generated resume to the user, the providing unit can select an appropriate display method by referring to the user's past operation history. For example, the providing unit can provide a display method that the user has used favorably in the past with priority. The providing unit can also suggest a specific display method based on the user's past operation history. Furthermore, the providing unit can customize the display interface based on the user's past operation history. This makes it possible to select the optimal display method based on the user's past operation history.

[0131] The application unit can estimate the user's emotions and determine the priority of applications based on the estimated user's emotions. For example, if the user is nervous, only the most important places to apply can be preferentially suggested. Also, if the user is relaxed, detailed places to apply can be suggested. Furthermore, if the user is in a hurry, only the most important places to apply can be preferentially suggested. In this way, the priority of applications can be determined based on the user's emotions.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The reception unit accepts information about the user's work history, skills, and aspirations. For example, the user enters their work history, skills, and aspirations into a dedicated platform. The user can enter detailed information such as past work experience, acquired qualifications, preferred skills, and future career aspirations. Step 2: The analysis unit learns and analyzes the information received by the reception unit. For example, the AI ​​analyzes the user's work history, skills, and aspirations, and generates an optimal resume based on the latest trends and demands of the job market. Step 3: The generator generates an optimal resume based on the information analyzed by the analyzer. For example, the AI ​​considers the skills and experience required in the current job market and generates a resume that maximizes the user's strengths. Step 4: The providing unit provides the resume generated by the generating unit to the user and allows the user to check and edit the resume. For example, the user can check the generated resume and enter corrections or additional information as necessary.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0162] 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.

[0163] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 reception unit that receives information about a user's work history, skills, and aspirations; an analysis unit that learns and analyzes the information received by the reception unit; a generation unit that generates an appropriate resume based on the information analyzed by the analysis unit; a providing unit that provides the resume generated by the generating unit to the user and enables the user to check and edit the resume. A system characterized by:

2. The reception unit Accepts detailed information about the user's work history, skills, and interests 2. The system of claim 1.

3. The analysis unit Use online job databases and industry reports to understand job market trends 2. The system of claim 1.

4. The generation unit Generate a resume that highlights your strengths 2. The system of claim 1.

5. The providing unit Provide the generated resume through a user interface for users to review and edit.

2. The system of claim 1.

6. The providing unit It has an application section that sends the generated resume to job sites and companies.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of inputting work history and skills based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past work history and skill input history to select the appropriate input method 2. The system of claim 1.

9. The reception unit Filtering based on the user's current occupation or interests when entering work experience and skills 2. The system of claim 1.

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