system

Abstract

The system according to the embodiment aims to detect grammatical and spelling errors in resume writing and suggest appropriate expressions. [Solution] A system according to an embodiment includes a detection unit, a presentation unit, a suggestion unit, an advice unit, an interface unit, and a feedback unit. The detection unit detects grammatical and spelling errors in input text. The suggestion unit suggests corrections based on the errors detected by the detection unit. The suggestion unit learns from past resume data and suggests appropriate expressions. The advice unit provides advice based on specific evaluation criteria and past success stories. The interface unit provides an interface that is easy for users to operate. The feedback unit provides feedback in real time.

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 technologies do not adequately detect grammatical and spelling errors and suggest appropriate expressions when creating resumes, so there is room for improvement.

[0005] The system according to the embodiment aims to detect grammatical and spelling errors in resume writing and suggest appropriate expressions. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a presentation unit, a suggestion unit, an advice unit, an interface unit, and a feedback unit. The detection unit detects grammatical and spelling errors in input text. The suggestion unit suggests corrections based on the errors detected by the detection unit. The suggestion unit learns from past resume data and suggests appropriate expressions. The advice unit provides advice based on specific evaluation criteria and past success stories. The interface unit provides an interface that is easy for users to operate. The feedback unit provides feedback in real time. [Effects of the Invention]

[0007] The system according to the embodiment can detect grammatical and spelling errors in resume writing and suggest appropriate expressions. [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) A resume creation support system according to an embodiment of the present invention applies a generation AI to detect grammatical and spelling errors in input text, suggest corrections, suggest optimal expressions, provide advice, provide an interface that is easy for users to operate, and provide feedback in real time. For example, the resume creation support system uses a generation AI to detect grammatical and spelling errors in input text. The generation AI then suggests corrections based on the detected errors. Furthermore, the generation AI learns from past resume data and suggests optimal expressions. The generation AI also provides advice based on general evaluation criteria and past success stories. Furthermore, an interface that is easy for users to operate is provided, and the generation AI provides feedback in real time. This allows users to create high-quality resumes in a short amount of time. For example, the generation AI detects grammatical and spelling errors and suggests corrections, allowing users to create accurate resumes. Furthermore, the generation AI suggests optimal expressions and provides advice, thereby more effectively conveying the content of the resume. Furthermore, by providing an easy-to-use interface and providing real-time feedback, users can quickly complete their resumes.

[0029] A resume creation support system according to an embodiment includes a detection unit, a presentation unit, a suggestion unit, an advice unit, an interface unit, and a feedback unit. The detection unit detects grammatical and spelling errors in input text. For example, the detection unit uses natural language processing technology to detect subject-verb agreement, spelling errors, punctuation errors, and the like. The detection unit can also perform grammatical and semantic analysis using a generation AI. The suggestion unit proposes corrections based on the errors detected by the detection unit. For example, the suggestion unit proposes corrections such as grammatical corrections, expression corrections, and style corrections. The suggestion unit can also generate appropriate corrections using a generation AI. The suggestion unit learns from past resume data and proposes optimal expressions. For example, the suggestion unit proposes industry-standard expressions and formal expressions. The suggestion unit can also propose effective ways to promote yourself and describe your work history using a generation AI. The advice unit provides advice based on common evaluation criteria and past success stories. For example, the advice unit provides advice based on grammatical accuracy and appropriateness of expression. The advice unit can also use a generation AI to provide feedback from a third-party perspective. The interface unit provides an interface that is easy for users to operate. For example, the interface unit can provide an intuitive design and a function that automatically generates a resume by simply entering the required information. The interface unit can also use a generation AI to optimize the interface based on the results of a usability test. The feedback unit provides feedback in real time. For example, the feedback unit can immediately present advice or suggested corrections in response to the input content. The feedback unit can also minimize the delay time in feedback using a generation AI. As a result, the resume creation support system according to the embodiment allows users to create high-quality resumes in a short amount of time.

[0030] The detection unit can detect grammatical and spelling errors in the input text using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the detection unit can use morphological analysis to divide the input text into words and detect grammatical errors. The detection unit can also use grammatical analysis to detect errors in subject-verb agreement and tense. The detection unit can also use semantic analysis to detect appropriate word usage based on the context. This allows for accurate detection of grammatical and spelling errors using natural language processing technology. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input the input text to a generation AI and have the generation AI detect grammatical and spelling errors.

[0031] The suggestion unit can suggest correction suggestions based on the mistakes detected by the detection unit. Examples of suggested corrections include, but are not limited to, grammar corrections, expression corrections, and style corrections. For example, the suggestion unit can suggest subject-verb agreement or tense corrections as grammar corrections. Furthermore, the suggestion unit can also suggest simplifying redundant expressions or selecting appropriate words as expression corrections. Furthermore, the suggestion unit can also suggest formal expressions or industry-standard expressions as style corrections. This allows appropriate correction suggestions to be presented based on the detected mistakes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the mistakes detected by the detection unit into the generation AI and cause the generation AI to generate correction suggestions.

[0032] The suggestion unit can learn from past resume data and suggest appropriate expressions. Examples of appropriate expressions include, but are not limited to, industry-standard expressions and formal expressions. For example, the suggestion unit can learn from past resume data and suggest effective ways to promote oneself and describe one's work history. The suggestion unit can also use a generation AI to suggest optimal expressions based on past success stories. The suggestion unit can also optimize the content of the resume based on industry-standard expressions and formal expressions. In this way, optimal expressions can be suggested by learning from past data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input past resume data into a generation AI and have the generation AI suggest optimal expressions.

[0033] The advice unit can provide advice based on general evaluation criteria and past success stories. General evaluation criteria include, but are not limited to, grammatical accuracy and appropriateness of expression. For example, the advice unit can point out grammatical errors and provide suggested corrections based on grammatical accuracy. The advice unit can also advise on simplifying redundant expressions and choosing appropriate words based on appropriateness of expression. The advice unit can also advise on effective self-promotion and work history writing based on past success stories. This allows advice to be provided from an objective perspective. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input general evaluation criteria and past success stories into the generation AI and cause the generation AI to generate advice.

[0034] The interface unit can provide an interface that is easy for users to operate. Examples of an easy-to-operate interface include, but are not limited to, an intuitive design and a function that automatically generates a resume by simply inputting required information. For example, the interface unit can provide an intuitive design, allowing users to easily operate the interface. The interface unit can also provide a function that automatically generates a resume by simply inputting required information. The interface unit can also use a generation AI to optimize the interface based on the results of a usability test. This improves convenience by providing an interface that is easy for users to operate. Some or all of the above-described processing in the interface unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the interface unit can input the results of a usability test into the generation AI and have the generation AI optimize the interface.

[0035] The feedback unit can provide feedback in real time. Real-time feedback includes, but is not limited to, immediate advice or suggested corrections for the input content. For example, the feedback unit can immediately provide advice or suggested corrections for the input content. The feedback unit can also minimize feedback delays using a generation AI. The feedback unit can also optimize the feedback format using a generation AI. This allows for rapid feedback by providing feedback in real time. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the input content to the generation AI and cause the generation AI to generate feedback.

[0036] During detection, the detection unit can prioritize detecting specific mistake patterns by referring to the user's past input history. For example, the detection unit can prioritize detecting grammatical mistakes that the user has made frequently in the past. The detection unit can also prioritize detecting words that the user has frequently misspelled in the past. The detection unit can also prioritize detecting mistakes of specific grammatical structures from the user's past input history. This allows specific mistake patterns to be prioritized by referring to the past input history. Past input history includes, for example, a specific period of time or a specific type of input, but is not limited to such examples. Specific mistake patterns include, for example, frequently occurring spelling mistakes, grammatical mistakes, etc., but are not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using or without the generation AI. For example, the detection unit can input the user's past input history into the generation AI and cause the generation AI to detect specific mistake patterns.

[0037] During detection, the detection unit can more accurately detect grammatical and spelling errors based on the context of the input text. For example, the detection unit can detect misuse of homonyms based on the context. The detection unit can also detect appropriate tense errors by taking the context into account. The detection unit can also detect appropriate preposition errors based on the context. This allows for more accurate detection of grammatical and spelling errors by taking the context into account. Examples of context include, but are not limited to, surrounding sentences and related topics. Some or all of the above-described processing in the detection unit may be performed using or without the generation AI. For example, the detection unit can input the context of the input text to the generation AI and cause the generation AI to detect grammatical and spelling errors.

[0038] During detection, the detection unit can identify errors in technical terminology according to the user's field of expertise. For example, the detection unit can identify errors in technical terminology in the medical field. The detection unit can also identify errors in technical terminology in the legal field. The detection unit can also identify errors in technical terminology in the IT field. This allows for accurate detection of technical errors by identifying errors in technical terminology according to the field of expertise. Examples of technical fields include, but are not limited to, the medical field and the technical field. Examples of technical terminology include, but are not limited to, technical terms in specific industries or fields. Some or all of the above-described processing by the detection unit may be performed using or without the generation AI. For example, the detection unit can input information about the user's field of expertise into the generation AI and cause the generation AI to identify errors in technical terminology.

[0039] During detection, the detection unit can detect region-specific grammatical and spelling errors by taking the user's geographical background into account. The detection unit can detect, for example, spelling differences between British English and American English. The detection unit can also detect misuse of region-specific slang and dialects. The detection unit can also detect errors in region-specific grammatical structures. This allows region-specific errors to be accurately detected by taking the geographical background into account. Examples of geographical background include, but are not limited to, specific regions or countries. Examples of region-specific grammatical and spelling errors include, but are not limited to, differences between British English and American English. Some or all of the above-described processing by the detection unit can be performed using or without the generation AI. For example, the detection unit can input information about the user's geographical background into the generation AI and cause the generation AI to detect region-specific grammatical and spelling errors.

[0040] During detection, the detection unit can analyze the user's social media activity and detect related mistakes. For example, the detection unit can detect misuse of phrases frequently used by the user on social media. The detection unit can also analyze the user's social media posts to detect related grammatical mistakes. The detection unit can also detect related mistakes by referring to the posts of the user's friends on social media. This allows for accurate detection of related mistakes by analyzing social media activity. Social media activity can include, but is not limited to, a specific platform or a specific period. Related mistakes can include, but are not limited to, frequently occurring spelling mistakes and grammatical mistakes. Some or all of the above-described processing in the detection unit can be performed using or without the generation AI. For example, the detection unit can input data on the user's social media activity into the generation AI and cause the generation AI to detect related mistakes.

[0041] The detection unit can customize the detection algorithm by reflecting the user's past feedback during detection. The detection unit can adjust the detection algorithm based on, for example, feedback provided by the user in the past. The detection unit can also preferentially detect specific mistake patterns based on the user's past feedback. The detection unit can also improve detection accuracy by reflecting the user's feedback. This allows the detection algorithm to be customized and accuracy to be improved by reflecting past feedback. Past feedback includes, for example, a specific period of time or a specific type of feedback, but is not limited to such examples. The detection algorithm includes, for example, parameter adjustment, algorithm selection, etc., but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using or without the generation AI. For example, the detection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the detection algorithm.

[0042] The suggestion unit can adjust the level of detail of the proposed revisions based on the importance of the mistake when presenting them. For example, the suggestion unit can present detailed revisions for serious mistakes. The suggestion unit can also present brief revisions for minor mistakes. The suggestion unit can also present moderately detailed revisions for medium mistakes. In this way, by adjusting the level of detail of the revisions based on the importance of the mistake, it is possible to provide appropriate revisions. Examples of the importance of the mistake include, but are not limited to, the severity of grammatical errors and the frequency of spelling mistakes. Examples of the level of detail of the revisions include, but are not limited to, simple revisions and detailed revisions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the importance of the mistake to the generation AI and cause the generation AI to adjust the level of detail of the revisions.

[0043] The presentation unit can apply different correction algorithms depending on the category of the error when presenting the error. For example, the presentation unit can apply a grammar correction algorithm to a grammatical error. The presentation unit can also apply a spelling correction algorithm to a spelling error. The presentation unit can also apply a style correction algorithm to a style error. This allows appropriate correction suggestions to be provided by applying different correction algorithms depending on the category of the error. Examples of error categories include, but are not limited to, grammatical errors, spelling errors, and style errors. Examples of correction algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-mentioned processing in the presentation unit can be performed using or without the generation AI. For example, the presentation unit can input information on the category of the error to the generation AI and cause the generation AI to apply the correction algorithm.

[0044] When presenting a revision suggestion, the presentation unit can improve the accuracy of the revision suggestion by referring to the user's past revision history. The presentation unit can improve the accuracy of the revision suggestion, for example, based on revision suggestions previously accepted by the user. The presentation unit can also learn specific revision patterns from the user's past revision history. The presentation unit can also analyze the user's past revision history and present an optimal revision suggestion. This can improve the accuracy of the revision suggestion by referring to the past revision history. The past revision history includes, for example, a specific period or a specific type of revision, but is not limited to such examples. The accuracy of the revision suggestion includes, for example, accuracy and appropriateness, but is not limited to such examples. Some or all of the above-mentioned processing in the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input the user's past revision history into the generation AI and cause the generation AI to improve the accuracy of the revision suggestion.

[0045] When presenting the proposed corrections, the presentation unit can prioritize the proposed corrections based on the time of the error. For example, the presentation unit prioritizes the proposed corrections for recent errors. The presentation unit can also present proposed corrections later for errors that occurred in the past. The presentation unit can also present proposed corrections for errors that occurred within a specific period of time in a priority order according to the period of time. This allows appropriate proposed corrections to be provided by prioritizing proposed corrections based on the time of the error. Examples of the time of the error include, but are not limited to, recent errors and past errors. Examples of the priority of proposed corrections include, but are not limited to, priority based on importance and priority based on frequency of occurrence. Some or all of the above-described processing by the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input information about the time of the error to the generation AI and have the generation AI determine the priority of the proposed corrections.

[0046] The presentation unit can adjust the order of proposed corrections based on the relevance of the mistakes when presenting them. For example, the presentation unit can prioritize the presentation of highly relevant mistakes as proposed corrections. The presentation unit can also present less relevant mistakes as proposed corrections later. The presentation unit can also adjust the order of proposed corrections according to the relevance of the mistakes. This allows appropriate proposals to be provided by adjusting the order of proposed corrections based on the relevance of the mistakes. Examples of the relevance of mistakes include, but are not limited to, mistakes within the same context and related grammatical mistakes. Examples of the order of proposed corrections include, but are not limited to, an order based on importance or relevance. Some or all of the above-described processing by the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input information on the relevance of mistakes to the generation AI and cause the generation AI to adjust the order of proposed corrections.

[0047] When presenting the revision proposal, the presentation unit can adjust the use of technical terms in the revision proposal depending on the user's level of expertise. For example, the presentation unit may present a revision proposal that uses a lot of technical terms to a user with high levels of expertise. The presentation unit may also present a revision proposal in simpler language to a user with low levels of expertise. The presentation unit may also present a revision proposal using appropriate technical terms depending on the user's level of expertise. This allows for providing an appropriate revision proposal by adjusting the use of technical terms depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. Examples of the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. Some or all of the above-described processing by the presentation unit may be performed using or without the generation AI. For example, the presentation unit may input information about the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the revision proposal.

[0048] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the resume. For example, the suggestion unit can make a detailed proposal for an important resume. The suggestion unit can also make a brief proposal for a minor resume. The suggestion unit can also make a moderately detailed proposal for a medium-level resume. This allows for providing an appropriate proposal by adjusting the level of detail of the proposal based on the importance of the resume. Examples of the importance of the resume include, but are not limited to, the size of the company to which the applicant is applying and the importance of the job type. Examples of the level of detail of the proposal include, but are not limited to, a simple proposal, a detailed proposal, etc. Some or all of the above-described processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the importance of the resume to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the resume. For example, the suggestion unit can suggest technical language for a resume of a technical job. The suggestion unit can also suggest creative language for a resume of a creative job. The suggestion unit can also suggest managerial language for a resume of a managerial job. This allows appropriate suggestions to be provided by applying different suggestion algorithms depending on the category of the resume. Resume categories include, but are not limited to, technical jobs, sales jobs, and managerial jobs. Proposal algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the resume category into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0050] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on suggestions accepted by the user in the past. The suggestion unit can also learn specific suggestion patterns from the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. This allows the accuracy of the suggestion to be improved by referring to the past suggestion results. Past suggestion results include, for example, a specific period or a specific type of suggestion, but are not limited to such examples. The accuracy of the suggestion includes, for example, accuracy and appropriateness, but are not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0051] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the resume. For example, the suggestion unit can prioritize proposals for resumes with an upcoming deadline. The suggestion unit can also postpone proposals for resumes with a more distant submission date. The suggestion unit can also prioritize proposals for resumes to be submitted within a specific period according to the period. This allows appropriate proposals to be provided by determining the priority of proposals based on the time of submission of the resume. Examples of the time of submission of the resume include, but are not limited to, recent submissions and past submissions. Examples of the priority of proposals include, but are not limited to, priority based on importance and priority based on submission time. Some or all of the above-described processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the time of submission of the resume into the generation AI and have the generation AI determine the priority of the proposals.

[0052] When making a proposal, the suggestion unit can adjust the order of the proposals based on the relevance of the resumes. For example, the suggestion unit may prioritize the proposal of highly relevant content. The suggestion unit may also postpone the proposal of less relevant content. The suggestion unit may also adjust the order of the proposals according to the relevance of the content. This allows appropriate proposals to be provided by adjusting the order of proposals based on the relevance of the resumes. Examples of the relevance of resumes include, but are not limited to, resumes of the same job type or resumes in a related industry. Examples of the order of proposals include, but are not limited to, an order based on importance or an order based on relevance. Some or all of the above-described processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input information about the relevance of resumes to the generation AI and cause the generation AI to adjust the order of proposals.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise. For example, the suggestion unit may make a proposal that uses a lot of technical terms for a user with high level of expertise. The suggestion unit may also make a proposal using simpler language for a user with low level of expertise. The suggestion unit may also make a proposal using appropriate technical terms depending on the user's level of expertise. This allows appropriate proposals to be provided by adjusting the use of technical terms depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. Examples of the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input information about the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the proposal.

[0054] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the resume. For example, the advice unit provides detailed advice for an important resume. The advice unit can also provide brief advice for a minor resume. The advice unit can also provide advice with appropriate detail for a medium-level resume. In this way, by adjusting the level of detail of the advice based on the importance of the resume, appropriate advice can be provided. Examples of the importance of the resume include, but are not limited to, the size of the company to which the applicant is applying and the importance of the job type. Examples of the level of detail of the advice include, but are not limited to, simple advice, detailed advice, etc. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information about the importance of the resume to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0055] When providing advice, the advice unit can apply different advice algorithms depending on the category of the resume. For example, the advice unit can provide technical advice for a resume of a technical job. The advice unit can also provide creative advice for a resume of a creative job. The advice unit can also provide managerial advice for a resume of a managerial job. This allows appropriate advice to be provided by applying different advice algorithms depending on the category of the resume. Resume categories include, but are not limited to, technical jobs, sales jobs, and managerial jobs. Advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input resume category information into the generation AI and cause the generation AI to apply the advice algorithm.

[0056] When providing advice, the advice unit can improve the accuracy of the advice by referring to past advice results of the user. The advice unit improves the accuracy of the advice, for example, based on advice accepted by the user in the past. The advice unit can also learn specific advice patterns from the user's past advice results. The advice unit can also analyze the user's past advice results and provide optimal advice. In this way, the accuracy of the advice can be improved by referring to past advice results. Past advice results include, for example, advice for a specific period or a specific type, but are not limited to such examples. The accuracy of the advice includes, for example, accuracy and appropriateness, but are not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using or without using the generation AI. For example, the advice unit can input the user's past advice results into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0057] When providing advice, the advice unit can determine the priority of advice based on the time of submission of the resume. For example, the advice unit can prioritize advice for resumes with an upcoming deadline. The advice unit can also postpone providing advice for resumes with a more distant submission date. The advice unit can also provide advice for resumes to be submitted within a specific period, prioritizing the advice based on the period. This allows appropriate advice to be provided by determining the priority of advice based on the time of submission of the resume. Examples of the time of submission of the resume include, but are not limited to, recent submission and past submission. Examples of the priority of advice include, but are not limited to, priority based on importance and priority based on submission time. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information about the time of submission of the resume to the generation AI and have the generation AI determine the priority of advice.

[0058] When providing advice, the advice unit can adjust the order of advice based on the relevance of the resume. For example, the advice unit prioritizes advice on highly relevant content. The advice unit can also postpone advice on less relevant content. The advice unit can also adjust the order of advice according to the relevance of the content. This allows appropriate advice to be provided by adjusting the order of advice based on the relevance of the resume. Examples of resume relevance include, but are not limited to, resumes in the same job category or resumes in a related industry. Examples of the order of advice include, but are not limited to, an order based on importance or an order based on relevance. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information on the relevance of the resume to the generation AI and cause the generation AI to adjust the order of advice.

[0059] When providing advice, the advice unit can adjust the use of technical terms in the advice depending on the user's level of expertise. For example, the advice unit can provide advice that uses a lot of technical terms to a user with high levels of expertise. The advice unit can also provide advice in simpler terms to a user with low levels of expertise. The advice unit can also provide advice using appropriate technical terms depending on the user's level of expertise. This allows appropriate advice to be provided by adjusting the use of technical terms depending on the user's level of expertise. The user's level of expertise includes, but is not limited to, beginner, intermediate, and advanced. The use of technical terms includes, but is not limited to, the definition and frequency of use of technical terms. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information about the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the advice.

[0060] When displaying an interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit can prioritize displaying an interface design that the user has previously preferred. The interface unit can also select the most efficient display method from the user's past operation history. The interface unit can also analyze the user's past operation history and provide the optimal interface display method. In this way, the optimal display method can be provided by referring to the past operation history. The past operation history includes, for example, a specific period of time or a specific type of operation, but is not limited to such examples. The optimal display method includes, for example, the results of a usability test, an intuitive operation method, etc. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input the user's past operation history into the generation AI and have the generation AI select the optimal display method.

[0061] The interface unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is creating a resume, the interface unit can prioritize displaying related information. Furthermore, if the user is making revisions, the interface unit can prioritize displaying tools necessary for the revisions. The interface unit can also provide optimal display content according to the user's current task. This allows appropriate information to be provided by customizing the display content according to the current task. Examples of the current task include, but are not limited to, a specific task or a specific project. Examples of customizing the display content include, but are not limited to, displaying information according to the task, changing the layout, and the like. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input information about the user's current task into the generation AI and have the generation AI customize the display content.

[0062] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can provide a simple and highly visible display method. This allows the optimal display method to be provided by taking into account the device information. Device information includes, but is not limited to, the device type, screen size, OS, etc. The optimal display method includes, but is not limited to, the results of usability tests, intuitive operation methods, etc. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0063] The interface unit can make the display content multilingual when displaying the interface according to the user's language setting. For example, the interface unit automatically sets the interface language based on the language setting of the user's device. The interface unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the interface unit can display the interface in that language. This allows appropriate information to be provided by making the display content multilingual according to the language setting. Examples of language settings include, but are not limited to, the language used and regional settings. Examples of multilingual support include, but are not limited to, the accuracy of translation and the types of supported languages. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input information about the user's language setting into the generation AI and have the generation AI set the display content to be multilingual.

[0064] The interface unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is creating a resume, the interface unit can prioritize displaying related information. Furthermore, if the user is making revisions, the interface unit can prioritize displaying tools necessary for the revisions. The interface unit can also provide optimal display content according to the user's current task. This allows appropriate information to be provided by customizing the display content according to the current task. Examples of the current task include, but are not limited to, a specific task or a specific project. Examples of customizing the display content include, but are not limited to, displaying information according to the task, changing the layout, and the like. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input information about the user's current task into the generation AI and have the generation AI customize the display content.

[0065] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can provide a simple and highly visible display method. This allows the optimal display method to be provided by taking into account the device information. Device information includes, but is not limited to, the device type, screen size, OS, etc. The optimal display method includes, but is not limited to, the results of usability tests, intuitive operation methods, etc. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0066] The feedback unit can adjust the level of detail of the feedback based on the importance of the resume when providing feedback. For example, the feedback unit can provide detailed feedback for an important resume. The feedback unit can also provide brief feedback for a minor resume. The feedback unit can also provide moderately detailed feedback for a medium-level resume. In this way, by adjusting the level of detail of the feedback based on the importance of the resume, appropriate feedback can be provided. Examples of the importance of the resume include, but are not limited to, the size of the company to which the applicant applied and the importance of the job type. Examples of the level of detail of the feedback include, but are not limited to, simple feedback, detailed feedback, etc. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input information about the importance of the resume to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0067] The feedback unit can apply different feedback algorithms depending on the resume category when providing feedback. For example, the feedback unit can provide technical feedback for a technical resume. The feedback unit can also provide creative feedback for a creative resume. The feedback unit can also provide managerial feedback for a managerial resume. This allows appropriate feedback to be provided by applying different feedback algorithms depending on the resume category. Resume categories include, but are not limited to, technical, sales, and managerial. Feedback algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-described processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input resume category information into the generation AI and cause the generation AI to apply the feedback algorithm.

[0068] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. The feedback unit can improve the accuracy of the feedback, for example, based on feedback the user has accepted in the past. The feedback unit can also learn specific feedback patterns from the user's past feedback results. The feedback unit can also analyze the user's past feedback results and provide optimal feedback. This can improve the accuracy of the feedback by referring to the past feedback results. Past feedback results include, for example, but are not limited to, feedback from a specific period or a specific type. The accuracy of the feedback includes, for example, but is not limited to, accuracy and appropriateness. Some or all of the above-described processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input the user's past feedback results into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0069] When providing feedback, the feedback unit can determine the priority of the feedback based on the time of submission of the resume. For example, the feedback unit can prioritize feedback for resumes with an upcoming deadline. The feedback unit can also provide feedback later for resumes with a more distant submission date. The feedback unit can also provide feedback for resumes to be submitted within a specific period in a priority order according to the period. This allows appropriate feedback to be provided by determining the priority of feedback based on the time of submission of the resume. Examples of the time of submission of the resume include, but are not limited to, recent submissions and past submissions. Examples of the priority of feedback include, but are not limited to, priority based on importance and priority based on submission time. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input information about the time of submission of the resume to the generation AI and have the generation AI determine the priority of the feedback.

[0070] The feedback unit can adjust the order of feedback based on the relevance of the resumes when providing feedback. For example, the feedback unit prioritizes feedback of highly relevant content. The feedback unit can also delay feedback of less relevant content. The feedback unit can also adjust the order of feedback according to the relevance of the content. This allows appropriate feedback to be provided by adjusting the order of feedback based on the relevance of the resumes. Examples of resume relevance include, but are not limited to, resumes in the same job category or resumes in a related industry. Examples of feedback order include, but are not limited to, an order based on importance or relevance. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input information about the relevance of the resumes to the generation AI and cause the generation AI to adjust the order of feedback.

[0071] The feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise. For example, the feedback unit can provide feedback using a lot of technical terms to a user with high level of expertise. The feedback unit can also provide feedback in simple language to a user with low level of expertise. The feedback unit can also provide feedback using appropriate technical terms depending on the user's level of expertise. This allows appropriate feedback to be provided by adjusting the use of technical terms depending on the user's level of expertise. The user's level of expertise can include, but is not limited to, beginner, intermediate, and advanced. The use of technical terms can include, but is not limited to, the definition and frequency of use of technical terms. Some or all of the above-described processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input information about the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the feedback.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The resume creation support system can also be equipped with a function that automatically analyzes a user's past work history, extracts relevant skills and experience, and reflects them in the resume. For example, if a user previously worked as a project manager, the system can emphasize project management skills and leadership abilities based on that experience. Also, if a user is knowledgeable in a particular technology, keywords related to that technology can be automatically added. Furthermore, if a user has experience in multiple jobs, the system can integrate the skills related to each job to present a comprehensive skill set. This allows the user to make the most of their past work history and create a more attractive resume.

[0074] The detector can detect grammatical and phrasal errors specific to a specific industry or profession based on the user's input. For example, in the medical industry, it can detect misuse of technical terms and inappropriate abbreviations. In the IT industry, it can also detect misuse of technical terms and inappropriate code. Furthermore, in the legal industry, it can detect legal errors and inappropriate citations. This allows the detector to accurately detect errors specific to a specific industry or profession and provide the user with appropriate correction suggestions.

[0075] The suggestion unit can learn the user's past resume creation history and suggest expressions that suit the user's preferences. For example, it can suggest similar expressions based on expressions and formats that the user has used in the past. Also, if the user is applying for a specific industry or job type, it can suggest expressions that are appropriate for that industry or job type. Furthermore, it can suggest effective expressions based on data on the user's past successful resumes. This makes it possible to suggest more appropriate expressions based on the user's preferences and past success stories.

[0076] The interface unit can analyze the user's operation history and prioritize the display of the most frequently used functions. For example, correction functions and suggestion functions that the user frequently uses can be placed in prominent positions in the interface. It can also automatically display custom templates that the user has used in the past. Furthermore, if the user repeatedly performs a specific operation, it can provide shortcuts that simplify that operation. This allows the interface to be optimized based on the user's operation history, improving operation efficiency.

[0077] The detector can detect grammatical and linguistic errors specific to a particular culture or region based on the user's input. For example, it can detect differences between British and American English, or misuse of slang or dialects used in a particular region. It can also detect the appropriateness of expressions in a particular culture. It can also detect errors in grammatical structures specific to a particular region. This allows the detector to accurately detect culturally and regionally specific errors and provide the user with appropriate correction suggestions.

[0078] The suggestion unit can learn the user's past suggestion history and make suggestions according to the user's preferences. For example, it can make similar suggestions based on suggestions the user has accepted in the past. Also, if the user is applying for a specific industry or job type, it can make suggestions suitable for that industry or job type. Furthermore, it can make effective suggestions based on data from the user's past successes in resumes. This allows it to make more appropriate suggestions based on the user's preferences and past successes.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The detection unit detects grammatical and spelling errors in the input text. For example, the detection unit uses natural language processing technology to detect subject-verb agreement, spelling errors, and punctuation errors. The detection unit can also use generative AI to perform grammatical and semantic analysis. Step 2: The suggestion unit suggests corrections based on the mistakes detected by the detection unit. For example, the suggestion unit suggests corrections such as grammar corrections, expression corrections, and style corrections. The suggestion unit can also generate appropriate correction suggestions using a generation AI. Step 3: The suggestion unit learns from past resume data and suggests optimal expressions. For example, the suggestion unit suggests industry-standard expressions or formal expressions. The suggestion unit can also use generative AI to suggest effective ways to promote yourself and describe your work history. Step 4: The advice module provides advice based on common evaluation criteria and past success stories. For example, the advice module may provide advice based on grammatical accuracy or appropriateness of expression. The advice module may also use generative AI to provide feedback from a third-party perspective. Step 5: The interface section provides an interface that is easy for users to operate. For example, the interface section can provide an intuitive design and a function that automatically generates a resume by simply entering the required information. The interface section can also use generative AI to optimize the interface based on the results of usability tests. Step 6: The feedback module provides real-time feedback. For example, the feedback module can provide immediate advice or corrections to the input. The feedback module can also use generative AI to minimize feedback latency.

[0081] (Example 2) A resume creation support system according to an embodiment of the present invention applies a generation AI to detect grammatical and spelling errors in input text, suggest corrections, suggest optimal expressions, provide advice, provide an interface that is easy for users to operate, and provide feedback in real time. For example, the resume creation support system uses a generation AI to detect grammatical and spelling errors in input text. The generation AI then suggests corrections based on the detected errors. Furthermore, the generation AI learns from past resume data and suggests optimal expressions. The generation AI also provides advice based on general evaluation criteria and past success stories. Furthermore, an interface that is easy for users to operate is provided, and the generation AI provides feedback in real time. This allows users to create high-quality resumes in a short amount of time. For example, the generation AI detects grammatical and spelling errors and suggests corrections, allowing users to create accurate resumes. Furthermore, the generation AI suggests optimal expressions and provides advice, thereby more effectively conveying the content of the resume. Furthermore, by providing an easy-to-use interface and providing real-time feedback, users can quickly complete their resumes.

[0082] A resume creation support system according to an embodiment includes a detection unit, a presentation unit, a suggestion unit, an advice unit, an interface unit, and a feedback unit. The detection unit detects grammatical and spelling errors in input text. For example, the detection unit uses natural language processing technology to detect subject-verb agreement, spelling errors, punctuation errors, and the like. The detection unit can also perform grammatical and semantic analysis using a generation AI. The suggestion unit proposes corrections based on the errors detected by the detection unit. For example, the suggestion unit proposes corrections such as grammatical corrections, expression corrections, and style corrections. The suggestion unit can also generate appropriate corrections using a generation AI. The suggestion unit learns from past resume data and proposes optimal expressions. For example, the suggestion unit proposes industry-standard expressions and formal expressions. The suggestion unit can also propose effective ways to promote yourself and describe your work history using a generation AI. The advice unit provides advice based on common evaluation criteria and past success stories. For example, the advice unit provides advice based on grammatical accuracy and appropriateness of expression. The advice unit can also use a generation AI to provide feedback from a third-party perspective. The interface unit provides an interface that is easy for users to operate. For example, the interface unit can provide an intuitive design and a function that automatically generates a resume by simply entering the required information. The interface unit can also use a generation AI to optimize the interface based on the results of a usability test. The feedback unit provides feedback in real time. For example, the feedback unit can immediately present advice or suggested corrections in response to the input content. The feedback unit can also minimize the delay time in feedback using a generation AI. As a result, the resume creation support system according to the embodiment allows users to create high-quality resumes in a short amount of time.

[0083] The detection unit can detect grammatical and spelling errors in the input text using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the detection unit can use morphological analysis to divide the input text into words and detect grammatical errors. The detection unit can also use grammatical analysis to detect errors in subject-verb agreement and tense. The detection unit can also use semantic analysis to detect appropriate word usage based on the context. This allows for accurate detection of grammatical and spelling errors using natural language processing technology. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input the input text to a generation AI and have the generation AI detect grammatical and spelling errors.

[0084] The suggestion unit can suggest correction suggestions based on the mistakes detected by the detection unit. Examples of suggested corrections include, but are not limited to, grammar corrections, expression corrections, and style corrections. For example, the suggestion unit can suggest subject-verb agreement or tense corrections as grammar corrections. Furthermore, the suggestion unit can also suggest simplifying redundant expressions or selecting appropriate words as expression corrections. Furthermore, the suggestion unit can also suggest formal expressions or industry-standard expressions as style corrections. This allows appropriate correction suggestions to be presented based on the detected mistakes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the mistakes detected by the detection unit into the generation AI and cause the generation AI to generate correction suggestions.

[0085] The suggestion unit can learn from past resume data and suggest appropriate expressions. Examples of appropriate expressions include, but are not limited to, industry-standard expressions and formal expressions. For example, the suggestion unit can learn from past resume data and suggest effective ways to promote oneself and describe one's work history. The suggestion unit can also use a generation AI to suggest optimal expressions based on past success stories. The suggestion unit can also optimize the content of the resume based on industry-standard expressions and formal expressions. In this way, optimal expressions can be suggested by learning from past data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input past resume data into a generation AI and have the generation AI suggest optimal expressions.

[0086] The advice unit can provide advice based on general evaluation criteria and past success stories. General evaluation criteria include, but are not limited to, grammatical accuracy and appropriateness of expression. For example, the advice unit can point out grammatical errors and provide suggested corrections based on grammatical accuracy. The advice unit can also advise on simplifying redundant expressions and choosing appropriate words based on appropriateness of expression. The advice unit can also advise on effective self-promotion and work history writing based on past success stories. This allows advice to be provided from an objective perspective. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input general evaluation criteria and past success stories into the generation AI and cause the generation AI to generate advice.

[0087] The interface unit can provide an interface that is easy for users to operate. Examples of an easy-to-operate interface include, but are not limited to, an intuitive design and a function that automatically generates a resume by simply inputting required information. For example, the interface unit can provide an intuitive design, allowing users to easily operate the interface. The interface unit can also provide a function that automatically generates a resume by simply inputting required information. The interface unit can also use a generation AI to optimize the interface based on the results of a usability test. This improves convenience by providing an interface that is easy for users to operate. Some or all of the above-described processing in the interface unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the interface unit can input the results of a usability test into the generation AI and have the generation AI optimize the interface.

[0088] The feedback unit can provide feedback in real time. Real-time feedback includes, but is not limited to, immediate advice or suggested corrections for the input content. For example, the feedback unit can immediately provide advice or suggested corrections for the input content. The feedback unit can also minimize feedback delays using a generation AI. The feedback unit can also optimize the feedback format using a generation AI. This allows for rapid feedback by providing feedback in real time. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the input content to the generation AI and cause the generation AI to generate feedback.

[0089] The detection unit can estimate the user's emotions and adjust the accuracy of detecting grammatical and spelling errors based on the estimated user's emotions. For example, if the user is stressed, the detection unit can increase the detection accuracy of the generation AI to detect errors in more detail. Furthermore, if the user is relaxed, the detection unit can relax the detection accuracy and detect only important errors. Furthermore, if the user is in a hurry, the detection unit can set the detection accuracy to a medium level to quickly detect errors. This enables more appropriate error detection by adjusting the detection accuracy according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the detection unit can input user emotion data into the generation AI and have the generation AI adjust its detection accuracy.

[0090] During detection, the detection unit can prioritize detecting specific mistake patterns by referring to the user's past input history. For example, the detection unit can prioritize detecting grammatical mistakes that the user has made frequently in the past. The detection unit can also prioritize detecting words that the user has frequently misspelled in the past. The detection unit can also prioritize detecting mistakes of specific grammatical structures from the user's past input history. This allows specific mistake patterns to be prioritized by referring to the past input history. Past input history includes, for example, a specific period of time or a specific type of input, but is not limited to such examples. Specific mistake patterns include, for example, frequently occurring spelling mistakes, grammatical mistakes, etc., but are not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using or without the generation AI. For example, the detection unit can input the user's past input history into the generation AI and cause the generation AI to detect specific mistake patterns.

[0091] During detection, the detection unit can more accurately detect grammatical and spelling errors based on the context of the input text. For example, the detection unit can detect misuse of homonyms based on the context. The detection unit can also detect appropriate tense errors by taking the context into account. The detection unit can also detect appropriate preposition errors based on the context. This allows for more accurate detection of grammatical and spelling errors by taking the context into account. Examples of context include, but are not limited to, surrounding sentences and related topics. Some or all of the above-described processing in the detection unit may be performed using or without the generation AI. For example, the detection unit can input the context of the input text to the generation AI and cause the generation AI to detect grammatical and spelling errors.

[0092] During detection, the detection unit can identify errors in technical terminology according to the user's field of expertise. For example, the detection unit can identify errors in technical terminology in the medical field. The detection unit can also identify errors in technical terminology in the legal field. The detection unit can also identify errors in technical terminology in the IT field. This allows for accurate detection of technical errors by identifying errors in technical terminology according to the field of expertise. Examples of technical fields include, but are not limited to, the medical field and the technical field. Examples of technical terminology include, but are not limited to, technical terms in specific industries or fields. Some or all of the above-described processing by the detection unit may be performed using or without the generation AI. For example, the detection unit can input information about the user's field of expertise into the generation AI and cause the generation AI to identify errors in technical terminology.

[0093] The detection unit can estimate the user's emotion and adjust the display method of the detection result based on the estimated user's emotion. For example, if the user is nervous, the detection unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the detection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the detection unit can provide a display method that focuses on the main points. This allows for more appropriate feedback to be provided by adjusting the display method according to the user's emotion. The user's emotion is estimated using technologies such as facial expression recognition, voice analysis, and text analysis. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the detection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0094] During detection, the detection unit can detect region-specific grammatical and spelling errors by taking the user's geographical background into account. The detection unit can detect, for example, spelling differences between British English and American English. The detection unit can also detect misuse of region-specific slang and dialects. The detection unit can also detect errors in region-specific grammatical structures. This allows region-specific errors to be accurately detected by taking the geographical background into account. Examples of geographical background include, but are not limited to, specific regions or countries. Examples of region-specific grammatical and spelling errors include, but are not limited to, differences between British English and American English. Some or all of the above-described processing by the detection unit can be performed using or without the generation AI. For example, the detection unit can input information about the user's geographical background into the generation AI and cause the generation AI to detect region-specific grammatical and spelling errors.

[0095] During detection, the detection unit can analyze the user's social media activity and detect related mistakes. For example, the detection unit can detect misuse of phrases frequently used by the user on social media. The detection unit can also analyze the user's social media posts to detect related grammatical mistakes. The detection unit can also detect related mistakes by referring to the posts of the user's friends on social media. This allows for accurate detection of related mistakes by analyzing social media activity. Social media activity can include, but is not limited to, a specific platform or a specific period. Related mistakes can include, but are not limited to, frequently occurring spelling mistakes and grammatical mistakes. Some or all of the above-described processing in the detection unit can be performed using or without the generation AI. For example, the detection unit can input data on the user's social media activity into the generation AI and cause the generation AI to detect related mistakes.

[0096] The detection unit can customize the detection algorithm by reflecting the user's past feedback during detection. The detection unit can adjust the detection algorithm based on, for example, feedback provided by the user in the past. The detection unit can also preferentially detect specific mistake patterns based on the user's past feedback. The detection unit can also improve detection accuracy by reflecting the user's feedback. This allows the detection algorithm to be customized and accuracy to be improved by reflecting past feedback. Past feedback includes, for example, a specific period of time or a specific type of feedback, but is not limited to such examples. The detection algorithm includes, for example, parameter adjustment, algorithm selection, etc., but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using or without the generation AI. For example, the detection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the detection algorithm.

[0097] The presentation unit can estimate the user's emotions and adjust the presentation method of the proposed revision based on the estimated user's emotions. For example, if the user is stressed, the presentation unit can present a concise and clear revision. Furthermore, if the user is relaxed, the presentation unit can present a revision with detailed explanations. Furthermore, if the user is in a hurry, the presentation unit can present a revision that can be quickly understood. This allows for providing a more appropriate revision by adjusting the presentation method of the proposed revision according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the proposed revision.

[0098] The suggestion unit can adjust the level of detail of the proposed revisions based on the importance of the mistake when presenting them. For example, the suggestion unit can present detailed revisions for serious mistakes. The suggestion unit can also present brief revisions for minor mistakes. The suggestion unit can also present moderately detailed revisions for medium mistakes. In this way, by adjusting the level of detail of the revisions based on the importance of the mistake, it is possible to provide appropriate revisions. Examples of the importance of the mistake include, but are not limited to, the severity of grammatical errors and the frequency of spelling mistakes. Examples of the level of detail of the revisions include, but are not limited to, simple revisions and detailed revisions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the importance of the mistake to the generation AI and cause the generation AI to adjust the level of detail of the revisions.

[0099] The presentation unit can apply different correction algorithms depending on the category of the error when presenting the error. For example, the presentation unit can apply a grammar correction algorithm to a grammatical error. The presentation unit can also apply a spelling correction algorithm to a spelling error. The presentation unit can also apply a style correction algorithm to a style error. This allows appropriate correction suggestions to be provided by applying different correction algorithms depending on the category of the error. Examples of error categories include, but are not limited to, grammatical errors, spelling errors, and style errors. Examples of correction algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-mentioned processing in the presentation unit can be performed using or without the generation AI. For example, the presentation unit can input information on the category of the error to the generation AI and cause the generation AI to apply the correction algorithm.

[0100] When presenting a revision suggestion, the presentation unit can improve the accuracy of the revision suggestion by referring to the user's past revision history. The presentation unit can improve the accuracy of the revision suggestion, for example, based on revision suggestions previously accepted by the user. The presentation unit can also learn specific revision patterns from the user's past revision history. The presentation unit can also analyze the user's past revision history and present an optimal revision suggestion. This can improve the accuracy of the revision suggestion by referring to the past revision history. The past revision history includes, for example, a specific period or a specific type of revision, but is not limited to such examples. The accuracy of the revision suggestion includes, for example, accuracy and appropriateness, but is not limited to such examples. Some or all of the above-mentioned processing in the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input the user's past revision history into the generation AI and cause the generation AI to improve the accuracy of the revision suggestion.

[0101] The presentation unit can estimate the user's emotions and adjust the length of the suggested revisions based on the estimated user emotions. For example, if the user is stressed, the presentation unit can present short, concise revisions. If the user is relaxed, the presentation unit can present longer revisions with detailed explanations. If the user is in a hurry, the presentation unit can present short, quickly understandable revisions. This allows for more appropriate revisions to be provided by adjusting the length of the revisions according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition, speech analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the revisions.

[0102] When presenting the proposed corrections, the presentation unit can prioritize the proposed corrections based on the time of the error. For example, the presentation unit prioritizes the proposed corrections for recent errors. The presentation unit can also present proposed corrections later for errors that occurred in the past. The presentation unit can also present proposed corrections for errors that occurred within a specific period of time in a priority order according to the period of time. This allows appropriate proposed corrections to be provided by prioritizing proposed corrections based on the time of the error. Examples of the time of the error include, but are not limited to, recent errors and past errors. Examples of the priority of proposed corrections include, but are not limited to, priority based on importance and priority based on frequency of occurrence. Some or all of the above-described processing by the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input information about the time of the error to the generation AI and have the generation AI determine the priority of the proposed corrections.

[0103] The presentation unit can adjust the order of proposed corrections based on the relevance of the mistakes when presenting them. For example, the presentation unit can prioritize the presentation of highly relevant mistakes as proposed corrections. The presentation unit can also present less relevant mistakes as proposed corrections later. The presentation unit can also adjust the order of proposed corrections according to the relevance of the mistakes. This allows appropriate proposals to be provided by adjusting the order of proposed corrections based on the relevance of the mistakes. Examples of the relevance of mistakes include, but are not limited to, mistakes within the same context and related grammatical mistakes. Examples of the order of proposed corrections include, but are not limited to, an order based on importance or relevance. Some or all of the above-described processing by the presentation unit may be performed using or without the generation AI. For example, the presentation unit can input information on the relevance of mistakes to the generation AI and cause the generation AI to adjust the order of proposed corrections.

[0104] When presenting the revision proposal, the presentation unit can adjust the use of technical terms in the revision proposal depending on the user's level of expertise. For example, the presentation unit may present a revision proposal that uses a lot of technical terms to a user with high levels of expertise. The presentation unit may also present a revision proposal in simpler language to a user with low levels of expertise. The presentation unit may also present a revision proposal using appropriate technical terms depending on the user's level of expertise. This allows for providing an appropriate revision proposal by adjusting the use of technical terms depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. Examples of the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. Some or all of the above-described processing by the presentation unit may be performed using or without the generation AI. For example, the presentation unit may input information about the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the revision proposal.

[0105] The suggestion unit can estimate the user's emotions and adjust the expression suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest concise and clear expressions. If the user is relaxed, the suggestion unit can also suggest expressions that include detailed explanations. If the user is in a hurry, the suggestion unit can also suggest expressions that can be quickly understood. This allows the expression suggestion method to be adjusted according to the user's emotions, thereby suggesting more appropriate expressions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression suggestion method.

[0106] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the resume. For example, the suggestion unit can make a detailed proposal for an important resume. The suggestion unit can also make a brief proposal for a minor resume. The suggestion unit can also make a moderately detailed proposal for a medium-level resume. This allows for providing an appropriate proposal by adjusting the level of detail of the proposal based on the importance of the resume. Examples of the importance of the resume include, but are not limited to, the size of the company to which the applicant is applying and the importance of the job type. Examples of the level of detail of the proposal include, but are not limited to, a simple proposal, a detailed proposal, etc. Some or all of the above-described processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the importance of the resume to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0107] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the resume. For example, the suggestion unit can suggest technical language for a resume of a technical job. The suggestion unit can also suggest creative language for a resume of a creative job. The suggestion unit can also suggest managerial language for a resume of a managerial job. This allows appropriate suggestions to be provided by applying different suggestion algorithms depending on the category of the resume. Resume categories include, but are not limited to, technical jobs, sales jobs, and managerial jobs. Proposal algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the resume category into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0108] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on suggestions accepted by the user in the past. The suggestion unit can also learn specific suggestion patterns from the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. This allows the accuracy of the suggestion to be improved by referring to the past suggestion results. Past suggestion results include, for example, a specific period or a specific type of suggestion, but are not limited to such examples. The accuracy of the suggestion includes, for example, accuracy and appropriateness, but are not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0109] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly understood. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestions according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, speech analysis, and text analysis. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0110] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the resume. For example, the suggestion unit can prioritize proposals for resumes with an upcoming deadline. The suggestion unit can also postpone proposals for resumes with a more distant submission date. The suggestion unit can also prioritize proposals for resumes to be submitted within a specific period according to the period. This allows appropriate proposals to be provided by determining the priority of proposals based on the time of submission of the resume. Examples of the time of submission of the resume include, but are not limited to, recent submissions and past submissions. Examples of the priority of proposals include, but are not limited to, priority based on importance and priority based on submission time. Some or all of the above-described processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input information about the time of submission of the resume into the generation AI and have the generation AI determine the priority of the proposals.

[0111] When making a proposal, the suggestion unit can adjust the order of the proposals based on the relevance of the resumes. For example, the suggestion unit may prioritize the proposal of highly relevant content. The suggestion unit may also postpone the proposal of less relevant content. The suggestion unit may also adjust the order of the proposals according to the relevance of the content. This allows appropriate proposals to be provided by adjusting the order of proposals based on the relevance of the resumes. Examples of the relevance of resumes include, but are not limited to, resumes of the same job type or resumes in a related industry. Examples of the order of proposals include, but are not limited to, an order based on importance or an order based on relevance. Some or all of the above-described processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input information about the relevance of resumes to the generation AI and cause the generation AI to adjust the order of proposals.

[0112] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise. For example, the suggestion unit may make a proposal that uses a lot of technical terms for a user with high level of expertise. The suggestion unit may also make a proposal using simpler language for a user with low level of expertise. The suggestion unit may also make a proposal using appropriate technical terms depending on the user's level of expertise. This allows appropriate proposals to be provided by adjusting the use of technical terms depending on the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, beginner, intermediate, and advanced. Examples of the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input information about the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the proposal.

[0113] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide concise and clear advice. Furthermore, if the user is relaxed, the advice unit can provide advice with detailed explanations. Furthermore, if the user is in a hurry, the advice unit can provide advice that can be quickly understood. This allows for more appropriate advice to be provided by adjusting the way the advice is expressed according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0114] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the resume. For example, the advice unit provides detailed advice for an important resume. The advice unit can also provide brief advice for a minor resume. The advice unit can also provide advice with appropriate detail for a medium-level resume. In this way, by adjusting the level of detail of the advice based on the importance of the resume, appropriate advice can be provided. Examples of the importance of the resume include, but are not limited to, the size of the company to which the applicant is applying and the importance of the job type. Examples of the level of detail of the advice include, but are not limited to, simple advice, detailed advice, etc. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information about the importance of the resume to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0115] When providing advice, the advice unit can apply different advice algorithms depending on the category of the resume. For example, the advice unit can provide technical advice for a resume of a technical job. The advice unit can also provide creative advice for a resume of a creative job. The advice unit can also provide managerial advice for a resume of a managerial job. This allows appropriate advice to be provided by applying different advice algorithms depending on the category of the resume. Resume categories include, but are not limited to, technical jobs, sales jobs, and managerial jobs. Advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input resume category information into the generation AI and cause the generation AI to apply the advice algorithm.

[0116] When providing advice, the advice unit can improve the accuracy of the advice by referring to past advice results of the user. The advice unit improves the accuracy of the advice, for example, based on advice accepted by the user in the past. The advice unit can also learn specific advice patterns from the user's past advice results. The advice unit can also analyze the user's past advice results and provide optimal advice. In this way, the accuracy of the advice can be improved by referring to past advice results. Past advice results include, for example, advice for a specific period or a specific type, but are not limited to such examples. The accuracy of the advice includes, for example, accuracy and appropriateness, but are not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using or without using the generation AI. For example, the advice unit can input the user's past advice results into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0117] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the advice unit can provide longer advice with detailed explanations. Furthermore, if the user is in a hurry, the advice unit can provide short, quickly understandable advice. By adjusting the length of the advice according to the user's emotions, more appropriate advice can be provided. The user's emotions are estimated using techniques such as facial expression recognition, voice analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0118] When providing advice, the advice unit can determine the priority of advice based on the time of submission of the resume. For example, the advice unit can prioritize advice for resumes with an upcoming deadline. The advice unit can also postpone providing advice for resumes with a more distant submission date. The advice unit can also provide advice for resumes to be submitted within a specific period, prioritizing the advice based on the period. This allows appropriate advice to be provided by determining the priority of advice based on the time of submission of the resume. Examples of the time of submission of the resume include, but are not limited to, recent submission and past submission. Examples of the priority of advice include, but are not limited to, priority based on importance and priority based on submission time. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information about the time of submission of the resume to the generation AI and have the generation AI determine the priority of advice.

[0119] When providing advice, the advice unit can adjust the order of advice based on the relevance of the resume. For example, the advice unit prioritizes advice on highly relevant content. The advice unit can also postpone advice on less relevant content. The advice unit can also adjust the order of advice according to the relevance of the content. This allows appropriate advice to be provided by adjusting the order of advice based on the relevance of the resume. Examples of resume relevance include, but are not limited to, resumes in the same job category or resumes in a related industry. Examples of the order of advice include, but are not limited to, an order based on importance or an order based on relevance. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information on the relevance of the resume to the generation AI and cause the generation AI to adjust the order of advice.

[0120] When providing advice, the advice unit can adjust the use of technical terms in the advice depending on the user's level of expertise. For example, the advice unit can provide advice that uses a lot of technical terms to a user with high levels of expertise. The advice unit can also provide advice in simpler terms to a user with low levels of expertise. The advice unit can also provide advice using appropriate technical terms depending on the user's level of expertise. This allows appropriate advice to be provided by adjusting the use of technical terms depending on the user's level of expertise. The user's level of expertise includes, but is not limited to, beginner, intermediate, and advanced. The use of technical terms includes, but is not limited to, the definition and frequency of use of technical terms. Some or all of the above-described processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input information about the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the advice.

[0121] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, the interface unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the interface unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the interface unit can provide a simple, highly visible interface to make input work easier. This allows for a more appropriate display by adjusting the interface display method according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the interface unit can input user emotion data into the generation AI and have the generation AI adjust the interface display method.

[0122] When displaying an interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit can prioritize displaying an interface design that the user has previously preferred. The interface unit can also select the most efficient display method from the user's past operation history. The interface unit can also analyze the user's past operation history and provide the optimal interface display method. In this way, the optimal display method can be provided by referring to the past operation history. The past operation history includes, for example, a specific period of time or a specific type of operation, but is not limited to such examples. The optimal display method includes, for example, the results of a usability test, an intuitive operation method, etc. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input the user's past operation history into the generation AI and have the generation AI select the optimal display method.

[0123] The interface unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is creating a resume, the interface unit can prioritize displaying related information. Furthermore, if the user is making revisions, the interface unit can prioritize displaying tools necessary for the revisions. The interface unit can also provide optimal display content according to the user's current task. This allows appropriate information to be provided by customizing the display content according to the current task. Examples of the current task include, but are not limited to, a specific task or a specific project. Examples of customizing the display content include, but are not limited to, displaying information according to the task, changing the layout, and the like. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input information about the user's current task into the generation AI and have the generation AI customize the display content.

[0124] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can provide a simple and highly visible display method. This allows the optimal display method to be provided by taking into account the device information. Device information includes, but is not limited to, the device type, screen size, OS, etc. The optimal display method includes, but is not limited to, the results of usability tests, intuitive operation methods, etc. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0125] The interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, if the user is nervous, the interface unit can simplify the operation procedures to reduce stress. Furthermore, if the user is enjoying themselves, the interface unit can also provide detailed operation procedures to make the task more enjoyable. Furthermore, if the user is tired, the interface unit can also minimize the operation procedures to make the task easier. This allows for more appropriate operation by adjusting the operation procedures according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the interface unit can input the user's emotion data into the generation AI and have the generation AI adjust the operation procedures.

[0126] The interface unit can make the display content multilingual when displaying the interface according to the user's language setting. For example, the interface unit automatically sets the interface language based on the language setting of the user's device. The interface unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the interface unit can display the interface in that language. This allows appropriate information to be provided by making the display content multilingual according to the language setting. Examples of language settings include, but are not limited to, the language used and regional settings. Examples of multilingual support include, but are not limited to, the accuracy of translation and the types of supported languages. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input information about the user's language setting into the generation AI and have the generation AI set the display content to be multilingual.

[0127] The interface unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is creating a resume, the interface unit can prioritize displaying related information. Furthermore, if the user is making revisions, the interface unit can prioritize displaying tools necessary for the revisions. The interface unit can also provide optimal display content according to the user's current task. This allows appropriate information to be provided by customizing the display content according to the current task. Examples of the current task include, but are not limited to, a specific task or a specific project. Examples of customizing the display content include, but are not limited to, displaying information according to the task, changing the layout, and the like. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input information about the user's current task into the generation AI and have the generation AI customize the display content.

[0128] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can provide a simple and highly visible display method. This allows the optimal display method to be provided by taking into account the device information. Device information includes, but is not limited to, the device type, screen size, OS, etc. The optimal display method includes, but is not limited to, the results of usability tests, intuitive operation methods, etc. Some or all of the above-described processing in the interface unit may be performed using or without the generation AI. For example, the interface unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0129] The feedback unit can estimate the user's emotions and adjust the feedback expression method based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide concise and clear feedback. If the user is relaxed, the feedback unit can provide feedback with detailed explanations. If the user is in a hurry, the feedback unit can provide quickly understandable feedback. This allows for more appropriate feedback to be provided by adjusting the feedback expression method according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback expression method.

[0130] The feedback unit can adjust the level of detail of the feedback based on the importance of the resume when providing feedback. For example, the feedback unit can provide detailed feedback for an important resume. The feedback unit can also provide brief feedback for a minor resume. The feedback unit can also provide moderately detailed feedback for a medium-level resume. In this way, by adjusting the level of detail of the feedback based on the importance of the resume, appropriate feedback can be provided. Examples of the importance of the resume include, but are not limited to, the size of the company to which the applicant applied and the importance of the job type. Examples of the level of detail of the feedback include, but are not limited to, simple feedback, detailed feedback, etc. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input information about the importance of the resume to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0131] The feedback unit can apply different feedback algorithms depending on the resume category when providing feedback. For example, the feedback unit can provide technical feedback for a technical resume. The feedback unit can also provide creative feedback for a creative resume. The feedback unit can also provide managerial feedback for a managerial resume. This allows appropriate feedback to be provided by applying different feedback algorithms depending on the resume category. Resume categories include, but are not limited to, technical, sales, and managerial. Feedback algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above-described processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input resume category information into the generation AI and cause the generation AI to apply the feedback algorithm.

[0132] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. The feedback unit can improve the accuracy of the feedback, for example, based on feedback the user has accepted in the past. The feedback unit can also learn specific feedback patterns from the user's past feedback results. The feedback unit can also analyze the user's past feedback results and provide optimal feedback. This can improve the accuracy of the feedback by referring to the past feedback results. Past feedback results include, for example, but are not limited to, feedback from a specific period or a specific type. The accuracy of the feedback includes, for example, but is not limited to, accuracy and appropriateness. Some or all of the above-described processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input the user's past feedback results into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0133] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide short, to-the-point feedback. If the user is relaxed, the feedback unit can provide longer feedback with detailed explanations. If the user is in a hurry, the feedback unit can provide short, quickly understandable feedback. This allows for more appropriate feedback by adjusting the length of the feedback according to the user's emotions. The user's emotions are estimated using techniques such as facial expression recognition, speech analysis, and text analysis. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the feedback.

[0134] When providing feedback, the feedback unit can determine the priority of the feedback based on the time of submission of the resume. For example, the feedback unit can prioritize feedback for resumes with an upcoming deadline. The feedback unit can also provide feedback later for resumes with a more distant submission date. The feedback unit can also provide feedback for resumes to be submitted within a specific period in a priority order according to the period. This allows appropriate feedback to be provided by determining the priority of feedback based on the time of submission of the resume. Examples of the time of submission of the resume include, but are not limited to, recent submissions and past submissions. Examples of the priority of feedback include, but are not limited to, priority based on importance and priority based on submission time. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input information about the time of submission of the resume to the generation AI and have the generation AI determine the priority of the feedback.

[0135] The feedback unit can adjust the order of feedback based on the relevance of the resumes when providing feedback. For example, the feedback unit prioritizes feedback of highly relevant content. The feedback unit can also delay feedback of less relevant content. The feedback unit can also adjust the order of feedback according to the relevance of the content. This allows appropriate feedback to be provided by adjusting the order of feedback based on the relevance of the resumes. Examples of resume relevance include, but are not limited to, resumes in the same job category or resumes in a related industry. Examples of feedback order include, but are not limited to, an order based on importance or relevance. Some or all of the above-described processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input information about the relevance of the resumes to the generation AI and cause the generation AI to adjust the order of feedback.

[0136] The feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise. For example, the feedback unit can provide feedback using a lot of technical terms to a user with high level of expertise. The feedback unit can also provide feedback in simple language to a user with low level of expertise. The feedback unit can also provide feedback using appropriate technical terms depending on the user's level of expertise. This allows appropriate feedback to be provided by adjusting the use of technical terms depending on the user's level of expertise. The user's level of expertise can include, but is not limited to, beginner, intermediate, and advanced. The use of technical terms can include, but is not limited to, the definition and frequency of use of technical terms. Some or all of the above-described processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input information about the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, presentation unit, suggestion unit, advice unit, interface unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14 and detects grammatical and spelling errors using generative AI. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents correction suggestions based on the detected errors. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and learns from past resume data and suggests optimal expressions. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on general evaluation criteria and past success stories. The interface unit is realized by the control unit 46A of the smart device 14 and provides an interface that is easy for the user to operate. The feedback unit is realized by the control unit 46A of the smart device 14 and provides feedback in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the detection unit, presentation unit, suggestion unit, advice unit, interface unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and detects grammatical and spelling errors using generative AI. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents correction suggestions based on the detected errors. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and learns past resume data and suggests optimal expressions. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on general evaluation criteria and past success stories. The interface unit is realized by the control unit 46A of the smart glasses 214 and provides an interface that is easy for the user to operate. The feedback unit is realized by the control unit 46A of the smart glasses 214 and provides feedback in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the detection unit, presentation unit, suggestion unit, advice unit, interface unit, and feedback unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the detection unit estimates the user's emotions using the camera 42 and microphone 238 of the headset-type terminal 314 and detects grammatical and spelling errors using generative AI. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents correction suggestions based on the detected errors. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and learns from past resume data and suggests optimal expressions. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on general evaluation criteria and past success stories. The interface unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface that is easy for the user to operate. The feedback unit is realized by the control unit 46A of the headset-type terminal 314 and provides feedback in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the detection unit, presentation unit, suggestion unit, advice unit, interface unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414 and detects grammatical and spelling errors using generative AI. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents correction suggestions based on the detected errors. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and learns from past resume data and suggests optimal expressions. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on general evaluation criteria and past success stories. The interface unit is realized by the control unit 46A of the robot 414 and provides an interface that is easy for the user to operate. The feedback unit is realized by the control unit 46A of the robot 414 and provides feedback in real time.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The resume creation support system can also be equipped with a function that automatically analyzes a user's past work history, extracts relevant skills and experience, and reflects them in the resume. For example, if a user previously worked as a project manager, the system can emphasize project management skills and leadership abilities based on that experience. Also, if a user is knowledgeable in a particular technology, keywords related to that technology can be automatically added. Furthermore, if a user has experience in multiple jobs, the system can integrate the skills related to each job to present a comprehensive skill set. This allows the user to make the most of their past work history and create a more attractive resume.

[0139] The detector can detect grammatical and phrasal errors specific to a specific industry or profession based on the user's input. For example, in the medical industry, it can detect misuse of technical terms and inappropriate abbreviations. In the IT industry, it can also detect misuse of technical terms and inappropriate code. Furthermore, in the legal industry, it can detect legal errors and inappropriate citations. This allows the detector to accurately detect errors specific to a specific industry or profession and provide the user with appropriate correction suggestions.

[0140] The presentation unit can estimate the user's emotions and adjust the presentation order of proposed revisions based on the estimated user's emotions. For example, if the user is feeling stressed, the most important proposed revisions can be presented first, and if the user is relaxed, detailed proposed revisions can be presented sequentially. Also, if the user is in a hurry, brief proposed revisions can be presented preferentially. In this way, by adjusting the presentation order of proposed revisions according to the user's emotions, more effective feedback can be provided.

[0141] The suggestion unit can learn the user's past resume creation history and suggest expressions that suit the user's preferences. For example, it can suggest similar expressions based on expressions and formats that the user has used in the past. Also, if the user is applying for a specific industry or job type, it can suggest expressions that are appropriate for that industry or job type. Furthermore, it can suggest effective expressions based on data on the user's past successful resumes. This makes it possible to suggest more appropriate expressions based on the user's preferences and past success stories.

[0142] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is confident, the advice unit can provide positive advice, and if the user is anxious, the advice unit can provide encouraging advice. Also, if the user is confused, the advice unit can provide clear and concise advice. In this way, by adjusting the content of the advice according to the user's emotions, more effective support can be provided.

[0143] The interface unit can analyze the user's operation history and prioritize the display of the most frequently used functions. For example, correction functions and suggestion functions that the user frequently uses can be placed in prominent positions in the interface. It can also automatically display custom templates that the user has used in the past. Furthermore, if the user repeatedly performs a specific operation, it can provide shortcuts that simplify that operation. This allows the interface to be optimized based on the user's operation history, improving operation efficiency.

[0144] The feedback unit can estimate the user's emotion and adjust the form of feedback based on the estimated user's emotion. For example, if the user is stressed, the feedback unit can provide concise and to-the-point feedback, and if the user is relaxed, the feedback unit can provide detailed feedback. Also, if the user is in a hurry, the feedback unit can provide quick and understandable feedback. Thus, by adjusting the form of feedback according to the user's emotion, more effective feedback can be provided.

[0145] The detector can detect grammatical and linguistic errors specific to a particular culture or region based on the user's input. For example, it can detect differences between British and American English, or misuse of slang or dialects used in a particular region. It can also detect the appropriateness of expressions in a particular culture. It can also detect errors in grammatical structures specific to a particular region. This allows the detector to accurately detect culturally and regionally specific errors and provide the user with appropriate correction suggestions.

[0146] The presentation unit can estimate the user's emotions and adjust the level of detail of the proposed revisions based on the estimated user's emotions. For example, if the user is stressed, a concise and clear proposed revision is presented, and if the user is relaxed, a proposed revision with detailed explanations is presented. Also, if the user is in a hurry, a proposed revision that can be quickly understood can be presented. In this way, by adjusting the level of detail of the proposed revisions according to the user's emotions, more appropriate proposed revisions can be provided.

[0147] The suggestion unit can learn the user's past suggestion history and make suggestions according to the user's preferences. For example, it can make similar suggestions based on suggestions the user has accepted in the past. Also, if the user is applying for a specific industry or job type, it can make suggestions suitable for that industry or job type. Furthermore, it can make effective suggestions based on data from the user's past successes in resumes. This allows it to make more appropriate suggestions based on the user's preferences and past successes.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The detection unit detects grammatical and spelling errors in the input text. For example, the detection unit uses natural language processing technology to detect subject-verb agreement, spelling errors, and punctuation errors. The detection unit can also use generative AI to perform grammatical and semantic analysis. Step 2: The suggestion unit suggests corrections based on the mistakes detected by the detection unit. For example, the suggestion unit suggests corrections such as grammar corrections, expression corrections, and style corrections. The suggestion unit can also generate appropriate correction suggestions using a generation AI. Step 3: The suggestion unit learns from past resume data and suggests optimal expressions. For example, the suggestion unit suggests industry-standard expressions or formal expressions. The suggestion unit can also use generative AI to suggest effective ways to promote yourself and describe your work history. Step 4: The advice module provides advice based on common evaluation criteria and past success stories. For example, the advice module may provide advice based on grammatical accuracy or appropriateness of expression. The advice module may also use generative AI to provide feedback from a third-party perspective. Step 5: The interface section provides an interface that is easy for users to operate. For example, the interface section can provide an intuitive design and a function that automatically generates a resume by simply entering the required information. The interface section can also use generative AI to optimize the interface based on the results of usability tests. Step 6: The feedback module provides real-time feedback. For example, the feedback module can provide immediate advice or corrections to the input. The feedback module can also use generative AI to minimize feedback latency.

[0150] 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.

[0151] 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.

[0152] 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.

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

[0155] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. 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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

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

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. 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 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.

[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 (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).

[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] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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."

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Explanation of symbols]

[0222] 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 detector for detecting grammatical and spelling errors in the input text; a suggestion unit that suggests corrections based on the errors detected by the detection unit; A suggestion section that learns from past resume data and suggests appropriate expressions; The Advice Department provides advice based on specific evaluation criteria and past success stories, and an interface unit that provides an interface that can be easily operated by a user; a feedback unit that provides feedback in real time; A system characterized by:

2. The detection unit Uses natural language processing techniques to detect grammatical and spelling errors in entered text 2. The system of claim 1.

3. The presentation unit Providing correction suggestions based on the errors detected by the detection unit 2. The system of claim 1.

4. The proposal unit Learn from past resume data and suggest appropriate expressions 2. The system of claim 1.

5. The advice unit Providing advice based on common evaluation criteria and past success stories 2. The system of claim 1.

6. The interface unit Provide an interface that is easy for users to operate 2. The system of claim 1.

7. The feedback unit Provide real-time feedback 2. The system of claim 1.

8. The detection unit Estimate user emotions and adjust the accuracy of detecting grammar and spelling errors based on the estimated user emotions.

2. The system of claim 1.

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