System

Abstract

An object of a system according to an embodiment is to propose an optimal career path based on various information of an individual and provide realistic support.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects personal information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a carrier path based on the analysis result obtained by the analysis unit. The support unit provides a realistic support for the career path proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately propose optimal career paths based on diverse individual information and provide realistic support.

[0005] The system according to the embodiment aims to propose an optimal career path based on a variety of individual information and provide practical support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects personal information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a career path based on the analysis results obtained by the analysis unit. The support unit provides practical support for the career path proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable career path based on various information about an individual and provide practical support. [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 career support system according to an embodiment of the present invention collects, analyzes, proposes, and provides support for individuals' career development. The system collects information about individuals, such as their career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, and aspirations and dreams, analyzes it using a generation AI, proposes career paths, and provides practical support. For example, a user inputs information about their career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, and aspirations and dreams. The generation AI then analyzes the input information and derives the individual's characteristics and tendencies. The generation AI uses specialized theories to analyze the individual's information from multiple angles. For example, it identifies the individual's strengths and weaknesses based on past experiences and interests and proposes an appropriate career path. Furthermore, the generation AI proposes practical support for the individual's aspirations and dreams. Specifically, it presents the necessary skills, qualifications, and specific methods. This allows the career support system to help individuals understand their own characteristics and tendencies and pioneer new career paths. Furthermore, the realistic support suggested by generative AI allows individuals to take concrete actions toward their desired goals and dreams. This allows the career support system to support individuals in their career development and also provides support to companies in hiring the right talent. For example, companies can understand the characteristics and tendencies of individuals and hire the right talent. This is expected to improve corporate productivity and efficiency.

[0029] A career support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects personal information. Examples of personal information include, but are not limited to, career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, and aspirations and dreams. The collection unit collects information, for example, through questionnaires or interviews. The collection unit can also collect information by analyzing resumes and social media data. The analysis unit uses a generation AI to analyze the collected information from multiple angles and derive individual characteristics and tendencies. The generation AI can, for example, analyze text data using natural language processing technology. The generation AI can also analyze image data using image recognition technology. Furthermore, the generation AI can analyze audio data using voice recognition technology. The proposal unit proposes a career path suitable for the individual based on the analysis results. The proposal unit, for example, uses skill matching technology to compare the individual's skills with the skills required for the job and propose an appropriate career path. The proposal unit can also analyze interests and propose a career path based on the individual's interests. The support unit provides realistic support for the proposed career path. For example, the support unit provides a training program and supports the acquisition of necessary skills. The support unit can also suggest specific steps for career change through consulting. Furthermore, the support unit can provide resources and necessary information and tools. As a result, the career support system according to the embodiment can support an individual's career development, suggest an appropriate career path, and provide realistic support.

[0030] The collection unit can collect information on an individual's career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations, and dreams. For example, the collection unit can collect information on an individual's career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations, and dreams through a questionnaire. The collection unit can also collect this information through interviews. For example, the collection unit can collect detailed information through individual interviews. The collection unit can also collect information on an individual's career history and qualifications through resume analysis. For example, the collection unit can digitize and analyze the contents of a resume. This enables more accurate career support by collecting multifaceted information on individuals. Some or all of the above-mentioned processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input questionnaire response data into a generation AI and have the generation AI analyze the response data.

[0031] The analysis unit can use the generative AI to analyze the collected information from multiple angles and derive individual characteristics and tendencies. The analysis unit can use, for example, the generative AI to analyze the collected information from multiple angles. The generative AI can analyze text data using natural language processing technology. The generative AI can also analyze image data using image recognition technology. The generative AI can also analyze audio data using voice recognition technology. For example, the generative AI can identify an individual's strengths and weaknesses based on their past experiences and interests. The generative AI can also analyze an individual's personality and tendencies and derive an appropriate career path. For example, the generative AI can evaluate an individual's personality traits using psychological theory. This allows the generative AI to analyze individual characteristics and tendencies with high accuracy. Some or all of the above-mentioned processing in the analysis unit can be performed using generative AI (generative AI or LLM). For example, the analysis unit can input collected text data into the generative AI and have the generative AI analyze the text data.

[0032] The suggestion unit can propose a career path suitable for the individual based on the analysis results. The suggestion unit proposes a career path suitable for the individual based on, for example, the analysis results. The suggestion unit, for example, uses skill matching technology to compare the individual's skills with the skills required for the occupation and propose a suitable career path. The suggestion unit can also analyze interests and propose a career path based on the individual's interests. For example, the suggestion unit can identify the individual's fields of interest or occupations and propose a career path based on them. The suggestion unit can also propose a career path based on the individual's aspirations and dreams. For example, the suggestion unit can propose specific steps toward the individual's goals. This makes it possible to propose an optimal career path for the individual based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and have the generation AI execute career path proposals.

[0033] The support unit can provide practical support for the proposed career path. For example, the support unit provides practical support for the proposed career path. For example, the support unit provides training programs and supports the acquisition of necessary skills. The support unit can also propose specific steps for career changes through consulting. For example, the support unit can suggest study methods for obtaining specific qualifications. The support unit can also provide resources and provide necessary information and tools. For example, the support unit can provide online courses and teaching materials. This allows for specific support for the proposed career path. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI. For example, the support unit can input support content for the proposed career path into a generation AI and have the generation AI execute the specific support method.

[0034] The support unit can present the required skills, qualifications, and detailed methods. For example, the support unit presents the required skills, qualifications, and detailed methods. For example, the support unit presents study methods for obtaining a specific qualification. The support unit can also present specific steps for career changes. For example, the support unit presents training programs for acquiring the skills required for a specific occupation. The support unit can also provide online courses and teaching materials to support acquiring the required skills. This makes it possible to present the specific skills and qualifications required for a career path. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can input information on the required skills and qualifications into the generation AI and have the generation AI execute specific methods.

[0035] The collection unit may include a protection unit that protects the privacy of the collected personal information. The collection unit may, for example, include a protection unit that protects the privacy of the collected personal information. The protection unit may protect the collected personal information using, for example, data encryption technology. The protection unit may also limit access to the personal information using access control technology. The protection unit may also anonymize the personal information using anonymization technology. For example, the protection unit may encrypt the personal information to prevent unauthorized access by third parties. The protection unit may also set access permissions so that only specific users can access the personal information. This protects the privacy of the personal information, allowing information to be provided with peace of mind. Some or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit may input the collected personal information into a generation AI and cause the generation AI to execute a privacy protection method.

[0036] The support unit may include a company support unit that supports companies in using the system to hire suitable personnel. For example, the support unit may include a company support unit that supports companies in using the system to hire suitable personnel. For example, the company support unit may screen candidates and select suitable personnel. The company support unit may also provide support for interviews to enable companies to effectively evaluate candidates. Furthermore, the company support unit may optimize the recruitment process to enable companies to efficiently conduct recruitment activities. For example, the company support unit may analyze candidates' resumes and select candidates with suitable skills and experience. The company support unit may also provide a list of interview questions to support companies in evaluating the suitability of candidates. This enables companies to support hiring suitable personnel. Some or all of the above-described processing in the company support unit may be performed using AI, or may be performed without AI. For example, the company support unit may input candidate information into a generation AI and have the generation AI execute screening and interview support methods.

[0037] The collection unit can analyze the user's past information provision history and select an appropriate collection method. The collection unit, for example, analyzes the user's past information provision history and selects an appropriate collection method. The collection unit, for example, prioritizes collecting information that was frequently provided in the past. The collection unit can also prioritize selecting collection methods (such as questionnaires and interviews) that were used in the past. Furthermore, the collection unit can collect information during a specific time period based on the past information provision history. For example, the collection unit prioritizes collecting information that the user frequently provided in the past. The collection unit can also select an optimal collection means based on collection methods used by the user in the past. In this way, the optimal collection method can be selected by analyzing the past information provision history. Some or all of the above-described processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's past information provision history to a generation AI and cause the generation AI to select a collection method.

[0038] The collection unit may perform filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit may perform filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit may preferentially collect relevant information based on the user's current living situation. The collection unit may also filter information that is likely to be of interest to the user based on the user's areas of interest. Furthermore, the collection unit may exclude unnecessary information based on the user's living situation and areas of interest. For example, the collection unit may preferentially collect relevant information based on the user's current living situation. The collection unit may also filter information that is likely to be of interest to the user based on the user's areas of interest. This allows for preferential collection of relevant information based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit may input data on the user's living situation and areas of interest to a generation AI and cause the generation AI to perform filtering.

[0039] The collection unit can select an appropriate collection means depending on the user's input method when collecting information. For example, the collection unit selects an appropriate collection means depending on the user's input method (voice, text, image, etc.) when collecting information. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. This makes it possible to select the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select a collection means.

[0040] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting nearby job information based on the user's current location. The collection unit can also collect related company information based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting area-specific information in consideration of the user's location information. For example, the collection unit prioritizes collecting nearby job information based on the user's current location. The collection unit can also collect related company information based on the user's geographical location information. In this way, highly relevant information can be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0041] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media posts and collect related job information. The collection unit can also collect related company information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect related information based on the user's interests on social media. For example, the collection unit can analyze the user's social media posts and collect related job information. The collection unit can also collect related company information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select an optimal collection means based on the user's past feedback. Furthermore, the collection unit can adjust the type of information to be collected by reflecting the user's feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select an optimal collection means based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. In this way, the level of detail of the analysis can be adjusted based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an appropriate analysis algorithm to career information. The analysis unit can also apply a psychological analysis algorithm to personality information. Furthermore, the analysis unit can apply an analysis algorithm that is highly relevant to things of interest. For example, the analysis unit applies an appropriate analysis algorithm to career information. The analysis unit can also apply a psychological analysis algorithm to personality information. This makes it possible to apply an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. Furthermore, the analysis unit can improve the analysis method by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can determine the analysis priority based on the time of submission of information during analysis. The analysis unit, for example, determines the analysis priority based on the time of submission of information during analysis. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted earlier. In this way, by determining the analysis priority based on the time of submission of information, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis based on the relevance of information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit uses technical terms according to the user's level of expertise. The analysis unit can also provide analysis results in simple language if the user does not have specialized knowledge. Furthermore, the analysis unit can adjust the way the analysis is presented based on the user's level of expertise. For example, the analysis unit uses technical terms according to the user's level of expertise. The analysis unit can also provide analysis results in simple language if the user does not have specialized knowledge. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0049] The proposal unit can adjust the level of detail of the proposal based on the importance of the career path when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the career path when making the proposal. For example, the proposal unit makes a detailed proposal for a career path with high importance. The proposal unit can also make a simplified proposal for a career path with low importance. Furthermore, the proposal unit can determine the priority of the proposal according to the importance of the career path. For example, the proposal unit makes a detailed proposal for a career path with high importance. The proposal unit can also make a simplified proposal for a career path with low importance. In this way, by adjusting the level of detail of the proposal based on the importance of the career path, it is possible to make a detailed proposal for a more important career path. Some or all of the above-mentioned processing in the proposal unit may be performed using AI or without AI. For example, the proposal unit can input career path importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0050] The proposal unit can apply different proposal algorithms depending on the career path category when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the career path category when making a proposal. For example, the proposal unit can apply an appropriate proposal algorithm to technical jobs. The proposal unit can also apply an appropriate proposal algorithm to managerial jobs. The proposal unit can also apply an appropriate proposal algorithm to creative jobs. For example, the proposal unit can apply an appropriate proposal algorithm to technical jobs. The proposal unit can also apply an appropriate proposal algorithm to managerial jobs. This makes it possible to provide more appropriate proposals by applying an appropriate proposal algorithm depending on the career path category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI or without AI. For example, the proposal unit can input career path category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0051] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. Furthermore, the suggestion unit can improve the suggestion method by referring to the user's past suggestion results. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using AI or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0052] The proposal unit can determine the priority of proposals based on the submission date of the career paths when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission date of the career paths when making proposals. The proposal unit, for example, prioritizes the most recent career paths. The proposal unit can also postpone career paths that were submitted earlier. The proposal unit can also adjust the order of proposals based on the submission date. For example, the proposal unit prioritizes the most recent career paths. The proposal unit can also postpone career paths that were submitted earlier. In this way, by determining the priority of proposals based on the submission date of the career paths, the most recent career paths can be prioritized. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input data on the submission date of career paths into the generation AI and cause the generation AI to determine the priority of proposals.

[0053] The proposal unit can adjust the order of proposals based on the relevance of the career paths when making proposals. The proposal unit, for example, adjusts the order of proposals based on the relevance of the career paths when making proposals. For example, the proposal unit prioritizes proposing highly relevant career paths. The proposal unit can also postpone less relevant career paths. Furthermore, the proposal unit can determine the order of proposals based on the relevance of the career paths. For example, the proposal unit prioritizes proposing highly relevant career paths. The proposal unit can also postpone less relevant career paths. In this way, by adjusting the order of proposals based on the relevance of the career paths, it is possible to prioritize proposing highly relevant career paths. Some or all of the above-described processing in the proposal unit may be performed using AI or without AI. For example, the proposal unit can input career path relevance data to a generation AI and cause the generation AI to adjust the order of proposals.

[0054] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit may use technical terms according to the user's level of expertise. The suggestion unit may also provide the proposal in simple language if the user does not have specialized knowledge. The suggestion unit may also adjust the way the proposal is expressed based on the user's level of expertise. For example, the suggestion unit may use technical terms according to the user's level of expertise. The suggestion unit may also provide the proposal in simple language if the user does not have specialized knowledge. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0055] The support unit can select an optimal support method by analyzing the user's past behavior when providing support. For example, the support unit can select an optimal support method by analyzing the user's past behavior when providing support. For example, the support unit can select an optimal support method based on the user's past behavior history. The support unit can also analyze the user's past behavior patterns and provide an effective support method. Furthermore, the support unit can improve the support method by referring to the user's past behavior data. For example, the support unit can select an optimal support method based on the user's past behavior history. The support unit can also analyze the user's past behavior patterns and provide an effective support method. In this way, the optimal support method can be selected by analyzing the user's past behavior. Some or all of the above-described processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's past behavior data into a generation AI and cause the generation AI to select a support method.

[0056] The support unit can customize the means of support based on the user's current living situation when providing support. For example, the support unit customizes the means of support based on the user's current living situation when providing support. For example, the support unit provides the optimal means of support based on the user's current living situation. The support unit can also customize the method of support taking the user's living situation into consideration. Furthermore, the support unit can adjust the content of support according to the user's living situation. For example, the support unit provides the optimal means of support based on the user's current living situation. The support unit can also customize the method of support taking the user's living situation into consideration. In this way, more appropriate support can be provided by customizing the means of support based on the user's current living situation. Some or all of the above-described processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's living situation data into a generation AI and cause the generation AI to customize the means of support.

[0057] The support unit can improve the support method by reflecting user feedback when providing support. For example, the support unit improves the support method by reflecting user feedback when providing support. For example, the support unit improves the support method based on user feedback. The support unit can also adjust the support means by referring to the user's past feedback. Furthermore, the support unit can optimize the content of support by reflecting user feedback. For example, the support unit improves the support method based on user feedback. The support unit can also adjust the support means by referring to the user's past feedback. In this way, more effective support can be provided by improving the support method by reflecting user feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI or may be performed without using AI. For example, the support unit can input user feedback data into a generation AI and cause the generation AI to improve the support method.

[0058] The support unit can select an optimal support method by taking into account the user's geographical location information when providing support. For example, the support unit selects an optimal support method by taking into account the user's geographical location information when providing support. For example, the support unit provides nearby support services based on the user's current location. The support unit can also select a relevant support method based on the user's geographical location information. Furthermore, the support unit can provide region-specific support by taking into account the user's location information. For example, the support unit provides nearby support services based on the user's current location. The support unit can also select a relevant support method based on the user's geographical location information. In this way, more appropriate support can be provided by selecting the optimal support method by taking into account the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's geographical location information to a generation AI and cause the generation AI to select an optimal support method.

[0059] The support unit can analyze the user's social media activities and suggest support methods when providing support. For example, the support unit can analyze the user's social media activities and suggest support methods when providing support. For example, the support unit can analyze the content of the user's social media posts and suggest relevant support methods. The support unit can also suggest effective support methods by referring to the activities of the user's friends on social media. Furthermore, the support unit can suggest optimal support methods based on the user's interests on social media. For example, the support unit can analyze the content of the user's social media posts and suggest relevant support methods. The support unit can also suggest effective support methods by referring to the activities of the user's friends on social media. In this way, relevant support methods can be suggested by analyzing the user's social media activities. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's social media data into a generation AI and cause the generation AI to suggest support methods.

[0060] The support unit can customize the support method by reflecting the user's past feedback when providing support. For example, the support unit customizes the support method by reflecting the user's past feedback when providing support. For example, the support unit improves the support method based on the user's past feedback. The support unit can also select an optimal support method based on the user's past feedback. Furthermore, the support unit can adjust the content of support by reflecting the user's feedback. For example, the support unit improves the support method based on the user's past feedback. The support unit can also select an optimal support method based on the user's past feedback. In this way, more appropriate support can be provided by customizing the support method by reflecting the user's past feedback. Some or all of the above-described processing in the support unit may be performed using AI or may be performed without using AI. For example, the support unit can input user feedback data to a generation AI and cause the generation AI to customize the support method.

[0061] The protection unit can adjust the level of detail of protection based on the importance of collected information during privacy protection. For example, the protection unit adjusts the level of detail of protection based on the importance of collected information during privacy protection. For example, the protection unit provides detailed privacy protection for information with high importance. The protection unit can also provide simplified privacy protection for information with low importance. Furthermore, the protection unit can determine the priority of privacy protection according to the importance of information. For example, the protection unit provides detailed privacy protection for information with high importance. The protection unit can also provide simplified privacy protection for information with low importance. In this way, by adjusting the level of detail of privacy protection based on the importance of collected information, more appropriate privacy protection can be provided. Some or all of the above-mentioned processing in the protection unit may be performed using AI or without AI. For example, the protection unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of privacy protection.

[0062] The protection unit can apply different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. The protection unit can also apply a highly relevant privacy protection algorithm to items of interest. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. This allows for more appropriate privacy protection by applying an appropriate privacy protection algorithm depending on the category of information. Some or all of the above-mentioned processing in the protection unit may be performed using AI or without AI. For example, the protection unit can input information category data to the generation AI and cause the generation AI to apply the protection algorithm.

[0063] The protection unit can determine the priority of protection based on the time of submission of information during privacy protection. For example, the protection unit determines the priority of protection based on the time of submission of information during privacy protection. For example, the protection unit prioritizes privacy protection of the most recent information. The protection unit can also postpone information that has been submitted earlier. Furthermore, the protection unit can adjust the order of privacy protection based on the time of submission. For example, the protection unit prioritizes privacy protection of the most recent information. The protection unit can also postpone information that has been submitted earlier. In this way, by determining the priority of privacy protection based on the time of submission of information, the most recent information can be prioritized for protection. Some or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit can input information submission time data to the generation AI and cause the generation AI to determine the priority of privacy protection.

[0064] The protection unit can adjust the order of protection based on the relevance of information during privacy protection. For example, the protection unit adjusts the order of protection based on the relevance of information during privacy protection. For example, the protection unit prioritizes privacy protection for highly relevant information. The protection unit can also postpone privacy protection for less relevant information. Furthermore, the protection unit can determine the order of privacy protection based on the relevance of information. For example, the protection unit prioritizes privacy protection for highly relevant information. The protection unit can also postpone privacy protection for less relevant information. In this way, by adjusting the order of privacy protection based on the relevance of information, highly relevant information can be prioritized for protection. Some or all of the above-described processing in the protection unit may be performed using AI or may be performed without using AI. For example, the protection unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of privacy protection.

[0065] The protection unit can apply different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. The protection unit can also apply a highly relevant privacy protection algorithm to items of interest. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. This allows for more appropriate privacy protection by applying an appropriate privacy protection algorithm depending on the category of information. Some or all of the above-mentioned processing in the protection unit may be performed using AI or without AI. For example, the protection unit can input information category data to the generation AI and cause the generation AI to apply the protection algorithm.

[0066] When supporting a company, the company support department can analyze the company's past hiring history and select the optimal support method. For example, when supporting a company, the company support department analyzes the company's past hiring history and selects the optimal support method. For example, the company support department selects the optimal support method based on the company's past hiring history. The company support department can also analyze the company's past hiring patterns and provide an effective support method. Furthermore, the company support department can improve the support method by referring to the company's past hiring data. For example, the company support department selects the optimal support method based on the company's past hiring history. The company support department can also analyze the company's past hiring patterns and provide an effective support method. In this way, the optimal support method can be selected by analyzing the company's past hiring history. Some or all of the above-mentioned processing in the company support department may be performed using AI, or may be performed without using AI. For example, the company support department can input the company's past hiring history data into the generation AI and have the generation AI select a support method.

[0067] The business support department can customize the means of support based on the current needs of the company when providing business support. For example, the business support department customizes the means of support based on the current needs of the company when providing business support. For example, the business support department provides the optimal means of support based on the current needs of the company. The business support department can also customize the method of support taking into account the needs of the company. Furthermore, the business support department can adjust the content of support according to the needs of the company. For example, the business support department provides the optimal means of support based on the current needs of the company. The business support department can also customize the method of support taking into account the needs of the company. In this way, by customizing the means of support based on the current needs of the company, more appropriate business support can be provided. Some or all of the above-mentioned processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department can input company needs data into a generation AI and have the generation AI customize the means of support.

[0068] The business support department can improve the support method by reflecting the company's feedback when supporting the company. For example, the business support department improves the support method by reflecting the company's feedback when supporting the company. For example, the business support department improves the support method based on the company's feedback. The business support department can also adjust the support means by referring to the company's past feedback. Furthermore, the business support department can optimize the content of the support by reflecting the company's feedback. For example, the business support department improves the support method based on the company's feedback. The business support department can also adjust the support means by referring to the company's past feedback. In this way, by reflecting the company's feedback and improving the support method, more effective business support can be provided. Some or all of the above-mentioned processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department can input the company's feedback data into a generation AI and have the generation AI improve the support method.

[0069] The business support unit can select an appropriate support method by taking into consideration the geographical location information of the business when providing business support. For example, the business support unit selects an appropriate support method by taking into consideration the geographical location information of the business when providing business support. For example, the business support unit provides nearby support services based on the current location of the business. The business support unit can also select a relevant support method based on the geographical location information of the business. Furthermore, the business support unit can provide region-specific support by taking into consideration the location information of the business. For example, the business support unit provides nearby support services based on the current location of the business. The business support unit can also select a relevant support method based on the geographical location information of the business. In this way, by selecting an appropriate support method by taking into consideration the geographical location information of the business, more appropriate business support can be provided. Some or all of the above-described processing in the business support unit may be performed using AI or without AI. For example, the business support unit can input the geographical location information of the business into the generation AI and cause the generation AI to select an appropriate support method.

[0070] The business support department can analyze the social media activities of the company and propose support measures when providing business support. For example, when providing business support, the business support department can analyze the social media activities of the company and propose support measures. For example, the business support department can analyze the content of the company's social media posts and propose relevant support methods. The business support department can also propose effective support measures by referring to the company's social media activities. Furthermore, the business support department can propose optimal support measures based on the company's social media interests. For example, the business support department can analyze the content of the company's social media posts and propose relevant support methods. The business support department can also propose effective support measures by referring to the company's social media activities. In this way, relevant support measures can be proposed by analyzing the company's social media activities. Some or all of the above-mentioned processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department can input the company's social media data into a generation AI and have the generation AI execute the proposal of support measures.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit can prioritize the collection method that the user has used favorably in the past. The collection unit can also collect information during specific time periods based on the past behavioral history. Furthermore, the collection unit can adjust the type of information to be collected based on the past behavioral history. This enables flexible information collection that reflects the user's past behavioral history.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with a high level of importance. Also, the analysis unit can perform a simplified analysis of information with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for flexible analysis based on the importance of the information.

[0074] The suggestion unit can apply different suggestion algorithms depending on the category of the career path. For example, the suggestion unit can apply an appropriate suggestion algorithm to technical jobs. It can also apply an appropriate suggestion algorithm to managerial jobs. It can also apply an appropriate suggestion algorithm to creative jobs. This enables flexible suggestions according to the category of the career path.

[0075] The support unit can customize the means of support based on the user's current living situation. For example, the support unit provides the most appropriate means of support based on the user's current living situation. The support unit can also customize the method of support taking the user's living situation into consideration. Furthermore, the support content can be adjusted according to the user's living situation. This enables flexible support based on the user's current living situation.

[0076] The protection unit can apply different privacy protection algorithms depending on the category of information. For example, the protection unit can apply an appropriate privacy protection algorithm to biographical information. Also, the protection unit can apply a psychological privacy protection algorithm to personality information. Furthermore, the protection unit can apply a highly relevant privacy protection algorithm to interests. This enables flexible privacy protection according to the category of information.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection department collects personal information. This includes career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations and dreams, etc. The collection department collects information through questionnaires and interviews, as well as by analyzing resumes and social media data. Step 2: The analysis unit uses the generation AI to analyze the collected information from multiple angles and derive individual characteristics and tendencies. The generation AI can analyze text data using natural language processing technology, image data using image recognition technology, and voice data using voice recognition technology. Step 3: The proposal unit proposes a career path suitable for the individual based on the analysis results. The proposal unit uses skill matching technology to compare the individual's skills with the skills required for the job and propose an appropriate career path. It can also analyze interests and propose a career path based on the individual's interests. Step 4: The support department provides practical support for the proposed career path. The support department offers training programs to help employees acquire the necessary skills, and can also suggest specific steps for career change through consulting. They can also provide resources and provide the necessary information and tools.

[0079] (Example 2) A career support system according to an embodiment of the present invention collects, analyzes, proposes, and provides support for individuals' career development. The system collects information about individuals, such as their career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, and aspirations and dreams, analyzes it using a generation AI, proposes career paths, and provides practical support. For example, a user inputs information about their career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, and aspirations and dreams. The generation AI then analyzes the input information and derives the individual's characteristics and tendencies. The generation AI uses specialized theories to analyze the individual's information from multiple angles. For example, it identifies the individual's strengths and weaknesses based on past experiences and interests and proposes an appropriate career path. Furthermore, the generation AI proposes practical support for the individual's aspirations and dreams. Specifically, it presents the necessary skills, qualifications, and specific methods. This allows the career support system to help individuals understand their own characteristics and tendencies and pioneer new career paths. Furthermore, the realistic support suggested by generative AI allows individuals to take concrete actions toward their desired goals and dreams. This allows the career support system to support individuals in their career development and also provides support to companies in hiring the right talent. For example, companies can understand the characteristics and tendencies of individuals and hire the right talent. This is expected to improve corporate productivity and efficiency.

[0080] A career support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects personal information. Examples of personal information include, but are not limited to, career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, and aspirations and dreams. The collection unit collects information, for example, through questionnaires or interviews. The collection unit can also collect information by analyzing resumes and social media data. The analysis unit uses a generation AI to analyze the collected information from multiple angles and derive individual characteristics and tendencies. The generation AI can, for example, analyze text data using natural language processing technology. The generation AI can also analyze image data using image recognition technology. Furthermore, the generation AI can analyze audio data using voice recognition technology. The proposal unit proposes a career path suitable for the individual based on the analysis results. The proposal unit, for example, uses skill matching technology to compare the individual's skills with the skills required for the job and propose an appropriate career path. The proposal unit can also analyze interests and propose a career path based on the individual's interests. The support unit provides realistic support for the proposed career path. For example, the support unit provides a training program and supports the acquisition of necessary skills. The support unit can also suggest specific steps for career change through consulting. Furthermore, the support unit can provide resources and necessary information and tools. As a result, the career support system according to the embodiment can support an individual's career development, suggest an appropriate career path, and provide realistic support.

[0081] The collection unit can collect information on an individual's career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations, and dreams. For example, the collection unit can collect information on an individual's career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations, and dreams through a questionnaire. The collection unit can also collect this information through interviews. For example, the collection unit can collect detailed information through individual interviews. The collection unit can also collect information on an individual's career history and qualifications through resume analysis. For example, the collection unit can digitize and analyze the contents of a resume. This enables more accurate career support by collecting multifaceted information on individuals. Some or all of the above-mentioned processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input questionnaire response data into a generation AI and have the generation AI analyze the response data.

[0082] The analysis unit can use the generative AI to analyze the collected information from multiple angles and derive individual characteristics and tendencies. The analysis unit can use, for example, the generative AI to analyze the collected information from multiple angles. The generative AI can analyze text data using natural language processing technology. The generative AI can also analyze image data using image recognition technology. The generative AI can also analyze audio data using voice recognition technology. For example, the generative AI can identify an individual's strengths and weaknesses based on their past experiences and interests. The generative AI can also analyze an individual's personality and tendencies and derive an appropriate career path. For example, the generative AI can evaluate an individual's personality traits using psychological theory. This allows the generative AI to analyze individual characteristics and tendencies with high accuracy. Some or all of the above-mentioned processing in the analysis unit can be performed using generative AI (generative AI or LLM). For example, the analysis unit can input collected text data into the generative AI and have the generative AI analyze the text data.

[0083] The suggestion unit can propose a career path suitable for the individual based on the analysis results. The suggestion unit proposes a career path suitable for the individual based on, for example, the analysis results. The suggestion unit, for example, uses skill matching technology to compare the individual's skills with the skills required for the occupation and propose a suitable career path. The suggestion unit can also analyze interests and propose a career path based on the individual's interests. For example, the suggestion unit can identify the individual's fields of interest or occupations and propose a career path based on them. The suggestion unit can also propose a career path based on the individual's aspirations and dreams. For example, the suggestion unit can propose specific steps toward the individual's goals. This makes it possible to propose an optimal career path for the individual based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and have the generation AI execute career path proposals.

[0084] The support unit can provide practical support for the proposed career path. For example, the support unit provides practical support for the proposed career path. For example, the support unit provides training programs and supports the acquisition of necessary skills. The support unit can also propose specific steps for career changes through consulting. For example, the support unit can suggest study methods for obtaining specific qualifications. The support unit can also provide resources and provide necessary information and tools. For example, the support unit can provide online courses and teaching materials. This allows for specific support for the proposed career path. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI. For example, the support unit can input support content for the proposed career path into a generation AI and have the generation AI execute the specific support method.

[0085] The support unit can present the required skills, qualifications, and detailed methods. For example, the support unit presents the required skills, qualifications, and detailed methods. For example, the support unit presents study methods for obtaining a specific qualification. The support unit can also present specific steps for career changes. For example, the support unit presents training programs for acquiring the skills required for a specific occupation. The support unit can also provide online courses and teaching materials to support acquiring the required skills. This makes it possible to present the specific skills and qualifications required for a career path. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can input information on the required skills and qualifications into the generation AI and have the generation AI execute specific methods.

[0086] The collection unit may include a protection unit that protects the privacy of the collected personal information. The collection unit may, for example, include a protection unit that protects the privacy of the collected personal information. The protection unit may protect the collected personal information using, for example, data encryption technology. The protection unit may also limit access to the personal information using access control technology. The protection unit may also anonymize the personal information using anonymization technology. For example, the protection unit may encrypt the personal information to prevent unauthorized access by third parties. The protection unit may also set access permissions so that only specific users can access the personal information. This protects the privacy of the personal information, allowing information to be provided with peace of mind. Some or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit may input the collected personal information into a generation AI and cause the generation AI to execute a privacy protection method.

[0087] The support unit may include a company support unit that supports companies in using the system to hire suitable personnel. For example, the support unit may include a company support unit that supports companies in using the system to hire suitable personnel. For example, the company support unit may screen candidates and select suitable personnel. The company support unit may also provide support for interviews to enable companies to effectively evaluate candidates. Furthermore, the company support unit may optimize the recruitment process to enable companies to efficiently conduct recruitment activities. For example, the company support unit may analyze candidates' resumes and select candidates with suitable skills and experience. The company support unit may also provide a list of interview questions to support companies in evaluating the suitability of candidates. This enables companies to support hiring suitable personnel. Some or all of the above-described processing in the company support unit may be performed using AI, or may be performed without AI. For example, the company support unit may input candidate information into a generation AI and have the generation AI execute screening and interview support methods.

[0088] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, the collection unit analyzes the user's facial expressions using facial expression recognition technology to estimate the emotions. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotions. The collection unit can also analyze the user's text data using text analysis technology to estimate the emotions. For example, if the user is feeling stressed, the collection unit collects information during a time when the user is able to relax. The collection unit can also actively collect information when the user is relaxed. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 collection unit may be performed using AI or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The collection unit can analyze the user's past information provision history and select an appropriate collection method. The collection unit, for example, analyzes the user's past information provision history and selects an appropriate collection method. The collection unit, for example, prioritizes collecting information that was frequently provided in the past. The collection unit can also prioritize selecting collection methods (such as questionnaires and interviews) that were used in the past. Furthermore, the collection unit can collect information during a specific time period based on the past information provision history. For example, the collection unit prioritizes collecting information that the user frequently provided in the past. The collection unit can also select an optimal collection means based on collection methods used by the user in the past. In this way, the optimal collection method can be selected by analyzing the past information provision history. Some or all of the above-described processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's past information provision history to a generation AI and cause the generation AI to select a collection method.

[0090] The collection unit may perform filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit may perform filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit may preferentially collect relevant information based on the user's current living situation. The collection unit may also filter information that is likely to be of interest to the user based on the user's areas of interest. Furthermore, the collection unit may exclude unnecessary information based on the user's living situation and areas of interest. For example, the collection unit may preferentially collect relevant information based on the user's current living situation. The collection unit may also filter information that is likely to be of interest to the user based on the user's areas of interest. This allows for preferential collection of relevant information based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit may input data on the user's living situation and areas of interest to a generation AI and cause the generation AI to perform filtering.

[0091] The collection unit can select an appropriate collection means depending on the user's input method when collecting information. For example, the collection unit selects an appropriate collection means depending on the user's input method (voice, text, image, etc.) when collecting information. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. This makes it possible to select the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select a collection means.

[0092] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. The collection unit, for example, analyzes the user's facial expressions using facial expression recognition technology to estimate emotions. The collection unit can also analyze the user's voice using voice analysis technology to estimate emotions. The collection unit can also analyze the user's text data using text analysis technology to estimate emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important information. When the user is relaxed, the collection unit can prioritize collecting more important information. This allows the priority of information to be collected to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 collection unit may be performed using AI or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting nearby job information based on the user's current location. The collection unit can also collect related company information based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting area-specific information in consideration of the user's location information. For example, the collection unit prioritizes collecting nearby job information based on the user's current location. The collection unit can also collect related company information based on the user's geographical location information. In this way, highly relevant information can be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0094] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media posts and collect related job information. The collection unit can also collect related company information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect related information based on the user's interests on social media. For example, the collection unit can analyze the user's social media posts and collect related job information. The collection unit can also collect related company information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0095] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select an optimal collection means based on the user's past feedback. Furthermore, the collection unit can adjust the type of information to be collected by reflecting the user's feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select an optimal collection means based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0096] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The analysis unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the analysis unit can analyze the user's text data using text analysis technology to estimate the emotion. For example, the analysis unit can provide a simple, highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. This allows the presentation method of the analysis to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. In this way, the level of detail of the analysis can be adjusted based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an appropriate analysis algorithm to career information. The analysis unit can also apply a psychological analysis algorithm to personality information. Furthermore, the analysis unit can apply an analysis algorithm that is highly relevant to things of interest. For example, the analysis unit applies an appropriate analysis algorithm to career information. The analysis unit can also apply a psychological analysis algorithm to personality information. This makes it possible to apply an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0099] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. Furthermore, the analysis unit can improve the analysis method by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0100] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The analysis unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the analysis unit can analyze the user's text data using text analysis technology to estimate the emotion. For example, the analysis unit can provide a short, concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0101] The analysis unit can determine the analysis priority based on the time of submission of information during analysis. The analysis unit, for example, determines the analysis priority based on the time of submission of information during analysis. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted earlier. In this way, by determining the analysis priority based on the time of submission of information, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0102] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis based on the relevance of information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0103] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit uses technical terms according to the user's level of expertise. The analysis unit can also provide analysis results in simple language if the user does not have specialized knowledge. Furthermore, the analysis unit can adjust the way the analysis is presented based on the user's level of expertise. For example, the analysis unit uses technical terms according to the user's level of expertise. The analysis unit can also provide analysis results in simple language if the user does not have specialized knowledge. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0104] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. For example, the suggestion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The suggestion unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The suggestion unit can also analyze the user's text data using text analysis technology to estimate the emotion. For example, the suggestion unit can provide simple, highly visible suggestions when the user is nervous. The suggestion unit can also provide detailed suggestions when the user is relaxed. This allows the suggestion unit to adjust the way the suggestion is presented based on the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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-described processing in the suggestion unit can be performed using AI or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0105] The proposal unit can adjust the level of detail of the proposal based on the importance of the career path when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the career path when making the proposal. For example, the proposal unit makes a detailed proposal for a career path with high importance. The proposal unit can also make a simplified proposal for a career path with low importance. Furthermore, the proposal unit can determine the priority of the proposal according to the importance of the career path. For example, the proposal unit makes a detailed proposal for a career path with high importance. The proposal unit can also make a simplified proposal for a career path with low importance. In this way, by adjusting the level of detail of the proposal based on the importance of the career path, it is possible to make a detailed proposal for a more important career path. Some or all of the above-mentioned processing in the proposal unit may be performed using AI or without AI. For example, the proposal unit can input career path importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0106] The proposal unit can apply different proposal algorithms depending on the career path category when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the career path category when making a proposal. For example, the proposal unit can apply an appropriate proposal algorithm to technical jobs. The proposal unit can also apply an appropriate proposal algorithm to managerial jobs. The proposal unit can also apply an appropriate proposal algorithm to creative jobs. For example, the proposal unit can apply an appropriate proposal algorithm to technical jobs. The proposal unit can also apply an appropriate proposal algorithm to managerial jobs. This makes it possible to provide more appropriate proposals by applying an appropriate proposal algorithm depending on the career path category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI or without AI. For example, the proposal unit can input career path category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0107] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. Furthermore, the suggestion unit can improve the suggestion method by referring to the user's past suggestion results. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using AI or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0108] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The suggestion unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The suggestion unit can also analyze the user's text data using text analysis technology to estimate the emotion. For example, the suggestion unit can provide short and concise suggestions when the user is in a hurry. The suggestion unit can also provide detailed suggestions when the user is relaxed. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 AI or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0109] The proposal unit can determine the priority of proposals based on the submission date of the career paths when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission date of the career paths when making proposals. The proposal unit, for example, prioritizes the most recent career paths. The proposal unit can also postpone career paths that were submitted earlier. The proposal unit can also adjust the order of proposals based on the submission date. For example, the proposal unit prioritizes the most recent career paths. The proposal unit can also postpone career paths that were submitted earlier. In this way, by determining the priority of proposals based on the submission date of the career paths, the most recent career paths can be prioritized. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input data on the submission date of career paths into the generation AI and cause the generation AI to determine the priority of proposals.

[0110] The proposal unit can adjust the order of proposals based on the relevance of the career paths when making proposals. The proposal unit, for example, adjusts the order of proposals based on the relevance of the career paths when making proposals. For example, the proposal unit prioritizes proposing highly relevant career paths. The proposal unit can also postpone less relevant career paths. Furthermore, the proposal unit can determine the order of proposals based on the relevance of the career paths. For example, the proposal unit prioritizes proposing highly relevant career paths. The proposal unit can also postpone less relevant career paths. In this way, by adjusting the order of proposals based on the relevance of the career paths, it is possible to prioritize proposing highly relevant career paths. Some or all of the above-described processing in the proposal unit may be performed using AI or without AI. For example, the proposal unit can input career path relevance data to a generation AI and cause the generation AI to adjust the order of proposals.

[0111] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit may use technical terms according to the user's level of expertise. The suggestion unit may also provide the proposal in simple language if the user does not have specialized knowledge. The suggestion unit may also adjust the way the proposal is expressed based on the user's level of expertise. For example, the suggestion unit may use technical terms according to the user's level of expertise. The suggestion unit may also provide the proposal in simple language if the user does not have specialized knowledge. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0112] The support unit can estimate the user's emotion and adjust the support method based on the estimated user's emotion. For example, the support unit estimates the user's emotion and adjusts the support method based on the estimated user's emotion. For example, the support unit analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The support unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The support unit can also analyze the user's text data using text analysis technology to estimate the emotion. For example, the support unit provides a support method that helps the user relax when the user is nervous. The support unit can also provide a proactive support method when the user is relaxed. This allows the support method to be adjusted according to the user's emotion, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be 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 support unit may be performed using AI or without AI. For example, the support unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0113] The support unit can select an optimal support method by analyzing the user's past behavior when providing support. For example, the support unit can select an optimal support method by analyzing the user's past behavior when providing support. For example, the support unit can select an optimal support method based on the user's past behavior history. The support unit can also analyze the user's past behavior patterns and provide an effective support method. Furthermore, the support unit can improve the support method by referring to the user's past behavior data. For example, the support unit can select an optimal support method based on the user's past behavior history. The support unit can also analyze the user's past behavior patterns and provide an effective support method. In this way, the optimal support method can be selected by analyzing the user's past behavior. Some or all of the above-described processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's past behavior data into a generation AI and cause the generation AI to select a support method.

[0114] The support unit can customize the means of support based on the user's current living situation when providing support. For example, the support unit customizes the means of support based on the user's current living situation when providing support. For example, the support unit provides the optimal means of support based on the user's current living situation. The support unit can also customize the method of support taking the user's living situation into consideration. Furthermore, the support unit can adjust the content of support according to the user's living situation. For example, the support unit provides the optimal means of support based on the user's current living situation. The support unit can also customize the method of support taking the user's living situation into consideration. In this way, more appropriate support can be provided by customizing the means of support based on the user's current living situation. Some or all of the above-described processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's living situation data into a generation AI and cause the generation AI to customize the means of support.

[0115] The support unit can improve the support method by reflecting user feedback when providing support. For example, the support unit improves the support method by reflecting user feedback when providing support. For example, the support unit improves the support method based on user feedback. The support unit can also adjust the support means by referring to the user's past feedback. Furthermore, the support unit can optimize the content of support by reflecting user feedback. For example, the support unit improves the support method based on user feedback. The support unit can also adjust the support means by referring to the user's past feedback. In this way, more effective support can be provided by improving the support method by reflecting user feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI or may be performed without using AI. For example, the support unit can input user feedback data into a generation AI and cause the generation AI to improve the support method.

[0116] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, the support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, the support unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The support unit can also analyze the user's voice using voice analysis technology to estimate the emotions. The support unit can also analyze the user's text data using text analysis technology to estimate the emotions. For example, if the user is feeling stressed, the support unit can postpone providing less important support. Also, if the user is relaxed, the support unit can prioritize providing more important support. This allows for more appropriate support to be provided by determining the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0117] The support unit can select an optimal support method by taking into account the user's geographical location information when providing support. For example, the support unit selects an optimal support method by taking into account the user's geographical location information when providing support. For example, the support unit provides nearby support services based on the user's current location. The support unit can also select a relevant support method based on the user's geographical location information. Furthermore, the support unit can provide region-specific support by taking into account the user's location information. For example, the support unit provides nearby support services based on the user's current location. The support unit can also select a relevant support method based on the user's geographical location information. In this way, more appropriate support can be provided by selecting the optimal support method by taking into account the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's geographical location information to a generation AI and cause the generation AI to select an optimal support method.

[0118] The support unit can analyze the user's social media activities and suggest support methods when providing support. For example, the support unit can analyze the user's social media activities and suggest support methods when providing support. For example, the support unit can analyze the content of the user's social media posts and suggest relevant support methods. The support unit can also suggest effective support methods by referring to the activities of the user's friends on social media. Furthermore, the support unit can suggest optimal support methods based on the user's interests on social media. For example, the support unit can analyze the content of the user's social media posts and suggest relevant support methods. The support unit can also suggest effective support methods by referring to the activities of the user's friends on social media. In this way, relevant support methods can be suggested by analyzing the user's social media activities. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI. For example, the support unit can input the user's social media data into a generation AI and cause the generation AI to suggest support methods.

[0119] The support unit can customize the support method by reflecting the user's past feedback when providing support. For example, the support unit customizes the support method by reflecting the user's past feedback when providing support. For example, the support unit improves the support method based on the user's past feedback. The support unit can also select an optimal support method based on the user's past feedback. Furthermore, the support unit can adjust the content of support by reflecting the user's feedback. For example, the support unit improves the support method based on the user's past feedback. The support unit can also select an optimal support method based on the user's past feedback. In this way, more appropriate support can be provided by customizing the support method by reflecting the user's past feedback. Some or all of the above-described processing in the support unit may be performed using AI or may be performed without using AI. For example, the support unit can input user feedback data to a generation AI and cause the generation AI to customize the support method.

[0120] The protection unit can estimate a user's emotion and adjust the privacy protection method based on the estimated user's emotion. For example, the protection unit can estimate a user's emotion and adjust the privacy protection method based on the estimated user's emotion. For example, the protection unit can analyze a user's facial expression using facial expression recognition technology to estimate the emotion. The protection unit can also analyze a user's voice using voice analysis technology to estimate the emotion. The protection unit can also analyze a user's text data using text analysis technology to estimate the emotion. For example, the protection unit can strengthen the level of privacy protection when the user is feeling anxious. The protection unit can also provide standard privacy protection when the user is relaxed. This allows for more appropriate privacy protection by adjusting the privacy protection method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 protection unit can be performed using AI or without AI. For example, the protection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0121] The protection unit can adjust the level of detail of protection based on the importance of collected information during privacy protection. For example, the protection unit adjusts the level of detail of protection based on the importance of collected information during privacy protection. For example, the protection unit provides detailed privacy protection for information with high importance. The protection unit can also provide simplified privacy protection for information with low importance. Furthermore, the protection unit can determine the priority of privacy protection according to the importance of information. For example, the protection unit provides detailed privacy protection for information with high importance. The protection unit can also provide simplified privacy protection for information with low importance. In this way, by adjusting the level of detail of privacy protection based on the importance of collected information, more appropriate privacy protection can be provided. Some or all of the above-mentioned processing in the protection unit may be performed using AI or without AI. For example, the protection unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of privacy protection.

[0122] The protection unit can apply different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. The protection unit can also apply a highly relevant privacy protection algorithm to items of interest. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. This allows for more appropriate privacy protection by applying an appropriate privacy protection algorithm depending on the category of information. Some or all of the above-mentioned processing in the protection unit may be performed using AI or without AI. For example, the protection unit can input information category data to the generation AI and cause the generation AI to apply the protection algorithm.

[0123] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user emotions. For example, the protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user emotions. For example, the protection unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The protection unit can also analyze the user's voice using voice analysis technology to estimate the emotions. The protection unit can also analyze the user's text data using text analysis technology to estimate the emotions. For example, if the user is feeling anxious, the protection unit can postpone privacy protection with a lower priority. Also, if the user is relaxed, the protection unit can prioritize privacy protection with a higher priority. This allows for more appropriate privacy protection by determining the priority of privacy protection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0124] The protection unit can determine the priority of protection based on the time of submission of information during privacy protection. For example, the protection unit determines the priority of protection based on the time of submission of information during privacy protection. For example, the protection unit prioritizes privacy protection of the most recent information. The protection unit can also postpone information that has been submitted earlier. Furthermore, the protection unit can adjust the order of privacy protection based on the time of submission. For example, the protection unit prioritizes privacy protection of the most recent information. The protection unit can also postpone information that has been submitted earlier. In this way, by determining the priority of privacy protection based on the time of submission of information, the most recent information can be prioritized for protection. Some or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit can input information submission time data to the generation AI and cause the generation AI to determine the priority of privacy protection.

[0125] The protection unit can adjust the order of protection based on the relevance of information during privacy protection. For example, the protection unit adjusts the order of protection based on the relevance of information during privacy protection. For example, the protection unit prioritizes privacy protection for highly relevant information. The protection unit can also postpone privacy protection for less relevant information. Furthermore, the protection unit can determine the order of privacy protection based on the relevance of information. For example, the protection unit prioritizes privacy protection for highly relevant information. The protection unit can also postpone privacy protection for less relevant information. In this way, by adjusting the order of privacy protection based on the relevance of information, highly relevant information can be prioritized for protection. Some or all of the above-described processing in the protection unit may be performed using AI or may be performed without using AI. For example, the protection unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of privacy protection.

[0126] The protection unit can apply different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies different protection algorithms depending on the category of information during privacy protection. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. The protection unit can also apply a highly relevant privacy protection algorithm to items of interest. For example, the protection unit applies an appropriate privacy protection algorithm to career information. The protection unit can also apply a psychological privacy protection algorithm to personality information. This allows for more appropriate privacy protection by applying an appropriate privacy protection algorithm depending on the category of information. Some or all of the above-mentioned processing in the protection unit may be performed using AI or without AI. For example, the protection unit can input information category data to the generation AI and cause the generation AI to apply the protection algorithm.

[0127] The business support unit can estimate a user's emotions and adjust the business support method based on the estimated user emotions. For example, the business support unit can estimate a user's emotions and adjust the business support method based on the estimated user emotions. For example, the business support unit can analyze a user's facial expression using facial expression recognition technology to estimate the user's emotions. The business support unit can also analyze a user's voice using voice analysis technology to estimate the user's emotions. Furthermore, the business support unit can analyze a user's text data using text analysis technology to estimate the user's emotions. For example, if the user is nervous, the business support unit can provide a business support method that helps the user relax. If the user is relaxed, the business support unit can provide a proactive business support method. This allows the business support method to be adjusted according to the user's emotions, thereby providing more appropriate business support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0128] When supporting a company, the company support department can analyze the company's past hiring history and select the optimal support method. For example, when supporting a company, the company support department analyzes the company's past hiring history and selects the optimal support method. For example, the company support department selects the optimal support method based on the company's past hiring history. The company support department can also analyze the company's past hiring patterns and provide an effective support method. Furthermore, the company support department can improve the support method by referring to the company's past hiring data. For example, the company support department selects the optimal support method based on the company's past hiring history. The company support department can also analyze the company's past hiring patterns and provide an effective support method. In this way, the optimal support method can be selected by analyzing the company's past hiring history. Some or all of the above-mentioned processing in the company support department may be performed using AI, or may be performed without using AI. For example, the company support department can input the company's past hiring history data into the generation AI and have the generation AI select a support method.

[0129] The business support department can customize the means of support based on the current needs of the company when providing business support. For example, the business support department customizes the means of support based on the current needs of the company when providing business support. For example, the business support department provides the optimal means of support based on the current needs of the company. The business support department can also customize the method of support taking into account the needs of the company. Furthermore, the business support department can adjust the content of support according to the needs of the company. For example, the business support department provides the optimal means of support based on the current needs of the company. The business support department can also customize the method of support taking into account the needs of the company. In this way, by customizing the means of support based on the current needs of the company, more appropriate business support can be provided. Some or all of the above-mentioned processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department can input company needs data into a generation AI and have the generation AI customize the means of support.

[0130] The business support department can improve the support method by reflecting the company's feedback when supporting the company. For example, the business support department improves the support method by reflecting the company's feedback when supporting the company. For example, the business support department improves the support method based on the company's feedback. The business support department can also adjust the support means by referring to the company's past feedback. Furthermore, the business support department can optimize the content of the support by reflecting the company's feedback. For example, the business support department improves the support method based on the company's feedback. The business support department can also adjust the support means by referring to the company's past feedback. In this way, by reflecting the company's feedback and improving the support method, more effective business support can be provided. Some or all of the above-mentioned processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department can input the company's feedback data into a generation AI and have the generation AI improve the support method.

[0131] The business support unit can estimate the user's emotions and determine the priority of business support based on the estimated user emotions. For example, the business support unit can estimate the user's emotions and determine the priority of business support based on the estimated user emotions. For example, the business support unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The business support unit can also analyze the user's voice using voice analysis technology to estimate the emotions. Furthermore, the business support unit can analyze the user's text data using text analysis technology to estimate the emotions. For example, if the user is feeling stressed, the business support unit can postpone providing less important support. Also, if the user is relaxed, the business support unit can prioritize providing more important support. This allows the business support to be provided more appropriately by determining the priority of business support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0132] The business support unit can select an appropriate support method by taking into consideration the geographical location information of the business when providing business support. For example, the business support unit selects an appropriate support method by taking into consideration the geographical location information of the business when providing business support. For example, the business support unit provides nearby support services based on the current location of the business. The business support unit can also select a relevant support method based on the geographical location information of the business. Furthermore, the business support unit can provide region-specific support by taking into consideration the location information of the business. For example, the business support unit provides nearby support services based on the current location of the business. The business support unit can also select a relevant support method based on the geographical location information of the business. In this way, by selecting an appropriate support method by taking into consideration the geographical location information of the business, more appropriate business support can be provided. Some or all of the above-described processing in the business support unit may be performed using AI or without AI. For example, the business support unit can input the geographical location information of the business into the generation AI and cause the generation AI to select an appropriate support method.

[0133] The business support department can analyze the social media activities of the company and propose support measures when providing business support. For example, when providing business support, the business support department can analyze the social media activities of the company and propose support measures. For example, the business support department can analyze the content of the company's social media posts and propose relevant support methods. The business support department can also propose effective support measures by referring to the company's social media activities. Furthermore, the business support department can propose optimal support measures based on the company's social media interests. For example, the business support department can analyze the content of the company's social media posts and propose relevant support methods. The business support department can also propose effective support measures by referring to the company's social media activities. In this way, relevant support measures can be proposed by analyzing the company's social media activities. Some or all of the above-mentioned processing in the business support department may be performed using AI, or may be performed without using AI. For example, the business support department can input the company's social media data into a generation AI and have the generation AI execute the proposal of support measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a multifaceted analysis of the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a career path suitable for the individual based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides practical support for the suggested career path. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support 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 collection unit collects personal information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a multifaceted analysis of the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a career path suitable for the individual based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides practical support for the suggested career path. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and support 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 collection unit collects personal information using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a multifaceted analysis of the collected information using a generative AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a career path suitable for the individual based on the analysis results. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides practical support for the proposed career path. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a multifaceted analysis of the collected information using generative AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a career path suitable for the individual based on the analysis results. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides practical support toward the proposed career path.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The collection unit can estimate the user's emotions and adjust the method of information collection based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can collect information in the form of simple questions. Alternatively, if the user is relaxed, the collection unit can collect information in the form of a detailed interview. Furthermore, the collection unit can adjust the timing of information collection according to the user's emotions. This enables flexible information collection according to the user's emotions.

[0136] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can postpone analysis of less important information. Alternatively, if the user is relaxed, the analysis unit can prioritize analysis of more important information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's emotions. This allows for flexible analysis according to the user's emotions.

[0137] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Alternatively, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, the suggestion unit can adjust the timing of the suggestions according to the user's emotions. This enables flexible suggestions according to the user's emotions.

[0138] The support unit can estimate the user's emotions and adjust the support method based on the estimated emotions. For example, if the user is feeling stressed, the support unit can provide a support method that helps the user relax. Also, if the user is relaxed, the support unit can provide a proactive support method. Furthermore, the support unit can adjust the timing of support according to the user's emotions. This enables flexible support according to the user's emotions.

[0139] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated emotions. For example, the protection unit can strengthen the level of privacy protection when the user feels anxious, or provide standard privacy protection when the user feels relaxed. Furthermore, the protection unit can adjust the timing of privacy protection according to the user's emotions. This enables flexible privacy protection according to the user's emotions.

[0140] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit can prioritize the collection method that the user has used favorably in the past. The collection unit can also collect information during specific time periods based on the past behavioral history. Furthermore, the collection unit can adjust the type of information to be collected based on the past behavioral history. This enables flexible information collection that reflects the user's past behavioral history.

[0141] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with a high level of importance. Also, the analysis unit can perform a simplified analysis of information with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for flexible analysis based on the importance of the information.

[0142] The suggestion unit can apply different suggestion algorithms depending on the category of the career path. For example, the suggestion unit can apply an appropriate suggestion algorithm to technical jobs. It can also apply an appropriate suggestion algorithm to managerial jobs. It can also apply an appropriate suggestion algorithm to creative jobs. This enables flexible suggestions according to the category of the career path.

[0143] The support unit can customize the means of support based on the user's current living situation. For example, the support unit provides the most appropriate means of support based on the user's current living situation. The support unit can also customize the method of support taking the user's living situation into consideration. Furthermore, the support content can be adjusted according to the user's living situation. This enables flexible support based on the user's current living situation.

[0144] The protection unit can apply different privacy protection algorithms depending on the category of information. For example, the protection unit can apply an appropriate privacy protection algorithm to biographical information. Also, the protection unit can apply a psychological privacy protection algorithm to personality information. Furthermore, the protection unit can apply a highly relevant privacy protection algorithm to interests. This enables flexible privacy protection according to the category of information.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The collection department collects personal information. This includes career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations and dreams, etc. The collection department collects information through questionnaires and interviews, as well as by analyzing resumes and social media data. Step 2: The analysis unit uses the generation AI to analyze the collected information from multiple angles and derive individual characteristics and tendencies. The generation AI can analyze text data using natural language processing technology, image data using image recognition technology, and voice data using voice recognition technology. Step 3: The proposal unit proposes a career path suitable for the individual based on the analysis results. The proposal unit uses skill matching technology to compare the individual's skills with the skills required for the job and propose an appropriate career path. It can also analyze interests and propose a career path based on the individual's interests. Step 4: The support department provides practical support for the proposed career path. The support department offers training programs to help employees acquire the necessary skills, and can also suggest specific steps for career change through consulting. They can also provide resources and provide the necessary information and tools.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 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 collection department that collects personal information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes a career path based on the analysis results obtained by the analysis unit; a support unit that provides practical support for the career path proposed by the proposal unit; Equipped with A system characterized by:

2. The collecting unit Collect information about the individual's career history, qualifications, personality, residence, experience, interests, strengths and weaknesses, family structure, past experiences, aspirations and dreams.

2. The system of claim 1.

3. The analysis unit Using generative AI, collected information is analyzed from multiple angles to derive individual characteristics and tendencies.

2. The system of claim 1.

4. The proposal unit Based on the analysis results, we suggest career paths suited to each individual.

2. The system of claim 1.

5. The support unit Providing practical support for the proposed career path 2. The system of claim 1.

6. The support unit Present the required skills, qualifications, and detailed methods 2. The system of claim 1.

7. The collecting unit Have a protection department to protect the privacy of collected personal information 2. The system of claim 1.

8. The support unit Establish a corporate support department that will assist companies in using this system to recruit suitable personnel.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

10. The collecting unit Analyze the user's past information provision history and select the appropriate collection method 2. The system of claim 1.

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