System

Abstract

An object of the system according to the embodiment is to enable a user to find an optimum job or career for the user and to consult about worries about the career.SOLUTION: A system includes a collection part, an analysis part, a consultation part, and a provision part. The collection unit collects information on a user. The analysis unit analyzes the information collected by the collection unit and presents an occupation and a career suitable for the user. The consulting part consults the trouble of the user in a chat form on the basis of the result obtained by the analysis part. The provision unit provides word-of-mouth information from other users on the basis of the advice provided by the consultation 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 technology has made it difficult for users to find the job or career that best suits them, and has limited means for them to seek advice about their career concerns.

[0005] The system according to the embodiment aims to enable users to find the job type and career that best suits them and to consult about career-related concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a consultation unit, and a provision unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and presents suitable occupations and careers for the user. The consultation unit consults with the user about their concerns in a chat format based on the results obtained by the analysis unit. The provision unit provides word-of-mouth information from other users based on the advice provided by the consultation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to find the job type or career that best suits them and to seek advice on career-related concerns. [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 and career change support system according to an embodiment of the present invention collects user information, analyzes it using a generation AI, presents optimal job types and careers, accepts consultations via chat, and provides word-of-mouth information from other users. The career and career change support system collects information such as the user's skills and experience, desired job types and companies, and the generation AI analyzes this information to present optimal job types and careers. Furthermore, users can consult about their career-related concerns via chat, and the generation AI provides appropriate advice. Users can also obtain real-world feedback and information through word-of-mouth from other users. For example, in a career and career change support system, a user inputs information such as their skills and experience, desired job types and companies, etc. For example, a user might input, "I have marketing experience and would like to work at an IT company." This information is then input into the generation AI. The generation AI then analyzes the input information and presents optimal job types and careers to the user. The generation AI then suggests optimal job types and careers based on the user's skills, experience, desired job types and companies, etc. For example, if a user inputs, "I have marketing experience and want to work at an IT company," the AI ​​generator will suggest job titles such as "digital marketing manager" or "product marketing specialist." Furthermore, the career and job change support system allows users to discuss their career concerns via chat, with the AI ​​providing appropriate advice. For example, if a user asks, "I don't know how to proceed with my job search," the AI ​​generator will provide advice such as, "First, update your resume, and then register on a job site." The career and job change support system also provides real-world feedback and information through reviews from other users. For example, if a user wants to know how easy it is to work at a certain company, they can read reviews posted by other users to obtain information about the actual working environment at that company. This allows the career and job change support system to help users find the job and career that best suits them and resolve their career concerns.In addition, users can obtain real-world opinions and information through word-of-mouth from other users, making their job search go more smoothly. This allows the career and job change support system to efficiently collect, analyze, consult, and provide information about users. For example, by entering information such as their skills, experience, desired job type, and company, the generation AI will suggest the most suitable job type and career, and users can discuss their career concerns in chat format. In addition, users can obtain real-world opinions and information through word-of-mouth from other users, making their job search go more smoothly.

[0029] A career and job change support system according to an embodiment includes a collection unit, an analysis unit, a consultation unit, and a provision unit. The collection unit collects user information. The user information includes, but is not limited to, skills, experience, desired job type, and company. The collection unit collects information using, for example, a questionnaire or online form. The collection unit can also collect information through interviews. For example, the collection unit collects information entered by a user into an online form. The collection unit can also collect detailed information through interviews with the user. The collection unit can also collect information by analyzing the user's social media activity. For example, the collection unit collects information related to companies the user follows on social media and topics in which the user shows interest. The analysis unit analyzes the information collected by the collection unit using a generation AI. The analysis can be performed using, for example, data mining, statistical analysis, or a machine learning algorithm. For example, the generation AI can suggest optimal jobs and careers based on the user's skills, experience, desired job type, and company. The analysis unit can also analyze the user's information and suggest appropriate career paths using the generation AI. For example, the generation AI compares the user's skill set with market demand to suggest the most suitable job. The consultation unit accepts the user's concerns in chat format, and the generation AI provides appropriate advice. Chat formats include, but are not limited to, text chat, voice chat, and video chat. For example, the consultation unit provides real-time advice to users in response to their concerns via text chat. The consultation unit can also provide appropriate advice to users through voice chat or video chat. For example, if a user asks for advice saying, "I don't know how to proceed with my job search," the generation AI provides advice such as, "First, I recommend updating your resume, and then registering on a job site." The provision unit provides word-of-mouth information from other users. Word-of-mouth information includes, but is not limited to, ratings, comments, and reviews from other users.For example, if a user wants to know how easy it is to work at Company X, the providing unit can obtain information about the actual working environment of Company X by viewing reviews posted by other users. The providing unit can also provide appropriate advice to the user based on the review information. For example, the providing unit can analyze other users' reviews and provide information such as "Company X has a good working environment." This allows the career and job change support system according to the embodiment to efficiently collect, analyze, consult, and provide user information. For example, by inputting information such as the user's skills, experience, desired job type, and company, the generation AI can suggest the most suitable job type and career, and the user can discuss career-related concerns in a chat format. Furthermore, by obtaining real-world feedback and information through reviews from other users, the user can more smoothly progress through their job search.

[0030] The collection unit can collect information on the user's skills or experience, and desired job or company. The collection unit collects, for example, information on the user's skills, experience, and desired job or company. Skills include, but are not limited to, technical skills, soft skills, and language skills. For example, the collection unit collects information on technical skills and soft skills entered by the user into an online form. The collection unit can also collect information on the user's work history, project experience, volunteer experience, and so on. For example, the collection unit collects detailed information on projects the user has been involved in in the past. The collection unit can also collect information on the user's desired job or company. For example, the collection unit collects a list of the user's desired job or company and organizes the information based on that list. By collecting detailed information about the user, more appropriate job or career can be presented. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information entered by the user into an online form into a generation AI, which then organizes the information.

[0031] The analysis unit can analyze the collected information and present the user with the most suitable occupation or career. The analysis unit can, for example, analyze the collected information and present the user with the most suitable occupation or career. Examples of analysis include, but are not limited to, data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can use data mining technology to analyze information such as the user's skills and experience, desired occupation and company, etc. The analysis unit can also use statistical analysis to present the most suitable occupation or career based on the user's information. For example, the analysis unit can compare the user's skill set with market demand to suggest the most suitable occupation. The analysis unit can also use machine learning algorithms to analyze the user's information and present an appropriate career path. For example, the generation AI can suggest the most suitable occupation or career based on information such as the user's skills and experience, desired occupation and company, etc. In this way, the analysis unit can present the most suitable occupation or career to the user by analyzing the collected information. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI, which can then suggest the most suitable job type or career.

[0032] The consultation unit accepts the user's concerns in chat format, and the generation AI can provide useful advice. The consultation unit accepts the user's concerns in chat format, and the generation AI can provide useful advice. Examples of chat formats include, but are not limited to, text chat, voice chat, and video chat. For example, the consultation unit may use the generation AI to provide real-time advice on the content of the user's consultation via text chat. The consultation unit can also provide appropriate advice for the user's concerns via voice chat or video chat. For example, if a user asks for advice saying, "I don't know how to proceed with my job search," the generation AI can provide advice such as, "I recommend you first update your resume and then register on a job site." This allows the user's concerns to be accepted in chat format and appropriate advice to be provided. Some or all of the above-described processing in the consultation unit may be performed using, or without, the generation AI. For example, the consultation unit may input the user's consultation content into the generation AI, and the generation AI can provide appropriate advice.

[0033] The providing unit can provide word-of-mouth information from other users. The providing unit, for example, provides word-of-mouth information from other users. Word-of-mouth information includes, but is not limited to, ratings, comments, and reviews from other users. For example, if a user wants to know how easy it is to work at Company X, the providing unit can obtain information about the actual working environment of Company X by viewing reviews posted by other users. The providing unit can also provide appropriate advice to the user based on the word-of-mouth information. For example, the providing unit can analyze word-of-mouth information from other users and provide information such as, "Company X has a good working environment." By providing word-of-mouth information from other users, the user can obtain real opinions and information from the workplace. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input word-of-mouth information from other users into a generation AI, which then organizes and provides the information.

[0034] The collection unit can analyze the user's past work history and skill set and select the optimal information collection method. The collection unit, for example, analyzes the user's past work history and skill set and selects the optimal information collection method. Past work history includes, but is not limited to, the place of employment, job title, and job content. For example, if the user has past marketing experience, the collection unit prioritizes collecting marketing-related information. Furthermore, if the user has IT skills, the collection unit can also collect IT-related information. For example, if the user has managerial experience, the collection unit collects managerial-related information. In this way, the optimal information collection method can be selected by analyzing the user's past work history and skill set. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs data on the user's past work history and skill set into a generation AI, which can select the optimal information collection method.

[0035] The collection unit can filter information based on the user's current career goals and areas of interest when collecting information. For example, the collection unit can filter information based on the user's current career goals and areas of interest when collecting information. Career goals include, but are not limited to, short-term goals, long-term goals, and achievement criteria. For example, if the user is interested in digital marketing, the collection unit can prioritize collecting information related to digital marketing. Furthermore, if the user is interested in startup companies, the collection unit can also collect startup-related information. For example, if the user is interested in remote work, the collection unit can collect remote work-related information. By filtering information based on the user's career goals and areas of interest, more relevant information can be collected. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's career goals and areas of interest into a generation AI, which can then filter the information.

[0036] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Also, when the user uses text input, the collection unit can collect information using text analysis technology. For example, when the user uses image input, the collection unit collects information using image recognition technology. This enables efficient information collection by selecting 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, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI, which then selects the optimal collection means.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the user is in Tokyo, the collection unit can prioritize collecting information about companies and job types in Tokyo. Also, if the user is in New York, the collection unit can prioritize collecting information about companies and job types in New York. For example, if the user desires remote work, the collection unit prioritizes collecting information without geographical restrictions. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI, which can then prioritize collecting highly relevant information.

[0038] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can analyze the user's social media activity and collect related information when collecting information. Social media activity includes, but is not limited to, post content, number of followers, and engagement. For example, the collection unit can collect information about companies the user follows on LinkedIn (registered trademark). The collection unit can also collect information related to topics the user is interested in on Twitter (registered trademark). For example, the collection unit can collect information related to groups the user joins on Facebook (registered trademark). This allows related information to be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input data about the user's social media activity into a generation AI, which can then collect related information.

[0039] 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. Past feedback includes, but is not limited to, the user's ratings, comments, and areas for improvement. For example, the collection unit prioritizes the use of information collection methods that the user previously preferred. The collection unit can also eliminate information collection methods that the user previously avoided. For example, the collection unit suggests a new information collection method 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, for example, 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, which can then customize the collection method.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's skills and experience during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's skills and experience during analysis. The importance of skills and experience includes, but is not limited to, industry standards and expert evaluations. For example, the analysis unit performs a detailed analysis if the user has advanced skills. The analysis unit can also perform a concise analysis if the user is a beginner. For example, the analysis unit performs an analysis with an appropriate level of detail if the user is an intermediate user. This allows for adjusting the level of detail of the analysis based on the importance of the user's skills and experience, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the user's skills and experience into the generation AI, which can then adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the user's occupation or career category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the user's occupation or career category during analysis. Occupational and career categories include, but are not limited to, technical, managerial, and sales occupations. For example, if the user works in marketing, the analysis unit can use an analysis algorithm specialized for marketing. Furthermore, if the user works in engineering, the analysis unit can use an analysis algorithm specialized for engineering. For example, if the user works in management, the analysis unit can use an analysis algorithm specialized for management. This allows for applying different analysis algorithms depending on the user's occupation or career category, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the user's occupation or career into a generation AI, which can then apply an appropriate analysis algorithm.

[0042] 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. Past analysis results include, but are not limited to, success stories, failure stories, and areas for improvement. For example, the analysis unit adjusts the current analysis based on the user's past analysis results. The analysis unit can also identify trends and improve accuracy from the user's past analysis results. For example, the analysis unit improves the analysis algorithm based on the user's past feedback. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can then improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority based on the time when the user inputs information during analysis. For example, the analysis unit determines the analysis priority based on the time when the user inputs information during analysis. The time when the information is input includes, but is not limited to, the input date and time, the input frequency, etc. For example, the analysis unit prioritizes analysis of information recently input by the user. The analysis unit can also analyze information previously input by the user. For example, the analysis unit prioritizes analysis of information input by the user during a specific time period. This allows for more appropriate analysis results to be provided by determining the analysis priority based on the time when the user inputs information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the time when the user inputs information into the generation AI, which then determines the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the user's information during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's information during analysis. Information relevance includes, but is not limited to, common topics and areas of interest. For example, the analysis unit can determine the order of analysis based on the relevance of the user's skills and desired job type. The analysis unit can also determine the order of analysis based on the relevance of the user's experience and desired companies. For example, the analysis unit can determine the order of analysis based on the relevance of the user's past work history and current career goals. This allows for adjusting the order of analysis based on the relevance of the user's information, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the user's information into a generation AI, which can then adjust the order of analysis.

[0045] 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. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the analysis unit can avoid using technical terms during analysis. Furthermore, if the user is an intermediate expert, the analysis unit can also use technical terms moderately during analysis. For example, if the user is an advanced expert, the analysis unit can use a lot of technical terms to perform a detailed analysis. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI, which can then adjust the use of technical terms.

[0046] The consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. For example, the consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. The past consultation history includes, but is not limited to, the content of the consultation, the response results, and feedback. For example, the consultation unit can provide advice for the current consultation based on the content of the user's past consultations. The consultation unit can also identify trends from the user's past consultation history and provide optimal advice. For example, the consultation unit can improve its advice method based on the user's past feedback. By doing so, more appropriate advice can be provided by referring to the user's past consultation history. Some or all of the above-described processing in the consultation unit can be performed using, or without, a generation AI. For example, the consultation unit can input the user's past consultation history data into the generation AI, which can then provide optimal advice.

[0047] The consultation unit can customize the content of the advice based on the user's current career situation during the consultation. The consultation unit, for example, customizes the content of the advice based on the user's current career situation during the consultation. The current career situation includes, but is not limited to, the user's current job type, position, skill level, etc. For example, if the user is job-hunting, the consultation unit provides advice regarding job changes. Furthermore, if the user is aiming for career advancement, the consultation unit can also provide advice regarding career advancement. For example, if the user is satisfied with their current job, the consultation unit provides advice regarding skill improvement in their current job. By customizing the content of the advice based on the user's current career situation, more appropriate advice can be provided. Some or all of the above-described processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's current career situation data into the generation AI, which can then customize the content of the advice.

[0048] The consultation unit can improve the advice method by reflecting user feedback during the consultation. The consultation unit, for example, improves the advice method by reflecting user feedback during the consultation. Feedback includes, for example, user ratings, comments, and areas for improvement, but is not limited to these examples. For example, the consultation unit adjusts current advice based on feedback on advice previously provided by the user. The consultation unit can also identify and improve advice trends from the user feedback. For example, the consultation unit proposes a new advice method based on the user feedback. In this way, the advice method can be improved by reflecting the user feedback. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input user feedback data into the generation AI, which can then improve the advice method.

[0049] The consultation unit can provide optimal advice by taking into account the user's geographical location information during the consultation. For example, the consultation unit can provide optimal advice by taking into account the user's geographical location information during the consultation. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in Tokyo, the consultation unit can provide advice about companies and job types in Tokyo. Furthermore, if the user is in New York, the consultation unit can provide advice about companies and job types in New York. For example, if the user desires remote work, the consultation unit can provide advice without geographical restrictions. This allows for more appropriate advice to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the consultation unit can be performed using, or without, a generation AI. For example, the consultation unit can input the user's geographical location information data into a generation AI, which can then provide optimal advice.

[0050] The consultation unit can analyze the user's social media activity and suggest a means of providing advice during a consultation. For example, the consultation unit can analyze the user's social media activity and suggest a means of providing advice during a consultation. Social media activity includes, but is not limited to, post content, follower count, and engagement. For example, the consultation unit can provide advice related to companies the user follows on LinkedIn. The consultation unit can also provide advice related to topics the user is interested in on Twitter. For example, the consultation unit can provide advice related to groups the user participates in on Facebook. This allows for analysis of the user's social media activity to suggest a more appropriate means of providing advice. Some or all of the above-described processing in the consultation unit can be performed using, or without, a generation AI. For example, the consultation unit can input the user's social media activity data into a generation AI, which can then suggest a means of providing advice.

[0051] The consultation unit can customize the advice method by reflecting the user's past feedback during the consultation. The consultation unit, for example, customizes the advice method by reflecting the user's past feedback during the consultation. Past feedback includes, for example, the user's ratings, comments, and areas for improvement, but is not limited to these examples. For example, the consultation unit prioritizes advice methods that the user has previously preferred. The consultation unit can also eliminate advice methods that the user has previously avoided. For example, the consultation unit proposes a new advice method based on the user's past feedback. In this way, the advice method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the consultation unit may be performed using, or without using, a generation AI. For example, the consultation unit can input the user's past feedback data into the generation AI, which can then customize the advice method.

[0052] The providing unit can provide optimal information by referring to the user's past review browsing history when providing review information. For example, the providing unit can provide optimal information by referring to the user's past review browsing history when providing review information. The past review browsing history includes, but is not limited to, the content of the reviewed reviews, ratings, and comments. For example, the providing unit provides relevant information based on the review information the user has previously viewed. The providing unit can also identify trends from the user's past browsing history and provide optimal information. For example, the providing unit can improve the information provision method based on the user's past feedback. By referring to the user's past review browsing history, more appropriate information can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's past review browsing history data into the generation AI, which can then provide optimal information.

[0053] The providing unit can customize the content of the information based on the user's current career situation when providing word-of-mouth information. For example, when providing word-of-mouth information, the providing unit customizes the content of the information based on the user's current career situation. The current career situation includes, but is not limited to, the user's current job type, position, and skill level. For example, if the user is job-hunting, the providing unit provides word-of-mouth information about job changes. Furthermore, if the user is aiming for career advancement, the providing unit can also provide word-of-mouth information about career advancement. For example, if the user is satisfied with their current job, the providing unit provides word-of-mouth information about improving skills in their current job. This allows the provision of more appropriate information by customizing the content of the information based on the user's current career situation. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current career situation data into the generation AI, which can then customize the content of the information.

[0054] The providing unit can improve the method of providing information by reflecting user feedback when providing word-of-mouth information. For example, the providing unit can improve the method of providing information by reflecting user feedback when providing word-of-mouth information. Feedback includes, but is not limited to, user ratings, comments, and improvements. For example, the providing unit can adjust the current information provided based on feedback on word-of-mouth information previously provided by the user. The providing unit can also identify and improve trends in information provision from user feedback. For example, the providing unit can propose a new information provision method based on user feedback. This can improve the method of providing information by reflecting user feedback. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI, which can then improve the method of providing information.

[0055] The providing unit can provide optimal information by taking into account the user's geographical location information when providing word-of-mouth information. For example, the providing unit can provide optimal information by taking into account the user's geographical location information when providing word-of-mouth information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in Tokyo, the providing unit can provide word-of-mouth information about companies and occupations in Tokyo. Furthermore, if the user is in New York, the providing unit can provide word-of-mouth information about companies and occupations in New York. For example, if the user desires remote work, the providing unit can provide word-of-mouth information without geographical restrictions. This allows for more appropriate information to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into a generation AI, which can then provide optimal information.

[0056] The providing unit may analyze the user's social media activity and provide related information when providing word-of-mouth information. For example, when providing word-of-mouth information, the providing unit may analyze the user's social media activity and provide related information. Social media activity may include, but is not limited to, post content, follower count, and engagement. For example, the providing unit may provide word-of-mouth information about places where the user checked in on social media. The providing unit may also analyze the user's social media posts and provide word-of-mouth information about related companies and occupations. For example, the providing unit may provide word-of-mouth information about related companies and occupations by referring to the activity of the user's friends on social media. This allows for more appropriate information to be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's social media activity data into a generation AI, which may then provide the related information.

[0057] The providing unit can customize the information provision method by reflecting the user's past feedback when providing word-of-mouth information. For example, the providing unit customizes the information provision method by reflecting the user's past feedback when providing word-of-mouth information. Past feedback includes, but is not limited to, the user's ratings, comments, and areas for improvement. For example, the providing unit prioritizes the use of information provision methods that the user has previously preferred. The providing unit can also eliminate information provision methods that the user has previously avoided. For example, the providing unit suggests a new information provision method based on the user's past feedback. This allows the information provision method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then customize the information provision method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in Tokyo, information about companies and job types in Tokyo can be prioritized. Also, if the user is in New York, information about companies and job types in New York can be prioritized. Furthermore, if the user wishes to work remotely, information without geographical restrictions can be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0060] When providing word-of-mouth information, the providing unit can provide optimal information by referring to the user's past review browsing history. For example, it can provide related information based on word-of-mouth information that the user has previously viewed. It can also identify trends from the user's past browsing history and provide optimal information. Furthermore, it can improve the method of providing information based on the user's past feedback. As a result, it is possible to provide more appropriate information by referring to the user's past review browsing history.

[0061] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis can be performed while avoiding technical terms. If the user is an intermediate user, the analysis can be performed using technical terms appropriately. Furthermore, if the user is an advanced user, detailed analysis can be performed using a lot of technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0062] When providing word-of-mouth information, the providing unit can customize the content of the information based on the user's current career situation. For example, if the user is looking for a new job, word-of-mouth information about changing jobs can be provided. Also, if the user is aiming for career advancement, word-of-mouth information about career advancement can be provided. Furthermore, if the user is satisfied with their current job, word-of-mouth information about improving skills in their current job can be provided. In this way, by customizing the content of the information based on the user's current career situation, more appropriate information can be provided.

[0063] The collection unit can analyze the user's social media activities and collect related information. For example, it can collect information about companies the user follows on LinkedIn. It can also collect information related to topics the user is interested in on Twitter. It can also collect information related to groups the user participates in on Facebook. In this way, it is possible to collect related information by analyzing the user's social media activities.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection department collects user information, including skills, experience, desired job type, and company. The collection department collects information through surveys, online forms, interviews, and analysis of social media activity. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is carried out using data mining, statistical analysis, and machine learning algorithms to suggest the most suitable job types and careers for the user. Step 3: The consultation department accepts users' concerns via chat, and the AI ​​generator provides appropriate advice. Chat formats include text chat, voice chat, and video chat. Step 4: The provider provides word-of-mouth information from other users. The word-of-mouth information includes ratings, comments, and reviews from other users, and provides appropriate advice to the user.

[0066] (Example 2) A career and career change support system according to an embodiment of the present invention collects user information, analyzes it using a generation AI, presents optimal job types and careers, accepts consultations via chat, and provides word-of-mouth information from other users. The career and career change support system collects information such as the user's skills and experience, desired job types and companies, and the generation AI analyzes this information to present optimal job types and careers. Furthermore, users can consult about their career-related concerns via chat, and the generation AI provides appropriate advice. Users can also obtain real-world feedback and information through word-of-mouth from other users. For example, in a career and career change support system, a user inputs information such as their skills and experience, desired job types and companies, etc. For example, a user might input, "I have marketing experience and would like to work at an IT company." This information is then input into the generation AI. The generation AI then analyzes the input information and presents optimal job types and careers to the user. The generation AI then suggests optimal job types and careers based on the user's skills, experience, desired job types and companies, etc. For example, if a user inputs, "I have marketing experience and want to work at an IT company," the AI ​​generator will suggest job titles such as "digital marketing manager" or "product marketing specialist." Furthermore, the career and job change support system allows users to discuss their career concerns via chat, with the AI ​​providing appropriate advice. For example, if a user asks, "I don't know how to proceed with my job search," the AI ​​generator will provide advice such as, "First, update your resume, and then register on a job site." The career and job change support system also provides real-world feedback and information through reviews from other users. For example, if a user wants to know how easy it is to work at a certain company, they can read reviews posted by other users to obtain information about the actual working environment at that company. This allows the career and job change support system to help users find the job and career that best suits them and resolve their career concerns.In addition, users can obtain real-world opinions and information through word-of-mouth from other users, making their job search go more smoothly. This allows the career and job change support system to efficiently collect, analyze, consult, and provide information about users. For example, by entering information such as their skills, experience, desired job type, and company, the generation AI will suggest the most suitable job type and career, and users can discuss their career concerns in chat format. In addition, users can obtain real-world opinions and information through word-of-mouth from other users, making their job search go more smoothly.

[0067] A career and job change support system according to an embodiment includes a collection unit, an analysis unit, a consultation unit, and a provision unit. The collection unit collects user information. The user information includes, but is not limited to, skills, experience, desired job type, and company. The collection unit collects information using, for example, a questionnaire or online form. The collection unit can also collect information through interviews. For example, the collection unit collects information entered by a user into an online form. The collection unit can also collect detailed information through interviews with the user. The collection unit can also collect information by analyzing the user's social media activity. For example, the collection unit collects information related to companies the user follows on social media and topics in which the user shows interest. The analysis unit analyzes the information collected by the collection unit using a generation AI. The analysis can be performed using, for example, data mining, statistical analysis, or a machine learning algorithm. For example, the generation AI can suggest optimal jobs and careers based on the user's skills, experience, desired job type, and company. The analysis unit can also analyze the user's information and suggest appropriate career paths using the generation AI. For example, the generation AI compares the user's skill set with market demand to suggest the most suitable job. The consultation unit accepts the user's concerns in chat format, and the generation AI provides appropriate advice. Chat formats include, but are not limited to, text chat, voice chat, and video chat. For example, the consultation unit provides real-time advice to users in response to their concerns via text chat. The consultation unit can also provide appropriate advice to users through voice chat or video chat. For example, if a user asks for advice saying, "I don't know how to proceed with my job search," the generation AI provides advice such as, "First, I recommend updating your resume, and then registering on a job site." The provision unit provides word-of-mouth information from other users. Word-of-mouth information includes, but is not limited to, ratings, comments, and reviews from other users.For example, if a user wants to know how easy it is to work at Company X, the providing unit can obtain information about the actual working environment of Company X by viewing reviews posted by other users. The providing unit can also provide appropriate advice to the user based on the review information. For example, the providing unit can analyze other users' reviews and provide information such as "Company X has a good working environment." This allows the career and job change support system according to the embodiment to efficiently collect, analyze, consult, and provide user information. For example, by inputting information such as the user's skills, experience, desired job type, and company, the generation AI can suggest the most suitable job type and career, and the user can discuss career-related concerns in a chat format. Furthermore, by obtaining real-world feedback and information through reviews from other users, the user can more smoothly progress through their job search.

[0068] The collection unit can collect information on the user's skills or experience, and desired job or company. The collection unit collects, for example, information on the user's skills, experience, and desired job or company. Skills include, but are not limited to, technical skills, soft skills, and language skills. For example, the collection unit collects information on technical skills and soft skills entered by the user into an online form. The collection unit can also collect information on the user's work history, project experience, volunteer experience, and so on. For example, the collection unit collects detailed information on projects the user has been involved in in the past. The collection unit can also collect information on the user's desired job or company. For example, the collection unit collects a list of the user's desired job or company and organizes the information based on that list. By collecting detailed information about the user, more appropriate job or career can be presented. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information entered by the user into an online form into a generation AI, which then organizes the information.

[0069] The analysis unit can analyze the collected information and present the user with the most suitable occupation or career. The analysis unit can, for example, analyze the collected information and present the user with the most suitable occupation or career. Examples of analysis include, but are not limited to, data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can use data mining technology to analyze information such as the user's skills and experience, desired occupation and company, etc. The analysis unit can also use statistical analysis to present the most suitable occupation or career based on the user's information. For example, the analysis unit can compare the user's skill set with market demand to suggest the most suitable occupation. The analysis unit can also use machine learning algorithms to analyze the user's information and present an appropriate career path. For example, the generation AI can suggest the most suitable occupation or career based on information such as the user's skills and experience, desired occupation and company, etc. In this way, the analysis unit can present the most suitable occupation or career to the user by analyzing the collected information. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI, which can then suggest the most suitable job type or career.

[0070] The consultation unit accepts the user's concerns in chat format, and the generation AI can provide useful advice. The consultation unit accepts the user's concerns in chat format, and the generation AI can provide useful advice. Examples of chat formats include, but are not limited to, text chat, voice chat, and video chat. For example, the consultation unit may use the generation AI to provide real-time advice on the content of the user's consultation via text chat. The consultation unit can also provide appropriate advice for the user's concerns via voice chat or video chat. For example, if a user asks for advice saying, "I don't know how to proceed with my job search," the generation AI can provide advice such as, "I recommend you first update your resume and then register on a job site." This allows the user's concerns to be accepted in chat format and appropriate advice to be provided. Some or all of the above-described processing in the consultation unit may be performed using, or without, the generation AI. For example, the consultation unit may input the user's consultation content into the generation AI, and the generation AI can provide appropriate advice.

[0071] The providing unit can provide word-of-mouth information from other users. The providing unit, for example, provides word-of-mouth information from other users. Word-of-mouth information includes, but is not limited to, ratings, comments, and reviews from other users. For example, if a user wants to know how easy it is to work at Company X, the providing unit can obtain information about the actual working environment of Company X by viewing reviews posted by other users. The providing unit can also provide appropriate advice to the user based on the word-of-mouth information. For example, the providing unit can analyze word-of-mouth information from other users and provide information such as, "Company X has a good working environment." By providing word-of-mouth information from other users, the user can obtain real opinions and information from the workplace. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input word-of-mouth information from other users into a generation AI, which then organizes and provides the information.

[0072] The collection unit can estimate the user's emotions and set the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is feeling stressed, the collection unit collects information during a relaxed time. The collection unit can also start information collection immediately if the user is concentrating. For example, if the user is tired, the collection unit collects information after a rest. This allows for 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then adjust the timing of information collection.

[0073] The collection unit can analyze the user's past work history and skill set and select the optimal information collection method. The collection unit, for example, analyzes the user's past work history and skill set and selects the optimal information collection method. Past work history includes, but is not limited to, the place of employment, job title, and job content. For example, if the user has past marketing experience, the collection unit prioritizes collecting marketing-related information. Furthermore, if the user has IT skills, the collection unit can also collect IT-related information. For example, if the user has managerial experience, the collection unit collects managerial-related information. In this way, the optimal information collection method can be selected by analyzing the user's past work history and skill set. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs data on the user's past work history and skill set into a generation AI, which can select the optimal information collection method.

[0074] The collection unit can filter information based on the user's current career goals and areas of interest when collecting information. For example, the collection unit can filter information based on the user's current career goals and areas of interest when collecting information. Career goals include, but are not limited to, short-term goals, long-term goals, and achievement criteria. For example, if the user is interested in digital marketing, the collection unit can prioritize collecting information related to digital marketing. Furthermore, if the user is interested in startup companies, the collection unit can also collect startup-related information. For example, if the user is interested in remote work, the collection unit can collect remote work-related information. By filtering information based on the user's career goals and areas of interest, more relevant information can be collected. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's career goals and areas of interest into a generation AI, which can then filter the information.

[0075] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Also, when the user uses text input, the collection unit can collect information using text analysis technology. For example, when the user uses image input, the collection unit collects information using image recognition technology. This enables efficient information collection by selecting 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, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI, which then selects the optimal collection means.

[0076] 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. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling anxious, the collection unit can prioritize collecting information that provides a sense of security. Also, if the user is excited, the collection unit can prioritize collecting challenging information. For example, if the user is relaxed, the collection unit can prioritize collecting interesting information. This allows more appropriate information to be collected by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then prioritize the information.

[0077] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the user is in Tokyo, the collection unit can prioritize collecting information about companies and job types in Tokyo. Also, if the user is in New York, the collection unit can prioritize collecting information about companies and job types in New York. For example, if the user desires remote work, the collection unit prioritizes collecting information without geographical restrictions. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI, which can then prioritize collecting highly relevant information.

[0078] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can analyze the user's social media activity and collect related information when collecting information. Social media activity includes, but is not limited to, post content, number of followers, and engagement. For example, the collection unit can collect information about companies the user follows on LinkedIn. The collection unit can also collect information related to topics the user is interested in on Twitter. For example, the collection unit can collect information related to groups the user joins on Facebook. This allows related information to be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input data about the user's social media activity into a generation AI, which can then collect related information.

[0079] 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. Past feedback includes, but is not limited to, the user's ratings, comments, and areas for improvement. For example, the collection unit prioritizes the use of information collection methods that the user previously preferred. The collection unit can also eliminate information collection methods that the user previously avoided. For example, the collection unit suggests a new information collection method 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, for example, 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, which can then customize the collection method.

[0080] The analysis unit can estimate the user's emotion and adjust the analysis presentation method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis presentation method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling anxious, the analysis unit uses a presentation method that conveys a sense of security. Furthermore, if the user is excited, the analysis unit can use a challenging presentation method. For example, if the user is relaxed, the analysis unit uses an interesting presentation method. This allows the analysis presentation method to be adjusted according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then adjust the way the analysis is expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's skills and experience during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's skills and experience during analysis. The importance of skills and experience includes, but is not limited to, industry standards and expert evaluations. For example, the analysis unit performs a detailed analysis if the user has advanced skills. The analysis unit can also perform a concise analysis if the user is a beginner. For example, the analysis unit performs an analysis with an appropriate level of detail if the user is an intermediate user. This allows for adjusting the level of detail of the analysis based on the importance of the user's skills and experience, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the user's skills and experience into the generation AI, which can then adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the user's occupation or career category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the user's occupation or career category during analysis. Occupational and career categories include, but are not limited to, technical, managerial, and sales occupations. For example, if the user works in marketing, the analysis unit can use an analysis algorithm specialized for marketing. Furthermore, if the user works in engineering, the analysis unit can use an analysis algorithm specialized for engineering. For example, if the user works in management, the analysis unit can use an analysis algorithm specialized for management. This allows for applying different analysis algorithms depending on the user's occupation or career category, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the user's occupation or career into a generation AI, which can then apply an appropriate analysis algorithm.

[0083] 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. Past analysis results include, but are not limited to, success stories, failure stories, and areas for improvement. For example, the analysis unit adjusts the current analysis based on the user's past analysis results. The analysis unit can also identify trends and improve accuracy from the user's past analysis results. For example, the analysis unit improves the analysis algorithm based on the user's past feedback. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can then improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can, for example, estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the analysis unit can perform a short and to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is excited, the analysis unit can perform a visually stimulating analysis. This allows for adjusting the length of the analysis according to the user's emotion to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the length of the analysis.

[0085] The analysis unit can determine the analysis priority based on the time when the user inputs information during analysis. For example, the analysis unit determines the analysis priority based on the time when the user inputs information during analysis. The time when the information is input includes, but is not limited to, the input date and time, the input frequency, etc. For example, the analysis unit prioritizes analysis of information recently input by the user. The analysis unit can also analyze information previously input by the user. For example, the analysis unit prioritizes analysis of information input by the user during a specific time period. This allows for more appropriate analysis results to be provided by determining the analysis priority based on the time when the user inputs information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the time when the user inputs information into the generation AI, which then determines the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the user's information during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the user's information during analysis. Information relevance includes, but is not limited to, common topics and areas of interest. For example, the analysis unit can determine the order of analysis based on the relevance of the user's skills and desired job type. The analysis unit can also determine the order of analysis based on the relevance of the user's experience and desired companies. For example, the analysis unit can determine the order of analysis based on the relevance of the user's past work history and current career goals. This allows for adjusting the order of analysis based on the relevance of the user's information, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the user's information into a generation AI, which can then adjust the order of analysis.

[0087] 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. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the analysis unit can avoid using technical terms during analysis. Furthermore, if the user is an intermediate expert, the analysis unit can also use technical terms moderately during analysis. For example, if the user is an advanced expert, the analysis unit can use a lot of technical terms to perform a detailed analysis. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI, which can then adjust the use of technical terms.

[0088] The consultation unit can estimate the user's emotions and adjust the consultation response method based on the estimated user emotions. The consultation unit, for example, estimates the user's emotions and adjusts the consultation response method based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is feeling anxious, the consultation unit uses a response method that provides a sense of security. Furthermore, if the user is excited, the consultation unit can also use a challenging response method. For example, if the user is relaxed, the consultation unit uses an interesting response method. This allows the consultation response method to be adjusted according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the consultation department can input the user's emotional data into the generation AI, which can then adjust how it responds to the consultation.

[0089] The consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. For example, the consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. The past consultation history includes, but is not limited to, the content of the consultation, the response results, and feedback. For example, the consultation unit can provide advice for the current consultation based on the content of the user's past consultations. The consultation unit can also identify trends from the user's past consultation history and provide optimal advice. For example, the consultation unit can improve its advice method based on the user's past feedback. By doing so, more appropriate advice can be provided by referring to the user's past consultation history. Some or all of the above-described processing in the consultation unit can be performed using, or without, a generation AI. For example, the consultation unit can input the user's past consultation history data into the generation AI, which can then provide optimal advice.

[0090] The consultation unit can customize the content of the advice based on the user's current career situation during the consultation. The consultation unit, for example, customizes the content of the advice based on the user's current career situation during the consultation. The current career situation includes, but is not limited to, the user's current job type, position, skill level, etc. For example, if the user is job-hunting, the consultation unit provides advice regarding job changes. Furthermore, if the user is aiming for career advancement, the consultation unit can also provide advice regarding career advancement. For example, if the user is satisfied with their current job, the consultation unit provides advice regarding skill improvement in their current job. By customizing the content of the advice based on the user's current career situation, more appropriate advice can be provided. Some or all of the above-described processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's current career situation data into the generation AI, which can then customize the content of the advice.

[0091] The consultation unit can improve the advice method by reflecting user feedback during the consultation. The consultation unit, for example, improves the advice method by reflecting user feedback during the consultation. Feedback includes, for example, user ratings, comments, and areas for improvement, but is not limited to these examples. For example, the consultation unit adjusts current advice based on feedback on advice previously provided by the user. The consultation unit can also identify and improve advice trends from the user feedback. For example, the consultation unit proposes a new advice method based on the user feedback. In this way, the advice method can be improved by reflecting the user feedback. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input user feedback data into the generation AI, which can then improve the advice method.

[0092] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated user emotions. The consultation unit, for example, estimates the user's emotions and determines the priority of consultations based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the consultation unit prioritizes consultations when the user is feeling strong anxiety. The consultation unit can also respond immediately when the user needs urgent consultation. For example, the consultation unit accepts consultations with normal priority when the user is relaxed. This enables more appropriate responses by determining the priority of consultations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, the generation AI. For example, the consultation unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of consultations.

[0093] The consultation unit can provide optimal advice by taking into account the user's geographical location information during the consultation. For example, the consultation unit can provide optimal advice by taking into account the user's geographical location information during the consultation. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in Tokyo, the consultation unit can provide advice about companies and job types in Tokyo. Furthermore, if the user is in New York, the consultation unit can provide advice about companies and job types in New York. For example, if the user desires remote work, the consultation unit can provide advice without geographical restrictions. This allows for more appropriate advice to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the consultation unit can be performed using, or without, a generation AI. For example, the consultation unit can input the user's geographical location information data into a generation AI, which can then provide optimal advice.

[0094] The consultation unit can analyze the user's social media activity and suggest a means of providing advice during a consultation. For example, the consultation unit can analyze the user's social media activity and suggest a means of providing advice during a consultation. Social media activity includes, but is not limited to, post content, follower count, and engagement. For example, the consultation unit can provide advice related to companies the user follows on LinkedIn. The consultation unit can also provide advice related to topics the user is interested in on Twitter. For example, the consultation unit can provide advice related to groups the user participates in on Facebook. This allows for analysis of the user's social media activity to suggest a more appropriate means of providing advice. Some or all of the above-described processing in the consultation unit can be performed using, or without, a generation AI. For example, the consultation unit can input the user's social media activity data into a generation AI, which can then suggest a means of providing advice.

[0095] The consultation unit can customize the advice method by reflecting the user's past feedback during the consultation. The consultation unit, for example, customizes the advice method by reflecting the user's past feedback during the consultation. Past feedback includes, for example, the user's ratings, comments, and areas for improvement, but is not limited to these examples. For example, the consultation unit prioritizes advice methods that the user has previously preferred. The consultation unit can also eliminate advice methods that the user has previously avoided. For example, the consultation unit proposes a new advice method based on the user's past feedback. In this way, the advice method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the consultation unit may be performed using, or without using, a generation AI. For example, the consultation unit can input the user's past feedback data into the generation AI, which can then customize the advice method.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing word-of-mouth information based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing word-of-mouth information based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is feeling anxious, the providing unit can preferentially provide word-of-mouth information that gives a sense of security. Furthermore, if the user is excited, the providing unit can also provide challenging word-of-mouth information. For example, if the user is relaxed, the providing unit can provide interesting word-of-mouth information. This allows the method of providing word-of-mouth information to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input user emotional data into the generating AI, which can then adjust how word-of-mouth information is provided.

[0097] The providing unit can provide optimal information by referring to the user's past review browsing history when providing review information. For example, the providing unit can provide optimal information by referring to the user's past review browsing history when providing review information. The past review browsing history includes, but is not limited to, the content of the reviewed reviews, ratings, and comments. For example, the providing unit provides relevant information based on the review information the user has previously viewed. The providing unit can also identify trends from the user's past browsing history and provide optimal information. For example, the providing unit can improve the information provision method based on the user's past feedback. By referring to the user's past review browsing history, more appropriate information can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's past review browsing history data into the generation AI, which can then provide optimal information.

[0098] The providing unit can customize the content of the information based on the user's current career situation when providing word-of-mouth information. For example, when providing word-of-mouth information, the providing unit customizes the content of the information based on the user's current career situation. The current career situation includes, but is not limited to, the user's current job type, position, and skill level. For example, if the user is job-hunting, the providing unit provides word-of-mouth information about job changes. Furthermore, if the user is aiming for career advancement, the providing unit can also provide word-of-mouth information about career advancement. For example, if the user is satisfied with their current job, the providing unit provides word-of-mouth information about improving skills in their current job. This allows the provision of more appropriate information by customizing the content of the information based on the user's current career situation. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current career situation data into the generation AI, which can then customize the content of the information.

[0099] The providing unit can improve the method of providing information by reflecting user feedback when providing word-of-mouth information. For example, the providing unit can improve the method of providing information by reflecting user feedback when providing word-of-mouth information. Feedback includes, but is not limited to, user ratings, comments, and improvements. For example, the providing unit can adjust the current information provided based on feedback on word-of-mouth information previously provided by the user. The providing unit can also identify and improve trends in information provision from user feedback. For example, the providing unit can propose a new information provision method based on user feedback. This can improve the method of providing information by reflecting user feedback. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI, which can then improve the method of providing information.

[0100] The providing unit can estimate the user's emotions and prioritize the word-of-mouth information based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and prioritizes the word-of-mouth information based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling anxious, the providing unit can prioritize word-of-mouth information that gives a sense of security. Furthermore, if the user is excited, the providing unit can also provide challenging word-of-mouth information. For example, if the user is relaxed, the providing unit can provide interesting word-of-mouth information. This allows the user to prioritize word-of-mouth information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input user emotion data into the generation AI, which can then prioritize the word-of-mouth information.

[0101] The providing unit can provide optimal information by taking into account the user's geographical location information when providing word-of-mouth information. For example, the providing unit can provide optimal information by taking into account the user's geographical location information when providing word-of-mouth information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in Tokyo, the providing unit can provide word-of-mouth information about companies and occupations in Tokyo. Furthermore, if the user is in New York, the providing unit can provide word-of-mouth information about companies and occupations in New York. For example, if the user desires remote work, the providing unit can provide word-of-mouth information without geographical restrictions. This allows for more appropriate information to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into a generation AI, which can then provide optimal information.

[0102] The providing unit may analyze the user's social media activity and provide related information when providing word-of-mouth information. For example, when providing word-of-mouth information, the providing unit may analyze the user's social media activity and provide related information. Social media activity may include, but is not limited to, post content, follower count, and engagement. For example, the providing unit may provide word-of-mouth information about places where the user checked in on social media. The providing unit may also analyze the user's social media posts and provide word-of-mouth information about related companies and occupations. For example, the providing unit may provide word-of-mouth information about related companies and occupations by referring to the activity of the user's friends on social media. This allows for more appropriate information to be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's social media activity data into a generation AI, which may then provide the related information.

[0103] The providing unit can customize the information provision method by reflecting the user's past feedback when providing word-of-mouth information. For example, the providing unit customizes the information provision method by reflecting the user's past feedback when providing word-of-mouth information. Past feedback includes, but is not limited to, the user's ratings, comments, and areas for improvement. For example, the providing unit prioritizes the use of information provision methods that the user has previously preferred. The providing unit can also eliminate information provision methods that the user has previously avoided. For example, the providing unit suggests a new information provision method based on the user's past feedback. This allows the information provision method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then customize the information provision method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, consultation unit, and provision 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 user information using the camera 42 and microphone 38B of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user information using a generation AI. The consultation unit is realized, for example, by the control unit 46A of the smart device 14 and accepts the user's concerns in a chat format, and the generation AI provides appropriate advice. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides word-of-mouth information from other users. The collection unit can, for example, estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, consultation unit, and provision 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 user information using the camera 42 and microphone 238 of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user information using a generation AI. The consultation unit is realized, for example, by the control unit 46A of the smart glasses 214 and accepts the user's concerns in a chat format, and the generation AI provides appropriate advice. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides word-of-mouth information from other users. The collection unit can, for example, estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, consultation unit, and provision 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 user information using the camera 42 and microphone 238 of the headset-type terminal 314, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user information using a generation AI. The consultation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and accepts the user's concerns in a chat format, and the generation AI provides appropriate advice. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides word-of-mouth information from other users. The collection unit can, for example, estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, consultation unit, and provision 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 user information using the camera 42 and microphone 238 of the robot 414, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user information using a generation AI. The consultation unit is realized, for example, by the control unit 46A of the robot 414, and accepts the user's concerns in a chat format, and the generation AI provides appropriate advice. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides word-of-mouth information from other users. The collection unit can, for example, estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can use an expression that gives a sense of security. If the user is excited, the analysis unit can use a challenging expression. If the user is relaxed, the analysis unit can use an interesting expression. This makes it possible to provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0106] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in Tokyo, information about companies and job types in Tokyo can be prioritized. Also, if the user is in New York, information about companies and job types in New York can be prioritized. Furthermore, if the user wishes to work remotely, information without geographical restrictions can be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0107] The consultation unit can estimate the user's emotions and adjust the consultation response method based on the estimated user's emotions. For example, if the user feels anxious, the consultation unit can use a response method that gives a sense of security. If the user feels excited, the consultation unit can use a challenging response method. Furthermore, if the user feels relaxed, the consultation unit can use an interesting response method. In this way, by adjusting the consultation response method according to the user's emotions, more appropriate advice can be provided.

[0108] When providing word-of-mouth information, the providing unit can provide optimal information by referring to the user's past review browsing history. For example, it can provide related information based on word-of-mouth information that the user has previously viewed. It can also identify trends from the user's past browsing history and provide optimal information. Furthermore, it can improve the method of providing information based on the user's past feedback. As a result, it is possible to provide more appropriate information by referring to the user's past review browsing history.

[0109] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, information that gives a sense of security can be preferentially collected. Also, if the user is excited, challenging information can be preferentially collected. Furthermore, if the user is relaxed, interesting information can be preferentially collected. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be collected.

[0110] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis can be performed while avoiding technical terms. If the user is an intermediate user, the analysis can be performed using technical terms appropriately. Furthermore, if the user is an advanced user, detailed analysis can be performed using a lot of technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0111] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated user's emotions. For example, if the user is feeling very anxious, the consultation can be accepted with priority. Also, if the user needs urgent consultation, it can respond immediately. Furthermore, if the user is relaxed, the consultation can be accepted with normal priority. In this way, by determining the priority of consultations according to the user's emotions, more appropriate responses can be made.

[0112] When providing word-of-mouth information, the providing unit can customize the content of the information based on the user's current career situation. For example, if the user is looking for a new job, word-of-mouth information about changing jobs can be provided. Also, if the user is aiming for career advancement, word-of-mouth information about career advancement can be provided. Furthermore, if the user is satisfied with their current job, word-of-mouth information about improving skills in their current job can be provided. In this way, by customizing the content of the information based on the user's current career situation, more appropriate information can be provided.

[0113] The collection unit can analyze the user's social media activities and collect related information. For example, it can collect information about companies the user follows on LinkedIn. It can also collect information related to topics the user is interested in on Twitter. It can also collect information related to groups the user participates in on Facebook. In this way, it is possible to collect related information by analyzing the user's social media activities.

[0114] The providing unit can estimate the user's emotions and adjust the method of providing word-of-mouth information based on the estimated user's emotions. For example, if the user is feeling anxious, word-of-mouth information that gives a sense of security can be provided preferentially. Also, if the user is excited, challenging word-of-mouth information can be provided. Furthermore, if the user is relaxed, interesting word-of-mouth information can be provided. In this way, by adjusting the method of providing word-of-mouth information according to the user's emotions, more appropriate information can be provided.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection department collects user information, including skills, experience, desired job type, and company. The collection department collects information through surveys, online forms, interviews, and analysis of social media activity. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is carried out using data mining, statistical analysis, and machine learning algorithms to suggest the most suitable job types and careers for the user. Step 3: The consultation department accepts users' concerns via chat, and the AI ​​generator provides appropriate advice. Chat formats include text chat, voice chat, and video chat. Step 4: The provider provides word-of-mouth information from other users. The word-of-mouth information includes ratings, comments, and reviews from other users, and provides appropriate advice to the user.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 AI 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 AI 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the 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.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 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.

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

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and presents suitable occupations and careers to the user; a consultation unit that allows the user to discuss their concerns in a chat format based on the results obtained by the analysis unit; a providing unit that provides word-of-mouth information from other users based on the advice provided by the consultation unit. A system characterized by:

2. The collecting unit Collect information about your skills or experience, desired job or company 2. The system of claim 1.

3. The analysis unit Analyze the collected information and suggest the most suitable job and career for the user 2. The system of claim 1.

4. The consultation department: Accepts user concerns in chat format, and generative AI provides useful advice 2. The system of claim 1.

5. The providing unit Provide reviews from other users 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and set the timing of information collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze the user's past work history and skill set to select the most appropriate information gathering method 2. The system of claim 1.

8. The collecting unit As information is collected, it is filtered based on the user's current career goals and areas of interest.

2. The system of claim 1.

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