system

Abstract

The system according to this embodiment aims to suggest the most suitable job type and company based on diverse user data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. The analysis unit analyzes the data collected by the collection unit. The generation unit generates job types and companies based on the data analyzed by the analysis unit. The proposal unit proposes the job types and companies generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to propose an optimal occupation or company based on various data of users, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal occupation or company based on various data of users.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a proposal unit. The data collection unit collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates job types and companies based on the data analyzed by the analysis unit. The proposal unit proposes the job types and companies generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable job type and company based on diverse user data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The career path generation system according to an embodiment of the present invention is a service in which a generating AI creates future job types and job postings based on the user's current skills and experience. This career path generation system collects multimodal data such as the user's career history, interests, career vision, values, hobbies, and lifestyle, and the generating AI analyzes this data to newly generate and propose the most suitable job types and companies for the user. In addition, the career path generation system generates a unique AI avatar based on the user's statements and thoughts and provides support such as interview practice and resume creation. Furthermore, the career path generation system provides a simulation of the user's own career path based on information of successful job changers. For example, the career path generation system collects multimodal data such as the user's career history, interests, career vision, values, hobbies, and lifestyle. In this case, not only information entered by the user is collected, but also public information such as SNS and blogs. For example, the user's interests and values ​​can be understood from blog posts and SNS posts that the user has posted in the past. Next, the generating AI analyzes the collected data in the career path generation system. The generating AI creates optimal job roles and companies for the user based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle. For example, if a user has experience in the IT industry and enjoys programming as a hobby, the generating AI can suggest the most suitable IT companies and job roles. Furthermore, the career path generation system generates a unique AI avatar based on the user's statements and thoughts. The generated AI avatar supports the user with tasks such as interview practice and resume creation. For example, when the user practices an interview, the AI ​​avatar acts as the interviewer and provides feedback on the user's answers. When creating a resume, it suggests the optimal resume format and content based on the user's background and skills. Finally, the career path generation system provides a simulation of the user's career path based on information from successful job changers. For example, the user can simulate what steps they should take by referring to the career paths of successful job changers in the same industry.This allows the career path generation system to help users visualize their own career path concretely and act systematically towards their goals. The system collects and analyzes data such as the user's work history, interests, career vision, values, hobbies, and lifestyle to generate and suggest the most suitable job types and companies.

[0029] The career path generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. The collection unit collects not only information entered by the user, but also publicly available information such as social media and blogs. For example, the collection unit can understand the user's interests and values ​​from blog posts and social media posts the user has made in the past. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generation AI to generate optimal job types and companies for the user based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle. For example, if the user has experience in the IT industry and their hobby is programming, the analysis unit can use the generation AI to propose optimal IT companies and job types for the user. The generation unit generates job types and companies based on the data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate optimal job types and companies for the user. The proposal unit proposes the job types and companies generated by the generation unit to the user. The proposal section, for example, proposes job types and companies generated using generation AI to the user. This allows the career path generation system according to the embodiment to collect and analyze data such as the user's work history, interests, career vision, values, hobbies, and lifestyle, and then generate and propose the most suitable job types and companies.

[0030] The data collection unit collects data on users' backgrounds, interests, career visions, values, hobbies, and lifestyles. Specifically, it collects not only information entered by users but also publicly available information such as social media posts and blogs. For example, it can understand a user's interests and values ​​from blog posts and social media posts they have previously made. The data collection unit automatically analyzes information such as resumes, work histories, and online profiles provided by users and stores it in a database. It also collects information on online courses users have taken, qualifications they have obtained, and projects they have participated in, to gain a detailed understanding of their skill sets. Furthermore, the data collection unit collects information on users' lifestyles and hobbies, providing data to understand the user as a whole. For example, by understanding what hobbies a user has and what activities they participate in, it is possible to propose career paths that match the user's values ​​and lifestyle. The data collection unit centrally manages this data and makes it accessible to the analysis and generation units. This allows the data collection unit to efficiently collect diverse user information and improve the accuracy and effectiveness of the entire system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, using generative AI, the analysis unit generates optimal job types and companies for users based on data such as their background, interests, career vision, values, hobbies, and lifestyle. Specifically, the generative AI uses natural language processing technology to analyze user input data and publicly available information to gain a detailed understanding of the user's skills, experience, and interests. For example, if a user has experience in the IT industry and their hobby is programming, the generative AI will identify the optimal IT company and job type based on the user's skill set and interests. Furthermore, the analysis unit considers the user's career vision and values ​​to design a long-term career path. For example, if a user aims for a management position in the future, the analysis unit will suggest job types and companies that will allow the user to acquire the necessary skills and experience based on the user's current skill set and goals. The analysis unit can utilize past data and statistical information to evaluate the probability of success of the user's career path and make optimal suggestions. In this way, the analysis unit can analyze diverse user information in detail and design the optimal career path for the user.

[0032] The generation unit generates job titles and companies based on data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate the most suitable job titles and companies for the user. Specifically, the generation AI generates the most suitable job titles and companies for the user based on the user's skill set, interests, and career vision. For example, if a user has experience in the IT industry and enjoys programming as a hobby, the generation AI will generate the most suitable IT company and job title for that user. The generation unit considers the user's skills, experience, and interests to generate the most suitable job titles and companies. Furthermore, the generation unit considers the user's career vision and values ​​to design a long-term career path. For example, if a user aims for a management position in the future, the generation unit generates job titles and companies that will allow the user to acquire the necessary skills and experience based on the user's current skill set and goals. The generation unit can utilize historical data and statistical information to evaluate the probability of success of the user's career path and make optimal suggestions. This allows the generation unit to analyze diverse user information in detail and design the most suitable career path for the user.

[0033] The Proposal Department proposes job types and companies generated by the Generation Department to the user. Specifically, it proposes job types and companies generated using generation AI. Based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle, the Proposal Department proposes the most suitable job types and companies for the user. For example, if the user has experience in the IT industry and their hobby is programming, the Proposal Department will propose the most suitable IT company and job type for the user. The Proposal Department considers the user's skills, experience, and interests to propose the most suitable job types and companies for the user. Furthermore, the Proposal Department considers the user's career vision and values ​​to design a long-term career path. For example, if the user aims for a management position in the future, the Proposal Department will propose job types and companies that will allow the user to acquire the necessary skills and experience based on the user's current skill set and goals. The Proposal Department can utilize past data and statistical information to evaluate the probability of success of the user's career path and make optimal suggestions. This allows the Proposal Department to analyze diverse user information in detail and design the most suitable career path for the user.

[0034] The avatar generation unit analyzes the user's statements and thoughts and generates an AI avatar. For example, the avatar generation unit uses a generation AI to generate a unique AI avatar based on the user's statements and thoughts. For example, when the user practices for an interview, the avatar generation unit has the AI ​​avatar act as the interviewer and provide feedback on the user's answers. Also, when the user is creating a resume, the avatar generation unit suggests the optimal resume format and content based on the user's experience and skills. In this way, a unique AI avatar can be generated based on the user's statements and thoughts, and support is provided for interview practice, resume creation, etc. Some or all of the above processing in the avatar generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the avatar generation unit can input the user's statements and thoughts into a generation AI and have the generation AI perform the generation of an AI avatar.

[0035] The simulation unit provides career path simulations based on information from successful job changers. For example, the simulation unit simulates what steps a user should take based on information from successful job changers. For example, the simulation unit can simulate what steps a user should take by referring to the career paths of successful job changers in the same industry. This allows the system to provide a simulation of one's own career path based on information from successful job changers. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input information from successful job changers into a generative AI and have the generative AI perform a career path simulation.

[0036] The data collection unit can collect publicly available information such as social media and blogs. For example, the data collection unit can understand a user's interests and values ​​from blog posts and social media posts the user has previously made. This allows the collection to include not only information entered by the user, but also publicly available information such as social media and blogs. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input publicly available information such as social media and blogs into a generative AI and have the generative AI perform the information collection.

[0037] The analysis unit can analyze the user's background and interests using natural language processing techniques. For example, the analysis unit can analyze the user's background and interests using natural language processing techniques. For example, the analysis unit can analyze the user's background and interests using morphological analysis. The analysis unit can also analyze the user's background and interests using grammatical analysis. Furthermore, the analysis unit can analyze the user's background and interests using semantic analysis. This allows the analysis of the user's background and interests to be performed using natural language processing techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's background and interests into a generative AI and have the generative AI perform analysis using natural language processing techniques.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and customize the collection method. This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can collect necessary data according to the progress of the user's current projects. This allows for the collection of highly relevant data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's current projects and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant job postings based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect data related to their current location in real time. This allows for the priority collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant job information. It can also collect relevant job information based on the user's interests and preferences on social media. Furthermore, the data collection unit can analyze a user's social media activity history and suggest the most suitable data collection method. This enables more appropriate data collection by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a history analysis algorithm to history data. It can also apply an interest analysis algorithm to interest data. Furthermore, it can apply a values ​​analysis algorithm to values ​​data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0046] The generation unit can improve the accuracy of generation by considering the interrelationships of data during generation. For example, the generation unit can generate the optimal job by considering the interrelationship between the user's background and interests. It can also generate the optimal company by considering the interrelationship between the user's values ​​and lifestyle. Furthermore, the generation unit can generate the optimal job and company by considering the interrelationship between the user's hobbies and career vision. By improving the accuracy of generation by considering the interrelationships of data, it is possible to generate more appropriate job and company. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the interrelationships of data into the generation AI and have the generation AI perform the improvement of generation accuracy.

[0047] The generation unit can perform generation while considering the attribute information of the data submitter. For example, the generation unit can generate the most suitable job types and companies by considering the user's age and gender. It can also generate the most suitable job types and companies by considering the user's educational background and work history. Furthermore, it can generate the most suitable job types and companies by considering the user's region and cultural background. In this way, by performing generation while considering the attribute information of the data submitter, it is possible to generate more appropriate job types and companies. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the attribute information of the data submitter into the generation AI and have the generation AI perform the generation.

[0048] The generation unit can perform generation while considering the geographical distribution of the data. For example, the generation unit can generate nearby job types and companies based on the user's place of residence. It can also generate optimal job types and companies based on the user's desired region. Furthermore, it can generate relevant job types and companies based on the user's desired relocation destination. By considering the geographical distribution of the data during generation, it is possible to generate more appropriate job types and companies. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the geographical distribution of the data into a generation AI and have the generation AI perform the generation.

[0049] The generation unit can improve the accuracy of its generation by referring to relevant literature during the generation process. For example, the generation unit can generate the most suitable job titles by referring to literature related to the user's career history. It can also generate the most suitable companies by referring to literature related to the user's interests. Furthermore, it can generate the most suitable job titles and companies by referring to literature related to the user's career vision. By improving the accuracy of generation by referring to relevant literature, it is possible to generate more appropriate job titles and companies. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input relevant literature into the generation AI and have the generation AI perform the task of improving the accuracy of its generation.

[0050] The proposal department can adjust the level of detail in a proposal based on the importance of the job type and company. For example, the proposal department will provide detailed proposals for high-priority job types and companies. Conversely, it can provide simplified proposals for low-priority job types and companies. The proposal department can also determine the priority of proposals according to the importance of the job type and company. This allows for efficient proposals by adjusting the level of detail based on the importance of the job type and company. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the importance of the job type and company into the generative AI and have the generative AI adjust the level of detail of the proposal.

[0051] The proposal department can apply different proposal algorithms depending on the job type and company category when making a proposal. For example, for IT positions, the proposal department can apply a proposal algorithm that emphasizes technical skills. For creative positions, the proposal department can also apply a proposal algorithm that emphasizes portfolios. Furthermore, for management positions, the proposal department can apply a proposal algorithm that emphasizes leadership skills. By applying different proposal algorithms depending on the job type and company category, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input the job type and company category into a generative AI and have the generative AI execute the application of the proposal algorithm.

[0052] The proposal department can prioritize proposals based on the job type and the submission timing of companies. For example, the proposal department will prioritize proposals for the most recent job types and companies. It can also postpone proposals for older job types and companies. Furthermore, the proposal department can adjust the proposal schedule based on the submission timing. This allows for efficient proposals by prioritizing proposals based on the submission timing of job types and companies. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the submission timing of job types and companies into a generative AI and have the generative AI determine the priority of proposals.

[0053] The proposal department can adjust the order of proposals based on the relevance of job types and companies. For example, the proposal department can prioritize proposing highly relevant job types and companies. It can also postpone proposing less relevant job types and companies. Furthermore, the proposal department can determine the order of proposals based on the relevance of job types and companies. This allows for efficient proposals by adjusting the order of proposals based on the relevance of job types and companies. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the relevance of job types and companies into a generative AI and have the generative AI adjust the order of proposals.

[0054] The avatar generation unit can generate the optimal avatar by analyzing the user's past statements and thoughts during avatar generation. For example, the avatar generation unit can generate an avatar that matches the user's speaking style based on the content of the user's past statements. The avatar generation unit can also analyze the user's thought patterns and generate an avatar that matches their thoughts. Furthermore, the avatar generation unit can comprehensively analyze the user's past statements and thoughts to generate the optimal avatar. This allows for more appropriate support by analyzing the user's past statements and thoughts to generate the optimal avatar. Some or all of the above-described processes in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's past statements and thoughts into a generation AI and have the generation AI generate the optimal avatar.

[0055] The avatar generation unit can customize the characteristics of the avatar based on the user's current lifestyle when generating the avatar. For example, if the user is busy, the avatar generation unit can generate a simple and efficient avatar. Conversely, if the user is relaxed, the avatar generation unit can generate an avatar that provides detailed support. Furthermore, the avatar generation unit can customize the characteristics of the avatar according to the user's lifestyle. This allows for more appropriate support by customizing the avatar's characteristics based on the user's current lifestyle. Some or all of the above-described processes in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's current lifestyle into the generation AI and have the generation AI perform the customization of the avatar's characteristics.

[0056] The avatar generation unit can generate the optimal avatar by considering the user's geographical location information during avatar generation. For example, if the user is in a specific region, the avatar generation unit will generate an avatar related to that region. The avatar generation unit can also generate highly relevant avatars based on the user's geographical location information. Furthermore, if the user is on the move, the avatar generation unit can generate an avatar related to the user's current location in real time. This enables more appropriate support by generating the optimal avatar based on the user's geographical location information. Some or all of the above processing in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal avatar.

[0057] The avatar generation unit can analyze the user's social media activity and suggest avatar characteristics during avatar generation. For example, the avatar generation unit can analyze the user's social media posts and suggest relevant avatar characteristics. It can also suggest optimal avatar characteristics based on the user's interests and preferences on social media. Furthermore, it can analyze the user's social media activity history and suggest optimal avatar characteristics. This allows for more appropriate support by analyzing the user's social media activity and suggesting avatar characteristics accordingly. Some or all of the above-described processes in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's social media activity into a generation AI and have the generation AI suggest avatar characteristics.

[0058] The simulation unit can analyze past data of successful job seekers during a simulation to select the optimal simulation method. For example, the simulation unit can select the optimal simulation method based on the career path of a successful job seeker. The simulation unit can also analyze the skill set of a successful job seeker to select the optimal simulation method. Furthermore, the simulation unit can select the optimal simulation method based on the job-hunting history of a successful job seeker. By analyzing past data of successful job seekers and selecting the optimal simulation method, more appropriate simulations become possible. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input past data of successful job seekers into a generative AI and have the generative AI select the optimal simulation method.

[0059] The simulation unit can customize the simulation methods based on the user's current lifestyle during the simulation. For example, if the user is busy, the simulation unit can provide a simple and efficient simulation. Alternatively, if the user is relaxed, the simulation unit can provide a detailed simulation. Furthermore, the simulation unit can customize the simulation methods according to the user's lifestyle. This allows for more appropriate simulations by customizing the simulation methods based on the user's current lifestyle. Some or all of the above-described processes in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the user's current lifestyle into a generative AI and have the generative AI perform the customization of the simulation methods.

[0060] The simulation unit can select the optimal simulation method during simulation, taking into account the user's geographical location information. For example, if the user is in a specific region, the simulation unit will provide a simulation relevant to that region. The simulation unit can also provide highly relevant simulations based on the user's geographical location information. Furthermore, if the user is on the move, the simulation unit can provide simulations relevant to their current location in real time. This allows for more appropriate simulations by selecting the optimal simulation method based on the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal simulation method.

[0061] The simulation unit can analyze the user's social media activity during simulation and propose simulation methods. For example, the simulation unit can analyze the content of the user's social media posts and propose relevant simulation methods. The simulation unit can also propose the optimal simulation method based on the user's interests and preferences on social media. Furthermore, the simulation unit can analyze the user's social media activity history and propose the optimal simulation method. By analyzing the user's social media activity and proposing simulation methods, more appropriate simulations become possible. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the user's social media activity into a generative AI and have the generative AI execute the proposal of simulation methods.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle, and generate and propose the most suitable job types and companies. Furthermore, the career path generation system can analyze the user's past data collection history and select the most suitable collection method. For example, it can prioritize data collection methods that the user has frequently used in the past. It can also propose the most efficient collection method based on the user's past data collection history. This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the user's past data collection history into the AI ​​and have the AI ​​select the optimal collection method.

[0064] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle, and generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can filter data based on the user's current projects and areas of interest. For example, it can prioritize the collection of data related to the projects the user is currently working on. It can also filter and collect highly relevant data based on the user's areas of interest. This allows the system to collect highly relevant data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current projects and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0065] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle, and generate and propose the most suitable job types and companies. Furthermore, the career path generation system can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0066] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle, and generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can improve the accuracy of its generation by considering the interrelationships of the data. For example, it can generate the most suitable job types by considering the interrelationship between the user's background and interests. It can also generate the most suitable companies by considering the interrelationship between the user's values ​​and lifestyle. By improving the accuracy of generation by considering the interrelationships of the data, it can generate more appropriate job types and companies. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the interrelationships of the data into the generation AI and have the generation AI perform the task of improving the accuracy of the generation.

[0067] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle to generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can analyze a user's social media activity and collect relevant data. For example, it can analyze a user's social media posts and collect relevant job information. It can also collect relevant job information based on a user's interests and preferences on social media. By analyzing a user's social media activity and collecting relevant data, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect the relevant data.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. The data collection unit collects not only information entered by the user, but also publicly available information such as social media and blogs. For example, the data collection unit can understand the user's interests and values ​​from the blog posts and social media posts the user has made in the past. Step 2: The analysis unit analyzes the data collected by the collection unit. Using generative AI, the analysis unit generates new job types and companies that are best suited to the user, based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle. For example, if a user has experience in the IT industry and their hobby is programming, the generative AI can be used to suggest the best IT companies and job types for the user. Step 3: The generation unit generates job titles and companies based on the data analyzed by the analysis unit. The generation unit uses generation AI to generate the most suitable job titles and companies for the user. Step 4: The Proposal Unit proposes job titles and companies generated by the Generation Unit to the user. The Proposal Unit proposes job titles and companies generated using the Generation AI to the user.

[0070] (Example of form 2) The career path generation system according to an embodiment of the present invention is a service in which a generating AI creates future job types and job postings based on the user's current skills and experience. This career path generation system collects multimodal data such as the user's career history, interests, career vision, values, hobbies, and lifestyle, and the generating AI analyzes this data to newly generate and propose the most suitable job types and companies for the user. In addition, the career path generation system generates a unique AI avatar based on the user's statements and thoughts and provides support such as interview practice and resume creation. Furthermore, the career path generation system provides a simulation of the user's own career path based on information of successful job changers. For example, the career path generation system collects multimodal data such as the user's career history, interests, career vision, values, hobbies, and lifestyle. In this case, not only information entered by the user is collected, but also public information such as SNS and blogs. For example, the user's interests and values ​​can be understood from blog posts and SNS posts that the user has posted in the past. Next, the generating AI analyzes the collected data in the career path generation system. The generating AI creates optimal job roles and companies for the user based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle. For example, if a user has experience in the IT industry and enjoys programming as a hobby, the generating AI can suggest the most suitable IT companies and job roles. Furthermore, the career path generation system generates a unique AI avatar based on the user's statements and thoughts. The generated AI avatar supports the user with tasks such as interview practice and resume creation. For example, when the user practices an interview, the AI ​​avatar acts as the interviewer and provides feedback on the user's answers. When creating a resume, it suggests the optimal resume format and content based on the user's background and skills. Finally, the career path generation system provides a simulation of the user's career path based on information from successful job changers. For example, the user can simulate what steps they should take by referring to the career paths of successful job changers in the same industry.This allows the career path generation system to help users visualize their own career path concretely and act systematically towards their goals. The system collects and analyzes data such as the user's work history, interests, career vision, values, hobbies, and lifestyle to generate and suggest the most suitable job types and companies.

[0071] The career path generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. The collection unit collects not only information entered by the user, but also publicly available information such as social media and blogs. For example, the collection unit can understand the user's interests and values ​​from blog posts and social media posts the user has made in the past. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generation AI to generate optimal job types and companies for the user based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle. For example, if the user has experience in the IT industry and their hobby is programming, the analysis unit can use the generation AI to propose optimal IT companies and job types for the user. The generation unit generates job types and companies based on the data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate optimal job types and companies for the user. The proposal unit proposes the job types and companies generated by the generation unit to the user. The proposal section, for example, proposes job types and companies generated using generation AI to the user. This allows the career path generation system according to the embodiment to collect and analyze data such as the user's work history, interests, career vision, values, hobbies, and lifestyle, and then generate and propose the most suitable job types and companies.

[0072] The data collection unit collects data on users' backgrounds, interests, career visions, values, hobbies, and lifestyles. Specifically, it collects not only information entered by users but also publicly available information such as social media posts and blogs. For example, it can understand a user's interests and values ​​from blog posts and social media posts they have previously made. The data collection unit automatically analyzes information such as resumes, work histories, and online profiles provided by users and stores it in a database. It also collects information on online courses users have taken, qualifications they have obtained, and projects they have participated in, to gain a detailed understanding of their skill sets. Furthermore, the data collection unit collects information on users' lifestyles and hobbies, providing data to understand the user as a whole. For example, by understanding what hobbies a user has and what activities they participate in, it is possible to propose career paths that match the user's values ​​and lifestyle. The data collection unit centrally manages this data and makes it accessible to the analysis and generation units. This allows the data collection unit to efficiently collect diverse user information and improve the accuracy and effectiveness of the entire system.

[0073] The analysis unit analyzes the data collected by the data collection unit. For example, using generative AI, the analysis unit generates optimal job types and companies for users based on data such as their background, interests, career vision, values, hobbies, and lifestyle. Specifically, the generative AI uses natural language processing technology to analyze user input data and publicly available information to gain a detailed understanding of the user's skills, experience, and interests. For example, if a user has experience in the IT industry and their hobby is programming, the generative AI will identify the optimal IT company and job type based on the user's skill set and interests. Furthermore, the analysis unit considers the user's career vision and values ​​to design a long-term career path. For example, if a user aims for a management position in the future, the analysis unit will suggest job types and companies that will allow the user to acquire the necessary skills and experience based on the user's current skill set and goals. The analysis unit can utilize past data and statistical information to evaluate the probability of success of the user's career path and make optimal suggestions. In this way, the analysis unit can analyze diverse user information in detail and design the optimal career path for the user.

[0074] The generation unit generates job titles and companies based on data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate the most suitable job titles and companies for the user. Specifically, the generation AI generates the most suitable job titles and companies for the user based on the user's skill set, interests, and career vision. For example, if a user has experience in the IT industry and enjoys programming as a hobby, the generation AI will generate the most suitable IT company and job title for that user. The generation unit considers the user's skills, experience, and interests to generate the most suitable job titles and companies. Furthermore, the generation unit considers the user's career vision and values ​​to design a long-term career path. For example, if a user aims for a management position in the future, the generation unit generates job titles and companies that will allow the user to acquire the necessary skills and experience based on the user's current skill set and goals. The generation unit can utilize historical data and statistical information to evaluate the probability of success of the user's career path and make optimal suggestions. This allows the generation unit to analyze diverse user information in detail and design the most suitable career path for the user.

[0075] The Proposal Department proposes job types and companies generated by the Generation Department to the user. Specifically, it proposes job types and companies generated using generation AI. Based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle, the Proposal Department proposes the most suitable job types and companies for the user. For example, if the user has experience in the IT industry and their hobby is programming, the Proposal Department will propose the most suitable IT company and job type for the user. The Proposal Department considers the user's skills, experience, and interests to propose the most suitable job types and companies for the user. Furthermore, the Proposal Department considers the user's career vision and values ​​to design a long-term career path. For example, if the user aims for a management position in the future, the Proposal Department will propose job types and companies that will allow the user to acquire the necessary skills and experience based on the user's current skill set and goals. The Proposal Department can utilize past data and statistical information to evaluate the probability of success of the user's career path and make optimal suggestions. This allows the Proposal Department to analyze diverse user information in detail and design the most suitable career path for the user.

[0076] The avatar generation unit analyzes the user's statements and thoughts and generates an AI avatar. For example, the avatar generation unit uses a generation AI to generate a unique AI avatar based on the user's statements and thoughts. For example, when the user practices for an interview, the avatar generation unit has the AI ​​avatar act as the interviewer and provide feedback on the user's answers. Also, when the user is creating a resume, the avatar generation unit suggests the optimal resume format and content based on the user's experience and skills. In this way, a unique AI avatar can be generated based on the user's statements and thoughts, and support is provided for interview practice, resume creation, etc. Some or all of the above processing in the avatar generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the avatar generation unit can input the user's statements and thoughts into a generation AI and have the generation AI perform the generation of an AI avatar.

[0077] The simulation unit provides career path simulations based on information from successful job changers. For example, the simulation unit simulates what steps a user should take based on information from successful job changers. For example, the simulation unit can simulate what steps a user should take by referring to the career paths of successful job changers in the same industry. This allows the system to provide a simulation of one's own career path based on information from successful job changers. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input information from successful job changers into a generative AI and have the generative AI perform a career path simulation.

[0078] The data collection unit can collect publicly available information such as social media and blogs. For example, the data collection unit can understand a user's interests and values ​​from blog posts and social media posts the user has previously made. This allows the collection to include not only information entered by the user, but also publicly available information such as social media and blogs. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input publicly available information such as social media and blogs into a generative AI and have the generative AI perform the information collection.

[0079] The analysis unit can analyze the user's background and interests using natural language processing techniques. For example, the analysis unit can analyze the user's background and interests using natural language processing techniques. For example, the analysis unit can analyze the user's background and interests using morphological analysis. The analysis unit can also analyze the user's background and interests using grammatical analysis. Furthermore, the analysis unit can analyze the user's background and interests using semantic analysis. This allows the analysis of the user's background and interests to be performed using natural language processing techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's background and interests into a generative AI and have the generative AI perform analysis using natural language processing techniques.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. The data collection unit can also collect detailed data when the user is focused. Furthermore, if the user is tired, the data collection unit can perform simplified data collection and collect detailed data later. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and customize the collection method. This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0082] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can collect necessary data according to the progress of the user's current projects. This allows for the collection of highly relevant data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's current projects and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting data that is of interest to the user. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is stressed, the data collection unit may also prioritize collecting simple data. This allows for more appropriate data collection by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant job postings based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect data related to their current location in real time. This allows for the priority collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant job information. It can also collect relevant job information based on the user's interests and preferences on social media. Furthermore, the data collection unit can analyze a user's social media activity history and suggest the most suitable data collection method. This enables more appropriate data collection by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect the relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can also provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a history analysis algorithm to history data. It can also apply an interest analysis algorithm to interest data. Furthermore, it can apply a values ​​analysis algorithm to values ​​data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0092] The generation unit can estimate the user's emotions and determine the priority of job types and companies to generate based on the estimated emotions. For example, if the user is excited, the generation unit will prioritize generating challenging job types and companies. If the user is relaxed, the generation unit can also prioritize generating stable job types and companies. Furthermore, if the user is stressed, the generation unit can also prioritize generating low-stress job types and companies. This allows for more appropriate suggestions by determining the priority of job types and companies to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of job types and companies.

[0093] The generation unit can improve the accuracy of generation by considering the interrelationships of data during generation. For example, the generation unit can generate the optimal job by considering the interrelationship between the user's background and interests. It can also generate the optimal company by considering the interrelationship between the user's values ​​and lifestyle. Furthermore, the generation unit can generate the optimal job and company by considering the interrelationship between the user's hobbies and career vision. By improving the accuracy of generation by considering the interrelationships of data, it is possible to generate more appropriate job and company. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the interrelationships of data into the generation AI and have the generation AI perform the improvement of generation accuracy.

[0094] The generation unit can perform generation while considering the attribute information of the data submitter. For example, the generation unit can generate the most suitable job types and companies by considering the user's age and gender. It can also generate the most suitable job types and companies by considering the user's educational background and work history. Furthermore, it can generate the most suitable job types and companies by considering the user's region and cultural background. In this way, by performing generation while considering the attribute information of the data submitter, it is possible to generate more appropriate job types and companies. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the attribute information of the data submitter into the generation AI and have the generation AI perform the generation.

[0095] The generation unit can estimate the user's emotions and adjust the way job titles and companies are displayed based on the estimated emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display. If the user is relaxed, the generation unit can also provide a display that includes detailed information. If the user is in a hurry, the generation unit can provide a display that gets straight to the point. By adjusting the way job titles and companies are displayed according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the display method.

[0096] The generation unit can perform generation while considering the geographical distribution of the data. For example, the generation unit can generate nearby job types and companies based on the user's place of residence. It can also generate optimal job types and companies based on the user's desired region. Furthermore, it can generate relevant job types and companies based on the user's desired relocation destination. By considering the geographical distribution of the data during generation, it is possible to generate more appropriate job types and companies. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the geographical distribution of the data into a generation AI and have the generation AI perform the generation.

[0097] The generation unit can improve the accuracy of its generation by referring to relevant literature during the generation process. For example, the generation unit can generate the most suitable job titles by referring to literature related to the user's career history. It can also generate the most suitable companies by referring to literature related to the user's interests. Furthermore, it can generate the most suitable job titles and companies by referring to literature related to the user's career vision. By improving the accuracy of generation by referring to relevant literature, it is possible to generate more appropriate job titles and companies. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input relevant literature into the generation AI and have the generation AI perform the task of improving the accuracy of its generation.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can provide a suggestion that gets straight to the point. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0099] The proposal department can adjust the level of detail in a proposal based on the importance of the job type and company. For example, the proposal department will provide detailed proposals for high-priority job types and companies. Conversely, it can provide simplified proposals for low-priority job types and companies. The proposal department can also determine the priority of proposals according to the importance of the job type and company. This allows for efficient proposals by adjusting the level of detail based on the importance of the job type and company. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the importance of the job type and company into the generative AI and have the generative AI adjust the level of detail of the proposal.

[0100] The proposal department can apply different proposal algorithms depending on the job type and company category when making a proposal. For example, for IT positions, the proposal department can apply a proposal algorithm that emphasizes technical skills. For creative positions, the proposal department can also apply a proposal algorithm that emphasizes portfolios. Furthermore, for management positions, the proposal department can apply a proposal algorithm that emphasizes leadership skills. By applying different proposal algorithms depending on the job type and company category, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input the job type and company category into a generative AI and have the generative AI execute the application of the proposal algorithm.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make short, concise suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is excited, the suggestion unit can make visually appealing suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0102] The proposal department can prioritize proposals based on the job type and the submission timing of companies. For example, the proposal department will prioritize proposals for the most recent job types and companies. It can also postpone proposals for older job types and companies. Furthermore, the proposal department can adjust the proposal schedule based on the submission timing. This allows for efficient proposals by prioritizing proposals based on the submission timing of job types and companies. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the submission timing of job types and companies into a generative AI and have the generative AI determine the priority of proposals.

[0103] The proposal department can adjust the order of proposals based on the relevance of job types and companies. For example, the proposal department can prioritize proposing highly relevant job types and companies. It can also postpone proposing less relevant job types and companies. Furthermore, the proposal department can determine the order of proposals based on the relevance of job types and companies. This allows for efficient proposals by adjusting the order of proposals based on the relevance of job types and companies. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the relevance of job types and companies into a generative AI and have the generative AI adjust the order of proposals.

[0104] The avatar generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is nervous, the avatar generation unit can generate an avatar with a calm expression. If the user is relaxed, the avatar generation unit can also generate an avatar with a cheerful expression. If the user is excited, the avatar generation unit can also generate an avatar with an energetic expression. By adjusting the avatar's expression according to the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the avatar generation unit may be performed using a generative AI, or not. For example, the avatar generation unit can input the user's emotion data into a generative AI and have the generative AI adjust the avatar's expression.

[0105] The avatar generation unit can generate the optimal avatar by analyzing the user's past statements and thoughts during avatar generation. For example, the avatar generation unit can generate an avatar that matches the user's speaking style based on the content of the user's past statements. The avatar generation unit can also analyze the user's thought patterns and generate an avatar that matches their thoughts. Furthermore, the avatar generation unit can comprehensively analyze the user's past statements and thoughts to generate the optimal avatar. This allows for more appropriate support by analyzing the user's past statements and thoughts to generate the optimal avatar. Some or all of the above-described processes in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's past statements and thoughts into a generation AI and have the generation AI generate the optimal avatar.

[0106] The avatar generation unit can customize the characteristics of the avatar based on the user's current lifestyle when generating the avatar. For example, if the user is busy, the avatar generation unit can generate a simple and efficient avatar. Conversely, if the user is relaxed, the avatar generation unit can generate an avatar that provides detailed support. Furthermore, the avatar generation unit can customize the characteristics of the avatar according to the user's lifestyle. This allows for more appropriate support by customizing the avatar's characteristics based on the user's current lifestyle. Some or all of the above-described processes in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's current lifestyle into the generation AI and have the generation AI perform the customization of the avatar's characteristics.

[0107] The avatar generation unit can estimate the user's emotions and determine avatar priorities based on the estimated emotions. For example, if the user is tense, the avatar generation unit will prioritize generating relaxing avatars. If the user is relaxed, the avatar generation unit can also prioritize generating avatars that provide detailed support. Furthermore, if the user is excited, the avatar generation unit can prioritize generating lively avatars. This allows for more appropriate support by prioritizing avatars according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the avatar generation unit may be performed using a generative AI, or not. For example, the avatar generation unit can input user emotion data into a generative AI and have the generative AI determine avatar priorities.

[0108] The avatar generation unit can generate the optimal avatar by considering the user's geographical location information during avatar generation. For example, if the user is in a specific region, the avatar generation unit will generate an avatar related to that region. The avatar generation unit can also generate highly relevant avatars based on the user's geographical location information. Furthermore, if the user is on the move, the avatar generation unit can generate an avatar related to the user's current location in real time. This enables more appropriate support by generating the optimal avatar based on the user's geographical location information. Some or all of the above processing in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal avatar.

[0109] The avatar generation unit can analyze the user's social media activity and suggest avatar characteristics during avatar generation. For example, the avatar generation unit can analyze the user's social media posts and suggest relevant avatar characteristics. It can also suggest optimal avatar characteristics based on the user's interests and preferences on social media. Furthermore, it can analyze the user's social media activity history and suggest optimal avatar characteristics. This allows for more appropriate support by analyzing the user's social media activity and suggesting avatar characteristics accordingly. Some or all of the above-described processes in the avatar generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar generation unit can input the user's social media activity into a generation AI and have the generation AI suggest avatar characteristics.

[0110] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide a detailed simulation. If the user is in a hurry, the simulation unit can provide a concise simulation that gets straight to the point. If the user is excited, the simulation unit can provide a visually appealing simulation. By adjusting the simulation method according to the user's emotions, a more appropriate simulation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using a generative AI, or not. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI adjust the simulation method.

[0111] The simulation unit can analyze past data of successful job seekers during a simulation to select the optimal simulation method. For example, the simulation unit can select the optimal simulation method based on the career path of a successful job seeker. The simulation unit can also analyze the skill set of a successful job seeker to select the optimal simulation method. Furthermore, the simulation unit can select the optimal simulation method based on the job-hunting history of a successful job seeker. By analyzing past data of successful job seekers and selecting the optimal simulation method, more appropriate simulations become possible. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input past data of successful job seekers into a generative AI and have the generative AI select the optimal simulation method.

[0112] The simulation unit can customize the simulation methods based on the user's current lifestyle during the simulation. For example, if the user is busy, the simulation unit can provide a simple and efficient simulation. Alternatively, if the user is relaxed, the simulation unit can provide a detailed simulation. Furthermore, the simulation unit can customize the simulation methods according to the user's lifestyle. This allows for more appropriate simulations by customizing the simulation methods based on the user's current lifestyle. Some or all of the above-described processes in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the user's current lifestyle into a generative AI and have the generative AI perform the customization of the simulation methods.

[0113] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is tense, the simulation unit may prioritize providing a relaxing simulation. If the user is relaxed, the simulation unit may also prioritize providing a detailed simulation. Furthermore, if the user is excited, the simulation unit may prioritize providing a visually appealing simulation. This allows for more appropriate simulations by prioritizing simulations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the simulation unit may be performed using, for example, a generative AI, or not. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI determine the simulation priorities.

[0114] The simulation unit can select the optimal simulation method during simulation, taking into account the user's geographical location information. For example, if the user is in a specific region, the simulation unit will provide a simulation relevant to that region. The simulation unit can also provide highly relevant simulations based on the user's geographical location information. Furthermore, if the user is on the move, the simulation unit can provide simulations relevant to their current location in real time. This allows for more appropriate simulations by selecting the optimal simulation method based on the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal simulation method.

[0115] The simulation unit can analyze the user's social media activity during simulation and propose simulation methods. For example, the simulation unit can analyze the content of the user's social media posts and propose relevant simulation methods. The simulation unit can also propose the optimal simulation method based on the user's interests and preferences on social media. Furthermore, the simulation unit can analyze the user's social media activity history and propose the optimal simulation method. By analyzing the user's social media activity and proposing simulation methods, more appropriate simulations become possible. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the user's social media activity into a generative AI and have the generative AI execute the proposal of simulation methods.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle to generate and propose the most suitable job types and companies. Furthermore, the career path generation system can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing job types and companies. Conversely, if the user is excited, it can suggest challenging job types and companies. This allows for more appropriate suggestions based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using or without generative AI. For example, the suggestion section can input the user's emotion data into the generative AI and have the generative AI adjust the suggestions.

[0118] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle, and generate and propose the most suitable job types and companies. Furthermore, the career path generation system can analyze the user's past data collection history and select the most suitable collection method. For example, it can prioritize data collection methods that the user has frequently used in the past. It can also propose the most efficient collection method based on the user's past data collection history. This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the user's past data collection history into the AI ​​and have the AI ​​select the optimal collection method.

[0119] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle to generate and suggest optimal job types and companies. Furthermore, the career path generation system can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, data collection can be temporarily stopped and resumed when the user is relaxed. Conversely, if the user is focused, detailed data collection can be performed at that time. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processing in the collection unit may be performed using generative AI, or not using generative AI. For example, the collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0120] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle, and generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can filter data based on the user's current projects and areas of interest. For example, it can prioritize the collection of data related to the projects the user is currently working on. It can also filter and collect highly relevant data based on the user's areas of interest. This allows the system to collect highly relevant data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current projects and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0121] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle, and generate and propose the most suitable job types and companies. Furthermore, the career path generation system can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the presentation of the analysis.

[0122] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle, and generate and propose the most suitable job types and companies. Furthermore, the career path generation system can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0123] A career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle to generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can estimate the user's emotions and determine the priority of job types and companies to generate based on the estimated emotions. For example, if the user is excited, it can prioritize generating challenging job types and companies. Conversely, if the user is relaxed, it can prioritize generating stable job types and companies. This allows for more appropriate suggestions by determining the priority of job types and companies according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of job types and companies.

[0124] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle, and generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can improve the accuracy of its generation by considering the interrelationships of the data. For example, it can generate the most suitable job types by considering the interrelationship between the user's background and interests. It can also generate the most suitable companies by considering the interrelationship between the user's values ​​and lifestyle. By improving the accuracy of generation by considering the interrelationships of the data, it can generate more appropriate job types and companies. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the interrelationships of the data into the generation AI and have the generation AI perform the task of improving the accuracy of the generation.

[0125] The career path generation system can collect and analyze data such as the user's background, interests, career vision, values, hobbies, and lifestyle to generate and propose the most suitable job types and companies. Furthermore, the career path generation system can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is stressed, it can provide a simple and highly visual presentation. If the user is relaxed, it can provide a presentation that includes more detailed information. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the suggestion section may be performed using generative AI or not. For example, the suggestion section can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the suggestions.

[0126] The career path generation system can collect and analyze data such as a user's background, interests, career vision, values, hobbies, and lifestyle to generate and suggest the most suitable job types and companies. Furthermore, the career path generation system can analyze a user's social media activity and collect relevant data. For example, it can analyze a user's social media posts and collect relevant job information. It can also collect relevant job information based on a user's interests and preferences on social media. By analyzing a user's social media activity and collecting relevant data, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect the relevant data.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. The data collection unit collects not only information entered by the user, but also publicly available information such as social media and blogs. For example, the data collection unit can understand the user's interests and values ​​from the blog posts and social media posts the user has made in the past. Step 2: The analysis unit analyzes the data collected by the collection unit. Using generative AI, the analysis unit generates new job types and companies that are best suited to the user, based on data such as the user's background, interests, career vision, values, hobbies, and lifestyle. For example, if a user has experience in the IT industry and their hobby is programming, the generative AI can be used to suggest the best IT companies and job types for the user. Step 3: The generation unit generates job titles and companies based on the data analyzed by the analysis unit. The generation unit uses generation AI to generate the most suitable job titles and companies for the user. Step 4: The Proposal Unit proposes job titles and companies generated by the Generation Unit to the user. The Proposal Unit proposes job titles and companies generated using the Generation AI to the user.

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, proposal unit, avatar generation unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates job types and companies based on the analysis results. The proposal unit is implemented, for example, by the control unit 46A of the smart device 14 and proposes the generated job types and companies to the user. The avatar generation unit is implemented, for example, by the control unit 46A of the smart device 14 and generates an AI avatar based on the user's statements and thoughts. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a career path simulation based on information of successful job changers. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, proposal unit, avatar generation unit, and simulation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates job types and companies based on the analysis results. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes the generated job types and companies to the user. The avatar generation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and generates an AI avatar based on the user's statements and thoughts. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a career path simulation based on information of successful job changers. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, proposal unit, avatar generation unit, and simulation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates job types and companies based on the analysis results. The proposal unit is implemented, for example, by the control unit 46A of the headset terminal 314 and proposes the generated job types and companies to the user. The avatar generation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and generates an AI avatar based on the user's statements and thoughts. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a career path simulation based on information of successful job changers. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, proposal unit, avatar generation unit, and simulation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates job types and companies based on the analysis results. The proposal unit is implemented, for example, by the control unit 46A of the robot 414 and proposes the generated job types and companies to the user. The avatar generation unit is implemented, for example, by the control unit 46A of the robot 414 and generates an AI avatar based on the user's statements and thoughts. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a career path simulation based on information of successful job changers. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) The data collection department collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates job types and companies based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes job types and companies generated by the generation unit to the user. A system characterized by the following features. (Note 2) It includes an avatar generation unit that analyzes the user's statements and thoughts and generates an AI avatar. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a simulation department that provides career path simulations based on information from successful job changers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect publicly available information from social media, blogs, etc. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the user's background and interests using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates user emotions and determines the priority of job roles and companies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the accuracy of the generation is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the data is generated while taking into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts how job titles and companies are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, we refer to relevant literature to improve the accuracy of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the job and the company. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the job type and company category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When submitting proposals, prioritize them based on job type and the submission deadline of the company. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of job roles and companies. The system described in Appendix 1, characterized by the features described herein. (Note 30) The avatar generation unit is, It estimates the user's emotions and adjusts the avatar's representation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The avatar generation unit is, When generating an avatar, the system analyzes the user's past statements and thoughts to create the most suitable avatar. The system described in Appendix 2, characterized by the features described herein. (Note 32) The avatar generation unit is, When creating an avatar, its characteristics are customized based on the user's current lifestyle. The system described in Appendix 2, characterized by the features described herein. (Note 33) The avatar generation unit is, It estimates the user's emotions and determines avatar priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The avatar generation unit is, When generating an avatar, the system takes the user's geographical location information into consideration to create the most suitable avatar. The system described in Appendix 2, characterized by the features described herein. (Note 35) The avatar generation unit is, When generating an avatar, the system analyzes the user's social media activity to suggest avatar characteristics. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned simulation unit, During the simulation, we analyze past data from successful job changers to select the optimal simulation method. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned simulation unit, During the simulation, the simulation method is customized based on the user's current living situation. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned simulation unit, During the simulation, we analyze the user's social media activity and propose simulation methods. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data such as the user's background, interests, career vision, values, hobbies, and lifestyle. An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates job types and companies based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes job types and companies generated by the generation unit to the user. A system characterized by the following features.

2. It includes an avatar generation unit that analyzes the user's statements and thoughts and generates an AI avatar. The system according to feature 1.

3. It includes a simulation department that provides career path simulations based on information from successful job changers. The system according to feature 1.

4. The aforementioned collection unit is Collect publicly available information from social media, blogs, etc. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the user's background and interests using natural language processing technology. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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