system

Abstract

The system according to this embodiment aims to propose an optimal career path based on the user's skill set and career goals, and to provide resources for acquiring the necessary skills. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input of the user's skill set and career goals. The analysis unit analyzes past data and current market trends based on the information received by the reception unit. The proposal unit proposes a career path based on the analysis results obtained by the analysis unit. The provision unit provides resources for acquiring skills based on the career path proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the chatbot's 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 prior art, there is a problem that an optimal career path has not been sufficiently proposed based on the user's skill set and career goals, and resources for acquiring the necessary skills have not been provided.

[0005] The system according to the embodiment aims to propose an optimal career path based on the user's skill set and career goals, and provide resources for acquiring the necessary skills.

Means for Solving the Problems

[0007] The system according to this embodiment can suggest an optimal career path based on the user's skill set and career goals, and provide resources for acquiring the necessary skills. [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 controls 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] [[ID=Z]] 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 support system according to an embodiment of the present invention is a system that analyzes a user's skill set and career goals, recommends the most suitable job, and provides resources for acquiring the necessary skills. This career support system takes the user's skill set and career goals as input, and an AI agent analyzes past data and current market trends to propose the optimal career path for the user. Furthermore, the career support system provides resources for the user to acquire the necessary skills. For example, if a user inputs "I want to become a data scientist," the career support system analyzes the user's current skill set. Next, the career support system analyzes past data and current market trends to propose the optimal career path for the user. For example, the career support system suggests the skills and experience necessary for the user to become a data scientist. Furthermore, the career support system provides resources for the user to acquire the necessary skills. For example, the career support system suggests online courses and training programs necessary for the user to become a data scientist. This allows the user to efficiently acquire the necessary skills. This mechanism improves the user's employment rate. By acquiring the necessary skills according to the suggestions of the career support system, users can gain more job opportunities. The average income of users also improves. By following the career path suggested by the career support system, users can earn higher incomes. Furthermore, their career satisfaction will also improve. By following the career path suggested by the career support system, users can achieve self-realization and lead more fulfilling professional lives. This career support system targets young to mid-career professionals in their 20s to 40s. These users face challenges such as limited opportunities for career growth and insufficient access to appropriate resources. To address these challenges, the career support system proposes the optimal career path for each individual user and provides resources to learn the necessary skills. This career support system utilizes generative AI to analyze individual user profiles and provide personalized career suggestions.This will give users a competitive edge in the market. Furthermore, advancements in AI have made it possible to provide personalized career advice to individual users. This career support system is expected to see significant growth in the career development services market. According to market research data and industry reports, the career development services market is projected to grow at an average annual rate of 6% over the next five years, with a TAM (Total Addressable Market) of 1 trillion yen and a SAM (Serviceable Market) of 30 billion yen. With increasing career awareness among young and mid-career professionals, and a growing demand for customized services tailored to individual needs, now is an excellent time to enter the market. This innovative career support system will evolve users' careers to the next level. The goal is to create a society where users can achieve self-realization and lead more fulfilling professional lives. This allows the career support system to efficiently analyze users' skill sets and career goals, providing optimal career paths and resources.

[0029] The career support system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input from the user regarding their skill set and career goals. The reception unit, for example, stores the user's entered skill set and career goals in a database. The reception unit can also process the user's input information in real time and transmit it to the analysis unit. The reception unit automatically classifies the user's input information so that the analysis unit can process it efficiently. For example, the reception unit classifies the user's entered skill set into technical skills, soft skills, industry-specific skills, etc. The reception unit classifies the user's entered career goals into short-term goals, long-term goals, specific job types or positions, etc. The analysis unit analyzes past data and current market trends based on the information received by the reception unit. The analysis unit, for example, evaluates the user's skill set based on past work history and skill evaluation data. The analysis unit analyzes current market trends to understand industry growth rates, recruitment trends, skill demand, etc. The analysis unit combines past data and current market trends to provide information for proposing the optimal career path to the user. For example, the Analysis Department identifies the skills and experience a user needs to become a data scientist based on their skill set and market trends. The Proposal Department proposes a career path based on the analysis results obtained by the Analysis Department. The Proposal Department, for example, presents a specific career path to the user and indicates the necessary skills and experience. The Proposal Department can also specifically show the steps a user needs to take to advance their career path. The Proposal Department provides information to help the user access the resources necessary to advance their career path. For example, the Proposal Department suggests online courses and training programs necessary for a user to become a data scientist. The Delivery Department provides resources to help users acquire the necessary skills based on the career path proposed by the Proposal Department. For example, the Delivery Department provides information on online courses and training programs to the user. The Delivery Department can also support the user in accessing the resources. The Delivery Department monitors the user's progress as they access the resources and provides support as needed.For example, the service provider monitors the user's progress while they are taking an online course and provides necessary support. This allows the career support system according to the embodiment to efficiently analyze the user's skill set and career goals and provide the optimal career path and resources.

[0030] The reception department receives user input regarding their skill sets and career goals. For example, the reception department stores the user's entered skill sets and career goals in a database. Specifically, information entered by users through web forms or applications is saved to the database in real time. The reception department can also process user input in real time and send it to the analytics department. For example, the user's entered skill sets and career goals are immediately saved to the database and simultaneously sent to the analytics department. The reception department automatically categorizes the user input to enable efficient processing by the analytics department. For example, the reception department categorizes the user's entered skill sets into technical skills, soft skills, industry-specific skills, etc. Technical skills include programming languages, database management, network construction, etc., while soft skills include communication skills, leadership, problem-solving skills, etc. Industry-specific skills include specialized knowledge and experience required in a particular industry. The reception department categorizes the user's entered career goals into short-term goals, long-term goals, specific job titles or positions, etc. Short-term goals include those to be achieved within a few months to a year, while long-term goals include those to be achieved within a few years. Specific job titles and positions include the specific job titles and positions that the user aspires to. This allows the reception department to efficiently classify user input information, enabling the analysis department to process it quickly and accurately. Furthermore, the reception department can regularly update user input information to maintain up-to-date data. For example, if a user acquires new skills or changes their career goals, the reception department updates this information and reflects it in the database. This allows the reception department to always provide analysis and recommendations based on the latest information.

[0031] The Analysis Department analyzes historical data and current market trends based on information received by the Reception Department. For example, the Analysis Department evaluates users' skill sets based on their past work history and skill assessment data. Specifically, it retrieves users' work history and skill assessment data from a database and analyzes it using AI. The AI ​​uses natural language processing technology to analyze the user's work history and skill assessment data and evaluate their skill set. The Analysis Department analyzes current market trends to understand industry growth rates, recruitment trends, and skill demand. For example, the AI ​​collects and analyzes online job postings and industry reports to understand current market trends. The Analysis Department combines historical data and current market trends to provide information that proposes the optimal career path for users. For example, the Analysis Department identifies the skills and experience necessary for a user to become a data scientist based on their skill set and market trends. This allows the Analysis Department to provide information that proposes the optimal career path for users based on their skill set and market trends. Furthermore, the analytics department can utilize historical data and statistical information to predict long-term career paths and conduct trend analysis. For example, it can analyze the evolution of career paths in specific occupations or industries based on historical data and predict future career paths. The analytics department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analytics department to not only grasp the situation in real time but also to predict long-term career paths and detect anomalies, thereby improving the reliability and security of the entire system.

[0032] The proposal department proposes career paths based on the analysis results obtained by the analysis department. For example, the proposal department presents specific career paths to users and indicates the necessary skills and experience. Specifically, the proposal department proposes the optimal career path for the user based on the user's skill set and career goals. The proposal department can also specifically show the steps necessary for the user to advance their career path. For example, it can show the skills and experience necessary for a user to become a data scientist and propose specific steps to achieve this. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal department suggests online courses and training programs necessary for the user to become a data scientist. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal department suggests online courses and training programs necessary for the user to become a data scientist. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal department suggests online courses and training programs necessary for the user to become a data scientist. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal team can suggest online courses and training programs necessary for users to become data scientists. This allows the proposal team to present users with concrete career paths and indicate the necessary skills and experience. Furthermore, the proposal team can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For instance, based on feedback received as users progress through the suggested career paths, the proposal team can revise the suggestions and propose more appropriate career paths. The proposal team can also monitor users' progress and provide support as needed. This allows the proposal team to present users with concrete career paths and indicate the necessary skills and experience.

[0033] The Service Provider provides resources to help users acquire the necessary skills based on the career path proposed by the Service Provider. For example, the Service Provider provides users with information on online courses and training programs. Specifically, the Service Provider provides information on online courses and training programs necessary for users to advance their proposed career path. The Service Provider can also support users in the process of using resources. For example, the Service Provider supports users in the process of registering for online courses and provides the necessary information. The Service Provider monitors users' progress in using resources and provides support as needed. For example, the Service Provider monitors users' progress in taking online courses and provides the necessary support. This allows the Service Provider to provide users with information on online courses and training programs and resources to help them acquire the necessary skills. Furthermore, the service provider can monitor users' progress and provide support as needed. For example, they can monitor users' progress while they are taking online courses and provide necessary support. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of their offerings. This allows the service provider to provide users with information on online courses and training programs, and to provide resources for acquiring the necessary skills.

[0034] The service provider can offer online courses or training programs. For example, the service provider can provide specific resources to help users acquire the skills they need. The service provider can also provide information for users to take online courses. For example, the service provider can provide users with information such as the types of online courses, the platforms on which they are offered, and how to take them. The service provider can also provide information for users to take training programs. For example, the service provider can provide users with information such as the types of training programs, the format of which they are offered, and how to take them. This allows the service provider to provide specific resources to help users acquire the skills they need. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide resources using an AI model that takes the user's skill set and career goals as input and outputs the optimal online course or training program.

[0035] The reception desk can analyze the user's past career history and select an input method. For example, the reception desk can suggest a similar input method based on the user's past career history. The reception desk can also automatically complete frequently used keywords from the user's past career history. The reception desk can also analyze the user's past career history and provide templates to reduce the effort required for input. This allows the reception desk to reduce the effort required for input by selecting the optimal input method based on the user's past career history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past career history data into a generating AI and have the generating AI select the optimal input method.

[0036] The reception desk can filter the input of skill sets and career goals based on the user's current occupation and areas of interest. For example, the reception desk can prioritize the input of relevant skill sets based on the user's current occupation. The reception desk can also customize the input of career goals based on the user's areas of interest. The reception desk can also suggest the most relevant input items by combining the user's occupation and areas of interest. This allows the reception desk to input more relevant information by filtering the input based on the user's occupation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's occupation and areas of interest into a generating AI and have the generating AI suggest the most relevant input items.

[0037] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when the user inputs their skill set and career goals. For example, the reception desk can prioritize displaying local job postings based on the user's geographical location. The reception desk can also suggest local training programs based on the user's geographical location. The reception desk can also provide local career event information based on the user's geographical location. In this way, the reception desk can provide region-specific information by prioritizing inputting highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0038] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their skill set and career goals. For example, the reception desk can automatically input the user's skill set of interest based on their social media activity. The reception desk can also suggest information related to the user's career goals based on their social media activity. The reception desk can also analyze the user's social media activity and suggest the optimal career path. This allows the reception desk to provide more personalized input by inputting relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0039] The analysis unit can adjust the accuracy of its analysis based on the level of detail of the user's skill set. For example, if the user's skill set is detailed, the analysis unit will perform a highly accurate analysis. If the user's skill set is vague, the analysis unit can also perform a broad analysis. The analysis unit can also adjust the depth of its analysis according to the level of detail of the user's skill set. This allows the analysis unit to provide more accurate analysis results by adjusting the accuracy of its analysis according to the level of detail of the user's skill set. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input detailed information about the user's skill set into a generating AI and have the generating AI adjust the accuracy of the analysis.

[0040] The analysis unit can apply different analytical methods depending on the user's career goal category during analysis. For example, if the user's career goal is a technical position, the analysis unit will apply an analytical algorithm specialized for technical positions. If the user's career goal is a management position, the analysis unit can also apply an analytical algorithm specialized for management positions. If the user's career goal is a creative position, the analysis unit can also apply an analytical algorithm specialized for creative positions. In this way, the analysis unit can provide more appropriate analytical results by applying different analytical algorithms depending on the user's career goal category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's career goal category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0041] The analysis unit can prioritize analyses based on when users submit their data. For example, if a user is in a hurry, the analysis unit will prioritize that user's analysis based on the submission date. If a user has more time, the analysis unit may also prioritize other users' analyses. The analysis unit can also adjust the analysis schedule based on the user's submission date. This allows the analysis unit to provide analysis results more quickly by prioritizing analyses based on the user's submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user submission date data into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant market data during the analysis process. For example, the analysis unit can perform highly accurate analysis based on the user's relevant market data. The analysis unit can also reflect the latest market trends by referring to the user's relevant market data. The analysis unit can also improve the reliability of its analysis results by utilizing the user's relevant market data. As a result, the analysis unit can provide more accurate analysis results by referring to the user's relevant market data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant market data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0043] The proposal unit can adjust the level of detail of its proposals based on the importance of the career paths. For example, if the career path is highly important, the proposal unit will provide a detailed proposal. If the career path is less important, the proposal unit can provide a concise proposal. The proposal unit can also adjust the depth of the proposal according to the importance of the career paths. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail according to the importance of the career paths. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input career path importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0044] The proposal unit can apply different proposal methods depending on the career path category when making a proposal. For example, if the career path is a technical position, the proposal unit will apply a proposal algorithm specialized for technical positions. If the career path is a management position, the proposal unit can also apply a proposal algorithm specialized for management positions. If the career path is a creative position, the proposal unit can also apply a proposal algorithm specialized for creative positions. In this way, the proposal unit can make more appropriate proposals by applying different proposal algorithms depending on the career path category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input career path category data into a generating AI and have the generating AI execute the application of different proposal methods.

[0045] The proposal department can prioritize proposals based on when the career path is submitted. For example, if the career path submission deadline is approaching, the proposal department will prioritize the proposal. If there is ample time before the career path submission deadline, the proposal department may also prioritize proposals from other users. The proposal department can also adjust the proposal schedule based on the career path submission deadline. This allows the proposal department to submit proposals more quickly by prioritizing them based on the career path submission deadline. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input career path submission deadline data into a generating AI and have the generating AI determine the priority of proposals.

[0046] The proposal unit can adjust the order of proposals based on relevant career path information when making proposals. For example, the proposal unit will prioritize proposals that are highly relevant to the career path. The proposal unit can also postpone proposals that are less relevant to the career path. The proposal unit can also adjust the order of proposals based on the relevance of the career path. This allows the proposal unit to make more relevant proposals by adjusting the order of proposals based on the relevance of the career path. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input career path relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0047] The service provider can adjust the accuracy of its resource provision based on the level of detail of the user's skill set. For example, if the user's skill set is detailed, the service provider can provide highly accurate resources. If the user's skill set is broad, the service provider can also provide a wide range of resources. The service provider can also adjust the depth of the resources provided according to the level of detail of the user's skill set. This allows the service provider to provide more appropriate resources by adjusting the accuracy of its provision according to the level of detail of the user's skill set. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input detailed information about the user's skill set into a generating AI and have the generating AI perform the adjustment of the accuracy of the provision.

[0048] The resource provisioning unit can apply different resource provisioning methods depending on the user's career goal category when providing resources. For example, if the career goal is a technical position, the provisioning unit will apply a resource provisioning algorithm specialized for technical positions. If the career goal is a management position, the provisioning unit can also apply a resource provisioning algorithm specialized for management positions. If the career goal is a creative position, the provisioning unit can also apply a resource provisioning algorithm specialized for creative positions. In this way, the provisioning unit can provide more appropriate resources by applying different resource provisioning algorithms depending on the user's career goal category. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the user's career goal category data into a generating AI and have the generating AI execute the application of different resource provisioning methods.

[0049] The service provider can provide the most suitable resources based on the user's geographical location when providing resources. For example, the service provider can prioritize providing local training programs based on the user's geographical location. The service provider can also provide local career event information based on the user's geographical location. The service provider can also prioritize providing local job postings based on the user's geographical location. In this way, the service provider can provide region-specific resources by providing the most suitable resources based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the most suitable resources.

[0050] The service provider can analyze a user's social media activity and provide relevant resources when providing resources. For example, the service provider can provide training programs of interest based on the user's social media activity. The service provider can also provide resources related to career goals based on the user's social media activity. The service provider can analyze a user's social media activity and provide the most suitable resources. This allows the service provider to provide more personalized resources by providing relevant resources based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant resources.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] Career support systems can consider a user's past learning history when analyzing their skill set and career goals. For example, they can analyze a user's history of online courses and training programs to suggest resources most relevant to their current skill set and career goals. They can also consider qualifications and certifications the user has obtained in the past and suggest career paths based on that. Furthermore, they can analyze a user's history of workshops and seminars and provide new learning opportunities based on that. In this way, career support systems can leverage a user's past learning history to provide more personalized career suggestions.

[0053] Career support systems can consider a user's health status when analyzing their skill set and career goals. For example, if a user inputs information about their health, the system can use that information to suggest a suitable career path. If a user is prone to stress, the system can suggest less stressful jobs and work styles. If a user has physical limitations, the system can suggest jobs or remote work opportunities that accommodate those limitations. Furthermore, it can provide the user with the resources necessary to maintain their health. In this way, career support systems can make more appropriate career suggestions by taking the user's health status into consideration.

[0054] Career support systems can consider a user's lifestyle when analyzing their skill set and career goals. For example, if a user inputs information about their family circumstances or hobbies, the system can use that information to suggest a suitable career path. If a user is unable to work full-time due to family circumstances, the system can suggest part-time or flexible work opportunities. Furthermore, if a user has specific hobbies or interests, the system can suggest occupations or industries related to those interests. It can also provide learning resources tailored to the user's lifestyle. This allows career support systems to provide more personalized career suggestions by taking the user's lifestyle into account.

[0055] Career support systems can consider a user's network when analyzing their skill set and career goals. For example, if a user inputs information about their social media or professional network, the system can use that information to suggest a suitable career path. If a user has a strong network in a particular industry or job type, the system can suggest a career path that leverages that network. It can also suggest events and communities to help the user expand their network. Furthermore, users can share learning resources using their network. This allows career support systems to provide more personalized career suggestions by taking the user's network into account.

[0056] Career support systems can consider a user's cultural background when analyzing their skill set and career goals. For example, if a user inputs information about their cultural background, the system can use that information to suggest a suitable career path. If a user is proficient in a particular culture or language, the system can suggest occupations and industries where they can utilize those skills. It can also provide learning resources based on the user's cultural background. Furthermore, it can suggest training programs to deepen the user's understanding of different cultures. In this way, career support systems can provide more personalized career suggestions by taking the user's cultural background into consideration.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives the user's skill set and career goals. The reception desk stores the user's entered skill set and career goals in a database, processes them in real time, and sends them to the analysis department. Furthermore, the reception desk automatically categorizes the information entered by the user, classifying it into categories such as technical skills, soft skills, industry-specific skills, short-term goals, long-term goals, and specific job titles or positions. Step 2: The analysis department analyzes historical data and current market trends based on the information received by the reception department. The analysis department evaluates the user's skill set based on past work history and skill evaluation data, and analyzes current market trends to understand industry growth rates, job market trends, and skill demand. This provides information to propose the optimal career path for the user. Step 3: The proposal team proposes career paths based on the analysis results obtained by the analysis team. The proposal team presents specific career paths to the user and indicates the necessary skills and experience. Furthermore, it provides information to help the user take the necessary steps and resources to advance their career path. Step 4: The Providing Department provides resources to help users acquire the necessary skills based on the career path proposed by the Proposing Department. The Providing Department provides users with information on online courses and training programs and supports them in the process of using the resources. Furthermore, the Providing Department monitors the user's progress as they use the resources and provides support as needed.

[0059] (Example of form 2) The career support system according to an embodiment of the present invention is a system that analyzes a user's skill set and career goals, recommends the most suitable job, and provides resources for acquiring the necessary skills. This career support system takes the user's skill set and career goals as input, and an AI agent analyzes past data and current market trends to propose the optimal career path for the user. Furthermore, the career support system provides resources for the user to acquire the necessary skills. For example, if a user inputs "I want to become a data scientist," the career support system analyzes the user's current skill set. Next, the career support system analyzes past data and current market trends to propose the optimal career path for the user. For example, the career support system suggests the skills and experience necessary for the user to become a data scientist. Furthermore, the career support system provides resources for the user to acquire the necessary skills. For example, the career support system suggests online courses and training programs necessary for the user to become a data scientist. This allows the user to efficiently acquire the necessary skills. This mechanism improves the user's employment rate. By acquiring the necessary skills according to the suggestions of the career support system, users can gain more job opportunities. The average income of users also improves. By following the career path suggested by the career support system, users can earn higher incomes. Furthermore, their career satisfaction will also improve. By following the career path suggested by the career support system, users can achieve self-realization and lead more fulfilling professional lives. This career support system targets young to mid-career professionals in their 20s to 40s. These users face challenges such as limited opportunities for career growth and insufficient access to appropriate resources. To address these challenges, the career support system proposes the optimal career path for each individual user and provides resources to learn the necessary skills. This career support system utilizes generative AI to analyze individual user profiles and provide personalized career suggestions.This will give users a competitive edge in the market. Furthermore, advancements in AI have made it possible to provide personalized career advice to individual users. This career support system is expected to see significant growth in the career development services market. According to market research data and industry reports, the career development services market is projected to grow at an average annual rate of 6% over the next five years, with a TAM (Total Addressable Market) of 1 trillion yen and a SAM (Serviceable Market) of 30 billion yen. With increasing career awareness among young and mid-career professionals, and a growing demand for customized services tailored to individual needs, now is an excellent time to enter the market. This innovative career support system will evolve users' careers to the next level. The goal is to create a society where users can achieve self-realization and lead more fulfilling professional lives. This allows the career support system to efficiently analyze users' skill sets and career goals, providing optimal career paths and resources.

[0060] The career support system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input from the user regarding their skill set and career goals. The reception unit, for example, stores the user's entered skill set and career goals in a database. The reception unit can also process the user's input information in real time and transmit it to the analysis unit. The reception unit automatically classifies the user's input information so that the analysis unit can process it efficiently. For example, the reception unit classifies the user's entered skill set into technical skills, soft skills, industry-specific skills, etc. The reception unit classifies the user's entered career goals into short-term goals, long-term goals, specific job types or positions, etc. The analysis unit analyzes past data and current market trends based on the information received by the reception unit. The analysis unit, for example, evaluates the user's skill set based on past work history and skill evaluation data. The analysis unit analyzes current market trends to understand industry growth rates, recruitment trends, skill demand, etc. The analysis unit combines past data and current market trends to provide information for proposing the optimal career path to the user. For example, the Analysis Department identifies the skills and experience a user needs to become a data scientist based on their skill set and market trends. The Proposal Department proposes a career path based on the analysis results obtained by the Analysis Department. The Proposal Department, for example, presents a specific career path to the user and indicates the necessary skills and experience. The Proposal Department can also specifically show the steps a user needs to take to advance their career path. The Proposal Department provides information to help the user access the resources necessary to advance their career path. For example, the Proposal Department suggests online courses and training programs necessary for a user to become a data scientist. The Delivery Department provides resources to help users acquire the necessary skills based on the career path proposed by the Proposal Department. For example, the Delivery Department provides information on online courses and training programs to the user. The Delivery Department can also support the user in accessing the resources. The Delivery Department monitors the user's progress as they access the resources and provides support as needed.For example, the service provider monitors the user's progress while they are taking an online course and provides necessary support. This allows the career support system according to the embodiment to efficiently analyze the user's skill set and career goals and provide the optimal career path and resources.

[0061] The reception department receives user input regarding their skill sets and career goals. For example, the reception department stores the user's entered skill sets and career goals in a database. Specifically, information entered by users through web forms or applications is saved to the database in real time. The reception department can also process user input in real time and send it to the analytics department. For example, the user's entered skill sets and career goals are immediately saved to the database and simultaneously sent to the analytics department. The reception department automatically categorizes the user input to enable efficient processing by the analytics department. For example, the reception department categorizes the user's entered skill sets into technical skills, soft skills, industry-specific skills, etc. Technical skills include programming languages, database management, network construction, etc., while soft skills include communication skills, leadership, problem-solving skills, etc. Industry-specific skills include specialized knowledge and experience required in a particular industry. The reception department categorizes the user's entered career goals into short-term goals, long-term goals, specific job titles or positions, etc. Short-term goals include those to be achieved within a few months to a year, while long-term goals include those to be achieved within a few years. Specific job titles and positions include the specific job titles and positions that the user aspires to. This allows the reception department to efficiently classify user input information, enabling the analysis department to process it quickly and accurately. Furthermore, the reception department can regularly update user input information to maintain up-to-date data. For example, if a user acquires new skills or changes their career goals, the reception department updates this information and reflects it in the database. This allows the reception department to always provide analysis and recommendations based on the latest information.

[0062] The Analysis Department analyzes historical data and current market trends based on information received by the Reception Department. For example, the Analysis Department evaluates users' skill sets based on their past work history and skill assessment data. Specifically, it retrieves users' work history and skill assessment data from a database and analyzes it using AI. The AI ​​uses natural language processing technology to analyze the user's work history and skill assessment data and evaluate their skill set. The Analysis Department analyzes current market trends to understand industry growth rates, recruitment trends, and skill demand. For example, the AI ​​collects and analyzes online job postings and industry reports to understand current market trends. The Analysis Department combines historical data and current market trends to provide information that proposes the optimal career path for users. For example, the Analysis Department identifies the skills and experience necessary for a user to become a data scientist based on their skill set and market trends. This allows the Analysis Department to provide information that proposes the optimal career path for users based on their skill set and market trends. Furthermore, the analytics department can utilize historical data and statistical information to predict long-term career paths and conduct trend analysis. For example, it can analyze the evolution of career paths in specific occupations or industries based on historical data and predict future career paths. The analytics department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analytics department to not only grasp the situation in real time but also to predict long-term career paths and detect anomalies, thereby improving the reliability and security of the entire system.

[0063] The proposal department proposes career paths based on the analysis results obtained by the analysis department. For example, the proposal department presents specific career paths to users and indicates the necessary skills and experience. Specifically, the proposal department proposes the optimal career path for the user based on the user's skill set and career goals. The proposal department can also specifically show the steps necessary for the user to advance their career path. For example, it can show the skills and experience necessary for a user to become a data scientist and propose specific steps to achieve this. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal department suggests online courses and training programs necessary for the user to become a data scientist. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal department suggests online courses and training programs necessary for the user to become a data scientist. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal department suggests online courses and training programs necessary for the user to become a data scientist. The proposal department provides information to provide the resources necessary for the user to advance their career path. For example, the proposal team can suggest online courses and training programs necessary for users to become data scientists. This allows the proposal team to present users with concrete career paths and indicate the necessary skills and experience. Furthermore, the proposal team can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For instance, based on feedback received as users progress through the suggested career paths, the proposal team can revise the suggestions and propose more appropriate career paths. The proposal team can also monitor users' progress and provide support as needed. This allows the proposal team to present users with concrete career paths and indicate the necessary skills and experience.

[0064] The Service Provider provides resources to help users acquire the necessary skills based on the career path proposed by the Service Provider. For example, the Service Provider provides users with information on online courses and training programs. Specifically, the Service Provider provides information on online courses and training programs necessary for users to advance their proposed career path. The Service Provider can also support users in the process of using resources. For example, the Service Provider supports users in the process of registering for online courses and provides the necessary information. The Service Provider monitors users' progress in using resources and provides support as needed. For example, the Service Provider monitors users' progress in taking online courses and provides the necessary support. This allows the Service Provider to provide users with information on online courses and training programs and resources to help them acquire the necessary skills. Furthermore, the service provider can monitor users' progress and provide support as needed. For example, they can monitor users' progress while they are taking online courses and provide necessary support. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of their offerings. This allows the service provider to provide users with information on online courses and training programs, and to provide resources for acquiring the necessary skills.

[0065] The service provider can offer online courses or training programs. For example, the service provider can provide specific resources to help users acquire the skills they need. The service provider can also provide information for users to take online courses. For example, the service provider can provide users with information such as the types of online courses, the platforms on which they are offered, and how to take them. The service provider can also provide information for users to take training programs. For example, the service provider can provide users with information such as the types of training programs, the format of which they are offered, and how to take them. This allows the service provider to provide specific resources to help users acquire the skills they need. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide resources using an AI model that takes the user's skill set and career goals as input and outputs the optimal online course or training program.

[0066] The reception desk can estimate the user's emotions and adjust the timing of skill set and career goal input based on the estimated emotions. For example, if the user is stressed, the reception desk can prompt input at a time when the user is relaxed. If the user is focused, the reception desk can also prompt input of detailed skill set and career goals at that time. If the user is tired, the reception desk can provide a simplified input form and allow the user to complete the details later. This allows the reception desk to prompt input at a more appropriate time by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation of the user.

[0067] The reception desk can analyze the user's past career history and select an input method. For example, the reception desk can suggest a similar input method based on the user's past career history. The reception desk can also automatically complete frequently used keywords from the user's past career history. The reception desk can also analyze the user's past career history and provide templates to reduce the effort required for input. This allows the reception desk to reduce the effort required for input by selecting the optimal input method based on the user's past career history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past career history data into a generating AI and have the generating AI select the optimal input method.

[0068] The reception desk can filter the input of skill sets and career goals based on the user's current occupation and areas of interest. For example, the reception desk can prioritize the input of relevant skill sets based on the user's current occupation. The reception desk can also customize the input of career goals based on the user's areas of interest. The reception desk can also suggest the most relevant input items by combining the user's occupation and areas of interest. This allows the reception desk to input more relevant information by filtering the input based on the user's occupation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's occupation and areas of interest into a generating AI and have the generating AI suggest the most relevant input items.

[0069] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of the skill sets and career goals to be entered. For example, if the user is anxious, the reception desk will prioritize the input of important skill sets and career goals. If the user is relaxed, the reception desk may also prompt for the input of detailed skill sets and career goals. If the user is feeling anxious, the reception desk may start with simple input and gradually prompt for more detailed input. This allows the reception desk to prioritize the input according to the user's emotions, thereby prioritizing the input of more important information. Emotion estimation is achieved using an emotion estimation function, for example, 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 reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when the user inputs their skill set and career goals. For example, the reception desk can prioritize displaying local job postings based on the user's geographical location. The reception desk can also suggest local training programs based on the user's geographical location. The reception desk can also provide local career event information based on the user's geographical location. In this way, the reception desk can provide region-specific information by prioritizing inputting highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0071] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their skill set and career goals. For example, the reception desk can automatically input the user's skill set of interest based on their social media activity. The reception desk can also suggest information related to the user's career goals based on their social media activity. The reception desk can also analyze the user's social media activity and suggest the optimal career path. This allows the reception desk to provide more personalized input by inputting relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis method of past data and current market trends based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis and provide comprehensive market trends. If the user is in a hurry, the analysis unit can also perform a concise data analysis that gets straight to the point. If the user is feeling anxious, the analysis unit can also highlight positive data to provide reassurance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis method 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The analysis unit can adjust the accuracy of its analysis based on the level of detail of the user's skill set. For example, if the user's skill set is detailed, the analysis unit will perform a highly accurate analysis. If the user's skill set is vague, the analysis unit can also perform a broad analysis. The analysis unit can also adjust the depth of its analysis according to the level of detail of the user's skill set. This allows the analysis unit to provide more accurate analysis results by adjusting the accuracy of its analysis according to the level of detail of the user's skill set. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input detailed information about the user's skill set into a generating AI and have the generating AI adjust the accuracy of the analysis.

[0074] The analysis unit can apply different analytical methods depending on the user's career goal category during analysis. For example, if the user's career goal is a technical position, the analysis unit will apply an analytical algorithm specialized for technical positions. If the user's career goal is a management position, the analysis unit can also apply an analytical algorithm specialized for management positions. If the user's career goal is a creative position, the analysis unit can also apply an analytical algorithm specialized for creative positions. In this way, the analysis unit can provide more appropriate analytical results by applying different analytical algorithms depending on the user's career goal category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's career goal category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the analysis unit can provide analysis results in a more appropriate format by adjusting the display method 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The analysis unit can prioritize analyses based on when users submit their data. For example, if a user is in a hurry, the analysis unit will prioritize that user's analysis based on the submission date. If a user has more time, the analysis unit may also prioritize other users' analyses. The analysis unit can also adjust the analysis schedule based on the user's submission date. This allows the analysis unit to provide analysis results more quickly by prioritizing analyses based on the user's submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user submission date data into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant market data during the analysis process. For example, the analysis unit can perform highly accurate analysis based on the user's relevant market data. The analysis unit can also reflect the latest market trends by referring to the user's relevant market data. The analysis unit can also improve the reliability of its analysis results by utilizing the user's relevant market data. As a result, the analysis unit can provide more accurate analysis results by referring to the user's relevant market data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant market data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust its career path suggestion method based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can suggest a detailed career path. If the user is in a hurry, the suggestion unit can also suggest a concise career path that gets straight to the point. If the user is feeling anxious, the suggestion unit can also suggest a positive career path to provide reassurance. In this way, the suggestion unit can suggest a more appropriate career path by adjusting its suggestion method 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 suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0079] The proposal unit can adjust the level of detail of its proposals based on the importance of the career paths. For example, if the career path is highly important, the proposal unit will provide a detailed proposal. If the career path is less important, the proposal unit can provide a concise proposal. The proposal unit can also adjust the depth of the proposal according to the importance of the career paths. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail according to the importance of the career paths. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input career path importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0080] The proposal unit can apply different proposal methods depending on the career path category when making a proposal. For example, if the career path is a technical position, the proposal unit will apply a proposal algorithm specialized for technical positions. If the career path is a management position, the proposal unit can also apply a proposal algorithm specialized for management positions. If the career path is a creative position, the proposal unit can also apply a proposal algorithm specialized for creative positions. In this way, the proposal unit can make more appropriate proposals by applying different proposal algorithms depending on the career path category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input career path category data into a generating AI and have the generating AI execute the application of different proposal methods.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. In this way, the suggestion unit can provide suggestions in a more appropriate format by adjusting the length of the suggestion 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0082] The proposal department can prioritize proposals based on when the career path is submitted. For example, if the career path submission deadline is approaching, the proposal department will prioritize the proposal. If there is ample time before the career path submission deadline, the proposal department may also prioritize proposals from other users. The proposal department can also adjust the proposal schedule based on the career path submission deadline. This allows the proposal department to submit proposals more quickly by prioritizing them based on the career path submission deadline. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input career path submission deadline data into a generating AI and have the generating AI determine the priority of proposals.

[0083] The proposal unit can adjust the order of proposals based on relevant career path information when making proposals. For example, the proposal unit will prioritize proposals that are highly relevant to the career path. The proposal unit can also postpone proposals that are less relevant to the career path. The proposal unit can also adjust the order of proposals based on the relevance of the career path. This allows the proposal unit to make more relevant proposals by adjusting the order of proposals based on the relevance of the career path. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input career path relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0084] The service provider can estimate the user's emotions and prioritize the resources to be provided based on the estimated emotions. For example, if the user is anxious, the service provider will prioritize providing important resources. If the user is relaxed, the service provider may also provide detailed resources. If the user is feeling anxious, the service provider may also prioritize providing resources to provide a sense of security. In this way, the service provider can provide more appropriate resources by prioritizing resources 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 service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The service provider can adjust the accuracy of its resource provision based on the level of detail of the user's skill set. For example, if the user's skill set is detailed, the service provider can provide highly accurate resources. If the user's skill set is broad, the service provider can also provide a wide range of resources. The service provider can also adjust the depth of the resources provided according to the level of detail of the user's skill set. This allows the service provider to provide more appropriate resources by adjusting the accuracy of its provision according to the level of detail of the user's skill set. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input detailed information about the user's skill set into a generating AI and have the generating AI perform the adjustment of the accuracy of the provision.

[0086] The resource provisioning unit can apply different resource provisioning methods depending on the user's career goal category when providing resources. For example, if the career goal is a technical position, the provisioning unit will apply a resource provisioning algorithm specialized for technical positions. If the career goal is a management position, the provisioning unit can also apply a resource provisioning algorithm specialized for management positions. If the career goal is a creative position, the provisioning unit can also apply a resource provisioning algorithm specialized for creative positions. In this way, the provisioning unit can provide more appropriate resources by applying different resource provisioning algorithms depending on the user's career goal category. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the user's career goal category data into a generating AI and have the generating AI execute the application of different resource provisioning methods.

[0087] The service provider can estimate the user's emotions and adjust how the resources are displayed based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. If the user is in a hurry, the service provider can provide a concise display. This allows the service provider to provide resources in a more appropriate format by adjusting the display 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 service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The service provider can provide the most suitable resources based on the user's geographical location when providing resources. For example, the service provider can prioritize providing local training programs based on the user's geographical location. The service provider can also provide local career event information based on the user's geographical location. The service provider can also prioritize providing local job postings based on the user's geographical location. In this way, the service provider can provide region-specific resources by providing the most suitable resources based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the most suitable resources.

[0089] The service provider can analyze a user's social media activity and provide relevant resources when providing resources. For example, the service provider can provide training programs of interest based on the user's social media activity. The service provider can also provide resources related to career goals based on the user's social media activity. The service provider can analyze a user's social media activity and provide the most suitable resources. This allows the service provider to provide more personalized resources by providing relevant resources based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant resources.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] Career support systems can consider a user's past learning history when analyzing their skill set and career goals. For example, they can analyze a user's history of online courses and training programs to suggest resources most relevant to their current skill set and career goals. They can also consider qualifications and certifications the user has obtained in the past and suggest career paths based on that. Furthermore, they can analyze a user's history of workshops and seminars and provide new learning opportunities based on that. In this way, career support systems can leverage a user's past learning history to provide more personalized career suggestions.

[0092] Career support systems can consider a user's health status when analyzing their skill set and career goals. For example, if a user inputs information about their health, the system can use that information to suggest a suitable career path. If a user is prone to stress, the system can suggest less stressful jobs and work styles. If a user has physical limitations, the system can suggest jobs or remote work opportunities that accommodate those limitations. Furthermore, it can provide the user with the resources necessary to maintain their health. In this way, career support systems can make more appropriate career suggestions by taking the user's health status into consideration.

[0093] Career support systems can consider a user's lifestyle when analyzing their skill set and career goals. For example, if a user inputs information about their family circumstances or hobbies, the system can use that information to suggest a suitable career path. If a user is unable to work full-time due to family circumstances, the system can suggest part-time or flexible work opportunities. Furthermore, if a user has specific hobbies or interests, the system can suggest occupations or industries related to those interests. It can also provide learning resources tailored to the user's lifestyle. This allows career support systems to provide more personalized career suggestions by taking the user's lifestyle into account.

[0094] Career support systems can consider a user's network when analyzing their skill set and career goals. For example, if a user inputs information about their social media or professional network, the system can use that information to suggest a suitable career path. If a user has a strong network in a particular industry or job type, the system can suggest a career path that leverages that network. It can also suggest events and communities to help the user expand their network. Furthermore, users can share learning resources using their network. This allows career support systems to provide more personalized career suggestions by taking the user's network into account.

[0095] Career support systems can consider a user's cultural background when analyzing their skill set and career goals. For example, if a user inputs information about their cultural background, the system can use that information to suggest a suitable career path. If a user is proficient in a particular culture or language, the system can suggest occupations and industries where they can utilize those skills. It can also provide learning resources based on the user's cultural background. Furthermore, it can suggest training programs to deepen the user's understanding of different cultures. In this way, career support systems can provide more personalized career suggestions by taking the user's cultural background into consideration.

[0096] A career support system can estimate a user's emotions and suggest career paths based on those emotions. For example, if a user is feeling anxious, the system can suggest a positive career path to provide reassurance. If a user is excited, it can suggest a challenging career path that leverages that excitement. If a user is relaxed, it can suggest a detailed career path. Furthermore, if a user is stressed, it can provide resources to alleviate that stress. In this way, the career support system can provide more appropriate career suggestions by suggesting career paths according to the user's emotions.

[0097] A career support system can estimate a user's emotions and provide learning resources based on those emotions. For example, if a user is focused, it can provide challenging learning resources. If a user is tired, it can provide easier resources. If a user is excited, it can provide interactive learning resources. Furthermore, if a user is relaxed, it can provide detailed learning resources. In this way, the career support system can support more effective learning by providing learning resources according to the user's emotions.

[0098] A career support system can estimate a user's emotions and support the setting of career goals based on those emotions. For example, if a user is feeling anxious, it can suggest realistic and achievable career goals. If a user is excited, it can suggest challenging career goals. If a user is relaxed, it can suggest long-term career goals. Furthermore, if a user is stressed, it can suggest career goals to reduce stress. In this way, the career support system can help users set more appropriate career goals by supporting them in setting career goals according to their emotions.

[0099] A career support system can estimate a user's emotions and monitor their career path progress based on those emotions. For example, if a user is stressed, the system can offer advice to slow down their progress. If a user is relaxed, it can offer advice to accelerate their progress. If a user is anxious, it can provide positive feedback to reassure them. Furthermore, if a user is excited, it can provide motivation to maintain that excitement. This allows the career support system to provide more effective career support by monitoring career path progress in accordance with the user's emotions.

[0100] A career support system can estimate a user's emotions and provide career path feedback based on those emotions. For example, if a user is feeling anxious, the system can provide reassurance by offering positive feedback. If a user is excited, it can provide motivation to maintain that excitement. If a user is relaxed, it can provide detailed feedback. Furthermore, if a user is stressed, it can provide advice to reduce stress. In this way, the career support system can provide more appropriate career support by offering career path feedback according to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives the user's skill set and career goals. The reception desk stores the user's entered skill set and career goals in a database, processes them in real time, and sends them to the analysis department. Furthermore, the reception desk automatically categorizes the information entered by the user, classifying it into categories such as technical skills, soft skills, industry-specific skills, short-term goals, long-term goals, and specific job titles or positions. Step 2: The analysis department analyzes historical data and current market trends based on the information received by the reception department. The analysis department evaluates the user's skill set based on past work history and skill evaluation data, and analyzes current market trends to understand industry growth rates, job market trends, and skill demand. This provides information to propose the optimal career path for the user. Step 3: The proposal team proposes career paths based on the analysis results obtained by the analysis team. The proposal team presents specific career paths to the user and indicates the necessary skills and experience. Furthermore, it provides information to help the user take the necessary steps and resources to advance their career path. Step 4: The Providing Department provides resources to help users acquire the necessary skills based on the career path proposed by the Proposing Department. The Providing Department provides users with information on online courses and training programs and supports them in the process of using the resources. Furthermore, the Providing Department monitors the user's progress as they use the resources and provides support as needed.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of the user's skill set and career goals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past data and current market trends. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path to the user. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with the necessary resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of the user's skill set and career goals. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes past data and current market trends. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path to the user. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the user with the necessary resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of the user's skill set and career goals. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past data and current market trends. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path to the user. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides the user with the necessary resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of the user's skill set and career goals. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past data and current market trends. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal career path to the user. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with the necessary resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A reception area that accepts input of the user's skill set and career goals, Based on the information received by the aforementioned reception department, an analysis department analyzes past data and current market trends. Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a career path, The system comprises a provisioning unit that provides resources for acquiring skills based on the career path proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Offering online courses or training programs The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of skill set and career goal input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Analyze the user's past career history and select the input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When users input their skill sets and career goals, the system filters them based on their current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and, based on those emotions, determines the priority of the skill set and career goals to input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter their skill sets and career goals, the system prioritizes inputting more relevant information based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter their skill sets and career goals, the system analyzes their social media usage and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis methods of historical data and current market trends based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, the accuracy of the analysis is adjusted based on detailed information about the user's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the user's career goal category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, the analysis prioritization is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, improve the accuracy of the analysis based on relevant market data from the user. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We estimate the user's emotions and adjust the career path suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the detailed information of the proposal based on the importance of the career path. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, different proposal methods will be applied depending on the career path category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the career path proposals were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on relevant information about career paths. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of resources to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing resources, we adjust the accuracy of the provision based on detailed information about the user's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing resources, different resource delivery methods are applied depending on the user's career goal category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how resources are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing resources, the system will provide the most suitable resources based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing resources, we analyze the user's social media usage and provide relevant resources. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 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. A reception area that accepts input of the user's skill set and career goals, Based on the information received by the aforementioned reception department, an analysis department analyzes past data and current market trends. Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a career path, The system comprises a provisioning unit that provides resources for acquiring skills based on the career path proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned supply unit is, Offering online courses or training programs The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of skill set and career goal input based on the estimated user emotions. The system according to feature 1.

4. The aforementioned reception unit is Analyze the user's past career history and select the input method. The system according to feature 1.

5. The aforementioned reception unit is When users input their skill sets and career goals, the system filters them based on their current occupation and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and, based on those emotions, determines the priority of the skill set and career goals to input. The system according to feature 1.

7. The aforementioned reception unit is When users enter their skill sets and career goals, the system prioritizes inputting more relevant information based on their geographical location. The system according to feature 1.

8. The aforementioned reception unit is When users enter their skill sets and career goals, the system analyzes their social media usage and inputs relevant information. The system according to feature 1.

9. The aforementioned analysis unit is We estimate user sentiment and adjust the analysis methods of historical data and current market trends based on the estimated user sentiment. The system according to feature 1.

Images