system

Abstract

The system according to this embodiment aims to analyze the user's company information, recommend the most suitable workplace, and provide real-time career advice. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a recommendation unit, and an advice unit. The reception unit inputs the user's company information. The analysis unit analyzes the information entered by the reception unit. The recommendation unit makes workplace recommendations based on the information analyzed by the analysis unit. The advice unit provides real-time career advice based on the workplaces recommended by the recommendation 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 conventional technology, there is a problem that it is difficult for a user to find an optimal workplace based on their company information and they cannot receive sufficient support in career selection.

[0005] The system according to the embodiment aims to analyze the user's company information, recommend an optimal workplace, and provide real-time career advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, and an advice unit. The reception unit inputs the user's company information. The analysis unit analyzes the information entered by the reception unit. The recommendation unit makes job recommendations based on the information analyzed by the analysis unit. The advice unit provides real-time career advice based on the job recommendations made by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's company information, recommend the most suitable workplace, and provide real-time career advice. [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, etc. 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] 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 Compass Agent according to an embodiment of the present invention is a platform that collects detailed information about Japanese companies and supports individuals' career choices. The Career Compass Agent is a system in which users input their own company information and receive comparisons and advice from other companies through AI technology. The Career Compass Agent consists of the following steps. First, the user inputs their own company information. For example, they input information such as the industry, job title, salary, and work location of their current workplace. This information is input into the AI ​​agent. Next, the AI ​​agent analyzes the input information. The AI ​​agent analyzes the company information using deep learning and provides personalized workplace recommendations based on the user's input data. For example, it lists companies that match the user's preferences and provides detailed information about those companies. Furthermore, the AI ​​agent provides real-time career advice. For example, if the user is considering changing jobs, it provides advice on the current market situation and how to proceed with job hunting. It also provides realistic workplace information to students who are job hunting and proposes career paths to currently employed people who are aiming for career advancement. Through this mechanism, users can make career decisions with confidence. For example, job seekers can find workplaces that match their preferences, students looking for employment can obtain realistic workplace information, and current employees aiming for career advancement can find the optimal career path. Furthermore, the AI ​​agent has an algorithm that learns from user feedback and self-evolves. This improves the accuracy of the service and increases user satisfaction. For example, 90% of users are satisfied with the service, and information search time can be reduced by 50%. In this way, Career Compass Agent utilizes AI technology to support users' career choices, reducing mismatches and improving the speed of information access. This can increase worker satisfaction and productivity, and improve the overall working environment for society. Thus, Career Compass Agent can support users' career choices, reducing mismatches and improving the speed of information access.

[0029] The Career Compass Agent according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, and an advice unit. The reception unit inputs the user's company information. The user's company information includes, but is not limited to, the company name, location, industry, and number of employees. The reception unit stores the company information entered by the user in a database, for example. The reception unit can also analyze the information entered by the user in real time. For example, the reception unit sends the information entered by the user to an AI agent and receives the analysis results. The analysis unit analyzes the information entered by the reception unit using deep learning. Deep learning analyzes company information using, for example, a neural network. For example, the analysis unit analyzes the company's performance and market trends based on the user's company information. The analysis unit can also evaluate the company's strengths and weaknesses based on the user's input data. For example, the analysis unit analyzes the company's financial data and employee satisfaction survey results to identify the company's strengths and weaknesses. The recommendation unit makes workplace recommendations based on the information analyzed by the analysis unit. The recommendation department, for example, lists companies that match the user's preferences. For instance, the recommendation department recommends appropriate companies based on the user's desired industry and work location. The recommendation department can also recommend the most suitable workplace based on the user's skills and experience. For example, the recommendation department analyzes the user's resume and work history to recommend a suitable workplace. The advice department provides real-time career advice based on the workplaces recommended by the recommendation department. For example, if the user is considering changing jobs, the advice department provides advice on the current market situation and how to proceed with job hunting. The advice department can also provide realistic workplace information to students who are job hunting. For example, the advice department provides information on company culture and work style. The advice department can also propose career paths to currently employed individuals aiming for career advancement. For example, the advice department provides career advancement advice based on the user's skills and experience. As a result, the Career Compass Agent according to this embodiment can efficiently analyze the user's company information and provide workplace recommendations and career advice.

[0030] The reception desk inputs the user's company information. This information includes, but is not limited to, company name, address, industry, and number of employees. The reception desk stores the company information entered by the user in a database. Specifically, the user enters company information through a dedicated input form, and this information is immediately saved to the database. The database is equipped with security measures to ensure that user information is stored safely. The reception desk can also analyze the information entered by the user in real time. For example, the reception desk sends the information entered by the user to an AI agent and receives the analysis results. The AI ​​agent quickly analyzes the entered information and evaluates the company's basic information and market position. This allows the reception desk to immediately analyze the information entered by the user and provide necessary feedback. Furthermore, the reception desk also has a function to verify the accuracy of the information entered by the user. For example, it automatically checks whether the entered company name and address are correct and notifies the user if there are any errors. This allows the reception desk to help users enter accurate information and improve the overall reliability of the system.

[0031] The analysis department uses deep learning to analyze information entered by the reception department. Deep learning, for example, uses neural networks to analyze company information. Specifically, the analysis department analyzes a company's performance and market trends based on the user's company information. The deep learning model has learned from a large amount of company data and can evaluate a company's performance and market competitiveness from the input information. The analysis department can also evaluate a company's strengths and weaknesses based on the user's input data. For example, the analysis department analyzes a company's financial data and employee satisfaction survey results to identify its strengths and weaknesses. This allows users to objectively understand their current situation and clarify areas for improvement and strengthening. Furthermore, the analysis department can predict future market trends based on past data and trends. For example, it analyzes past performance data to evaluate future growth potential and risks. This allows users to obtain reference information when formulating future strategies. The analysis department also has a function to visually display these analysis results, helping users understand them intuitively. For example, it uses graphs and charts to visually display company performance and market trends. This allows the analytics department to provide support to users in easily understanding information and making appropriate decisions.

[0032] The recommendation department makes workplace recommendations based on information analyzed by the analysis department. For example, the recommendation department lists companies that match the user's preferences. Specifically, it recommends appropriate companies based on the user's desired industry and work location. The recommendation department searches company information in the database based on the user's entered preferences and selects the best candidates. The recommendation department can also recommend the best workplace based on the user's skills and experience. For example, the recommendation department analyzes the user's resume and work history to recommend a suitable workplace. Using AI-based analysis, it evaluates the user's skill set and past experience in detail and identifies the workplace that is most suitable. Furthermore, the recommendation department also takes into account information about the company's culture and work style. For example, it evaluates employee satisfaction and initiatives for work style reform and recommends companies that provide a comfortable working environment for the user. In this way, the recommendation department can help users find a workplace where they can be satisfied in the long term. When providing the recommendation results to the user, the recommendation department generates a report that includes detailed information. For example, the recommendation department can create reports that include an overview of the recommended company, its performance, and employee testimonials, enabling users to make informed decisions. This allows the recommendation department to provide comprehensive support to users in finding the best workplace.

[0033] The Advice Department provides real-time career advice based on workplaces recommended by the Recommendation Department. For example, if a user is considering changing jobs, the Advice Department will advise them on the current market situation and how to proceed with their job search. Specifically, the Advice Department will propose the optimal job change strategy to the user based on the latest job postings and market trends. The Advice Department can also provide realistic workplace information to students who are job hunting. For example, the Advice Department will provide information on company culture and work styles. This allows students to obtain reference information to find a workplace that suits them. The Advice Department can also propose career paths to currently employed individuals who are aiming for career advancement. For example, the Advice Department will provide advice for career advancement based on the user's skills and experience. Specifically, it will make concrete suggestions on what skills the user should acquire and what kind of experience they should gain. Furthermore, the Advice Department can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it will evaluate the results and satisfaction level of users after receiving advice and revise the advice based on the results. In this way, the Advice Department can always provide users with the best possible career advice and support their career development.

[0034] The learning unit can learn from user feedback. For example, the learning unit can collect user ratings and comments to improve the system's accuracy. For example, the learning unit can analyze user feedback and adjust the system's algorithms. The learning unit can also develop new features and services based on user feedback. For example, the learning unit can expand the system's functionality according to user requests. This improves the system's accuracy by learning from user feedback. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input user feedback data into a generating AI and have the generating AI perform the analysis of the feedback.

[0035] The evolutionary unit can be equipped with self-evolving algorithms. For example, the evolutionary unit can improve system performance using evolutionary or adaptive algorithms. For instance, the evolutionary unit can evolve its algorithms based on user feedback and system usage. Furthermore, the evolutionary unit can monitor system performance in real time and adjust the algorithms as needed. For example, it can monitor system response time and accuracy and apply the optimal algorithm. This self-evolving algorithm improves system performance. Some or all of the above-described processes in the evolutionary unit may be performed using AI or not. For example, the evolutionary unit can input system usage data into a generating AI and have the generating AI perform algorithm evolution.

[0036] The analysis department can analyze corporate information using deep learning. For example, the analysis department can analyze corporate information using neural networks. For example, the analysis department can analyze a company's financial data and market trends to identify its strengths and weaknesses. The analysis department can also evaluate a company's performance based on user input data. For example, the analysis department can analyze a company's performance and employee satisfaction to evaluate the company. By using deep learning, the accuracy of the analysis of corporate information is improved. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input corporate information data into a generating AI and have the generating AI perform the analysis of the corporate information.

[0037] The recommendation system can provide personalized job recommendations based on user input data. For example, the recommendation system can recommend suitable companies based on the user's desired industry and work location. For example, the recommendation system can recommend the most suitable workplace based on the user's skills and experience. The recommendation system can also analyze the user's resume and work history to recommend suitable workplaces. For example, the recommendation system can provide personalized job recommendations based on the user's career goals. In this way, by providing personalized job recommendations based on the user's input data, it can recommend workplaces that are suitable for the user. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user input data into a generating AI and have the generating AI perform personalized job recommendations.

[0038] The advice department can provide real-time career advice. For example, if a user is considering changing jobs, the advice department can advise them on the current market situation and how to proceed with their job search. The advice department can also provide students who are job hunting with realistic workplace information. For example, the advice department can provide information on company culture and work styles. The advice department can also propose career paths to currently employed individuals who are aiming for career advancement. For example, the advice department can provide advice for career advancement based on the user's skills and experience. By providing real-time career advice, the advice department can quickly support the user's career choices. Some or all of the above processes in the advice department may be performed using AI or not. For example, the advice department can input the user's career data into a generating AI and have the generating AI execute real-time career advice.

[0039] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. It can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can customize input methods by referring to information the user has previously entered. This allows the reception desk to provide the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0040] The reception desk can filter company information input based on the user's current work environment and areas of interest. For example, the reception desk can display only relevant information based on the user's current work environment. It can also filter the input information based on the user's areas of interest. Furthermore, the reception desk can prioritize the input information based on the user's work environment and areas of interest. This allows for the provision of highly relevant information by filtering information based on the user's work environment and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's work environment data into a generating AI and have the generating AI perform the information filtering.

[0041] The reception desk can prioritize inputting highly relevant information when a user enters company information, taking into account their geographical location. For example, the reception desk can prioritize inputting relevant company information based on the user's current location. Furthermore, the reception desk can suggest the optimal input method, taking into account the user's geographical location. In addition, the reception desk can determine the priority of information to be entered based on the user's geographical location. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0042] The reception desk can analyze a user's social media activity and input relevant information when they enter company information. For example, the reception desk can analyze a user's social media activity and input relevant company information. The reception desk can also determine the priority of the information to be entered based on the user's social media activity. Furthermore, the reception desk can customize the information to be entered by referring to the user's social media activity. This allows for the efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of the company information. For example, the analysis unit will perform a detailed analysis on important company information. It can also perform a concise analysis on less important company information. Furthermore, the analysis unit can dynamically adjust the level of detail of its analysis based on the importance of the company information. This allows for efficient information analysis by adjusting the level of detail of the analysis based on the importance of the company information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input company information data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis department can apply different analysis algorithms depending on the company category during the analysis. For example, the analysis department can apply a technical analysis algorithm to IT companies. It can also apply an analysis algorithm related to production efficiency to manufacturing companies. Furthermore, it can apply an analysis algorithm related to customer satisfaction to service companies. By applying different analysis algorithms depending on the company category, appropriate analysis results can be provided. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input company category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis department can determine the priority of analysis based on the timing of company information submission during the analysis process. For example, the analysis department can prioritize the analysis of the most recent company information. Conversely, the analysis department can also lower the priority of analysis for older company information. Furthermore, the analysis department can dynamically adjust the analysis priority based on the timing of company information submission. This allows for the prioritization of analysis of the most recent information by determining the analysis priority based on the timing of company information submission. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input company information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of companies during the analysis process. For example, the analysis unit can prioritize the analysis of companies of high user interest. Conversely, the analysis unit can also postpone the analysis of companies of low user interest. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of companies. This allows for the priority provision of information important to users by adjusting the order of analysis based on company relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input company relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The recommendation department can adjust the level of detail in recommendations based on the importance of the companies. For example, it can provide detailed recommendation information to important companies, and concise recommendation information to less important companies. Furthermore, the recommendation department can dynamically adjust the level of detail in recommendations based on the importance of the companies. This allows for efficient information recommendation by adjusting the level of detail in recommendations based on the importance of the companies. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input company importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recommendations.

[0048] The recommendation system can apply different recommendation algorithms depending on the company category during the recommendation process. For example, it might apply a technical recommendation algorithm to IT companies. It could also apply a recommendation algorithm related to production efficiency to manufacturing companies. Furthermore, it could apply a recommendation algorithm related to customer satisfaction to service companies. This allows for the provision of appropriate recommendation information by applying different recommendation algorithms depending on the company category. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input company category data into a generating AI and have the generating AI perform the application of recommendation algorithms.

[0049] The recommendation department can determine the priority of recommendations based on the timing of company information submission. For example, the recommendation department will prioritize recommendations for the most recent company information. Conversely, the recommendation department can also lower the priority of recommendations for older company information. Furthermore, the recommendation department can dynamically adjust the recommendation priority based on the timing of company information submission. This allows for prioritizing the recommendation of the most recent information by determining the recommendation priority based on the submission timing of company information. Some or all of the above processing in the recommendation department may be performed using AI or not. For example, the recommendation department can input company information submission timing data into a generating AI and have the generating AI perform the determination of recommendation priority.

[0050] The recommendation system can adjust the order of recommendations based on the relevance of the companies. For example, the recommendation system will prioritize recommendations for companies that the user is highly interested in. It can also postpone recommendations for companies that the user is less interested in. Furthermore, the recommendation system can dynamically adjust the order of recommendations based on the relevance of the companies. This allows the system to prioritize providing users with information that is important to them by adjusting the order of recommendations based on the relevance of the companies. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input company relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0051] The advice unit can adjust the level of detail in its advice based on the user's career goals. For example, the advice unit can provide detailed advice according to the user's career goals. It can also provide concise advice based on the user's career goals. Furthermore, the advice unit can dynamically adjust the level of detail in its advice according to the user's career goals. This allows for efficient career advice by adjusting the level of detail based on the user's career goals. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's career goal data into a generating AI and have the generating AI perform the adjustment of the level of detail in its advice.

[0052] The advice unit can apply different advice algorithms depending on the user's job type when providing advice. For example, the advice unit can provide technical career advice to IT professionals. It can also provide advice to sales professionals to improve their sales skills. Furthermore, it can provide advice to managers to improve their leadership skills. By applying different advice algorithms depending on the user's job type, it can provide appropriate career advice. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's job type data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0053] The advice unit can prioritize advice based on the user's career stage. For example, it can prioritize basic career advice for new employees. It can also prioritize advice for skill development for mid-career employees. Furthermore, it can prioritize advice for leadership improvement for managers. By prioritizing advice based on the user's career stage, it can provide appropriate career advice. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's career stage data into a generating AI and have the generating AI determine the priority of advice.

[0054] The advice unit can adjust the order of advice based on the user's relevant information when providing advice. For example, the advice unit may prioritize relevant advice based on the user's current work environment. It can also prioritize relevant advice based on the user's career goals. Furthermore, it can prioritize relevant advice based on the user's past career history. By adjusting the order of advice based on the user's relevant information, it can prioritize providing information that is important to the user. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's relevant information data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0055] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also dynamically adjust the learning algorithm by analyzing past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Thus, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0056] The learning unit can weight the training data based on when the company information was submitted during training. For example, the learning unit can give a higher weight to the latest company information and a lower weight to older company information. Furthermore, the learning unit can dynamically adjust the weighting of the training data based on when the company information was submitted. This allows for priority learning of the latest information by weighting the training data based on when the company information was submitted. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input company information submission date data into a generating AI and have the generating AI perform the weighting of the training data.

[0057] The evolutionary unit can optimize the evolutionary algorithm by referring to past evolutionary data during evolution. For example, the evolutionary unit can select the optimal evolutionary algorithm based on past evolutionary data. The evolutionary unit can also dynamically adjust the evolutionary algorithm by analyzing past evolutionary data. Furthermore, the evolutionary unit can improve the accuracy of the evolutionary algorithm by referring to past evolutionary data. In this way, the accuracy of the evolutionary algorithm is improved by referring to past evolutionary data. Some or all of the above processes in the evolutionary unit may be performed using AI or not. For example, the evolutionary unit can input past evolutionary data into a generating AI and have the generating AI perform the optimization of the evolutionary algorithm.

[0058] The evolution unit can weight evolutionary data based on the submission date of company information during the evolution process. For example, the evolution unit can give higher weight to the latest company information and lower weight to older company information. Furthermore, the evolution unit can dynamically adjust the weighting of evolutionary data based on the submission date of company information. This allows for priority evolution of the latest information by weighting evolutionary data based on the submission date of company information. Some or all of the above processing in the evolution unit may be performed using AI or not. For example, the evolution unit can input company information submission date data into a generating AI and have the generating AI perform the weighting of evolutionary data.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] Career Compass Agent can analyze a user's past career choice history and provide information to help them make future career choices. For example, it can analyze trends in past workplaces and recommend workplaces that are a good fit for the user. It can also provide specific advice to users based on past successes and failures in career choices. Furthermore, it can predict a user's career path based on their past career choice history and provide information to help them make future career choices. In this way, by utilizing a user's past career choice history, it can provide more appropriate career advice.

[0061] Career Compass Agent can recommend the most suitable workplaces by taking into account the user's geographical location. For example, it can prioritize recommending workplaces with short commute times based on the user's current location. It can also provide local job information based on the user's geographical location. Furthermore, it can analyze local labor market trends while considering the user's geographical location and recommend the most suitable workplaces. In this way, by utilizing the user's geographical location, it can provide more appropriate workplace recommendations.

[0062] Career Compass Agent can analyze users' social media activity and provide relevant workplace information. For example, it can recommend relevant workplaces based on users' interests and preferences on social media. It can also provide information about workplace culture and work styles based on users' social media activity history. Furthermore, it can leverage users' social media networks to provide workplace reputation and word-of-mouth information. In this way, by utilizing users' social media activity, it can provide more appropriate workplace information.

[0063] Career Compass Agent can learn from users' past feedback to improve the system's accuracy. For example, it can learn users' preferences and tendencies based on past feedback to provide more appropriate job recommendations. It can also analyze past feedback to adjust the system's algorithms. Furthermore, it can develop new features and services based on past feedback. In this way, leveraging users' past feedback can improve the system's accuracy.

[0064] Career Compass Agent can provide information to help users make future career choices based on their past career selection history. For example, it can analyze trends in past workplaces and recommend workplaces that are a good fit for the user. It can also provide specific advice to users based on past successes and failures in career choices. Furthermore, it can predict the user's career path based on their past career selection history and provide information to help them make future career choices. In this way, by utilizing the user's past career selection history, it can provide more appropriate career advice.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk enters the user's company information. This information includes company name, address, industry, and number of employees. The reception desk saves the company information entered by the user to a database. The reception desk can also analyze the information entered by the user in real time. Step 2: The analysis department uses deep learning to analyze the information entered by the reception department. For example, the analysis department analyzes the company's performance and market trends based on the user's company information. The analysis department can also evaluate the company's strengths and weaknesses based on the user's input data. Step 3: The recommendation department makes job recommendations based on the information analyzed by the analysis department. For example, the recommendation department recommends suitable companies based on the user's desired industry and work location. The recommendation department can also recommend the most suitable workplace based on the user's skills and experience. Step 4: The Advice Department provides real-time career advice based on the workplaces recommended by the Recommendation Department. For example, if a user is considering changing jobs, the Advice Department will advise them on the current market situation and how to proceed with their job search. The Advice Department can also provide students who are job hunting with real-world workplace information.

[0067] (Example of form 2) The Career Compass Agent according to an embodiment of the present invention is a platform that collects detailed information about Japanese companies and supports individuals' career choices. The Career Compass Agent is a system in which users input their own company information and receive comparisons and advice from other companies through AI technology. The Career Compass Agent consists of the following steps. First, the user inputs their own company information. For example, they input information such as the industry, job title, salary, and work location of their current workplace. This information is input into the AI ​​agent. Next, the AI ​​agent analyzes the input information. The AI ​​agent analyzes the company information using deep learning and provides personalized workplace recommendations based on the user's input data. For example, it lists companies that match the user's preferences and provides detailed information about those companies. Furthermore, the AI ​​agent provides real-time career advice. For example, if the user is considering changing jobs, it provides advice on the current market situation and how to proceed with job hunting. It also provides realistic workplace information to students who are job hunting and proposes career paths to currently employed people who are aiming for career advancement. Through this mechanism, users can make career decisions with confidence. For example, job seekers can find workplaces that match their preferences, students looking for employment can obtain realistic workplace information, and current employees aiming for career advancement can find the optimal career path. Furthermore, the AI ​​agent has an algorithm that learns from user feedback and self-evolves. This improves the accuracy of the service and increases user satisfaction. For example, 90% of users are satisfied with the service, and information search time can be reduced by 50%. In this way, Career Compass Agent utilizes AI technology to support users' career choices, reducing mismatches and improving the speed of information access. This can increase worker satisfaction and productivity, and improve the overall working environment for society. Thus, Career Compass Agent can support users' career choices, reducing mismatches and improving the speed of information access.

[0068] The Career Compass Agent according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, and an advice unit. The reception unit inputs the user's company information. The user's company information includes, but is not limited to, the company name, location, industry, and number of employees. The reception unit stores the company information entered by the user in a database, for example. The reception unit can also analyze the information entered by the user in real time. For example, the reception unit sends the information entered by the user to an AI agent and receives the analysis results. The analysis unit analyzes the information entered by the reception unit using deep learning. Deep learning analyzes company information using, for example, a neural network. For example, the analysis unit analyzes the company's performance and market trends based on the user's company information. The analysis unit can also evaluate the company's strengths and weaknesses based on the user's input data. For example, the analysis unit analyzes the company's financial data and employee satisfaction survey results to identify the company's strengths and weaknesses. The recommendation unit makes workplace recommendations based on the information analyzed by the analysis unit. The recommendation department, for example, lists companies that match the user's preferences. For instance, the recommendation department recommends appropriate companies based on the user's desired industry and work location. The recommendation department can also recommend the most suitable workplace based on the user's skills and experience. For example, the recommendation department analyzes the user's resume and work history to recommend a suitable workplace. The advice department provides real-time career advice based on the workplaces recommended by the recommendation department. For example, if the user is considering changing jobs, the advice department provides advice on the current market situation and how to proceed with job hunting. The advice department can also provide realistic workplace information to students who are job hunting. For example, the advice department provides information on company culture and work style. The advice department can also propose career paths to currently employed individuals aiming for career advancement. For example, the advice department provides career advancement advice based on the user's skills and experience. As a result, the Career Compass Agent according to this embodiment can efficiently analyze the user's company information and provide workplace recommendations and career advice.

[0069] The reception desk inputs the user's company information. This information includes, but is not limited to, company name, address, industry, and number of employees. The reception desk stores the company information entered by the user in a database. Specifically, the user enters company information through a dedicated input form, and this information is immediately saved to the database. The database is equipped with security measures to ensure that user information is stored safely. The reception desk can also analyze the information entered by the user in real time. For example, the reception desk sends the information entered by the user to an AI agent and receives the analysis results. The AI ​​agent quickly analyzes the entered information and evaluates the company's basic information and market position. This allows the reception desk to immediately analyze the information entered by the user and provide necessary feedback. Furthermore, the reception desk also has a function to verify the accuracy of the information entered by the user. For example, it automatically checks whether the entered company name and address are correct and notifies the user if there are any errors. This allows the reception desk to help users enter accurate information and improve the overall reliability of the system.

[0070] The analysis department uses deep learning to analyze information entered by the reception department. Deep learning, for example, uses neural networks to analyze company information. Specifically, the analysis department analyzes a company's performance and market trends based on the user's company information. The deep learning model has learned from a large amount of company data and can evaluate a company's performance and market competitiveness from the input information. The analysis department can also evaluate a company's strengths and weaknesses based on the user's input data. For example, the analysis department analyzes a company's financial data and employee satisfaction survey results to identify its strengths and weaknesses. This allows users to objectively understand their current situation and clarify areas for improvement and strengthening. Furthermore, the analysis department can predict future market trends based on past data and trends. For example, it analyzes past performance data to evaluate future growth potential and risks. This allows users to obtain reference information when formulating future strategies. The analysis department also has a function to visually display these analysis results, helping users understand them intuitively. For example, it uses graphs and charts to visually display company performance and market trends. This allows the analytics department to provide support to users in easily understanding information and making appropriate decisions.

[0071] The recommendation department makes workplace recommendations based on information analyzed by the analysis department. For example, the recommendation department lists companies that match the user's preferences. Specifically, it recommends appropriate companies based on the user's desired industry and work location. The recommendation department searches company information in the database based on the user's entered preferences and selects the best candidates. The recommendation department can also recommend the best workplace based on the user's skills and experience. For example, the recommendation department analyzes the user's resume and work history to recommend a suitable workplace. Using AI-based analysis, it evaluates the user's skill set and past experience in detail and identifies the workplace that is most suitable. Furthermore, the recommendation department also takes into account information about the company's culture and work style. For example, it evaluates employee satisfaction and initiatives for work style reform and recommends companies that provide a comfortable working environment for the user. In this way, the recommendation department can help users find a workplace where they can be satisfied in the long term. When providing the recommendation results to the user, the recommendation department generates a report that includes detailed information. For example, the recommendation department can create reports that include an overview of the recommended company, its performance, and employee testimonials, enabling users to make informed decisions. This allows the recommendation department to provide comprehensive support to users in finding the best workplace.

[0072] The Advice Department provides real-time career advice based on workplaces recommended by the Recommendation Department. For example, if a user is considering changing jobs, the Advice Department will advise them on the current market situation and how to proceed with their job search. Specifically, the Advice Department will propose the optimal job change strategy to the user based on the latest job postings and market trends. The Advice Department can also provide realistic workplace information to students who are job hunting. For example, the Advice Department will provide information on company culture and work styles. This allows students to obtain reference information to find a workplace that suits them. The Advice Department can also propose career paths to currently employed individuals who are aiming for career advancement. For example, the Advice Department will provide advice for career advancement based on the user's skills and experience. Specifically, it will make concrete suggestions on what skills the user should acquire and what kind of experience they should gain. Furthermore, the Advice Department can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it will evaluate the results and satisfaction level of users after receiving advice and revise the advice based on the results. In this way, the Advice Department can always provide users with the best possible career advice and support their career development.

[0073] The learning unit can learn from user feedback. For example, the learning unit can collect user ratings and comments to improve the system's accuracy. For example, the learning unit can analyze user feedback and adjust the system's algorithms. The learning unit can also develop new features and services based on user feedback. For example, the learning unit can expand the system's functionality according to user requests. This improves the system's accuracy by learning from user feedback. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input user feedback data into a generating AI and have the generating AI perform the analysis of the feedback.

[0074] The evolutionary unit can be equipped with self-evolving algorithms. For example, the evolutionary unit can improve system performance using evolutionary or adaptive algorithms. For instance, the evolutionary unit can evolve its algorithms based on user feedback and system usage. Furthermore, the evolutionary unit can monitor system performance in real time and adjust the algorithms as needed. For example, it can monitor system response time and accuracy and apply the optimal algorithm. This self-evolving algorithm improves system performance. Some or all of the above-described processes in the evolutionary unit may be performed using AI or not. For example, the evolutionary unit can input system usage data into a generating AI and have the generating AI perform algorithm evolution.

[0075] The analysis department can analyze corporate information using deep learning. For example, the analysis department can analyze corporate information using neural networks. For example, the analysis department can analyze a company's financial data and market trends to identify its strengths and weaknesses. The analysis department can also evaluate a company's performance based on user input data. For example, the analysis department can analyze a company's performance and employee satisfaction to evaluate the company. By using deep learning, the accuracy of the analysis of corporate information is improved. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input corporate information data into a generating AI and have the generating AI perform the analysis of the corporate information.

[0076] The recommendation system can provide personalized job recommendations based on user input data. For example, the recommendation system can recommend suitable companies based on the user's desired industry and work location. For example, the recommendation system can recommend the most suitable workplace based on the user's skills and experience. The recommendation system can also analyze the user's resume and work history to recommend suitable workplaces. For example, the recommendation system can provide personalized job recommendations based on the user's career goals. In this way, by providing personalized job recommendations based on the user's input data, it can recommend workplaces that are suitable for the user. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user input data into a generating AI and have the generating AI perform personalized job recommendations.

[0077] The advice department can provide real-time career advice. For example, if a user is considering changing jobs, the advice department can advise them on the current market situation and how to proceed with their job search. The advice department can also provide students who are job hunting with realistic workplace information. For example, the advice department can provide information on company culture and work styles. The advice department can also propose career paths to currently employed individuals who are aiming for career advancement. For example, the advice department can provide advice for career advancement based on the user's skills and experience. By providing real-time career advice, the advice department can quickly support the user's career choices. Some or all of the above processes in the advice department may be performed using AI or not. For example, the advice department can input the user's career data into a generating AI and have the generating AI execute real-time career advice.

[0078] The reception desk can estimate the user's emotions and adjust the timing of company information input based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to provide time for relaxation. Conversely, if the user is relaxed, the reception desk can speed up the input timing to efficiently collect information. Furthermore, if the user is in a hurry, the reception desk can optimize the input timing to allow for quick information entry. In this way, the burden on the user can be reduced by adjusting the input timing 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. It can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can customize input methods by referring to information the user has previously entered. This allows the reception desk to provide the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0080] The reception desk can filter company information input based on the user's current work environment and areas of interest. For example, the reception desk can display only relevant information based on the user's current work environment. It can also filter the input information based on the user's areas of interest. Furthermore, the reception desk can prioritize the input information based on the user's work environment and areas of interest. This allows for the provision of highly relevant information by filtering information based on the user's work environment and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's work environment data into a generating AI and have the generating AI perform the information filtering.

[0081] The reception desk can estimate the user's emotions and prioritize the company information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize the input of important information. If the user is relaxed, the reception desk can also prioritize the input of detailed information. Furthermore, if the user is in a hurry, the reception desk can quickly input the most important information. This enables efficient information entry by prioritizing input information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can prioritize inputting highly relevant information when a user enters company information, taking into account their geographical location. For example, the reception desk can prioritize inputting relevant company information based on the user's current location. Furthermore, the reception desk can suggest the optimal input method, taking into account the user's geographical location. In addition, the reception desk can determine the priority of information to be entered based on the user's geographical location. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0083] The reception desk can analyze a user's social media activity and input relevant information when they enter company information. For example, the reception desk can analyze a user's social media activity and input relevant company information. The reception desk can also determine the priority of the information to be entered based on the user's social media activity. Furthermore, the reception desk can customize the information to be entered by referring to the user's social media activity. This allows for the efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. 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 or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of its analysis based on the importance of the company information. For example, the analysis unit will perform a detailed analysis on important company information. It can also perform a concise analysis on less important company information. Furthermore, the analysis unit can dynamically adjust the level of detail of its analysis based on the importance of the company information. This allows for efficient information analysis by adjusting the level of detail of the analysis based on the importance of the company information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input company information data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis department can apply different analysis algorithms depending on the company category during the analysis. For example, the analysis department can apply a technical analysis algorithm to IT companies. It can also apply an analysis algorithm related to production efficiency to manufacturing companies. Furthermore, it can apply an analysis algorithm related to customer satisfaction to service companies. By applying different analysis algorithms depending on the company category, appropriate analysis results can be provided. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input company category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis department can determine the priority of analysis based on the timing of company information submission during the analysis process. For example, the analysis department can prioritize the analysis of the most recent company information. Conversely, the analysis department can also lower the priority of analysis for older company information. Furthermore, the analysis department can dynamically adjust the analysis priority based on the timing of company information submission. This allows for the prioritization of analysis of the most recent information by determining the analysis priority based on the timing of company information submission. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input company information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0089] The analysis unit can adjust the order of analysis based on the relevance of companies during the analysis process. For example, the analysis unit can prioritize the analysis of companies of high user interest. Conversely, the analysis unit can also postpone the analysis of companies of low user interest. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of companies. This allows for the priority provision of information important to users by adjusting the order of analysis based on company relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input company relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations that get straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand recommendations. By adjusting the way recommendations are presented according to the user's emotions, the system can provide recommendations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0091] The recommendation department can adjust the level of detail in recommendations based on the importance of the companies. For example, it can provide detailed recommendation information to important companies, and concise recommendation information to less important companies. Furthermore, the recommendation department can dynamically adjust the level of detail in recommendations based on the importance of the companies. This allows for efficient information recommendation by adjusting the level of detail in recommendations based on the importance of the companies. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input company importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recommendations.

[0092] The recommendation system can apply different recommendation algorithms depending on the company category during the recommendation process. For example, it might apply a technical recommendation algorithm to IT companies. It could also apply a recommendation algorithm related to production efficiency to manufacturing companies. Furthermore, it could apply a recommendation algorithm related to customer satisfaction to service companies. This allows for the provision of appropriate recommendation information by applying different recommendation algorithms depending on the company category. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input company category data into a generating AI and have the generating AI perform the application of recommendation algorithms.

[0093] The recommendation section can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is in a hurry, the recommendation section can provide short, concise recommendations. If the user is relaxed, it can provide detailed recommendations. Furthermore, if the user is stressed, it can provide visually easy-to-understand recommendations. By adjusting the length of recommendations according to the user's emotions, the recommendation section can provide the most suitable recommendations for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI adjust the length of recommendations.

[0094] The recommendation department can determine the priority of recommendations based on the timing of company information submission. For example, the recommendation department will prioritize recommendations for the most recent company information. Conversely, the recommendation department can also lower the priority of recommendations for older company information. Furthermore, the recommendation department can dynamically adjust the recommendation priority based on the timing of company information submission. This allows for prioritizing the recommendation of the most recent information by determining the recommendation priority based on the submission timing of company information. Some or all of the above processing in the recommendation department may be performed using AI or not. For example, the recommendation department can input company information submission timing data into a generating AI and have the generating AI perform the determination of recommendation priority.

[0095] The recommendation system can adjust the order of recommendations based on the relevance of the companies. For example, the recommendation system will prioritize recommendations for companies that the user is highly interested in. It can also postpone recommendations for companies that the user is less interested in. Furthermore, the recommendation system can dynamically adjust the order of recommendations based on the relevance of the companies. This allows the system to prioritize providing users with information that is important to them by adjusting the order of recommendations based on the relevance of the companies. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input company relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0096] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is relaxed, the advice unit can provide detailed advice. If the user is in a hurry, it can provide concise advice that gets straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand advice. By adjusting the way advice is presented according to the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way it presents the advice.

[0097] The advice unit can adjust the level of detail in its advice based on the user's career goals. For example, the advice unit can provide detailed advice according to the user's career goals. It can also provide concise advice based on the user's career goals. Furthermore, the advice unit can dynamically adjust the level of detail in its advice according to the user's career goals. This allows for efficient career advice by adjusting the level of detail based on the user's career goals. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's career goal data into a generating AI and have the generating AI perform the adjustment of the level of detail in its advice.

[0098] The advice unit can apply different advice algorithms depending on the user's job type when providing advice. For example, the advice unit can provide technical career advice to IT professionals. It can also provide advice to sales professionals to improve their sales skills. Furthermore, it can provide advice to managers to improve their leadership skills. By applying different advice algorithms depending on the user's job type, it can provide appropriate career advice. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's job type data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0099] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is stressed, the advice unit can provide visually easy-to-understand advice. By adjusting the length of the advice according to the user's emotions, the system can provide the most appropriate advice for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0100] The advice unit can prioritize advice based on the user's career stage. For example, it can prioritize basic career advice for new employees. It can also prioritize advice for skill development for mid-career employees. Furthermore, it can prioritize advice for leadership improvement for managers. By prioritizing advice based on the user's career stage, it can provide appropriate career advice. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's career stage data into a generating AI and have the generating AI determine the priority of advice.

[0101] The advice unit can adjust the order of advice based on the user's relevant information when providing advice. For example, the advice unit may prioritize relevant advice based on the user's current work environment. It can also prioritize relevant advice based on the user's career goals. Furthermore, it can prioritize relevant advice based on the user's past career history. By adjusting the order of advice based on the user's relevant information, it can prioritize providing information that is important to the user. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's relevant information data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0102] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data that gets straight to the point. Furthermore, if the user is stressed, the learning unit can select visually easy-to-understand training data. This allows for efficient learning by selecting training data 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 learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0103] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also dynamically adjust the learning algorithm by analyzing past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Thus, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0104] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is in a hurry. Furthermore, it can adjust the learning frequency if the user is stressed. This allows for efficient learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0105] The learning unit can weight the training data based on when the company information was submitted during training. For example, the learning unit can give a higher weight to the latest company information and a lower weight to older company information. Furthermore, the learning unit can dynamically adjust the weighting of the training data based on when the company information was submitted. This allows for priority learning of the latest information by weighting the training data based on when the company information was submitted. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input company information submission date data into a generating AI and have the generating AI perform the weighting of the training data.

[0106] The evolution unit can estimate the user's emotions and adjust the evolution algorithm based on the estimated emotions. For example, if the user is relaxed, the evolution unit can apply a detailed evolution algorithm. If the user is in a hurry, it can apply a concise evolution algorithm. Furthermore, if the user is stressed, it can apply a visually easy-to-understand evolution algorithm. This allows for efficient evolution by adjusting the evolution algorithm 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 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 evolution unit may be performed using AI or not. For example, the evolution unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evolution algorithm.

[0107] The evolutionary unit can optimize the evolutionary algorithm by referring to past evolutionary data during evolution. For example, the evolutionary unit can select the optimal evolutionary algorithm based on past evolutionary data. The evolutionary unit can also dynamically adjust the evolutionary algorithm by analyzing past evolutionary data. Furthermore, the evolutionary unit can improve the accuracy of the evolutionary algorithm by referring to past evolutionary data. In this way, the accuracy of the evolutionary algorithm is improved by referring to past evolutionary data. Some or all of the above processes in the evolutionary unit may be performed using AI or not. For example, the evolutionary unit can input past evolutionary data into a generating AI and have the generating AI perform the optimization of the evolutionary algorithm.

[0108] The evolution unit can estimate the user's emotions and adjust the evolution frequency based on the estimated emotions. For example, the evolution unit can increase the evolution frequency when the user is relaxed. It can also decrease the evolution frequency when the user is in a hurry. Furthermore, it can adjust the evolution frequency when the user is stressed. This allows for efficient evolution by adjusting the evolution frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evolution unit may be performed using AI or not. For example, the evolution unit can input user emotion data into the generative AI and have the generative AI adjust the evolution frequency.

[0109] The evolution unit can weight evolutionary data based on the submission date of company information during the evolution process. For example, the evolution unit can give higher weight to the latest company information and lower weight to older company information. Furthermore, the evolution unit can dynamically adjust the weighting of evolutionary data based on the submission date of company information. This allows for priority evolution of the latest information by weighting evolutionary data based on the submission date of company information. Some or all of the above processing in the evolution unit may be performed using AI or not. For example, the evolution unit can input company information submission date data into a generating AI and have the generating AI perform the weighting of evolutionary data.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The Career Compass agent can estimate a user's emotions and adjust the content of career advice based on those emotions. For example, if a user is feeling stressed, the advice unit will provide relaxing advice. If the user is relaxed, it can provide more detailed career advice. Furthermore, if the user is in a hurry, it can provide concise advice that gets straight to the point. In this way, by providing appropriate career advice tailored to the user's emotions, user satisfaction can be increased.

[0112] Career Compass Agent can analyze a user's past career choice history and provide information to help them make future career choices. For example, it can analyze trends in past workplaces and recommend workplaces that are a good fit for the user. It can also provide specific advice to users based on past successes and failures in career choices. Furthermore, it can predict a user's career path based on their past career choice history and provide information to help them make future career choices. In this way, by utilizing a user's past career choice history, it can provide more appropriate career advice.

[0113] The Career Compass agent can estimate a user's emotions and adjust the way job recommendations are presented based on those emotions. For example, if a user is relaxed, it can provide detailed job recommendations. If a user is in a hurry, it can provide concise job recommendations that get straight to the point. Furthermore, if a user is stressed, it can provide visually easy-to-understand job recommendations. By providing job recommendations tailored to the user's emotions, it can deliver information that is easy for the user to understand.

[0114] Career Compass Agent can recommend the most suitable workplaces by taking into account the user's geographical location. For example, it can prioritize recommending workplaces with short commute times based on the user's current location. It can also provide local job information based on the user's geographical location. Furthermore, it can analyze local labor market trends while considering the user's geographical location and recommend the most suitable workplaces. In this way, by utilizing the user's geographical location, it can provide more appropriate workplace recommendations.

[0115] Career Compass Agent can estimate a user's emotions and prioritize job recommendations based on those emotions. For example, if a user is stressed, it will prioritize recommending important jobs. If a user is relaxed, it can prioritize providing detailed job information. Furthermore, if a user is in a hurry, it can quickly recommend the most important jobs. This allows for efficient job recommendations by prioritizing job recommendations according to the user's emotions.

[0116] Career Compass Agent can analyze users' social media activity and provide relevant workplace information. For example, it can recommend relevant workplaces based on users' interests and preferences on social media. It can also provide information about workplace culture and work styles based on users' social media activity history. Furthermore, it can leverage users' social media networks to provide workplace reputation and word-of-mouth information. In this way, by utilizing users' social media activity, it can provide more appropriate workplace information.

[0117] The Career Compass Agent can estimate a user's emotions and suggest career paths based on those emotions. For example, if the user is relaxed, it can suggest detailed career paths. If the user is in a hurry, it can suggest concise career paths that get straight to the point. Furthermore, if the user is stressed, it can suggest career paths that are easy to understand visually. In this way, by suggesting career paths that are tailored to the user's emotions, it can provide information that is easy for the user to understand.

[0118] Career Compass Agent can learn from users' past feedback to improve the system's accuracy. For example, it can learn users' preferences and tendencies based on past feedback to provide more appropriate job recommendations. It can also analyze past feedback to adjust the system's algorithms. Furthermore, it can develop new features and services based on past feedback. In this way, leveraging users' past feedback can improve the system's accuracy.

[0119] The Career Compass agent can estimate a user's emotions and select training data based on those emotions. For example, if the user is relaxed, it can select detailed training data. If the user is in a hurry, it can select concise training data that gets straight to the point. Furthermore, if the user is stressed, it can select visually easy-to-understand training data. This allows for more efficient learning by selecting training data that matches the user's emotions.

[0120] Career Compass Agent can provide information to help users make future career choices based on their past career selection history. For example, it can analyze trends in past workplaces and recommend workplaces that are a good fit for the user. It can also provide specific advice to users based on past successes and failures in career choices. Furthermore, it can predict the user's career path based on their past career selection history and provide information to help them make future career choices. In this way, by utilizing the user's past career selection history, it can provide more appropriate career advice.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk enters the user's company information. This information includes company name, address, industry, and number of employees. The reception desk saves the company information entered by the user to a database. The reception desk can also analyze the information entered by the user in real time. Step 2: The analysis department uses deep learning to analyze the information entered by the reception department. For example, the analysis department analyzes the company's performance and market trends based on the user's company information. The analysis department can also evaluate the company's strengths and weaknesses based on the user's input data. Step 3: The recommendation department makes job recommendations based on the information analyzed by the analysis department. For example, the recommendation department recommends suitable companies based on the user's desired industry and work location. The recommendation department can also recommend the most suitable workplace based on the user's skills and experience. Step 4: The Advice Department provides real-time career advice based on the workplaces recommended by the Recommendation Department. For example, if a user is considering changing jobs, the Advice Department will advise them on the current market situation and how to proceed with their job search. The Advice Department can also provide students who are job hunting with real-world workplace information.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, advice unit, learning unit, and evolution 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, which inputs the user's company information and stores it in a database. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes company information using deep learning. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which lists companies that match the user's preferences. The advice unit is implemented by the control unit 46A of the smart device 14, which provides real-time career advice. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns user feedback and improves the accuracy of the system. The evolution unit is implemented by the specific processing unit 290 of the data processing unit 12, which improves the performance of the system using a self-evolving algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, advice unit, learning unit, and evolution 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, which inputs the user's company information and stores it in a database. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes company information using deep learning. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which lists companies that match the user's preferences. The advice unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides real-time career advice. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which learns user feedback and improves the accuracy of the system. The evolution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which improves the performance of the system using a self-evolving algorithm. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, advice unit, learning unit, and evolution 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, which inputs the user's company information and stores it in a database. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes company information using deep learning. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which lists companies that match the user's preferences. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides real-time career advice. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which learns user feedback and improves the accuracy of the system. The evolution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which improves the performance of the system using a self-evolving algorithm. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, advice unit, learning unit, and evolution 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, which inputs the user's company information and stores it in a database. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes company information using deep learning. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which lists companies that match the user's preferences. The advice unit is implemented by, for example, the control unit 46A of the robot 414, which provides real-time career advice. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which learns user feedback and improves the accuracy of the system. The evolution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which improves the performance of the system using a self-evolving algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception area where users enter their company information, An analysis unit analyzes the information entered by the reception unit, Based on the information analyzed by the aforementioned analysis department, the recommendation department makes workplace recommendations, The Advice Department provides real-time career advice based on the workplaces recommended by the aforementioned Recommendation Department, Equipped with A system characterized by the following features. (Note 2) It also includes a learning unit that learns from user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is further equipped with an evolutionary unit that has a self-evolving algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyzing corporate information using deep learning The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, Personalized job recommendations based on user input data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Providing real-time career advice The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of company information input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering company information, filtering is performed based on the user's current work environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the company information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering company information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering company information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the company information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the company category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the company information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the companies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, When making a recommendation, adjust the level of detail based on the importance of the company. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the company category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making a recommendation, we will determine the priority of recommendations based on the timing of submission of company information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the companies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, adjust the level of detail based on the user's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's job title. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we prioritize the advice based on the user's career stage. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, the order of advice is adjusted based on the user's relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned learning unit, During training, the training data is weighted based on when the company information was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned evolutionary section is It estimates the user's emotions and adjusts the evolutionary algorithm based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned evolutionary section is During evolution, the evolutionary algorithm is optimized by referring to past evolutionary data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned evolutionary section is It estimates the user's emotions and adjusts the evolution frequency based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned evolutionary section is During evolution, evolutionary data is weighted based on when company information was submitted. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 where users enter their company information, An analysis unit analyzes the information entered by the reception unit, Based on the information analyzed by the aforementioned analysis department, the recommendation department makes workplace recommendations, The Advice Department provides real-time career advice based on the workplaces recommended by the aforementioned Recommendation Department, Equipped with A system characterized by the following features.

2. It also includes a learning unit that learns from user feedback. The system according to feature 1.

3. It is further equipped with an evolutionary unit that has a self-evolving algorithm. The system according to feature 1.

4. The aforementioned analysis unit is Analyzing corporate information using deep learning The system according to feature 1.

5. The aforementioned recommendation department, Personalized job recommendations based on user input data. The system according to feature 1.

6. The aforementioned advice section, Providing real-time career advice The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of company information input based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned reception unit is When entering company information, filtering is performed based on the user's current work environment and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the company information to be entered based on those estimated emotions. The system according to feature 1.

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