system

Abstract

The system according to this embodiment aims to promote appropriate matching and interaction among employees and to foster an innovation culture within the company. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a presentation unit, an arrangement unit, and a promotion unit. The collection unit collects employees' departments, job descriptions, skills, and networking preferences. The analysis unit analyzes the information collected by the collection unit. The presentation unit presents appropriate matching candidates based on the information analyzed by the analysis unit. The arrangement unit arranges interviews with the matching candidates presented by the presentation unit. The promotion unit facilitates the interviews arranged by the arrangement 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently perform appropriate matching and communication among employees, and there is room for improvement in preparing an environment for promoting innovation within the company.

[0005] The system according to the embodiment aims to promote appropriate matching and communication among employees and cultivate an innovation culture within the company.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a presentation unit, an arrangement unit, and a promotion unit. The collection unit collects employees' departments, job descriptions, skills, and preferences for networking. The analysis unit analyzes the information collected by the collection unit. The presentation unit presents appropriate matching candidates based on the information analyzed by the analysis unit. The arrangement unit arranges interviews with the matching candidates presented by the presentation unit. The promotion unit facilitates the interviews arranged by the arrangement unit. [Effects of the Invention]

[0007] The system according to this embodiment can facilitate appropriate matching and interaction among employees and foster an innovation culture within the company. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 matching system according to an embodiment of the present invention is an AI agent-based matching system available in the lounge of a specific office building. This matching system collects and analyzes employees' departments, job descriptions, skills, and networking preferences, and presents appropriate matching candidates. Furthermore, it arranges meetings and facilitates conversations in the lounge, supporting natural encounters and collaborative relationships. For example, the matching system allows employees to input their skills and networking preferences, and the AI ​​agent collects this information. This information is analyzed by the AI ​​agent, and appropriate matching candidates are presented. For example, an employee who wishes to share their expertise will be matched with an employee in the same field of expertise. Next, the matching system coordinates the meeting date and time between the matched employees and facilitates conversations in the lounge. In this process, the AI ​​agent supports the progress of the meeting and helps ensure that natural conversations take place. Furthermore, the matching system has a function to evaluate ideas in real time. For example, the AI ​​agent evaluates ideas generated during conversations between employees in real time and provides feedback. This allows for effective results even in short meetings. The matching system also protects intellectual property and designs feedback mechanisms. For example, it allows users to select idea evaluation methods such as stage-gate analysis, SWOT analysis, scoring models, and cost-benefit analysis. This enables efficient and effective idea management. The system fosters natural encounters and collaborations among employees, leading to the generation of new ideas and collaborations. Furthermore, it cultivates an innovation culture within the company, resulting in expected benefits such as improved operational efficiency, time savings, cost reductions, and the creation of new revenue opportunities. If successful, the system can also be customized and offered to other companies, generating licensing revenue and solution sales. In this way, the matching system collects, analyzes, presents, arranges, and facilitates employee information, enabling appropriate matching and supporting natural encounters and collaborations.

[0029] The matching system according to this embodiment comprises a collection unit, an analysis unit, a presentation unit, an arrangement unit, and a promotion unit. The collection unit collects employees' departments, job descriptions, skills, and interaction preferences. The collection unit can collect information, for example, when employees input their skills and interaction preferences. The collection unit can also automatically acquire employees' job descriptions and department information. For example, the collection unit can acquire employees' job descriptions from a database and collect skill information from an input form. Furthermore, the collection unit can collect employees' interaction preferences in the form of a questionnaire. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information, for example, using data mining techniques. Furthermore, the analysis unit can analyze employees' skills and interaction preferences using machine learning algorithms. For example, the analysis unit can analyze employees' skill information using a clustering algorithm and classify their interaction preferences. Furthermore, the analysis unit can analyze employees' job descriptions using statistical analysis techniques. The presentation unit presents appropriate matching candidates based on the information analyzed by the analysis unit. The Presentation Department can present matching candidates based, for example, on the degree of skill match or the relevance of job content. The Presentation Department can also present matching candidates based on employees' desire for interaction. For example, the Presentation Department can present employees with the same expertise to employees who wish to share their expertise. Furthermore, the Presentation Department can present employees with relevant skills to employees who wish to discuss ideas. The Arrangement Department arranges interviews between the matching candidates presented by the Presentation Department. The Arrangement Department can, for example, coordinate the date and time of the interview and facilitate conversation in a lounge. The Arrangement Department can also support the progress of the interview and help ensure natural conversation. For example, the Arrangement Department can coordinate the interview schedule and reserve a lounge. Furthermore, the Arrangement Department can support the progress of the interview and follow up on the conversation. The Facilitation Department facilitates the interviews arranged by the Arrangement Department. The Facilitation Department can, for example, support the progress of the interview and help ensure natural conversation. The Facilitation Department can also follow up on the interview and confirm the outcome of the conversation.For example, the Facilitation Department supports the progress of the interview and follows up on the dialogue. Furthermore, the Facilitation Department can also confirm the results of the dialogue and plan the next interview. In this way, the matching system according to the embodiment can collect, analyze, present, arrange, and facilitate employee information to perform appropriate matching and support natural encounters and collaborative relationships.

[0030] The data collection department collects employee information including department, job responsibilities, skills, and desired networking opportunities. For example, employees can input their skills and desired networking opportunities. Specifically, employees fill out a dedicated input form detailing their skill sets and desired networking activities. This form has a user-friendly interface and is designed to allow employees to easily input information. The data collection department can also automatically acquire employee job responsibilities and departmental information. For instance, it integrates with the company's HR database to obtain real-time information on employee job responsibilities and departments. This integration eliminates the need for employees to manually input information and ensures accurate data collection. Furthermore, the data collection department can collect employee networking preferences through questionnaires. These questionnaires are distributed to employees regularly and include questions to understand their networking preferences and interests. This allows the data collection department to collect data that reflects employees' latest needs and desires. The collected data is stored in a secure database and managed for access by the analytics department. The data collection department flexibly adjusts the frequency and method of data collection to achieve optimal data collection tailored to the company's needs and circumstances. For example, when a new project is launched, information on employees with relevant skills and experience can be focused on gathering data. This allows the data collection department to effectively utilize internal resources and provide a foundation for supporting appropriate matching.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and can reveal employee skill levels and interaction preferences. The analysis unit can also analyze employee skills and interaction preferences using machine learning algorithms. For example, the analysis unit can analyze employee skill information using clustering algorithms to group employees with similar skills. This promotes interaction among employees with the same skill set. Furthermore, the analysis unit can analyze employee work content using statistical analysis techniques. Statistical analysis techniques are methods for revealing data distribution and correlations, and are used to understand employee work content and inter-departmental relationships. For example, it can analyze the distribution of skills within a specific department or the sharing of skills between different departments. The analysis unit combines these techniques to perform a comprehensive analysis based on employee skills, interaction preferences, and work content. The analysis results are output in a format usable by the presentation and arrangement units, and can be used to present appropriate matching candidates and arrange interviews. The analysis unit continuously improves its algorithms and updates its data to enhance the accuracy of its analysis results. This allows the analysis unit to always provide highly accurate analysis based on the latest information, maximizing the overall effectiveness of the system.

[0032] The presentation unit presents appropriate matching candidates based on information analyzed by the analysis unit. For example, the presentation unit can present matching candidates based on skill matching and the relevance of job content. Specifically, the presentation unit compares employees' skill sets and job content and lists the most relevant matching candidates. This list is displayed in a user-friendly interface for easy viewing by employees. The presentation unit can also present matching candidates based on employees' interaction preferences. For example, it can present employees with the same expertise to those who wish to share their expertise, making it easier for employees to share specialized knowledge and experience. Furthermore, the presentation unit can present employees with relevant skills to those seeking advice on ideas, allowing employees to receive appropriate advice on new ideas and projects. The presentation unit takes measures to protect employee privacy when presenting matching candidates. For example, the information presented is limited to the minimum necessary, ensuring that employees' personal information is not inappropriately disclosed. The presentation unit flexibly adjusts the frequency and method of presenting matching candidates to achieve optimal matching tailored to employee needs and circumstances. For example, when a new project is launched, the system can prioritize presenting candidates with the relevant skills and experience for the project. This allows the matching department to support effective matching between employees and strengthen collaborative relationships within the company.

[0033] The Arrangement Department arranges interviews for matching candidates presented by the Presentation Department. For example, the Arrangement Department can coordinate interview dates and times and facilitate conversations in a lounge setting. Specifically, the Arrangement Department checks employees' schedules and coordinates a mutually convenient date and time. This coordination is done using a dedicated scheduling system, allowing for efficient and rapid determination of interview dates and times. The Arrangement Department can also support the interview process and facilitate natural conversation. For example, the Arrangement Department coordinates interview schedules and reserves lounges. The lounges are designed to provide a relaxed atmosphere for conversation, offering an environment where employees can interact naturally. Furthermore, the Arrangement Department can support the interview process and follow up on the conversations. For example, before an interview, the Arrangement Department clarifies the purpose and agenda and notifies employees in advance. This ensures smooth interviews and effective conversations. After the interview, the Arrangement Department follows up to review the interview's results and plan for the next meeting. This allows the Arrangement Department to maximize the effectiveness of the interviews and strengthen collaborative relationships among employees. The arrangement department takes measures to protect employee privacy when arranging interviews. For example, the content and schedule of interviews are limited to the bare minimum necessary, and employees' personal information is not disclosed inappropriately. This allows the arrangement department to support effective interviews while building trust among employees.

[0034] The Facilitation Department facilitates the interviews arranged by the Arrangement Department. For example, the Facilitation Department can support the progress of the interview and help ensure that natural dialogue takes place. Specifically, the Facilitation Department performs facilitation to ensure the smooth progress of the interview. Facilitation is a technique to make the flow of dialogue smooth and enable participants to actively exchange opinions. At the beginning of the interview, the Facilitation Department reconfirms the purpose and agenda with the participants and clarifies the direction of the dialogue. The Facilitation Department also organizes important points during the dialogue and supports participants in making them easy to understand. Furthermore, the Facilitation Department can follow up on the interview and confirm the results of the dialogue. For example, they can conduct a survey with participants after the interview to understand the effectiveness of the interview and areas for improvement. This allows the Facilitation Department to gather information that will be useful when planning the next interview. The Facilitation Department can also confirm the results of the dialogue and plan the next interview. For example, based on the ideas and suggestions obtained in the interview, they can set the agenda for the next interview and promote continuous dialogue. This allows the Promotion Department to strengthen cooperation among employees and improve communication throughout the company. When facilitating interviews, the Promotion Department will take measures to protect employee privacy. For example, the content and results of interviews will be limited to the minimum necessary, and employees' personal information will not be inappropriately disclosed. This allows the Promotion Department to support effective interviews while building trust among employees.

[0035] The evaluation unit can evaluate ideas in real time. For example, it can evaluate ideas generated during conversations between employees in real time. The evaluation unit can analyze and evaluate ideas during conversations using, for example, natural language processing technology. The evaluation unit can also evaluate ideas using machine learning algorithms. For example, it can analyze the content of an idea and calculate a score based on evaluation criteria. Furthermore, the evaluation unit can provide real-time feedback to enhance the effectiveness of conversations. For example, it can provide immediate feedback on ideas generated during conversations. This allows for effective results even in short meetings by evaluating ideas in real time. Some or all of the above processing in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input ideas generated during conversations into a generative AI, which can then evaluate the ideas.

[0036] The protection unit can protect intellectual property. For example, the protection unit can support patent applications for ideas. For example, the protection unit can automate and efficiently carry out patent application procedures. The protection unit can also support copyright registration. For example, the protection unit can automate and quickly carry out copyright registration procedures. Furthermore, the protection unit can also support the creation of non-disclosure agreements. For example, the protection unit can provide non-disclosure agreement templates and assist in the creation of agreements. This allows for efficient and effective management of ideas by protecting intellectual property. Some or all of the above processes in the protection unit may be carried out using, for example, generative AI, or not using generative AI. For example, the protection unit can input the patent application procedures into generative AI, which can automate the procedures.

[0037] The design department can design a feedback mechanism. For example, the design department can design a method for collecting feedback. For example, the design department can design a method for collecting feedback in the form of a questionnaire. The design department can also design evaluation criteria for feedback. For example, the design department can design evaluation criteria based on the importance and urgency of the feedback. Furthermore, the design department can also design a feedback improvement process. For example, the design department can design a process for proposing and implementing improvement measures based on feedback. By designing a feedback mechanism, efficient and effective idea management becomes possible. Some or all of the above processes in the design department may be performed using, for example, generative AI, or not using generative AI. For example, the design department can input the feedback collection method into a generative AI, and the generative AI can design the collection method.

[0038] The evaluation unit can select evaluation methods such as stage-gate analysis, SWOT analysis, scoring models, and cost-benefit analysis. For example, the evaluation unit can evaluate ideas using the stage-gate analysis. For example, the evaluation unit can evaluate ideas according to their progress. The evaluation unit can also evaluate ideas using SWOT analysis. For example, the evaluation unit can analyze and evaluate the strengths, weaknesses, opportunities, and threats of an idea. Furthermore, the evaluation unit can also evaluate ideas using a scoring model. For example, the evaluation unit can score each element of an idea and perform an overall evaluation. By selecting a variety of evaluation methods, the accuracy of idea evaluation can be improved. Some or all of the above processes in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input the idea evaluation method into the generative AI, and the generative AI can select the evaluation method.

[0039] The protection unit can protect the intellectual property of an idea. For example, the protection unit can support patent applications for ideas. For example, the protection unit can automate and efficiently carry out the patent application process. The protection unit can also support copyright registration. For example, the protection unit can automate and quickly carry out the copyright registration process. Furthermore, the protection unit can support the creation of non-disclosure agreements. For example, the protection unit can provide templates for non-disclosure agreements and assist in their creation. In this way, the value of an idea can be protected by protecting its intellectual property. Some or all of the above processes in the protection unit may be carried out using, for example, generative AI, or not using generative AI. For example, the protection unit can input the patent application procedure into a generative AI, which can automate the procedure.

[0040] The design department can perform efficient and effective idea management. For example, the design department can design methods for collecting ideas. For example, the design department can design methods for collecting ideas in the form of questionnaires. The design department can also design criteria for evaluating ideas. For example, the design department can design evaluation criteria based on the importance and urgency of ideas. Furthermore, the design department can design idea management tools. For example, the design department can design software tools to support idea management. This promotes the utilization of ideas by enabling efficient and effective idea management. Some or all of the above processes in the design department may be performed using, for example, generative AI, or not using generative AI. For example, the design department can input the idea collection method into the generative AI, and the generative AI can design the collection method.

[0041] The data collection unit can analyze an employee's past interaction history and select the optimal information collection method. For example, the data collection unit can select the optimal information collection method based on the employee's past participation in events and meetings. For example, the data collection unit can analyze the skills and work content of people the employee has interacted with in the past and collect relevant information. The data collection unit can also optimize information collection for specific times and locations based on the employee's past interaction history. For example, the data collection unit can analyze an employee's past interaction history and select the optimal information collection method. This allows the optimal information collection method to be selected by analyzing past interaction history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input an employee's past interaction history into a generative AI, which can then select the optimal information collection method.

[0042] The data collection unit can filter information based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. The data collection unit can also filter and collect relevant information based on an employee's areas of interest. Furthermore, the data collection unit can select and collect necessary information according to the employee's current work. For example, the data collection unit can prioritize collecting information related to an employee's current projects. This allows for efficient collection of necessary information by filtering it based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input an employee's current projects and areas of interest into a generative AI, which can then filter the information.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of employees during information gathering. For example, the data collection unit can prioritize the collection of information related to the employee's current location. For example, the data collection unit can collect information on nearby events and meetings based on the employee's geographical location. The data collection unit can also select the optimal information collection method by considering the employee's location. For example, the data collection unit can prioritize the collection of highly relevant information based on the employee's geographical location. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the employee's geographical location into a generative AI, which can then prioritize the collection of highly relevant information.

[0044] The data collection unit can analyze employees' social media activities and collect relevant information during the information gathering process. For example, the data collection unit can analyze the content of employees' social media posts and collect relevant information. For example, the data collection unit can collect relevant information based on information about accounts and groups that employees follow. The data collection unit can also collect information related to areas of interest from employees' social media activities. For example, the data collection unit can analyze employees' social media activities and collect relevant information. This allows for the efficient collection of relevant information by analyzing social media activities. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input employees' social media activities into a generative AI, which can then collect relevant information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on information of high importance, and a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. For example, the analysis unit will prioritize the analysis of information of high importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the information into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a technical analysis algorithm to technical information. For example, the analysis unit can apply a market analysis algorithm to market information. The analysis unit can also apply a skill matching algorithm to employee skill information. For example, the analysis unit applies a technical analysis algorithm to technical information. By applying analysis algorithms according to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the timing of information collection. For example, the analysis unit can prioritize the analysis of the latest information. For example, the analysis unit can lower the priority of analysis of older information. The analysis unit can also adjust the order of analysis according to the timing of information collection. For example, the analysis unit prioritizes the analysis of the latest information. This allows for the prioritization of analysis of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the timing of information collection into the generating AI, and the generating AI can determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can lower the priority of analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI, and the generative AI can adjust the order of analysis.

[0049] The presentation unit can adjust the level of detail presented based on the importance of the matching candidates. For example, the presentation unit can present detailed information for high-importance matching candidates, and concise information for low-importance matching candidates. The presentation unit can also determine the priority of presentation according to the importance of the matching candidates. For example, the presentation unit can prioritize the presentation of high-importance matching candidates. This allows for efficient information presentation by adjusting the level of detail according to the importance of the matching candidates. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the importance of the matching candidates into the generative AI, which can then adjust the level of detail of the presentation.

[0050] The presentation unit can apply different presentation algorithms depending on the category of the matching candidate. For example, for technical matching candidates, the presentation unit can provide a presentation method that emphasizes technical information. For example, for market matching candidates, the presentation unit can provide a presentation method that emphasizes market information. Furthermore, for skill matching candidates, the presentation unit can provide a presentation method that emphasizes skill information. For example, for technical matching candidates, the presentation unit can provide a presentation method that emphasizes technical information. This makes it possible to present appropriate information by applying a presentation algorithm according to the category of the matching candidate. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the category of the matching candidate into a generative AI, and the generative AI can apply an appropriate presentation algorithm.

[0051] The presentation unit can determine the priority of presentations based on the collection timing of matching candidates. For example, the presentation unit can prioritize the presentation of the most recent matching candidates. For example, the presentation unit can lower the priority of presentations of older matching candidates. The presentation unit can also adjust the order of presentations according to the collection timing of matching candidates. For example, the presentation unit can prioritize the presentation of the most recent matching candidates. This ensures that the latest information is presented preferentially by determining the priority of presentations based on the collection timing of matching candidates. Some or all of the above processing in the presentation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the presentation unit can input the collection timing of matching candidates into a generating AI, and the generating AI can determine the priority of presentations.

[0052] The presentation unit can adjust the presentation order based on the relevance of the matching candidates. For example, the presentation unit can prioritize the presentation of highly relevant matching candidates. For example, the presentation unit can lower the priority of presentation of less relevant matching candidates. The presentation unit can also adjust the presentation order according to the relevance of the matching candidates. For example, the presentation unit prioritizes the presentation of highly relevant matching candidates. This allows for efficient information presentation by adjusting the presentation order based on the relevance of the matching candidates. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the relevance of the matching candidates into a generative AI, and the generative AI can adjust the presentation order.

[0053] The arrangement unit can select the optimal arrangement method when arranging an interview by referring to the employee's past interview history. For example, the arrangement unit can select the optimal arrangement method based on the history of interviews the employee has previously participated in. For example, the arrangement unit can optimize interview arrangements for specific times and locations based on the employee's past interview history. The arrangement unit can also analyze the employee's past interview history and select the most effective arrangement method. For example, the arrangement unit can refer to the employee's past interview history and select the optimal arrangement method. This allows the optimal arrangement method to be selected by referring to past interview history. Some or all of the above processing in the arrangement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the arrangement unit can input the employee's past interview history into a generating AI, and the generating AI can select the optimal arrangement method.

[0054] The scheduling unit can customize the scheduling method based on the employee's current schedule when arranging an interview. For example, the scheduling unit can refer to the employee's schedule and arrange an interview during an available time slot. For example, the scheduling unit can select the optimal interview location based on the employee's schedule. The scheduling unit can also adjust the length and content of the interview, taking the employee's schedule into consideration. For example, the scheduling unit can refer to the employee's schedule and arrange an interview during an available time slot. This allows for efficient interview scheduling by customizing the scheduling method based on the current schedule. Some or all of the above processing in the scheduling unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the scheduling unit can input the employee's schedule into a generating AI, which can then customize the scheduling method.

[0055] The arrangement unit can select the optimal arrangement method when arranging interviews, taking into account the employee's geographical location information. For example, the arrangement unit can prioritize arranging interviews related to the employee's current location. For example, the arrangement unit can select nearby interview locations based on the employee's geographical location information. The arrangement unit can also select the optimal interview arrangement method by taking into account the employee's location information. For example, the arrangement unit can prioritize arranging highly relevant interviews based on the employee's geographical location information. This makes it possible to arrange interviews optimally by taking geographical location information into consideration. Some or all of the above processing in the arrangement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the arrangement unit can input the employee's geographical location information into a generating AI, and the generating AI can select the optimal arrangement method.

[0056] The arrangement department can analyze an employee's social media activity and propose arrangement methods when arranging an interview. For example, the arrangement department can analyze the content of an employee's social media posts and propose relevant interviews. For example, the arrangement department can propose relevant interviews based on information about accounts and groups that an employee follows. Furthermore, the arrangement department can propose interviews related to an employee's areas of interest based on their social media activity. For example, the arrangement department can analyze an employee's social media activity and propose relevant interviews. This allows for the efficient arrangement of relevant interviews by analyzing social media activity. Some or all of the above processing in the arrangement department may be performed using, for example, a generative AI, or not using a generative AI. For example, the arrangement department can input an employee's social media activity into a generative AI, which can then propose arrangement methods.

[0057] The Facilitation Department can select the optimal facilitation method by referring to the employee's past dialogue history when facilitating dialogue. For example, the Facilitation Department can select the optimal facilitation method based on the employee's past dialogue history. For example, the Facilitation Department can optimize dialogue facilitation for specific times and locations based on the employee's past dialogue history. The Facilitation Department can also analyze the employee's past dialogue history and select the most effective facilitation method. For example, the Facilitation Department can refer to the employee's past dialogue history and select the optimal facilitation method. This allows the Facilitation Department to select the optimal facilitation method by referring to past dialogue history. Some or all of the above processing in the Facilitation Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Facilitation Department can input the employee's past dialogue history into a generative AI, and the generative AI can select the optimal facilitation method.

[0058] The Facilitation Unit can customize the means of facilitating dialogue based on the employee's current project. For example, the Facilitation Unit can provide dialogue facilitation methods related to the project the employee is currently working on. For example, the Facilitation Unit can select the optimal means of facilitating dialogue based on the employee's project. The Facilitation Unit can also customize the means of facilitating dialogue by considering the content of the employee's project. For example, the Facilitation Unit can provide dialogue facilitation methods related to the employee's current project. This allows for efficient dialogue facilitation by customizing the means of facilitating dialogue based on the current project. Some or all of the above processing in the Facilitation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Facilitation Unit can input the employee's project content into a generative AI, which can then customize the means of facilitating dialogue.

[0059] The Facilitation Department can select the optimal facilitation method when facilitating dialogue, taking into account the employee's geographical location. For example, the Facilitation Department can prioritize facilitating dialogues related to the employee's current location. For example, the Facilitation Department can select nearby dialogue locations based on the employee's geographical location. Furthermore, the Facilitation Department can select the optimal facilitation method by considering the employee's location. For example, the Facilitation Department can prioritize facilitating highly relevant dialogues based on the employee's geographical location. This makes it possible to facilitate dialogue optimally by considering geographical location. Some or all of the above processing in the Facilitation Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Facilitation Department can input the employee's geographical location into a generative AI, which can then select the optimal facilitation method.

[0060] The Facilitation Department can analyze employees' social media activity and propose means of facilitating dialogue. For example, the Facilitation Department can analyze the content of employees' social media posts and propose relevant dialogues. For example, the Facilitation Department can propose relevant dialogues based on information about accounts and groups that employees follow. Furthermore, the Facilitation Department can propose dialogues related to areas of interest based on employees' social media activity. For example, the Facilitation Department can analyze employees' social media activity and propose relevant dialogues. In this way, relevant dialogues can be efficiently facilitated by analyzing social media activity. Some or all of the above processing in the Facilitation Department may be performed using, for example, generative AI, or not using generative AI. For example, the Facilitation Department can input employees' social media activity into generative AI, and the generative AI can propose means of facilitating dialogue.

[0061] The evaluation unit can select the optimal evaluation method by referring to past evaluation data when evaluating an idea. For example, the evaluation unit selects the optimal evaluation method based on past evaluation data. For example, the evaluation unit can optimize a specific evaluation method from past evaluation data. The evaluation unit can also analyze past evaluation data and select the most effective evaluation method. For example, the evaluation unit selects the optimal evaluation method by referring to past evaluation data. This allows the optimal evaluation method to be selected by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input past evaluation data into a generative AI, and the generative AI can select the optimal evaluation method.

[0062] The evaluation unit can apply different evaluation algorithms depending on the category of the idea during the idea evaluation process. For example, the evaluation unit can apply a technical evaluation algorithm to a technical idea. For example, the evaluation unit can apply a market evaluation algorithm to a market idea. The evaluation unit can also apply a skills evaluation algorithm to a skills-related idea. For example, the evaluation unit can apply a technical evaluation algorithm to a technical idea. This improves evaluation accuracy by applying an evaluation algorithm according to the category of the idea. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the category of the idea into a generative AI, and the generative AI can apply an appropriate evaluation algorithm.

[0063] The evaluation unit can weight ideas based on when they were submitted. For example, it can prioritize the evaluation of the newest ideas. For example, it can lower the priority of older ideas. The evaluation unit can also adjust the weighting of the evaluation according to when the ideas were submitted. For example, it can prioritize the evaluation of the newest ideas. This allows for prioritizing the evaluation of the newest ideas by weighting them based on when they were submitted. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input the submission dates of ideas into a generative AI, and the generative AI can perform the weighting of the evaluation.

[0064] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the idea during the idea evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the idea. For example, the evaluation unit can adjust the evaluation criteria based on relevant literature on the idea. The evaluation unit can also analyze relevant literature on the idea and select the most effective evaluation method. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the idea. This allows the evaluation to improve accuracy by referring to relevant literature on the idea. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input relevant literature on the idea into a generative AI, which can then improve the accuracy of the evaluation.

[0065] The protection unit can select the optimal protection method by referring to past protection data when protecting intellectual property. For example, the protection unit selects the optimal protection method based on past protection data. For example, the protection unit can optimize a specific protection method from past protection data. The protection unit can also analyze past protection data and select the most effective protection method. For example, the protection unit selects the optimal protection method by referring to past protection data. This allows the optimal protection method to be selected by referring to past protection data. Some or all of the above processing in the protection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the protection unit can input past protection data into a generating AI, and the generating AI can select the optimal protection method.

[0066] The protection unit can apply different protection algorithms depending on the category of the idea when protecting intellectual property. For example, the protection unit can apply a technology protection algorithm to a technical idea. For example, the protection unit can apply a market protection algorithm to a market idea. Furthermore, the protection unit can apply a skills protection algorithm to a skills-related idea. For example, the protection unit can apply a technology protection algorithm to a technical idea. This improves the accuracy of protection by applying a protection algorithm according to the category of the idea. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input the category of the idea into a generative AI, and the generative AI can apply an appropriate protection algorithm.

[0067] The protection unit can weight protection based on the submission date of the idea when protecting intellectual property. For example, the protection unit can prioritize protection for the newest ideas. For example, the protection unit can lower the priority of protection for older ideas. The protection unit can also adjust the weighting of protection according to the submission date of the idea. For example, the protection unit will prioritize protection for the newest ideas. This allows for priority protection of the newest ideas by weighting protection based on the submission date of the idea. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input the submission date of the idea into a generative AI, and the generative AI can perform the weighting of protection.

[0068] The protection unit can improve the accuracy of protection by referring to relevant literature on ideas when protecting intellectual property. For example, the protection unit can improve the accuracy of protection by referring to relevant literature on ideas. For example, the protection unit can adjust the criteria for protection based on relevant literature on ideas. The protection unit can also analyze relevant literature on ideas and select the most effective protection method. For example, the protection unit can improve the accuracy of protection by referring to relevant literature on ideas. This allows for improved accuracy of protection by referring to relevant literature on ideas. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input relevant literature on ideas into a generative AI, which can then improve the accuracy of protection.

[0069] The design department can select the optimal design method by referring to past feedback data when designing a feedback mechanism. For example, the design department can select the optimal design method based on past feedback data. For example, the design department can optimize a specific design method from past feedback data. The design department can also analyze past feedback data and select the most effective design method. For example, the design department can select the optimal design method by referring to past feedback data. This allows the design department to select the optimal design method by referring to past feedback data. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or without using a generative AI. For example, the design department can input past feedback data into a generative AI, and the generative AI can select the optimal design method.

[0070] The design department can apply different design algorithms depending on the category of feedback when designing a feedback mechanism. For example, the design department can apply a technical design algorithm to technical feedback. For example, the design department can apply a market design algorithm to market feedback. The design department can also apply a skill design algorithm to skill-related feedback. For example, the design department can apply a technical design algorithm to technical feedback. By applying a design algorithm according to the category of feedback, the design accuracy is improved. Some or all of the above processing in the design department may be performed using, for example, generative AI, or without generative AI. For example, the design department can input the feedback category into a generative AI, and the generative AI can apply an appropriate design algorithm.

[0071] The design department can weight the design based on the timing of feedback submission when designing the feedback mechanism. For example, the design department can prioritize the design for the most recent feedback. For example, the design department can lower the priority of the design for older feedback. The design department can also adjust the design weighting according to the timing of feedback submission. For example, the design department can prioritize the design for the most recent feedback. This allows the design department to prioritize the design for the most recent feedback by weighting the design based on the timing of feedback submission. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not using a generative AI. For example, the design department can input the timing of feedback submission into a generative AI, and the generative AI can perform the design weighting.

[0072] The design department can improve the accuracy of its design by referring to relevant literature on feedback when designing a feedback mechanism. For example, the design department can improve the accuracy of its design by referring to literature related to feedback. For example, the design department can adjust the design criteria based on relevant literature on feedback. The design department can also analyze relevant literature on feedback and select the most effective design method. For example, the design department can improve the accuracy of its design by referring to relevant literature on feedback. In this way, the accuracy of the design can be improved by referring to relevant literature on feedback. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not using a generative AI. For example, the design department can input relevant literature on feedback into a generative AI, and the generative AI can improve the accuracy of the design.

[0073] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0074] The data collection unit can analyze an employee's past interaction history and select the optimal information collection method. For example, the data collection unit can select the optimal information collection method based on the employee's past participation in events and meetings. For example, the data collection unit can analyze the skills and work content of people the employee has interacted with in the past and collect relevant information. The data collection unit can also optimize information collection for specific times and locations based on the employee's past interaction history. For example, the data collection unit can analyze an employee's past interaction history and select the optimal information collection method. This allows the optimal information collection method to be selected by analyzing past interaction history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input an employee's past interaction history into a generative AI, which can then select the optimal information collection method.

[0075] The data collection unit can filter information based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. The data collection unit can also filter and collect relevant information based on an employee's areas of interest. Furthermore, the data collection unit can select and collect necessary information according to the employee's current work. For example, the data collection unit can prioritize collecting information related to an employee's current projects. This allows for efficient collection of necessary information by filtering it based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input an employee's current projects and areas of interest into a generative AI, which can then filter the information.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of employees during information gathering. For example, the data collection unit can prioritize the collection of information related to the employee's current location. For example, the data collection unit can collect information on nearby events and meetings based on the employee's geographical location. The data collection unit can also select the optimal information collection method by considering the employee's location. For example, the data collection unit can prioritize the collection of highly relevant information based on the employee's geographical location. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the employee's geographical location into a generative AI, which can then prioritize the collection of highly relevant information.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on information of high importance, and a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. For example, the analysis unit will prioritize the analysis of information of high importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the information into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a technical analysis algorithm to technical information. For example, the analysis unit can apply a market analysis algorithm to market information. The analysis unit can also apply a skill matching algorithm to employee skill information. For example, the analysis unit applies a technical analysis algorithm to technical information. By applying analysis algorithms according to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0079] The analysis unit can determine the priority of analysis based on the timing of information collection. For example, the analysis unit can prioritize the analysis of the latest information. For example, the analysis unit can lower the priority of analysis of older information. The analysis unit can also adjust the order of analysis according to the timing of information collection. For example, the analysis unit prioritizes the analysis of the latest information. This allows for the prioritization of analysis of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the timing of information collection into the generating AI, and the generating AI can determine the priority of analysis.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The data collection unit collects employee information such as department, job responsibilities, skills, and desired networking opportunities. For example, employees can input their skills and desired networking opportunities to gather information. The data collection unit can also automatically retrieve employee job responsibilities and department information from a database. Furthermore, the data collection unit can collect employee networking opportunities through a questionnaire. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it can analyze employees' skills and interaction preferences using data mining techniques and machine learning algorithms. The analysis unit analyzes skill information using clustering algorithms and classifies interaction preferences. It can also analyze employees' work content using statistical analysis techniques. Step 3: The presentation unit presents appropriate matching candidates based on the information analyzed by the analysis unit. For example, it can present matching candidates based on the degree of skill match or the relevance of job content. It can also present employees with the same expertise to employees who wish to share their expertise, or present employees with relevant skills to employees who wish to discuss ideas. Step 4: The Arrangement Department arranges interviews for the matching candidates presented by the Presentation Department. For example, they can coordinate the date and time of the interview and facilitate conversation in the lounge. They can also support the flow of the interview and help ensure that a natural conversation takes place. Step 5: The Facilitation Department facilitates the interviews arranged by the Arrangement Department. For example, they can support the flow of the interview and help ensure a natural conversation takes place. They can also follow up on the interview and confirm the outcome of the conversation. Furthermore, they can confirm the outcome of the conversation and plan for the next interview.

[0082] (Example of form 2) The matching system according to an embodiment of the present invention is an AI agent-based matching system available in the lounge of a specific office building. This matching system collects and analyzes employees' departments, job descriptions, skills, and networking preferences, and presents appropriate matching candidates. Furthermore, it arranges meetings and facilitates conversations in the lounge, supporting natural encounters and collaborative relationships. For example, the matching system allows employees to input their skills and networking preferences, and the AI ​​agent collects this information. This information is analyzed by the AI ​​agent, and appropriate matching candidates are presented. For example, an employee who wishes to share their expertise will be matched with an employee in the same field of expertise. Next, the matching system coordinates the meeting date and time between the matched employees and facilitates conversations in the lounge. In this process, the AI ​​agent supports the progress of the meeting and helps ensure that natural conversations take place. Furthermore, the matching system has a function to evaluate ideas in real time. For example, the AI ​​agent evaluates ideas generated during conversations between employees in real time and provides feedback. This allows for effective results even in short meetings. The matching system also protects intellectual property and designs feedback mechanisms. For example, it allows users to select idea evaluation methods such as stage-gate analysis, SWOT analysis, scoring models, and cost-benefit analysis. This enables efficient and effective idea management. The system fosters natural encounters and collaborations among employees, leading to the generation of new ideas and collaborations. Furthermore, it cultivates an innovation culture within the company, resulting in expected benefits such as improved operational efficiency, time savings, cost reductions, and the creation of new revenue opportunities. If successful, the system can also be customized and offered to other companies, generating licensing revenue and solution sales. In this way, the matching system collects, analyzes, presents, arranges, and facilitates employee information, enabling appropriate matching and supporting natural encounters and collaborations.

[0083] The matching system according to this embodiment comprises a collection unit, an analysis unit, a presentation unit, an arrangement unit, and a promotion unit. The collection unit collects employees' departments, job descriptions, skills, and interaction preferences. The collection unit can collect information, for example, when employees input their skills and interaction preferences. The collection unit can also automatically acquire employees' job descriptions and department information. For example, the collection unit can acquire employees' job descriptions from a database and collect skill information from an input form. Furthermore, the collection unit can collect employees' interaction preferences in the form of a questionnaire. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information, for example, using data mining techniques. Furthermore, the analysis unit can analyze employees' skills and interaction preferences using machine learning algorithms. For example, the analysis unit can analyze employees' skill information using a clustering algorithm and classify their interaction preferences. Furthermore, the analysis unit can analyze employees' job descriptions using statistical analysis techniques. The presentation unit presents appropriate matching candidates based on the information analyzed by the analysis unit. The Presentation Department can present matching candidates based, for example, on the degree of skill match or the relevance of job content. The Presentation Department can also present matching candidates based on employees' desire for interaction. For example, the Presentation Department can present employees with the same expertise to employees who wish to share their expertise. Furthermore, the Presentation Department can present employees with relevant skills to employees who wish to discuss ideas. The Arrangement Department arranges interviews between the matching candidates presented by the Presentation Department. The Arrangement Department can, for example, coordinate the date and time of the interview and facilitate conversation in a lounge. The Arrangement Department can also support the progress of the interview and help ensure natural conversation. For example, the Arrangement Department can coordinate the interview schedule and reserve a lounge. Furthermore, the Arrangement Department can support the progress of the interview and follow up on the conversation. The Facilitation Department facilitates the interviews arranged by the Arrangement Department. The Facilitation Department can, for example, support the progress of the interview and help ensure natural conversation. The Facilitation Department can also follow up on the interview and confirm the outcome of the conversation.For example, the Facilitation Department supports the progress of the interview and follows up on the dialogue. Furthermore, the Facilitation Department can also confirm the results of the dialogue and plan the next interview. In this way, the matching system according to the embodiment can collect, analyze, present, arrange, and facilitate employee information to perform appropriate matching and support natural encounters and collaborative relationships.

[0084] The data collection department collects employee information including department, job responsibilities, skills, and desired networking opportunities. For example, employees can input their skills and desired networking opportunities. Specifically, employees fill out a dedicated input form detailing their skill sets and desired networking activities. This form has a user-friendly interface and is designed to allow employees to easily input information. The data collection department can also automatically acquire employee job responsibilities and departmental information. For instance, it integrates with the company's HR database to obtain real-time information on employee job responsibilities and departments. This integration eliminates the need for employees to manually input information and ensures accurate data collection. Furthermore, the data collection department can collect employee networking preferences through questionnaires. These questionnaires are distributed to employees regularly and include questions to understand their networking preferences and interests. This allows the data collection department to collect data that reflects employees' latest needs and desires. The collected data is stored in a secure database and managed for access by the analytics department. The data collection department flexibly adjusts the frequency and method of data collection to achieve optimal data collection tailored to the company's needs and circumstances. For example, when a new project is launched, information on employees with relevant skills and experience can be focused on gathering data. This allows the data collection department to effectively utilize internal resources and provide a foundation for supporting appropriate matching.

[0085] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and can reveal employee skill levels and interaction preferences. The analysis unit can also analyze employee skills and interaction preferences using machine learning algorithms. For example, the analysis unit can analyze employee skill information using clustering algorithms to group employees with similar skills. This promotes interaction among employees with the same skill set. Furthermore, the analysis unit can analyze employee work content using statistical analysis techniques. Statistical analysis techniques are methods for revealing data distribution and correlations, and are used to understand employee work content and inter-departmental relationships. For example, it can analyze the distribution of skills within a specific department or the sharing of skills between different departments. The analysis unit combines these techniques to perform a comprehensive analysis based on employee skills, interaction preferences, and work content. The analysis results are output in a format usable by the presentation and arrangement units, and can be used to present appropriate matching candidates and arrange interviews. The analysis unit continuously improves its algorithms and updates its data to enhance the accuracy of its analysis results. This allows the analysis unit to always provide highly accurate analysis based on the latest information, maximizing the overall effectiveness of the system.

[0086] The presentation unit presents appropriate matching candidates based on information analyzed by the analysis unit. For example, the presentation unit can present matching candidates based on skill matching and the relevance of job content. Specifically, the presentation unit compares employees' skill sets and job content and lists the most relevant matching candidates. This list is displayed in a user-friendly interface for easy viewing by employees. The presentation unit can also present matching candidates based on employees' interaction preferences. For example, it can present employees with the same expertise to those who wish to share their expertise, making it easier for employees to share specialized knowledge and experience. Furthermore, the presentation unit can present employees with relevant skills to those seeking advice on ideas, allowing employees to receive appropriate advice on new ideas and projects. The presentation unit takes measures to protect employee privacy when presenting matching candidates. For example, the information presented is limited to the minimum necessary, ensuring that employees' personal information is not inappropriately disclosed. The presentation unit flexibly adjusts the frequency and method of presenting matching candidates to achieve optimal matching tailored to employee needs and circumstances. For example, when a new project is launched, the system can prioritize presenting candidates with the relevant skills and experience for the project. This allows the matching department to support effective matching between employees and strengthen collaborative relationships within the company.

[0087] The Arrangement Department arranges interviews for matching candidates presented by the Presentation Department. For example, the Arrangement Department can coordinate interview dates and times and facilitate conversations in a lounge setting. Specifically, the Arrangement Department checks employees' schedules and coordinates a mutually convenient date and time. This coordination is done using a dedicated scheduling system, allowing for efficient and rapid determination of interview dates and times. The Arrangement Department can also support the interview process and facilitate natural conversation. For example, the Arrangement Department coordinates interview schedules and reserves lounges. The lounges are designed to provide a relaxed atmosphere for conversation, offering an environment where employees can interact naturally. Furthermore, the Arrangement Department can support the interview process and follow up on the conversations. For example, before an interview, the Arrangement Department clarifies the purpose and agenda and notifies employees in advance. This ensures smooth interviews and effective conversations. After the interview, the Arrangement Department follows up to review the interview's results and plan for the next meeting. This allows the Arrangement Department to maximize the effectiveness of the interviews and strengthen collaborative relationships among employees. The arrangement department takes measures to protect employee privacy when arranging interviews. For example, the content and schedule of interviews are limited to the bare minimum necessary, and employees' personal information is not disclosed inappropriately. This allows the arrangement department to support effective interviews while building trust among employees.

[0088] The Facilitation Department facilitates the interviews arranged by the Arrangement Department. For example, the Facilitation Department can support the progress of the interview and help ensure that natural dialogue takes place. Specifically, the Facilitation Department performs facilitation to ensure the smooth progress of the interview. Facilitation is a technique to make the flow of dialogue smooth and enable participants to actively exchange opinions. At the beginning of the interview, the Facilitation Department reconfirms the purpose and agenda with the participants and clarifies the direction of the dialogue. The Facilitation Department also organizes important points during the dialogue and supports participants in making them easy to understand. Furthermore, the Facilitation Department can follow up on the interview and confirm the results of the dialogue. For example, they can conduct a survey with participants after the interview to understand the effectiveness of the interview and areas for improvement. This allows the Facilitation Department to gather information that will be useful when planning the next interview. The Facilitation Department can also confirm the results of the dialogue and plan the next interview. For example, based on the ideas and suggestions obtained in the interview, they can set the agenda for the next interview and promote continuous dialogue. This allows the Promotion Department to strengthen cooperation among employees and improve communication throughout the company. When facilitating interviews, the Promotion Department will take measures to protect employee privacy. For example, the content and results of interviews will be limited to the minimum necessary, and employees' personal information will not be inappropriately disclosed. This allows the Promotion Department to support effective interviews while building trust among employees.

[0089] The evaluation unit can evaluate ideas in real time. For example, it can evaluate ideas generated during conversations between employees in real time. The evaluation unit can analyze and evaluate ideas during conversations using, for example, natural language processing technology. The evaluation unit can also evaluate ideas using machine learning algorithms. For example, it can analyze the content of an idea and calculate a score based on evaluation criteria. Furthermore, the evaluation unit can provide real-time feedback to enhance the effectiveness of conversations. For example, it can provide immediate feedback on ideas generated during conversations. This allows for effective results even in short meetings by evaluating ideas in real time. Some or all of the above processing in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input ideas generated during conversations into a generative AI, which can then evaluate the ideas.

[0090] The protection unit can protect intellectual property. For example, the protection unit can support patent applications for ideas. For example, the protection unit can automate and efficiently carry out patent application procedures. The protection unit can also support copyright registration. For example, the protection unit can automate and quickly carry out copyright registration procedures. Furthermore, the protection unit can also support the creation of non-disclosure agreements. For example, the protection unit can provide non-disclosure agreement templates and assist in the creation of agreements. This allows for efficient and effective management of ideas by protecting intellectual property. Some or all of the above processes in the protection unit may be carried out using, for example, generative AI, or not using generative AI. For example, the protection unit can input the patent application procedures into generative AI, which can automate the procedures.

[0091] The design department can design a feedback mechanism. For example, the design department can design a method for collecting feedback. For example, the design department can design a method for collecting feedback in the form of a questionnaire. The design department can also design evaluation criteria for feedback. For example, the design department can design evaluation criteria based on the importance and urgency of the feedback. Furthermore, the design department can also design a feedback improvement process. For example, the design department can design a process for proposing and implementing improvement measures based on feedback. By designing a feedback mechanism, efficient and effective idea management becomes possible. Some or all of the above processes in the design department may be performed using, for example, generative AI, or not using generative AI. For example, the design department can input the feedback collection method into a generative AI, and the generative AI can design the collection method.

[0092] The evaluation unit can select evaluation methods such as stage-gate analysis, SWOT analysis, scoring models, and cost-benefit analysis. For example, the evaluation unit can evaluate ideas using the stage-gate analysis. For example, the evaluation unit can evaluate ideas according to their progress. The evaluation unit can also evaluate ideas using SWOT analysis. For example, the evaluation unit can analyze and evaluate the strengths, weaknesses, opportunities, and threats of an idea. Furthermore, the evaluation unit can also evaluate ideas using a scoring model. For example, the evaluation unit can score each element of an idea and perform an overall evaluation. By selecting a variety of evaluation methods, the accuracy of idea evaluation can be improved. Some or all of the above processes in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input the idea evaluation method into the generative AI, and the generative AI can select the evaluation method.

[0093] The protection unit can protect the intellectual property of an idea. For example, the protection unit can support patent applications for ideas. For example, the protection unit can automate and efficiently carry out the patent application process. The protection unit can also support copyright registration. For example, the protection unit can automate and quickly carry out the copyright registration process. Furthermore, the protection unit can support the creation of non-disclosure agreements. For example, the protection unit can provide templates for non-disclosure agreements and assist in their creation. In this way, the value of an idea can be protected by protecting its intellectual property. Some or all of the above processes in the protection unit may be carried out using, for example, generative AI, or not using generative AI. For example, the protection unit can input the patent application procedure into a generative AI, which can automate the procedure.

[0094] The design department can perform efficient and effective idea management. For example, the design department can design methods for collecting ideas. For example, the design department can design methods for collecting ideas in the form of questionnaires. The design department can also design criteria for evaluating ideas. For example, the design department can design evaluation criteria based on the importance and urgency of ideas. Furthermore, the design department can design idea management tools. For example, the design department can design software tools to support idea management. This promotes the utilization of ideas by enabling efficient and effective idea management. Some or all of the above processes in the design department may be performed using, for example, generative AI, or not using generative AI. For example, the design department can input the idea collection method into the generative AI, and the generative AI can design the collection method.

[0095] The data collection unit can estimate employees' emotions and adjust the timing of information collection based on the estimated emotions. For example, if an employee is feeling stressed, the data collection unit will collect information during a relaxed time. For example, if an employee is concentrating, the data collection unit can collect information in a way that does not interrupt their concentration. Furthermore, if an employee is relaxed, the data collection unit can select the timing for collecting detailed information. For example, the data collection unit estimates the employee's emotions and collects information during a relaxed time. This allows for more appropriate information collection by adjusting the timing of information collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input employee emotion data into a generative AI, which can then estimate the emotions.

[0096] The data collection unit can analyze an employee's past interaction history and select the optimal information collection method. For example, the data collection unit can select the optimal information collection method based on the employee's past participation in events and meetings. For example, the data collection unit can analyze the skills and work content of people the employee has interacted with in the past and collect relevant information. The data collection unit can also optimize information collection for specific times and locations based on the employee's past interaction history. For example, the data collection unit can analyze an employee's past interaction history and select the optimal information collection method. This allows the optimal information collection method to be selected by analyzing past interaction history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input an employee's past interaction history into a generative AI, which can then select the optimal information collection method.

[0097] The data collection unit can filter information based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. The data collection unit can also filter and collect relevant information based on an employee's areas of interest. Furthermore, the data collection unit can select and collect necessary information according to the employee's current work. For example, the data collection unit can prioritize collecting information related to an employee's current projects. This allows for efficient collection of necessary information by filtering it based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input an employee's current projects and areas of interest into a generative AI, which can then filter the information.

[0098] The data collection unit can estimate employees' emotions and determine the priority of information to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting information of high importance. For example, if an employee is relaxed, the data collection unit can prioritize collecting detailed information. Also, if an employee is focused, the data collection unit can prioritize collecting information relevant to their work. For example, the data collection unit estimates employees' emotions and prioritizes collecting information of high importance. This allows for the priority collection of important information by determining the priority of information according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input employee emotion data into a generative AI, which can then estimate the emotions.

[0099] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of employees during information gathering. For example, the data collection unit can prioritize the collection of information related to the employee's current location. For example, the data collection unit can collect information on nearby events and meetings based on the employee's geographical location. The data collection unit can also select the optimal information collection method by considering the employee's location. For example, the data collection unit can prioritize the collection of highly relevant information based on the employee's geographical location. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the employee's geographical location into a generative AI, which can then prioritize the collection of highly relevant information.

[0100] The data collection unit can analyze employees' social media activities and collect relevant information during the information gathering process. For example, the data collection unit can analyze the content of employees' social media posts and collect relevant information. For example, the data collection unit can collect relevant information based on information about accounts and groups that employees follow. The data collection unit can also collect information related to areas of interest from employees' social media activities. For example, the data collection unit can analyze employees' social media activities and collect relevant information. This allows for the efficient collection of relevant information by analyzing social media activities. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input employees' social media activities into a generative AI, which can then collect relevant information.

[0101] The analysis unit can estimate an employee's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is relaxed, the analysis unit can provide detailed analysis results. If an employee is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. The analysis unit can also provide visually stimulating analysis results if an employee is excited. For example, the analysis unit estimates an employee's emotions and provides detailed analysis results when the employee is relaxed. By adjusting the presentation of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input employee emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0102] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on information of high importance, and a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. For example, the analysis unit will prioritize the analysis of information of high importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the information into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0103] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a technical analysis algorithm to technical information. For example, the analysis unit can apply a market analysis algorithm to market information. The analysis unit can also apply a skill matching algorithm to employee skill information. For example, the analysis unit applies a technical analysis algorithm to technical information. By applying analysis algorithms according to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0104] The analysis unit can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if an employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if an employee is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit estimates an employee's emotions and provides a short, concise analysis result if the employee is in a hurry. By adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0105] The analysis unit can determine the priority of analysis based on the timing of information collection. For example, the analysis unit can prioritize the analysis of the latest information. For example, the analysis unit can lower the priority of analysis of older information. The analysis unit can also adjust the order of analysis according to the timing of information collection. For example, the analysis unit prioritizes the analysis of the latest information. This allows for the prioritization of analysis of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the timing of information collection into the generating AI, and the generating AI can determine the priority of analysis.

[0106] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can lower the priority of analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI, and the generative AI can adjust the order of analysis.

[0107] The presentation unit can estimate an employee's emotions and adjust the presentation format based on the estimated emotions. For example, if an employee is relaxed, the presentation unit can provide a presentation format that includes detailed information. If an employee is in a hurry, the presentation unit can provide a concise presentation format that gets straight to the point. Furthermore, if an employee is excited, the presentation unit can provide a visually stimulating presentation format. For example, the presentation unit estimates an employee's emotions and provides a presentation format that includes detailed information when the employee is relaxed. This allows for more appropriate information presentation by adjusting the presentation format according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using a generative AI, or not. For example, the presentation unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0108] The presentation unit can adjust the level of detail presented based on the importance of the matching candidates. For example, the presentation unit can present detailed information for high-importance matching candidates, and concise information for low-importance matching candidates. The presentation unit can also determine the priority of presentation according to the importance of the matching candidates. For example, the presentation unit can prioritize the presentation of high-importance matching candidates. This allows for efficient information presentation by adjusting the level of detail according to the importance of the matching candidates. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the importance of the matching candidates into the generative AI, which can then adjust the level of detail of the presentation.

[0109] The presentation unit can apply different presentation algorithms depending on the category of the matching candidate. For example, for technical matching candidates, the presentation unit can provide a presentation method that emphasizes technical information. For example, for market matching candidates, the presentation unit can provide a presentation method that emphasizes market information. Furthermore, for skill matching candidates, the presentation unit can provide a presentation method that emphasizes skill information. For example, for technical matching candidates, the presentation unit can provide a presentation method that emphasizes technical information. This makes it possible to present appropriate information by applying a presentation algorithm according to the category of the matching candidate. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the category of the matching candidate into a generative AI, and the generative AI can apply an appropriate presentation algorithm.

[0110] The presentation unit can estimate an employee's emotions and adjust the length of the presentation based on the estimated emotions. For example, if an employee is in a hurry, the presentation unit can provide a short, concise presentation. For example, if an employee is relaxed, the presentation unit can provide a presentation that includes detailed information. Furthermore, if an employee is excited, the presentation unit can provide a visually stimulating presentation. For example, the presentation unit estimates an employee's emotions and provides a short, concise presentation if they are in a hurry. This allows for more appropriate information presentation by adjusting the length of the presentation according to the employee'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 presentation unit may be performed using a generative AI, for example, or without a generative AI. For example, the presentation unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0111] The presentation unit can determine the priority of presentations based on the collection timing of matching candidates. For example, the presentation unit can prioritize the presentation of the most recent matching candidates. For example, the presentation unit can lower the priority of presentations of older matching candidates. The presentation unit can also adjust the order of presentations according to the collection timing of matching candidates. For example, the presentation unit can prioritize the presentation of the most recent matching candidates. This ensures that the latest information is presented preferentially by determining the priority of presentations based on the collection timing of matching candidates. Some or all of the above processing in the presentation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the presentation unit can input the collection timing of matching candidates into a generating AI, and the generating AI can determine the priority of presentations.

[0112] The presentation unit can adjust the presentation order based on the relevance of the matching candidates. For example, the presentation unit can prioritize the presentation of highly relevant matching candidates. For example, the presentation unit can lower the priority of presentation of less relevant matching candidates. The presentation unit can also adjust the presentation order according to the relevance of the matching candidates. For example, the presentation unit prioritizes the presentation of highly relevant matching candidates. This allows for efficient information presentation by adjusting the presentation order based on the relevance of the matching candidates. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the relevance of the matching candidates into a generative AI, and the generative AI can adjust the presentation order.

[0113] The arrangement unit can estimate an employee's emotions and adjust the interview arrangement method based on the estimated emotions. For example, if an employee is relaxed, the arrangement unit can provide a detailed interview arrangement. For example, if an employee is in a hurry, the arrangement unit can provide a concise interview arrangement. Furthermore, if an employee is excited, the arrangement unit can provide a visually stimulating interview arrangement. For example, the arrangement unit estimates an employee's emotions and provides a detailed interview arrangement if they are relaxed. This allows for more appropriate interview arrangements by adjusting the interview arrangement method according to the employee'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 arrangement unit may be performed using a generative AI, or not using a generative AI. For example, the arrangement unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0114] The arrangement unit can select the optimal arrangement method when arranging an interview by referring to the employee's past interview history. For example, the arrangement unit can select the optimal arrangement method based on the history of interviews the employee has previously participated in. For example, the arrangement unit can optimize interview arrangements for specific times and locations based on the employee's past interview history. The arrangement unit can also analyze the employee's past interview history and select the most effective arrangement method. For example, the arrangement unit can refer to the employee's past interview history and select the optimal arrangement method. This allows the optimal arrangement method to be selected by referring to past interview history. Some or all of the above processing in the arrangement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the arrangement unit can input the employee's past interview history into a generating AI, and the generating AI can select the optimal arrangement method.

[0115] The scheduling unit can customize the scheduling method based on the employee's current schedule when arranging an interview. For example, the scheduling unit can refer to the employee's schedule and arrange an interview during an available time slot. For example, the scheduling unit can select the optimal interview location based on the employee's schedule. The scheduling unit can also adjust the length and content of the interview, taking the employee's schedule into consideration. For example, the scheduling unit can refer to the employee's schedule and arrange an interview during an available time slot. This allows for efficient interview scheduling by customizing the scheduling method based on the current schedule. Some or all of the above processing in the scheduling unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the scheduling unit can input the employee's schedule into a generating AI, which can then customize the scheduling method.

[0116] The scheduling unit can estimate an employee's emotions and determine the priority of interviews based on the estimated emotions. For example, if an employee is feeling stressed, the scheduling unit will prioritize scheduling high-priority interviews. For example, if an employee is relaxed, the scheduling unit can prioritize scheduling detailed interviews. Also, if an employee is focused, the scheduling unit can prioritize scheduling work-related interviews. For example, the scheduling unit estimates an employee's emotions and prioritizes scheduling high-priority interviews if the employee is feeling stressed. This allows important interviews to be prioritized by determining the priority of interviews according to the employee'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 scheduling unit may be performed using a generative AI, or not using a generative AI. For example, the scheduling unit can input employee emotion data into a generative AI, which can then estimate the emotions.

[0117] The arrangement unit can select the optimal arrangement method when arranging interviews, taking into account the employee's geographical location information. For example, the arrangement unit can prioritize arranging interviews related to the employee's current location. For example, the arrangement unit can select nearby interview locations based on the employee's geographical location information. The arrangement unit can also select the optimal interview arrangement method by taking into account the employee's location information. For example, the arrangement unit can prioritize arranging highly relevant interviews based on the employee's geographical location information. This makes it possible to arrange interviews optimally by taking geographical location information into consideration. Some or all of the above processing in the arrangement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the arrangement unit can input the employee's geographical location information into a generating AI, and the generating AI can select the optimal arrangement method.

[0118] The arrangement department can analyze an employee's social media activity and propose arrangement methods when arranging an interview. For example, the arrangement department can analyze the content of an employee's social media posts and propose relevant interviews. For example, the arrangement department can propose relevant interviews based on information about accounts and groups that an employee follows. Furthermore, the arrangement department can propose interviews related to an employee's areas of interest based on their social media activity. For example, the arrangement department can analyze an employee's social media activity and propose relevant interviews. This allows for the efficient arrangement of relevant interviews by analyzing social media activity. Some or all of the above processing in the arrangement department may be performed using, for example, a generative AI, or not using a generative AI. For example, the arrangement department can input an employee's social media activity into a generative AI, which can then propose arrangement methods.

[0119] The facilitation unit can estimate an employee's emotions and adjust the dialogue facilitation method based on the estimated emotions. For example, if an employee is relaxed, the facilitation unit can provide a detailed dialogue facilitation method. For example, if an employee is in a hurry, the facilitation unit can provide a concise dialogue facilitation method. Furthermore, if an employee is excited, the facilitation unit can provide a visually stimulating dialogue facilitation method. For example, the facilitation unit estimates an employee's emotions and provides a detailed dialogue facilitation method when the employee is relaxed. This allows for more appropriate dialogue facilitation by adjusting the dialogue facilitation method according to the employee'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 facilitation unit may be performed using a generative AI, for example, or without a generative AI. For example, the facilitation unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0120] The Facilitation Department can select the optimal facilitation method by referring to the employee's past dialogue history when facilitating dialogue. For example, the Facilitation Department can select the optimal facilitation method based on the employee's past dialogue history. For example, the Facilitation Department can optimize dialogue facilitation for specific times and locations based on the employee's past dialogue history. The Facilitation Department can also analyze the employee's past dialogue history and select the most effective facilitation method. For example, the Facilitation Department can refer to the employee's past dialogue history and select the optimal facilitation method. This allows the Facilitation Department to select the optimal facilitation method by referring to past dialogue history. Some or all of the above processing in the Facilitation Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Facilitation Department can input the employee's past dialogue history into a generative AI, and the generative AI can select the optimal facilitation method.

[0121] The Facilitation Unit can customize the means of facilitating dialogue based on the employee's current project. For example, the Facilitation Unit can provide dialogue facilitation methods related to the project the employee is currently working on. For example, the Facilitation Unit can select the optimal means of facilitating dialogue based on the employee's project. The Facilitation Unit can also customize the means of facilitating dialogue by considering the content of the employee's project. For example, the Facilitation Unit can provide dialogue facilitation methods related to the employee's current project. This allows for efficient dialogue facilitation by customizing the means of facilitating dialogue based on the current project. Some or all of the above processing in the Facilitation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Facilitation Unit can input the employee's project content into a generative AI, which can then customize the means of facilitating dialogue.

[0122] The Facilitation Department can estimate an employee's emotions and determine the priority of dialogue facilitation based on the estimated emotions. For example, if an employee is stressed, the Facilitation Department will prioritize facilitating high-priority dialogues. For example, if an employee is relaxed, the Facilitation Department can prioritize facilitating detailed dialogues. Also, if an employee is focused, the Facilitation Department can prioritize facilitating work-related dialogues. For example, the Facilitation Department estimates an employee's emotions and prioritizes facilitating high-priority dialogues if the employee is stressed. This allows important dialogues to be prioritized by determining the priority of dialogue facilitation according to the employee'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 Facilitation Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Facilitation Department can input employee emotion data into a generative AI, which can then estimate the emotions.

[0123] The Facilitation Department can select the optimal facilitation method when facilitating dialogue, taking into account the employee's geographical location. For example, the Facilitation Department can prioritize facilitating dialogues related to the employee's current location. For example, the Facilitation Department can select nearby dialogue locations based on the employee's geographical location. Furthermore, the Facilitation Department can select the optimal facilitation method by considering the employee's location. For example, the Facilitation Department can prioritize facilitating highly relevant dialogues based on the employee's geographical location. This makes it possible to facilitate dialogue optimally by considering geographical location. Some or all of the above processing in the Facilitation Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Facilitation Department can input the employee's geographical location into a generative AI, which can then select the optimal facilitation method.

[0124] The Facilitation Department can analyze employees' social media activity and propose means of facilitating dialogue. For example, the Facilitation Department can analyze the content of employees' social media posts and propose relevant dialogues. For example, the Facilitation Department can propose relevant dialogues based on information about accounts and groups that employees follow. Furthermore, the Facilitation Department can propose dialogues related to areas of interest based on employees' social media activity. For example, the Facilitation Department can analyze employees' social media activity and propose relevant dialogues. In this way, relevant dialogues can be efficiently facilitated by analyzing social media activity. Some or all of the above processing in the Facilitation Department may be performed using, for example, generative AI, or not using generative AI. For example, the Facilitation Department can input employees' social media activity into generative AI, and the generative AI can propose means of facilitating dialogue.

[0125] The evaluation unit can estimate an employee's emotions and adjust the idea evaluation method based on the estimated emotions. For example, if an employee is relaxed, the evaluation unit can provide a detailed evaluation method. For example, if an employee is in a hurry, the evaluation unit can provide a concise evaluation method. Furthermore, if an employee is excited, the evaluation unit can provide a visually stimulating evaluation method. For example, the evaluation unit estimates an employee's emotions and provides a detailed evaluation method when the employee is relaxed. This allows for more appropriate evaluations by adjusting the idea evaluation method according to the employee'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 evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0126] The evaluation unit can select the optimal evaluation method by referring to past evaluation data when evaluating an idea. For example, the evaluation unit selects the optimal evaluation method based on past evaluation data. For example, the evaluation unit can optimize a specific evaluation method from past evaluation data. The evaluation unit can also analyze past evaluation data and select the most effective evaluation method. For example, the evaluation unit selects the optimal evaluation method by referring to past evaluation data. This allows the optimal evaluation method to be selected by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input past evaluation data into a generative AI, and the generative AI can select the optimal evaluation method.

[0127] The evaluation unit can apply different evaluation algorithms depending on the category of the idea during the idea evaluation process. For example, the evaluation unit can apply a technical evaluation algorithm to a technical idea. For example, the evaluation unit can apply a market evaluation algorithm to a market idea. The evaluation unit can also apply a skills evaluation algorithm to a skills-related idea. For example, the evaluation unit can apply a technical evaluation algorithm to a technical idea. This improves evaluation accuracy by applying an evaluation algorithm according to the category of the idea. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the category of the idea into a generative AI, and the generative AI can apply an appropriate evaluation algorithm.

[0128] The evaluation department can estimate an employee's emotions and determine evaluation priorities based on the estimated emotions. For example, if an employee is stressed, the evaluation department can prioritize high-importance evaluations. For example, if an employee is relaxed, the evaluation department can prioritize detailed evaluations. Also, if an employee is focused, the evaluation department can prioritize work-related evaluations. For example, the evaluation department estimates an employee's emotions and prioritizes high-importance evaluations if the employee is stressed. This allows for prioritizing important evaluations by determining evaluation priorities according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation department can input employee emotion data into a generative AI, which can then estimate the emotions.

[0129] The evaluation unit can weight ideas based on when they were submitted. For example, it can prioritize the evaluation of the newest ideas. For example, it can lower the priority of older ideas. The evaluation unit can also adjust the weighting of the evaluation according to when the ideas were submitted. For example, it can prioritize the evaluation of the newest ideas. This allows for prioritizing the evaluation of the newest ideas by weighting them based on when they were submitted. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input the submission dates of ideas into a generative AI, and the generative AI can perform the weighting of the evaluation.

[0130] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the idea during the idea evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the idea. For example, the evaluation unit can adjust the evaluation criteria based on relevant literature on the idea. The evaluation unit can also analyze relevant literature on the idea and select the most effective evaluation method. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the idea. This allows the evaluation to improve accuracy by referring to relevant literature on the idea. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input relevant literature on the idea into a generative AI, which can then improve the accuracy of the evaluation.

[0131] The protection unit can estimate an employee's emotions and adjust the method of intellectual property protection based on the estimated emotions. For example, if an employee is relaxed, the protection unit can provide a detailed method of intellectual property protection. For example, if an employee is in a hurry, the protection unit can provide a concise method of intellectual property protection. Furthermore, if an employee is excited, the protection unit can provide a visually stimulating method of intellectual property protection. For example, the protection unit estimates an employee's emotions and provides a detailed method of intellectual property protection when the employee is relaxed. This allows for more appropriate protection by adjusting the method of intellectual property protection according to the employee'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 protection unit may be performed using a generative AI, for example, or without a generative AI. For example, the protection unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0132] The protection unit can select the optimal protection method by referring to past protection data when protecting intellectual property. For example, the protection unit selects the optimal protection method based on past protection data. For example, the protection unit can optimize a specific protection method from past protection data. The protection unit can also analyze past protection data and select the most effective protection method. For example, the protection unit selects the optimal protection method by referring to past protection data. This allows the optimal protection method to be selected by referring to past protection data. Some or all of the above processing in the protection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the protection unit can input past protection data into a generating AI, and the generating AI can select the optimal protection method.

[0133] The protection unit can apply different protection algorithms depending on the category of the idea when protecting intellectual property. For example, the protection unit can apply a technology protection algorithm to a technical idea. For example, the protection unit can apply a market protection algorithm to a market idea. Furthermore, the protection unit can apply a skills protection algorithm to a skills-related idea. For example, the protection unit can apply a technology protection algorithm to a technical idea. This improves the accuracy of protection by applying a protection algorithm according to the category of the idea. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input the category of the idea into a generative AI, and the generative AI can apply an appropriate protection algorithm.

[0134] The protection unit can estimate an employee's emotions and determine the priority of protection based on the estimated emotions. For example, if an employee is stressed, the protection unit will prioritize high-priority protection. For example, if an employee is relaxed, the protection unit will prioritize detailed protection. The protection unit can also prioritize work-related protection if an employee is focused. For example, the protection unit estimates an employee's emotions and prioritizes high-priority protection if they are stressed. This allows important protection to be prioritized by determining the priority of protection according to the employee'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 protection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the protection unit can input employee emotion data into a generative AI, which can then estimate the emotions.

[0135] The protection unit can weight protection based on the submission date of the idea when protecting intellectual property. For example, the protection unit can prioritize protection for the newest ideas. For example, the protection unit can lower the priority of protection for older ideas. The protection unit can also adjust the weighting of protection according to the submission date of the idea. For example, the protection unit will prioritize protection for the newest ideas. This allows for priority protection of the newest ideas by weighting protection based on the submission date of the idea. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input the submission date of the idea into a generative AI, and the generative AI can perform the weighting of protection.

[0136] The protection unit can improve the accuracy of protection by referring to relevant literature on ideas when protecting intellectual property. For example, the protection unit can improve the accuracy of protection by referring to relevant literature on ideas. For example, the protection unit can adjust the criteria for protection based on relevant literature on ideas. The protection unit can also analyze relevant literature on ideas and select the most effective protection method. For example, the protection unit can improve the accuracy of protection by referring to relevant literature on ideas. This allows for improved accuracy of protection by referring to relevant literature on ideas. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input relevant literature on ideas into a generative AI, which can then improve the accuracy of protection.

[0137] The design department can estimate employees' emotions and adjust the design of the feedback mechanism based on the estimated emotions. For example, if an employee is relaxed, the design department can provide a detailed feedback mechanism. If an employee is in a hurry, the design department can provide a concise feedback mechanism. The design department can also provide a visually stimulating feedback mechanism if an employee is excited. For example, the design department estimates employees' emotions and provides a detailed feedback mechanism when they are relaxed. This allows for more appropriate feedback by adjusting the design of the feedback mechanism according to the employee'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 design department may be performed using, for example, generative AI, or not using generative AI. For example, the design department can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0138] The design department can select the optimal design method by referring to past feedback data when designing a feedback mechanism. For example, the design department can select the optimal design method based on past feedback data. For example, the design department can optimize a specific design method from past feedback data. The design department can also analyze past feedback data and select the most effective design method. For example, the design department can select the optimal design method by referring to past feedback data. This allows the design department to select the optimal design method by referring to past feedback data. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or without using a generative AI. For example, the design department can input past feedback data into a generative AI, and the generative AI can select the optimal design method.

[0139] The design department can apply different design algorithms depending on the category of feedback when designing a feedback mechanism. For example, the design department can apply a technical design algorithm to technical feedback. For example, the design department can apply a market design algorithm to market feedback. The design department can also apply a skill design algorithm to skill-related feedback. For example, the design department can apply a technical design algorithm to technical feedback. By applying a design algorithm according to the category of feedback, the design accuracy is improved. Some or all of the above processing in the design department may be performed using, for example, generative AI, or without generative AI. For example, the design department can input the feedback category into a generative AI, and the generative AI can apply an appropriate design algorithm.

[0140] The design department can estimate employees' emotions and prioritize feedback based on those emotions. For example, if an employee is stressed, the design department can prioritize high-priority feedback. If an employee is relaxed, the design department can prioritize detailed feedback. Furthermore, if an employee is focused, the design department can prioritize work-related feedback. For example, the design department can estimate employees' emotions and prioritize high-priority feedback if they are stressed. This allows important feedback to be prioritized by determining feedback priorities according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using, for example, generative AI, or not. For example, the design department can input employee emotion data into a generative AI, which can then estimate emotions.

[0141] The design department can weight the design based on the timing of feedback submission when designing the feedback mechanism. For example, the design department can prioritize the design for the most recent feedback. For example, the design department can lower the priority of the design for older feedback. The design department can also adjust the design weighting according to the timing of feedback submission. For example, the design department can prioritize the design for the most recent feedback. This allows the design department to prioritize the design for the most recent feedback by weighting the design based on the timing of feedback submission. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not using a generative AI. For example, the design department can input the timing of feedback submission into a generative AI, and the generative AI can perform the design weighting.

[0142] The design department can improve the accuracy of its design by referring to relevant literature on feedback when designing a feedback mechanism. For example, the design department can improve the accuracy of its design by referring to literature related to feedback. For example, the design department can adjust the design criteria based on relevant literature on feedback. The design department can also analyze relevant literature on feedback and select the most effective design method. For example, the design department can improve the accuracy of its design by referring to relevant literature on feedback. In this way, the accuracy of the design can be improved by referring to relevant literature on feedback. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not using a generative AI. For example, the design department can input relevant literature on feedback into a generative AI, and the generative AI can improve the accuracy of the design.

[0143] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0144] The data collection unit can estimate employees' emotions and adjust the timing of information collection based on the estimated emotions. For example, if an employee is feeling stressed, the data collection unit will collect information during a relaxed time. For example, if an employee is concentrating, the data collection unit can collect information in a way that does not interrupt their concentration. Furthermore, if an employee is relaxed, the data collection unit can select the timing for collecting detailed information. For example, the data collection unit estimates the employee's emotions and collects information during a relaxed time. This allows for more appropriate information collection by adjusting the timing of information collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input employee emotion data into a generative AI, which can then estimate the emotions.

[0145] The data collection unit can analyze an employee's past interaction history and select the optimal information collection method. For example, the data collection unit can select the optimal information collection method based on the employee's past participation in events and meetings. For example, the data collection unit can analyze the skills and work content of people the employee has interacted with in the past and collect relevant information. The data collection unit can also optimize information collection for specific times and locations based on the employee's past interaction history. For example, the data collection unit can analyze an employee's past interaction history and select the optimal information collection method. This allows the optimal information collection method to be selected by analyzing past interaction history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input an employee's past interaction history into a generative AI, which can then select the optimal information collection method.

[0146] The data collection unit can filter information based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. The data collection unit can also filter and collect relevant information based on an employee's areas of interest. Furthermore, the data collection unit can select and collect necessary information according to the employee's current work. For example, the data collection unit can prioritize collecting information related to an employee's current projects. This allows for efficient collection of necessary information by filtering it based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input an employee's current projects and areas of interest into a generative AI, which can then filter the information.

[0147] The data collection unit can estimate employees' emotions and determine the priority of information to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting information of high importance. For example, if an employee is relaxed, the data collection unit can prioritize collecting detailed information. Also, if an employee is focused, the data collection unit can prioritize collecting information relevant to their work. For example, the data collection unit estimates employees' emotions and prioritizes collecting information of high importance. This allows for the priority collection of important information by determining the priority of information according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input employee emotion data into a generative AI, which can then estimate the emotions.

[0148] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of employees during information gathering. For example, the data collection unit can prioritize the collection of information related to the employee's current location. For example, the data collection unit can collect information on nearby events and meetings based on the employee's geographical location. The data collection unit can also select the optimal information collection method by considering the employee's location. For example, the data collection unit can prioritize the collection of highly relevant information based on the employee's geographical location. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the employee's geographical location into a generative AI, which can then prioritize the collection of highly relevant information.

[0149] The analysis unit can estimate an employee's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is relaxed, the analysis unit can provide detailed analysis results. If an employee is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. The analysis unit can also provide visually stimulating analysis results if an employee is excited. For example, the analysis unit estimates an employee's emotions and provides detailed analysis results when the employee is relaxed. By adjusting the presentation of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input employee emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0150] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis on information of high importance, and a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. For example, the analysis unit will prioritize the analysis of information of high importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the information into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0151] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a technical analysis algorithm to technical information. For example, the analysis unit can apply a market analysis algorithm to market information. The analysis unit can also apply a skill matching algorithm to employee skill information. For example, the analysis unit applies a technical analysis algorithm to technical information. By applying analysis algorithms according to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0152] The analysis unit can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if an employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if an employee is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit estimates an employee's emotions and provides a short, concise analysis result if the employee is in a hurry. By adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input employee emotion data into a generative AI, which can then perform emotion estimation.

[0153] The analysis unit can determine the priority of analysis based on the timing of information collection. For example, the analysis unit can prioritize the analysis of the latest information. For example, the analysis unit can lower the priority of analysis of older information. The analysis unit can also adjust the order of analysis according to the timing of information collection. For example, the analysis unit prioritizes the analysis of the latest information. This allows for the prioritization of analysis of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the timing of information collection into the generating AI, and the generating AI can determine the priority of analysis.

[0154] The following briefly describes the processing flow for example form 2.

[0155] Step 1: The data collection unit collects employee information such as department, job responsibilities, skills, and desired networking opportunities. For example, employees can input their skills and desired networking opportunities to gather information. The data collection unit can also automatically retrieve employee job responsibilities and department information from a database. Furthermore, the data collection unit can collect employee networking opportunities through a questionnaire. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it can analyze employees' skills and interaction preferences using data mining techniques and machine learning algorithms. The analysis unit analyzes skill information using clustering algorithms and classifies interaction preferences. It can also analyze employees' work content using statistical analysis techniques. Step 3: The presentation unit presents appropriate matching candidates based on the information analyzed by the analysis unit. For example, it can present matching candidates based on the degree of skill match or the relevance of job content. It can also present employees with the same expertise to employees who wish to share their expertise, or present employees with relevant skills to employees who wish to discuss ideas. Step 4: The Arrangement Department arranges interviews for the matching candidates presented by the Presentation Department. For example, they can coordinate the date and time of the interview and facilitate conversation in the lounge. They can also support the flow of the interview and help ensure that a natural conversation takes place. Step 5: The Facilitation Department facilitates the interviews arranged by the Arrangement Department. For example, they can support the flow of the interview and help ensure a natural conversation takes place. They can also follow up on the interview and confirm the outcome of the conversation. Furthermore, they can confirm the outcome of the conversation and plan for the next interview.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, arrangement unit, promotion unit, evaluation unit, protection unit, and design unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee information using the reception device 38 and camera 42 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The presentation unit presents matching candidates using, for example, the output device 40 of the smart device 14. The arrangement unit adjusts the date and time of the interview and facilitates the conversation in the lounge using, for example, the specific processing unit 290 of the data processing unit 12. The promotion unit supports the progress of the interview using, for example, the control unit 46A of the smart device 14. The evaluation unit evaluates ideas in real time using, for example, the specific processing unit 290 of the data processing unit 12. The protection unit protects intellectual property using, for example, the specific processing unit 290 of the data processing unit 12. The design department, for example, designs the feedback mechanism using the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] 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).

[0166] 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.

[0167] 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.

[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 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.

[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 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.

[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 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, arrangement unit, promotion unit, evaluation unit, protection unit, and design unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects employee information using the microphone 238 and camera 42 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The presentation unit presents matching candidates using, for example, the speaker 240 of the smart glasses 214. The arrangement unit adjusts the date and time of the interview and facilitates the conversation in the lounge using, for example, the identification processing unit 290 of the data processing unit 12. The promotion unit supports the progress of the interview using, for example, the control unit 46A of the smart glasses 214. The evaluation unit evaluates ideas in real time using, for example, the identification processing unit 290 of the data processing unit 12. The protection unit protects intellectual property using, for example, the identification processing unit 290 of the data processing unit 12. The design department, for example, designs the feedback mechanism using the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0176] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, arrangement unit, promotion unit, evaluation unit, protection unit, and design unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee information using the microphone 238 and camera 42 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The presentation unit presents matching candidates using, for example, the display 343 of the headset terminal 314. The arrangement unit adjusts the date and time of the interview and facilitates the conversation in the lounge using, for example, the specific processing unit 290 of the data processing unit 12. The promotion unit supports the progress of the interview using, for example, the control unit 46A of the headset terminal 314. The evaluation unit evaluates ideas in real time using, for example, the specific processing unit 290 of the data processing unit 12. The protection unit protects intellectual property using, for example, the specific processing unit 290 of the data processing unit 12. The design department, for example, designs the feedback mechanism using the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0192] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, arrangement unit, promotion unit, evaluation unit, protection unit, and design unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee information using the microphone 238 and camera 42 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The presentation unit presents matching candidates using, for example, the speaker 240 of the robot 414. The arrangement unit adjusts the date and time of the interview and facilitates the conversation in the lounge using, for example, the specific processing unit 290 of the data processing unit 12. The promotion unit supports the progress of the interview using, for example, the control unit 46A of the robot 414. The evaluation unit evaluates ideas in real time using, for example, the specific processing unit 290 of the data processing unit 12. The protection unit protects intellectual property using, for example, the specific processing unit 290 of the data processing unit 12. The design department, for example, designs the feedback mechanism using the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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."

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] (Note 1) The collection department gathers information on employees' departments, job responsibilities, skills, and desired networking opportunities. An analysis unit analyzes the information collected by the aforementioned collection unit, A presentation unit presents appropriate matching candidates based on the information analyzed by the aforementioned analysis unit, An arrangement unit that arranges interviews for matching candidates presented by the aforementioned presentation unit, The system comprises a facilitator that facilitates the interview arranged by the aforementioned facilitator. A system characterized by the following features. (Note 2) It has an evaluation unit that assesses ideas in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a protection unit for protecting intellectual property. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a design department for designing feedback mechanisms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Evaluation methods such as stage-gate analysis, SWOT analysis, scoring models, and cost-benefit analysis can be selected. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned protective part is Protecting the intellectual property of ideas The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned design department, To manage ideas efficiently and effectively The system described in Appendix 4, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze employees' past interaction history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filter it based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate employees' emotions and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze employees' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the emotions of our employees and adjust the representation of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the emotions of employees and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Prioritize analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, The system estimates employees' emotions and adjusts the presentation style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, Adjust the level of detail in the presentation based on the importance of the matching candidates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, Apply different suggestion algorithms depending on the category of the matching candidate. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, The system estimates the employee's emotions and adjusts the length of the presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, The priority of presentations will be determined based on when the matching candidates were collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, Adjust the order of presentation based on the relevance of the matching candidates. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned arrangement section is, We estimate the emotions of our employees and adjust the interview arrangements based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned arrangement section is, When arranging interviews, the most suitable arrangement method is selected by referring to the employee's past interview history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned arrangement section is, When arranging an interview, customize the arrangement method based on the employee's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned arrangement section is, The system estimates employees' emotions and prioritizes interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned arrangement section is, When arranging interviews, the most suitable arrangement method will be selected, taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned arrangement section is, When arranging interviews, we analyze employees' social media activity and propose methods for arranging them. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned promotion unit is We estimate the emotions of our employees and adjust the methods of facilitating dialogue based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned promotion unit is When facilitating dialogue, the system selects the most suitable method by referring to the employee's past dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned promotion unit is When facilitating dialogue, customize the facilitation methods based on the employee's current project. The system according to Appendix 1, characterized in that... (Appendix 35) The promotion unit estimates the emotions of employees and determines the priority order of dialogue promotion based on the estimated emotions of employees The system according to Appendix 1, characterized in that... (Appendix 36) The promotion unit selects an optimal promotion method considering the geographical location information of employees during dialogue promotion The system according to Appendix 1, characterized in that... (Appendix 37) The promotion unit analyzes the social media activities of employees and proposes means of promotion during dialogue promotion The system according to Appendix 1, characterized in that... (Appendix 38) The evaluation unit estimates the emotions of employees and adjusts the method of idea evaluation based on the estimated emotions of employees The system according to Appendix 2, characterized in that... (Appendix 39) The evaluation unit selects an optimal evaluation method by referring to past evaluation data during idea evaluation The system according to Appendix 2, characterized in that... (Appendix 40) The evaluation unit applies different evaluation algorithms according to the category of ideas during idea evaluation The system according to Appendix 2, characterized in that... (Appendix 41) The evaluation unit estimates the emotions of employees and determines the priority order of evaluation based on the estimated emotions of employees The system according to Appendix 2, characterized in that... (Appendix 42) The evaluation unit performs weighted evaluation based on the submission time of ideas during idea evaluation The system according to Appendix 2, characterized in that... (Supplementary Note 43) The evaluation unit refers to the related literature of the idea during idea evaluation to improve the accuracy of evaluation for the system according to Supplementary Note 2, characterized in that. (Supplementary Note 44) The protection unit estimates the emotions of employees and adjusts the method of intellectual property protection based on the estimated emotions of employees for the system according to Supplementary Note 3, characterized in that. (Supplementary Note 45) The protection unit refers to the past protection data during intellectual property protection to select the optimal protection method for the system according to Supplementary Note 3, characterized in that. (Supplementary Note 46) The protection unit applies different protection algorithms according to the category of the idea during intellectual property protection for the system according to Supplementary Note 3, characterized in that. (Supplementary Note 47) The protection unit estimates the emotions of employees and determines the protection priority based on the estimated emotions of employees for the system according to Supplementary Note 3, characterized in that. (Supplementary Note 48) The protection unit performs protection weighting based on the submission time of the idea during intellectual property protection for the system according to Supplementary Note 3, characterized in that. (Supplementary Note 49) The protection unit refers to the related literature of the idea during intellectual property protection to improve the accuracy of protection for the system according to Supplementary Note 3, characterized in that. (Supplementary Note 50) The design unit estimates the emotions of employees and adjusts the design method of the feedback mechanism based on the estimated emotions of employees for the system according to Supplementary Note 4, characterized in that. (Supplementary Note 51) The design unit When designing a feedback mechanism, the optimal design method is selected by referring to past feedback data. The system described in Appendix 4, characterized by the features described herein. (Note 52) The aforementioned design department, When designing a feedback mechanism, different design algorithms are applied depending on the feedback category. The system described in Appendix 4, characterized by the features described herein. (Note 53) The aforementioned design department, The system estimates employees' emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 54) The aforementioned design department, When designing a feedback mechanism, weight the design based on when the feedback is submitted. The system described in Appendix 4, characterized by the features described herein. (Note 55) The aforementioned design department, When designing a feedback mechanism, refer to relevant literature on feedback to improve the accuracy of the design. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0228] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department gathers information on employees' departments, job responsibilities, skills, and desired networking opportunities. An analysis unit analyzes the information collected by the aforementioned collection unit, A presentation unit presents appropriate matching candidates based on the information analyzed by the aforementioned analysis unit, An arrangement unit that arranges interviews for matching candidates presented by the aforementioned presentation unit, The system comprises a facilitator that facilitates the interview arranged by the aforementioned facilitator. A system characterized by the following features.

2. It has an evaluation unit that assesses ideas in real time. The system according to feature 1.

3. It is equipped with a protection unit for protecting intellectual property. The system according to feature 1.

4. It includes a design department for designing feedback mechanisms. The system according to feature 1.

5. The evaluation unit described above, Evaluation methods such as stage-gate analysis, SWOT analysis, scoring models, and cost-benefit analysis can be selected. The system according to feature 2.

6. The aforementioned protective part is Protecting the intellectual property of ideas The system according to claim 3.

7. The aforementioned design department, To manage ideas efficiently and effectively The system according to feature 4.

8. The aforementioned collection unit is We estimate employees' emotions and adjust the timing of information gathering based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze employees' past interaction history to select the most suitable information gathering method. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, filter it based on employees' current projects and areas of interest. The system according to feature 1.

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