system

Abstract

The system according to this embodiment aims to quickly and accurately determine who knows what within the company. [Solution] The system according to the embodiment comprises a database unit, a graph database unit, a learning unit, a question reception unit, and an answer generation unit. The database unit stores job and occupation-related data. The graph database unit stores communication information between employees. The learning unit learns from the database unit and the graph database unit. The question reception unit receives questions from users. The answer generation unit generates appropriate answers to questions received by the question reception unit based on the information learned by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly and accurately grasp who in the company knew what.

[0005] The system according to the embodiment aims to quickly and accurately grasp who in the company knew what.

Means for Solving the Problems

[0006] ​The system according to this embodiment comprises a database unit, a graph database unit, a learning unit, a question reception unit, and an answer generation unit. The database unit stores job and occupation-related data. The graph database unit stores communication information between employees. The learning unit learns from the database unit and the graph database unit. The question reception unit receives questions from users. The answer generation unit generates appropriate answers to questions received by the question reception unit based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately determine who knows what 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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 2 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM, 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) An information provision system according to an embodiment of the present invention is a system that enables AI to answer who knows what within a company. This information provision system uses data such as job titles and job types, and a graph database as training data. Next, the generating AI learns from daily work content and communication networking, and provides appropriate answers to questions. For example, if a company is looking for someone knowledgeable about forestry, the generating AI will provide information such as "a specific employee is communicating with an expert" or "an employee previously planned a specific project." The main components of this system are as follows: job title-related database, graph database, learning unit, question reception unit, and answer generation unit. This system makes it possible to quickly grasp who knows what within a company and to efficiently consult with the appropriate person. First, a job title-related database and a graph database are used as training data. Next, the generating AI learns from daily work content and communication networking, and provides appropriate answers to questions. For example, if a company is looking for someone knowledgeable about forestry, the generating AI will provide information such as "a specific employee is communicating with an expert" or "an employee previously planned a specific project." The main components of this system are as follows: job / occupation-related database, graph database, learning unit, question reception unit, and answer generation unit. This system allows for quick identification of who knows what within the company, enabling efficient consultation with the appropriate person. Thus, the information provision system allows for quick identification of who knows what within the company, enabling efficient consultation with the appropriate person.

[0029] The information provision system according to this embodiment comprises a database unit, a graph database unit, a learning unit, a question reception unit, and an answer generation unit. The database unit stores job / occupation-related data. The database unit can store data such as job content, occupation classification, and position. The database unit may include AI processing. The graph database unit stores communication information between employees. The graph database unit can store information such as email exchanges, meeting records, and chat logs. The graph database unit may include AI processing. The learning unit learns the database unit and the graph database unit. The learning unit includes generation AI processing. The learning unit learns the job / occupation-related database and the graph database using, for example, machine learning algorithms and data mining techniques. The question reception unit receives questions from users. The question reception unit can receive questions in, for example, text format, audio format, image format, etc. The question reception unit may include AI processing. The answer generation unit generates appropriate answers to questions received by the question reception unit based on the information learned by the learning unit. The response generation unit includes processing by a generation AI. The response generation unit generates appropriate responses based on criteria such as accuracy, relevance, and level of detail. As a result, the information provision system according to the embodiment can quickly grasp who knows what within the company and efficiently consult with the appropriate person.

[0030] The database section stores job and occupation-related data. Specifically, it can store detailed data such as job description, job classification, position, skill set, years of experience, and past project history. This provides the database section with a foundation for gaining a detailed understanding of employees' expertise and experience. The database section may also include AI processing. For example, natural language processing (NLP) technology can be used to automatically extract information from employee resumes and work reports and store it in the database. The database section also regularly updates and maintains the data to keep it up-to-date. This ensures that the database section always provides the latest job and occupation-related data, improving the accuracy and reliability of the entire system. Furthermore, the database section manages access rights to ensure the protection of confidential information. For example, it can be configured so that only employees with specific positions or departments can access the data. This allows the database section to provide necessary information appropriately while ensuring information security.

[0031] The graph database section stores communication information among employees. Specifically, it can store information such as email exchanges, meeting records, chat logs, project management tool updates, and editing history of collaborative documents. This provides a foundation for gaining a detailed understanding of employee relationships and communication patterns. The graph database section may also include AI processing. For example, it can use social network analysis (SNA) technology to visualize employee communication networks and identify key hubs and bridges. The graph database section can also analyze the frequency and content of communications to evaluate team collaboration and information flow. This allows the graph database section to provide valuable insights for improving the efficiency and effectiveness of communication within the organization. Furthermore, the graph database section takes measures to ensure data privacy and security. For example, it can anonymize personal information and encrypt data to prevent the leakage of confidential information. This allows the graph database section to provide a secure and reliable platform for managing communication information.

[0032] The learning unit learns from the database unit and the graph database unit. Specifically, it learns from job-related databases and graph databases using machine learning algorithms and data mining techniques. The learning unit also includes generative AI processing. For example, the generative AI can suggest optimal project team formations based on employees' skill sets and experience. It can also analyze communication patterns among employees and provide advice to strengthen team collaboration. Furthermore, the learning unit can predict future trends and risks based on historical data. For example, it can predict when demand for specific skill sets will increase or the risks associated with project progress, allowing for proactive countermeasures. This enables the learning unit to provide valuable insights to improve the efficiency and productivity of the entire organization. In addition, the learning unit can continuously update its learning results, enabling highly accurate predictions and suggestions based on the latest information. This allows the learning unit to play a crucial role in adapting to the ever-changing business environment and maintaining the organization's competitiveness.

[0033] The question reception unit receives questions from users. Specifically, it can accept questions in various formats, including text, audio, and image. The question reception unit may also incorporate AI processing. For example, it can use natural language processing (NLP) technology to analyze user questions and classify them into appropriate categories. It can also use speech recognition technology to convert audio questions into text and analyze them. Furthermore, it can use image recognition technology to analyze image questions and extract relevant information. This allows the question reception unit to handle diverse question formats from users and quickly and accurately understand the content of the questions. In addition, the question reception unit can learn the user's past question history and behavior patterns to provide personalized responses. For example, it can identify topics and areas of interest that a particular user frequently asks about and prioritize providing relevant information. This allows the question reception unit to improve user satisfaction and achieve efficient information delivery.

[0034] The answer generation unit generates appropriate answers to questions received by the question reception unit, based on information learned by the learning unit. Specifically, this includes processing by the generation AI. For example, the generation AI extracts information from relevant databases and graph databases based on the question content and generates an answer. The generation AI generates appropriate answers based on criteria such as accuracy, relevance, and level of detail. For example, if a user asks a question about a specific job, the generation AI can provide information on the job content, required skill set, and related projects. Also, if a user asks about communication history with a specific employee, the generation AI can extract relevant emails and meeting records from the graph database and generate an answer. Furthermore, the answer generation unit can evaluate the quality of the generated answers and make corrections or additions as needed. This allows the answer generation unit to provide users with high-quality information and achieve quick and accurate answers. In addition, the answer generation unit can continuously improve the accuracy and quality of answers by collecting user feedback and using it as learning data for the generation AI. This allows the answer generation unit to always provide the latest information and highly accurate answers, improving user satisfaction.

[0035] The learning unit can learn from job / occupation-related databases and graph databases. The learning unit learns from job / occupation-related databases and graph databases using, for example, machine learning algorithms and data mining techniques. This allows it to provide more accurate information by learning from job / occupation-related databases and graph databases. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input job / occupation-related databases and graph databases into a generative AI, which can then perform the learning process.

[0036] The question reception unit can receive questions from users. The question reception unit can receive questions in various formats, such as text, audio, and image. By receiving questions from users, it can provide appropriate answers. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user questions into the AI, which can then receive the questions.

[0037] The answer generation unit can generate appropriate answers to questions received by the question reception unit, based on information learned by the learning unit. The answer generation unit generates appropriate answers based on criteria such as accuracy, relevance, and level of detail. This allows for quick and accurate responses to user questions by generating appropriate answers based on learned information. Some or all of the above-described processes in the answer generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the answer generation unit can input questions received by the question reception unit into a generation AI, which can then generate an appropriate answer.

[0038] The response generation unit can provide information on which specific employees are communicating with experts. For example, the response generation unit can provide information on which specific employees are exchanging emails with experts or discussing with experts in meetings. By providing information on which employees are communicating with experts, it is possible to identify employees with the appropriate expertise. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input information on which specific employees are communicating with experts into a generation AI, and the generation AI can provide that information.

[0039] The response generation unit can provide information about an employee's past involvement in planning a specific project. For example, the response generation unit can provide information about an employee's past leadership role in a specific project or information about their experience creating project proposals. By providing information about employees who have planned specific projects, it is possible to identify employees with project experience. Some or all of the above-described processes in the response generation unit may be performed using a generation AI, or they may not. For example, the response generation unit can input information about an employee's past involvement in planning a specific project into a generation AI, and the generation AI can then provide that information.

[0040] The database unit can classify data when acquiring job-related data, taking into account employees' skill levels and years of experience. For example, the database unit can classify data into beginner, intermediate, and advanced categories according to employees' skill levels. It can also classify data into new employees, mid-career employees, and veteran employees based on employees' years of experience. Furthermore, the database unit can combine employees' skill sets and years of experience to perform optimal data classification. This allows for more appropriate data management by classifying data while considering employees' skill levels and years of experience. Some or all of the above processing in the database unit may be performed using AI, or not. For example, the database unit can input employee skill level and years of experience data into an AI, which can then classify the data.

[0041] The database unit can incorporate employee self-assessments and supervisor evaluations when building the database. For example, the database unit can incorporate employee self-assessments into the database and perform data classification based on those assessments. It can also incorporate supervisor evaluations into the database and set data priorities based on those evaluations. Furthermore, the database unit can combine self-assessments and supervisor evaluations to perform comprehensive data classification. This allows for more accurate data management by incorporating employee self-assessments and supervisor evaluations. Some or all of the above processes in the database unit may be performed using AI, or not. For example, the database unit can input employee self-assessment and supervisor evaluation data into an AI, which can then classify the data.

[0042] The database unit can add employee qualifications and certifications when building the database. For example, the database unit can add employee qualifications to the database and perform data classification based on those qualifications. The database unit can also reflect employee certifications in the database and set data priorities based on those certifications. Furthermore, the database unit can combine qualifications and certifications to perform comprehensive data classification. This allows for more accurate data management by adding employee qualifications and certifications. Some or all of the above processes in the database unit may be performed using AI or not. For example, the database unit can input employee qualifications and certifications into an AI, which can then classify the data.

[0043] The database unit can include information about training and seminars attended by employees when updating the database. For example, the database unit can add information about training attended by employees to the database and classify the data based on the training content. The database unit can also reflect information about seminars attended by employees in the database and set data priorities based on the seminar content. Furthermore, the database unit can combine training and seminar information to perform comprehensive data classification. This allows for more accurate data management by including information about training and seminars attended by employees. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input information about employee training and seminars into AI, and the AI ​​can classify the data.

[0044] The graph database unit can generate nodes and edges when building a graph database, taking into account the frequency and content of communication between employees. For example, the graph database unit can generate nodes and edges based on the frequency of email exchanges between employees. It can also generate nodes and edges based on the frequency of meeting participation among employees. Furthermore, it can generate nodes and edges based on the frequency of collaborative project work among employees. This allows for more accurate data management by generating nodes and edges while considering the frequency and content of communication between employees. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input employee communication data into AI, and the AI ​​can generate nodes and edges.

[0045] The graph database unit can reflect project progress and deliverables when updating the graph database. For example, the graph database unit can reflect project progress in the database and display ongoing projects. The graph database unit can also add project deliverables to the database and perform data classification based on deliverables. Furthermore, the graph database unit can combine project progress and deliverables to perform comprehensive data classification. This allows for more accurate data management by reflecting project progress and deliverables. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input project progress and deliverable data into AI, which can then classify the data.

[0046] The graph database unit can add employee department and team information when building the graph database. For example, the graph database unit can add employee department information to the database and perform data classification based on department. The graph database unit can also reflect employee team information in the database and set data priorities based on teams. Furthermore, the graph database unit can combine department and team information to perform comprehensive data classification. This allows for more accurate data management by adding employee department and team information. Some or all of the above processes in the graph database unit may be performed using AI or not. For example, the graph database unit can input employee department and team information data into AI, which can then classify the data.

[0047] The graph database unit can include employees' participation history in company events and activities when updating the graph database. For example, the graph database unit can add information about company events attended by employees to the database and classify the data based on the event content. The graph database unit can also reflect information about company activities attended by employees in the database and set data priorities based on the activity content. Furthermore, the graph database unit can combine participation history of company events and activities to perform comprehensive data classification. This enables more accurate data management by including employees' participation history in company events and activities. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input data on employees' participation history in company events and activities into an AI, which can then classify the data.

[0048] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Thus, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input past learning data into a generative AI, and the generative AI can optimize the learning algorithm.

[0049] The learning unit can improve its learning accuracy by incorporating employee work performance data during the learning process. For example, the learning unit can add employee work performance data to the learning data to improve learning accuracy. The learning unit can also analyze the work performance data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can refer to the work performance data to improve the accuracy of the learning algorithm. In this way, learning accuracy is improved by incorporating employee work performance data. Some or all of the above processes in the learning unit may be performed using generative AI or not. For example, the learning unit can input employee work performance data into a generative AI, which can then optimize the learning algorithm.

[0050] The learning unit can improve its learning accuracy by incorporating employee self-assessment data during the learning process. For example, the learning unit can add employee self-assessment data to the learning data to improve learning accuracy. The learning unit can also analyze the self-assessment data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can refer to the self-assessment data to improve the accuracy of the learning algorithm. In this way, incorporating employee self-assessment data improves learning accuracy. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input employee self-assessment data into a generative AI, which can then optimize the learning algorithm.

[0051] The learning unit can improve its learning accuracy by incorporating outcome data from projects in which employees participated during the learning process. For example, the learning unit can add outcome data from projects in which employees participated to the learning data to improve learning accuracy. The learning unit can also analyze the project outcome data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can refer to the project outcome data to improve the accuracy of the learning algorithm. In this way, learning accuracy is improved by incorporating outcome data from projects in which employees participated. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input outcome data from projects in which employees participated into a generative AI, and the generative AI can optimize the learning algorithm.

[0052] The question reception unit can recommend the most suitable answerer by referring to the user's past question history when a question is received. For example, the question reception unit can automatically recommend the most suitable answerer based on the content of questions the user has asked in the past. The question reception unit can also analyze the user's past question history and recommend employees with relevant expertise. Furthermore, the question reception unit can recommend employees who are knowledgeable in a specific field based on the user's past question history. In this way, the most suitable answerer can be recommended by referring to the user's past question history. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input the user's past question history into AI, and the AI ​​can recommend the most suitable answerer.

[0053] The question reception unit can automatically categorize questions upon receipt, based on their content. For example, the question reception unit can analyze the content of the question and automatically categorize it. It can also classify questions into appropriate categories based on keywords. Furthermore, the question reception unit can classify questions into multiple categories depending on their content. This allows for more appropriate question management by categorizing questions according to their content. Some or all of the above processing in the question reception unit may be performed using AI, or not. For example, the question reception unit can input the content of the question into an AI, which can then categorize it.

[0054] The question reception unit can filter questions based on the user's job duties and position when a question is received. For example, the question reception unit can display only relevant questions based on the user's job duties. It can also filter appropriate questions based on the user's position. Furthermore, the question reception unit can combine the user's job duties and position to filter the most suitable questions. This allows for more appropriate question management by filtering questions based on the user's job duties and position. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user job duty and position data into an AI, which can then filter the questions.

[0055] The question reception unit can prioritize questions based on the user's current projects and areas of interest when a question is received. For example, the question reception unit may prioritize displaying questions related to the user's current projects. The question reception unit can also set question priorities based on the user's areas of interest. Furthermore, the question reception unit can combine the user's projects and areas of interest to determine the optimal question priorities. This allows for more appropriate question management by prioritizing questions based on the user's current projects and areas of interest. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input data on the user's projects and areas of interest into an AI, which can then determine the question priorities.

[0056] The answer generation unit can adjust the level of detail in the answer based on the importance of the question when generating an answer. For example, if the question is of high importance, the answer generation unit will provide a detailed answer. Conversely, if the question is of low importance, the answer generation unit can also provide a concise answer. Furthermore, the answer generation unit can adjust the level of detail in the answer according to the importance of the question. This allows for more appropriate answers by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input question importance data into the generation AI, and the generation AI can adjust the level of detail in the answer.

[0057] The answer generation unit can apply different answer algorithms depending on the question category when generating an answer. For example, the answer generation unit can apply a specialized answer algorithm to technical questions. It can also apply a concise answer algorithm to general questions. Furthermore, the answer generation unit can select the optimal answer algorithm depending on the question category. By applying different answer algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input question category data into a generation AI, which can then apply the optimal answer algorithm.

[0058] The answer generation unit can determine the priority of answers based on when the questions were submitted. For example, if the question was submitted early, the answer generation unit will prioritize generating an answer. Conversely, if the question was submitted late, the answer generation unit may postpone generating an answer. Furthermore, the answer generation unit can adjust the priority of answers according to when the questions were submitted. This allows for more appropriate answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input data on when the questions were submitted into a generation AI, and the generation AI can determine the priority of answers.

[0059] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit will prioritize generating answers when the questions are highly relevant. It can also postpone generating answers when the questions are less relevant. Furthermore, the answer generation unit can adjust the order of answers according to the relevance of the questions. By adjusting the order of answers based on the relevance of the questions, more appropriate answers can be provided. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input data on the relevance of the questions into a generation AI, and the generation AI can adjust the order of the answers.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The information provision system, in its database section, can classify data considering employees' skill levels and years of experience. For example, data can be classified into beginner, intermediate, and advanced categories according to the employee's skill level. It can also classify data into new employees, mid-career employees, and veteran employees based on their years of experience. Furthermore, it can combine employee skill sets and years of experience to achieve optimal data classification. This allows for more appropriate data management by classifying data while considering employees' skill levels and years of experience. Some or all of the above processing in the database section may be performed using AI, or not. For example, the database section can input employee skill level and years of experience data into an AI, which can then classify the data.

[0062] The information provision system can incorporate employee self-assessments and supervisor evaluations into its database. For example, employee self-assessments can be incorporated into the database, and data classification can be performed based on these assessments. Supervisor evaluations can also be incorporated into the database, and data priorities can be set based on these evaluations. Furthermore, self-assessments and supervisor evaluations can be combined to perform comprehensive data classification. This allows for more accurate data management by incorporating employee self-assessments and supervisor evaluations. Some or all of the above processing in the database may be performed using AI, or not. For example, the database can input employee self-assessment and supervisor evaluation data into an AI, which can then classify the data.

[0063] The information provision system allows for the addition of employee qualifications and certification information in the database section. For example, employee qualification information can be added to the database, and data classification can be performed based on qualifications. Furthermore, employee certification information can be reflected in the database, and data priorities can be set based on certifications. In addition, qualifications and certification information can be combined to perform comprehensive data classification. This allows for more accurate data management by adding employee qualifications and certification information. Some or all of the above processing in the database section may be performed using AI, or not. For example, the database section can input employee qualification and certification data into the AI, which can then classify the data.

[0064] The information provision system can include information on training and seminars attended by employees in its database. For example, information on employee training can be added to the database, and data classification can be performed based on the training content. Information on employee seminars can also be reflected in the database, and data priorities can be set based on the seminar content. Furthermore, training and seminar information can be combined to perform comprehensive data classification. This allows for more accurate data management by including information on employee training and seminars. Some or all of the above processing in the database may be performed using AI, or not. For example, the database can input employee training and seminar information into an AI, which can then classify the data.

[0065] The information provision system can generate nodes and edges in its graph database section, taking into account the frequency and content of communication between employees. For example, it can generate nodes and edges based on the frequency of email exchanges between employees. It can also generate nodes and edges based on the frequency of meeting participation among employees. Furthermore, it can generate nodes and edges based on the frequency of collaborative project work among employees. This allows for more accurate data management by generating nodes and edges while considering the frequency and content of communication between employees. Some or all of the above processing in the graph database section may be performed using AI, or it may be performed without AI. For example, the graph database section can input employee communication data into AI, and the AI ​​can generate nodes and edges.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The database section stores job and occupation-related data. For example, it can store data such as job description, occupation classification, and position. The database section may also include AI processing. Step 2: The graph database section stores communication information between employees. For example, it can store information such as email exchanges, meeting records, and chat logs. The graph database section may also include AI processing. Step 3: The learning unit learns the database unit and the graph database unit. For example, it learns job / occupation-related databases and graph databases using machine learning algorithms and data mining techniques. The learning unit also includes generative AI processing. Step 4: The question reception unit receives questions from users. For example, it can accept questions in text format, audio format, image format, etc. The question reception unit may also include AI processing. Step 5: The answer generation unit generates appropriate answers to questions received by the question reception unit, based on the information learned by the learning unit. For example, it generates appropriate answers based on criteria such as accuracy, relevance, and level of detail. The answer generation unit includes processing by the generating AI.

[0068] (Example of form 2) An information provision system according to an embodiment of the present invention is a system that enables AI to answer who knows what within a company. This information provision system uses data such as job titles and job types, and a graph database as training data. Next, the generating AI learns from daily work content and communication networking, and provides appropriate answers to questions. For example, if a company is looking for someone knowledgeable about forestry, the generating AI will provide information such as "a specific employee is communicating with an expert" or "an employee previously planned a specific project." The main components of this system are as follows: job title-related database, graph database, learning unit, question reception unit, and answer generation unit. This system makes it possible to quickly grasp who knows what within a company and to efficiently consult with the appropriate person. First, a job title-related database and a graph database are used as training data. Next, the generating AI learns from daily work content and communication networking, and provides appropriate answers to questions. For example, if a company is looking for someone knowledgeable about forestry, the generating AI will provide information such as "a specific employee is communicating with an expert" or "an employee previously planned a specific project." The main components of this system are as follows: job / occupation-related database, graph database, learning unit, question reception unit, and answer generation unit. This system allows for quick identification of who knows what within the company, enabling efficient consultation with the appropriate person. Thus, the information provision system allows for quick identification of who knows what within the company, enabling efficient consultation with the appropriate person.

[0069] The information provision system according to this embodiment comprises a database unit, a graph database unit, a learning unit, a question reception unit, and an answer generation unit. The database unit stores job / occupation-related data. The database unit can store data such as job content, occupation classification, and position. The database unit may include AI processing. The graph database unit stores communication information between employees. The graph database unit can store information such as email exchanges, meeting records, and chat logs. The graph database unit may include AI processing. The learning unit learns the database unit and the graph database unit. The learning unit includes generation AI processing. The learning unit learns the job / occupation-related database and the graph database using, for example, machine learning algorithms and data mining techniques. The question reception unit receives questions from users. The question reception unit can receive questions in, for example, text format, audio format, image format, etc. The question reception unit may include AI processing. The answer generation unit generates appropriate answers to questions received by the question reception unit based on the information learned by the learning unit. The response generation unit includes processing by a generation AI. The response generation unit generates appropriate responses based on criteria such as accuracy, relevance, and level of detail. As a result, the information provision system according to the embodiment can quickly grasp who knows what within the company and efficiently consult with the appropriate person.

[0070] The database section stores job and occupation-related data. Specifically, it can store detailed data such as job description, job classification, position, skill set, years of experience, and past project history. This provides the database section with a foundation for gaining a detailed understanding of employees' expertise and experience. The database section may also include AI processing. For example, natural language processing (NLP) technology can be used to automatically extract information from employee resumes and work reports and store it in the database. The database section also regularly updates and maintains the data to keep it up-to-date. This ensures that the database section always provides the latest job and occupation-related data, improving the accuracy and reliability of the entire system. Furthermore, the database section manages access rights to ensure the protection of confidential information. For example, it can be configured so that only employees with specific positions or departments can access the data. This allows the database section to provide necessary information appropriately while ensuring information security.

[0071] The graph database section stores communication information among employees. Specifically, it can store information such as email exchanges, meeting records, chat logs, project management tool updates, and editing history of collaborative documents. This provides a foundation for gaining a detailed understanding of employee relationships and communication patterns. The graph database section may also include AI processing. For example, it can use social network analysis (SNA) technology to visualize employee communication networks and identify key hubs and bridges. The graph database section can also analyze the frequency and content of communications to evaluate team collaboration and information flow. This allows the graph database section to provide valuable insights for improving the efficiency and effectiveness of communication within the organization. Furthermore, the graph database section takes measures to ensure data privacy and security. For example, it can anonymize personal information and encrypt data to prevent the leakage of confidential information. This allows the graph database section to provide a secure and reliable platform for managing communication information.

[0072] The learning unit learns from the database unit and the graph database unit. Specifically, it learns from job-related databases and graph databases using machine learning algorithms and data mining techniques. The learning unit also includes generative AI processing. For example, the generative AI can suggest optimal project team formations based on employees' skill sets and experience. It can also analyze communication patterns among employees and provide advice to strengthen team collaboration. Furthermore, the learning unit can predict future trends and risks based on historical data. For example, it can predict when demand for specific skill sets will increase or the risks associated with project progress, allowing for proactive countermeasures. This enables the learning unit to provide valuable insights to improve the efficiency and productivity of the entire organization. In addition, the learning unit can continuously update its learning results, enabling highly accurate predictions and suggestions based on the latest information. This allows the learning unit to play a crucial role in adapting to the ever-changing business environment and maintaining the organization's competitiveness.

[0073] The question reception unit receives questions from users. Specifically, it can accept questions in various formats, including text, audio, and image. The question reception unit may also incorporate AI processing. For example, it can use natural language processing (NLP) technology to analyze user questions and classify them into appropriate categories. It can also use speech recognition technology to convert audio questions into text and analyze them. Furthermore, it can use image recognition technology to analyze image questions and extract relevant information. This allows the question reception unit to handle diverse question formats from users and quickly and accurately understand the content of the questions. In addition, the question reception unit can learn the user's past question history and behavior patterns to provide personalized responses. For example, it can identify topics and areas of interest that a particular user frequently asks about and prioritize providing relevant information. This allows the question reception unit to improve user satisfaction and achieve efficient information delivery.

[0074] The answer generation unit generates appropriate answers to questions received by the question reception unit, based on information learned by the learning unit. Specifically, this includes processing by the generation AI. For example, the generation AI extracts information from relevant databases and graph databases based on the question content and generates an answer. The generation AI generates appropriate answers based on criteria such as accuracy, relevance, and level of detail. For example, if a user asks a question about a specific job, the generation AI can provide information on the job content, required skill set, and related projects. Also, if a user asks about communication history with a specific employee, the generation AI can extract relevant emails and meeting records from the graph database and generate an answer. Furthermore, the answer generation unit can evaluate the quality of the generated answers and make corrections or additions as needed. This allows the answer generation unit to provide users with high-quality information and achieve quick and accurate answers. In addition, the answer generation unit can continuously improve the accuracy and quality of answers by collecting user feedback and using it as learning data for the generation AI. This allows the answer generation unit to always provide the latest information and highly accurate answers, improving user satisfaction.

[0075] The learning unit can learn from job / occupation-related databases and graph databases. The learning unit learns from job / occupation-related databases and graph databases using, for example, machine learning algorithms and data mining techniques. This allows it to provide more accurate information by learning from job / occupation-related databases and graph databases. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input job / occupation-related databases and graph databases into a generative AI, which can then perform the learning process.

[0076] The question reception unit can receive questions from users. The question reception unit can receive questions in various formats, such as text, audio, and image. By receiving questions from users, it can provide appropriate answers. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user questions into the AI, which can then receive the questions.

[0077] The answer generation unit can generate appropriate answers to questions received by the question reception unit, based on information learned by the learning unit. The answer generation unit generates appropriate answers based on criteria such as accuracy, relevance, and level of detail. This allows for quick and accurate responses to user questions by generating appropriate answers based on learned information. Some or all of the above-described processes in the answer generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the answer generation unit can input questions received by the question reception unit into a generation AI, which can then generate an appropriate answer.

[0078] The response generation unit can provide information on which specific employees are communicating with experts. For example, the response generation unit can provide information on which specific employees are exchanging emails with experts or discussing with experts in meetings. By providing information on which employees are communicating with experts, it is possible to identify employees with the appropriate expertise. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input information on which specific employees are communicating with experts into a generation AI, and the generation AI can provide that information.

[0079] The response generation unit can provide information about an employee's past involvement in planning a specific project. For example, the response generation unit can provide information about an employee's past leadership role in a specific project or information about their experience creating project proposals. By providing information about employees who have planned specific projects, it is possible to identify employees with project experience. Some or all of the above-described processes in the response generation unit may be performed using a generation AI, or they may not. For example, the response generation unit can input information about an employee's past involvement in planning a specific project into a generation AI, and the generation AI can then provide that information.

[0080] The database unit can estimate the user's emotions and adjust the data update frequency based on the estimated emotions. For example, if the user is stressed, the database unit can set a lower data update frequency to avoid providing too much information. Conversely, if the user is relaxed, the database unit can set a higher data update frequency to provide the latest information. Furthermore, if the user is in a hurry, the database unit can prioritize updating only the most important data. This allows for the provision of appropriate information to the user by adjusting the data update frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the data update frequency.

[0081] The database unit can classify data when acquiring job-related data, taking into account employees' skill levels and years of experience. For example, the database unit can classify data into beginner, intermediate, and advanced categories according to employees' skill levels. It can also classify data into new employees, mid-career employees, and veteran employees based on employees' years of experience. Furthermore, the database unit can combine employees' skill sets and years of experience to perform optimal data classification. This allows for more appropriate data management by classifying data while considering employees' skill levels and years of experience. Some or all of the above processing in the database unit may be performed using AI, or not. For example, the database unit can input employee skill level and years of experience data into an AI, which can then classify the data.

[0082] The database unit can incorporate employee self-assessments and supervisor evaluations when building the database. For example, the database unit can incorporate employee self-assessments into the database and perform data classification based on those assessments. It can also incorporate supervisor evaluations into the database and set data priorities based on those evaluations. Furthermore, the database unit can combine self-assessments and supervisor evaluations to perform comprehensive data classification. This allows for more accurate data management by incorporating employee self-assessments and supervisor evaluations. Some or all of the above processes in the database unit may be performed using AI, or not. For example, the database unit can input employee self-assessment and supervisor evaluation data into an AI, which can then classify the data.

[0083] The database unit can estimate the user's emotions and adjust the data display method based on the estimated emotions. For example, if the user is stressed, the database unit can provide a simple and highly visible display method. If the user is relaxed, the database unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the database unit can provide a concise display method. This allows for the provision of appropriate information to the user by adjusting the data display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the data display method.

[0084] The database unit can add employee qualifications and certifications when building the database. For example, the database unit can add employee qualifications to the database and perform data classification based on those qualifications. The database unit can also reflect employee certifications in the database and set data priorities based on those certifications. Furthermore, the database unit can combine qualifications and certifications to perform comprehensive data classification. This allows for more accurate data management by adding employee qualifications and certifications. Some or all of the above processes in the database unit may be performed using AI or not. For example, the database unit can input employee qualifications and certifications into an AI, which can then classify the data.

[0085] The database unit can include information about training and seminars attended by employees when updating the database. For example, the database unit can add information about training attended by employees to the database and classify the data based on the training content. The database unit can also reflect information about seminars attended by employees in the database and set data priorities based on the seminar content. Furthermore, the database unit can combine training and seminar information to perform comprehensive data classification. This allows for more accurate data management by including information about training and seminars attended by employees. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input information about employee training and seminars into AI, and the AI ​​can classify the data.

[0086] The graph database unit can estimate the user's emotions and adjust the graph display method based on the estimated emotions. For example, if the user is stressed, the graph database unit can provide a simple and highly visible graph display. If the user is relaxed, the graph database unit can also provide a graph display containing detailed information. Furthermore, if the user is in a hurry, the graph database unit can provide a graph display that gets straight to the point. In this way, by adjusting the graph display method according to the user's emotions, it is possible to provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the graph database unit may be performed using AI or not using AI. For example, the graph database unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the graph display method.

[0087] The graph database unit can generate nodes and edges when building a graph database, taking into account the frequency and content of communication between employees. For example, the graph database unit can generate nodes and edges based on the frequency of email exchanges between employees. It can also generate nodes and edges based on the frequency of meeting participation among employees. Furthermore, it can generate nodes and edges based on the frequency of collaborative project work among employees. This allows for more accurate data management by generating nodes and edges while considering the frequency and content of communication between employees. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input employee communication data into AI, and the AI ​​can generate nodes and edges.

[0088] The graph database unit can reflect project progress and deliverables when updating the graph database. For example, the graph database unit can reflect project progress in the database and display ongoing projects. The graph database unit can also add project deliverables to the database and perform data classification based on deliverables. Furthermore, the graph database unit can combine project progress and deliverables to perform comprehensive data classification. This allows for more accurate data management by reflecting project progress and deliverables. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input project progress and deliverable data into AI, which can then classify the data.

[0089] The graph database unit can estimate the user's emotions and adjust the graph layout based on the estimated emotions. For example, if the user is stressed, the graph database unit can provide a simple and highly visible layout. If the user is relaxed, the graph database unit can also provide a layout containing detailed information. Furthermore, if the user is in a hurry, the graph database unit can provide a layout that gets straight to the point. This allows for the provision of appropriate information to the user by adjusting the graph layout according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the graph layout.

[0090] The graph database unit can add employee department and team information when building the graph database. For example, the graph database unit can add employee department information to the database and perform data classification based on department. The graph database unit can also reflect employee team information in the database and set data priorities based on teams. Furthermore, the graph database unit can combine department and team information to perform comprehensive data classification. This allows for more accurate data management by adding employee department and team information. Some or all of the above processes in the graph database unit may be performed using AI or not. For example, the graph database unit can input employee department and team information data into AI, which can then classify the data.

[0091] The graph database unit can include employees' participation history in company events and activities when updating the graph database. For example, the graph database unit can add information about company events attended by employees to the database and classify the data based on the event content. The graph database unit can also reflect information about company activities attended by employees in the database and set data priorities based on the activity content. Furthermore, the graph database unit can combine participation history of company events and activities to perform comprehensive data classification. This enables more accurate data management by including employees' participation history in company events and activities. Some or all of the above processing in the graph database unit may be performed using AI or not. For example, the graph database unit can input data on employees' participation history in company events and activities into an AI, which can then classify the data.

[0092] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select training data that focuses on the essentials. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This allows for more effective learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI or not. For example, the learning unit can input user emotion data into a generative AI, which can estimate the emotions and select training data.

[0093] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Thus, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input past learning data into a generative AI, and the generative AI can optimize the learning algorithm.

[0094] The learning unit can improve its learning accuracy by incorporating employee work performance data during the learning process. For example, the learning unit can add employee work performance data to the learning data to improve learning accuracy. The learning unit can also analyze the work performance data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can refer to the work performance data to improve the accuracy of the learning algorithm. In this way, learning accuracy is improved by incorporating employee work performance data. Some or all of the above processes in the learning unit may be performed using generative AI or not. For example, the learning unit can input employee work performance data into a generative AI, which can then optimize the learning algorithm.

[0095] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit can set a higher learning frequency. Conversely, if the user is in a hurry, the learning unit can set a lower learning frequency. Furthermore, if the user is excited, the learning unit can adjust the learning frequency. This allows for more effective learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using the generative AI or not. For example, the learning unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the learning frequency.

[0096] The learning unit can improve its learning accuracy by incorporating employee self-assessment data during the learning process. For example, the learning unit can add employee self-assessment data to the learning data to improve learning accuracy. The learning unit can also analyze the self-assessment data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can refer to the self-assessment data to improve the accuracy of the learning algorithm. In this way, incorporating employee self-assessment data improves learning accuracy. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input employee self-assessment data into a generative AI, which can then optimize the learning algorithm.

[0097] The learning unit can improve its learning accuracy by incorporating outcome data from projects in which employees participated during the learning process. For example, the learning unit can add outcome data from projects in which employees participated to the learning data to improve learning accuracy. The learning unit can also analyze the project outcome data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can refer to the project outcome data to improve the accuracy of the learning algorithm. In this way, learning accuracy is improved by incorporating outcome data from projects in which employees participated. Some or all of the above processes in the learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input outcome data from projects in which employees participated into a generative AI, and the generative AI can optimize the learning algorithm.

[0098] The question reception unit can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, if the user is stressed, the question reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the question reception unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the question reception unit can prioritize voice input and receive questions quickly. This allows for more appropriate question reception by adjusting the question reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the question reception method.

[0099] The question reception unit can recommend the most suitable answerer by referring to the user's past question history when a question is received. For example, the question reception unit can automatically recommend the most suitable answerer based on the content of questions the user has asked in the past. The question reception unit can also analyze the user's past question history and recommend employees with relevant expertise. Furthermore, the question reception unit can recommend employees who are knowledgeable in a specific field based on the user's past question history. In this way, the most suitable answerer can be recommended by referring to the user's past question history. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input the user's past question history into AI, and the AI ​​can recommend the most suitable answerer.

[0100] The question reception unit can automatically categorize questions upon receipt, based on their content. For example, the question reception unit can analyze the content of the question and automatically categorize it. It can also classify questions into appropriate categories based on keywords. Furthermore, the question reception unit can classify questions into multiple categories depending on their content. This allows for more appropriate question management by categorizing questions according to their content. Some or all of the above processing in the question reception unit may be performed using AI, or not. For example, the question reception unit can input the content of the question into an AI, which can then categorize it.

[0101] The question reception unit can estimate the user's emotions and determine the priority of questions based on the estimated emotions. For example, if the user is nervous, the question reception unit will set a higher priority for the question. Conversely, if the user is relaxed, the question reception unit can also set a lower priority for the question. Furthermore, if the user is in a hurry, the question reception unit can quickly determine the priority of the question. This allows for more appropriate question management by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the questions.

[0102] The question reception unit can filter questions based on the user's job duties and position when a question is received. For example, the question reception unit can display only relevant questions based on the user's job duties. It can also filter appropriate questions based on the user's position. Furthermore, the question reception unit can combine the user's job duties and position to filter the most suitable questions. This allows for more appropriate question management by filtering questions based on the user's job duties and position. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user job duty and position data into an AI, which can then filter the questions.

[0103] The question reception unit can prioritize questions based on the user's current projects and areas of interest when a question is received. For example, the question reception unit may prioritize displaying questions related to the user's current projects. The question reception unit can also set question priorities based on the user's areas of interest. Furthermore, the question reception unit can combine the user's projects and areas of interest to determine the optimal question priorities. This allows for more appropriate question management by prioritizing questions based on the user's current projects and areas of interest. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input data on the user's projects and areas of interest into an AI, which can then determine the question priorities.

[0104] The response generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is nervous, the response generation unit can provide a simple and easily understandable response. If the user is relaxed, the response generation unit can also provide a response that includes detailed information. Furthermore, if the user is in a hurry, the response generation unit can provide a concise response. By adjusting the way the response is expressed according to the user's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the response generation unit may be performed using the generative AI or not. For example, the response generation unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the way the response is expressed.

[0105] The answer generation unit can adjust the level of detail in the answer based on the importance of the question when generating an answer. For example, if the question is of high importance, the answer generation unit will provide a detailed answer. Conversely, if the question is of low importance, the answer generation unit can also provide a concise answer. Furthermore, the answer generation unit can adjust the level of detail in the answer according to the importance of the question. This allows for more appropriate answers by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input question importance data into the generation AI, and the generation AI can adjust the level of detail in the answer.

[0106] The answer generation unit can apply different answer algorithms depending on the question category when generating an answer. For example, the answer generation unit can apply a specialized answer algorithm to technical questions. It can also apply a concise answer algorithm to general questions. Furthermore, the answer generation unit can select the optimal answer algorithm depending on the question category. By applying different answer algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input question category data into a generation AI, which can then apply the optimal answer algorithm.

[0107] The response generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is nervous, the response generation unit can provide a short, to-the-point response. If the user is relaxed, the response generation unit can also provide a longer response with more detailed explanations. Furthermore, if the user is in a hurry, the response generation unit can provide a quick and concise response. By adjusting the length of the response according to the user's emotions, more appropriate responses can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the response generation unit may be performed using or without a generative AI. For example, the response generation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the response.

[0108] The answer generation unit can determine the priority of answers based on when the questions were submitted. For example, if the question was submitted early, the answer generation unit will prioritize generating an answer. Conversely, if the question was submitted late, the answer generation unit may postpone generating an answer. Furthermore, the answer generation unit can adjust the priority of answers according to when the questions were submitted. This allows for more appropriate answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input data on when the questions were submitted into a generation AI, and the generation AI can determine the priority of answers.

[0109] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit will prioritize generating answers when the questions are highly relevant. It can also postpone generating answers when the questions are less relevant. Furthermore, the answer generation unit can adjust the order of answers according to the relevance of the questions. By adjusting the order of answers based on the relevance of the questions, more appropriate answers can be provided. Some or all of the above processing in the answer generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answer generation unit can input data on the relevance of the questions into a generation AI, and the generation AI can adjust the order of the answers.

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

[0111] The information provision system can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided and the input steps minimized. If the user is relaxed, detailed input options can be provided and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to receive questions quickly. This allows for more appropriate question reception by adjusting the question reception method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. 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 question reception unit may be performed using AI or not. For example, the question reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the question reception method.

[0112] The information provision system can estimate the user's emotions and adjust the way data is displayed based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the way data is displayed according to the user's emotions, it becomes possible to provide the user with appropriate information. Emotion estimation is achieved 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 database section may be performed using AI or not using AI. For example, the database section can input user emotion data into the generative AI, which can estimate the emotions and adjust the way the data is displayed.

[0113] The information provision system can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, detailed training data can be selected. If the user is in a hurry, concise training data can be selected. Furthermore, if the user is excited, visually stimulating training data can be selected. This allows for more effective learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI or not. For example, the learning unit can input user emotion data into a generative AI, which can estimate the emotions and select training data.

[0114] The information provision system can estimate the user's emotions and adjust the way it expresses its responses based on those emotions. For example, if the user is nervous, it can provide a simple and easily understandable response. If the user is relaxed, it can provide a response that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise response. By adjusting the way it expresses responses according to the user's emotions, it becomes possible to provide more appropriate responses. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response generation unit may be performed using or without a generative AI. For example, the response generation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way it expresses the responses.

[0115] The information provision system can estimate the user's emotions and determine the priority of questions based on the estimated emotions. For example, if the user is nervous, the question can be given a higher priority. Conversely, if the user is relaxed, the question can be given a lower priority. Furthermore, if the user is in a hurry, the question priority can be determined quickly. This allows for more appropriate question management by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of questions.

[0116] The information provision system, in its database section, can classify data considering employees' skill levels and years of experience. For example, data can be classified into beginner, intermediate, and advanced categories according to the employee's skill level. It can also classify data into new employees, mid-career employees, and veteran employees based on their years of experience. Furthermore, it can combine employee skill sets and years of experience to achieve optimal data classification. This allows for more appropriate data management by classifying data while considering employees' skill levels and years of experience. Some or all of the above processing in the database section may be performed using AI, or not. For example, the database section can input employee skill level and years of experience data into an AI, which can then classify the data.

[0117] The information provision system can incorporate employee self-assessments and supervisor evaluations into its database. For example, employee self-assessments can be incorporated into the database, and data classification can be performed based on these assessments. Supervisor evaluations can also be incorporated into the database, and data priorities can be set based on these evaluations. Furthermore, self-assessments and supervisor evaluations can be combined to perform comprehensive data classification. This allows for more accurate data management by incorporating employee self-assessments and supervisor evaluations. Some or all of the above processing in the database may be performed using AI, or not. For example, the database can input employee self-assessment and supervisor evaluation data into an AI, which can then classify the data.

[0118] The information provision system allows for the addition of employee qualifications and certification information in the database section. For example, employee qualification information can be added to the database, and data classification can be performed based on qualifications. Furthermore, employee certification information can be reflected in the database, and data priorities can be set based on certifications. In addition, qualifications and certification information can be combined to perform comprehensive data classification. This allows for more accurate data management by adding employee qualifications and certification information. Some or all of the above processing in the database section may be performed using AI, or not. For example, the database section can input employee qualification and certification data into the AI, which can then classify the data.

[0119] The information provision system can include information on training and seminars attended by employees in its database. For example, information on employee training can be added to the database, and data classification can be performed based on the training content. Information on employee seminars can also be reflected in the database, and data priorities can be set based on the seminar content. Furthermore, training and seminar information can be combined to perform comprehensive data classification. This allows for more accurate data management by including information on employee training and seminars. Some or all of the above processing in the database may be performed using AI, or not. For example, the database can input employee training and seminar information into an AI, which can then classify the data.

[0120] The information provision system can generate nodes and edges in its graph database section, taking into account the frequency and content of communication between employees. For example, it can generate nodes and edges based on the frequency of email exchanges between employees. It can also generate nodes and edges based on the frequency of meeting participation among employees. Furthermore, it can generate nodes and edges based on the frequency of collaborative project work among employees. This allows for more accurate data management by generating nodes and edges while considering the frequency and content of communication between employees. Some or all of the above processing in the graph database section may be performed using AI, or it may be performed without AI. For example, the graph database section can input employee communication data into AI, and the AI ​​can generate nodes and edges.

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

[0122] Step 1: The database section stores job and occupation-related data. For example, it can store data such as job description, occupation classification, and position. The database section may also include AI processing. Step 2: The graph database section stores communication information between employees. For example, it can store information such as email exchanges, meeting records, and chat logs. The graph database section may also include AI processing. Step 3: The learning unit learns the database unit and the graph database unit. For example, it learns job / occupation-related databases and graph databases using machine learning algorithms and data mining techniques. The learning unit also includes generative AI processing. Step 4: The question reception unit receives questions from users. For example, it can accept questions in text format, audio format, image format, etc. The question reception unit may also include AI processing. Step 5: The answer generation unit generates appropriate answers to questions received by the question reception unit, based on the information learned by the learning unit. For example, it generates appropriate answers based on criteria such as accuracy, relevance, and level of detail. The answer generation unit includes processing by the generating AI.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the database unit, graph database unit, learning unit, question reception unit, and answer generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the database unit is implemented by the database 24 of the data processing unit 12. The graph database unit is implemented by the database 24 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The question reception unit is implemented by the reception device 38 of the smart device 14. The answer generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice 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.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the database unit, graph database unit, learning unit, question reception unit, and answer generation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the database unit is implemented by the database 24 of the data processing unit 12. The graph database unit is implemented by the database 24 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The question reception unit is implemented by the microphone 238 of the smart glasses 214. The answer generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice 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.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the database unit, graph database unit, learning unit, question reception unit, and answer generation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the database unit is implemented by the database 24 of the data processing unit 12. The graph database unit is implemented by the database 24 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The question reception unit is implemented by the microphone 238 of the headset terminal 314. The answer generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice 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.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the database unit, graph database unit, learning unit, question receiving unit, and answer generation unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the database unit is implemented by the database 24 of the data processing unit 12. The graph database unit is implemented by the database 24 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The question receiving unit is implemented by the microphone 238 of the robot 414. The answer generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A database unit that stores job and occupation-related data, A graph database section that stores communication information between employees, A learning unit that learns the database unit and the graph database unit, A question reception department that accepts questions from users, The system includes: an answer generation unit that generates an appropriate answer to a question received by the question receiving unit, based on information learned by the learning unit; and an answer generation unit that generates an appropriate answer to a question received by the question receiving unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn about job / occupation-related databases and graph databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned question reception department, We accept questions from users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned response generation unit, The learning unit generates appropriate answers to questions received by the question receiving unit based on the information it has learned. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned response generation unit, Provide information on when specific employees are communicating with experts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned response generation unit, Provide information about an employee's previous plans for a specific project. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned database unit is It estimates the user's emotions and adjusts the data update frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned database unit is When acquiring job-related data, classify the data considering the employee's skill level and years of experience. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned database unit is When building the database, employee self-assessments and supervisor evaluations should be incorporated. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned database unit is It estimates the user's emotions and adjusts how data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned database unit is When building the database, add employee qualifications and certification information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned database unit is When updating the database, include information about training sessions and seminars attended by employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned graph database unit is It estimates the user's emotions and adjusts how the graph is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned graph database unit is When building a graph database, nodes and edges are generated considering the frequency and content of communication among employees. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned graph database unit is When updating the graph database, reflect the project's progress and deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned graph database unit is It estimates the user's emotions and adjusts the graph layout based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned graph database unit is When building a graph database, add employee department and team information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned graph database unit is When updating the graph database, include employees' participation history in company events and activities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During the learning process, employee work performance data is incorporated to improve learning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During the learning process, employee self-assessment data is incorporated to improve learning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During the learning process, we incorporate outcome data from projects in which employees participated to improve learning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question reception department, The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned question reception department, When a question is submitted, the system recommends the most suitable answerer by referring to the user's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question reception department, When a question is submitted, it will be automatically categorized according to its content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question reception department, The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned question reception department, When a question is submitted, the system filters the question based on the user's job title or position. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned question reception department, When receiving questions, we prioritize them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned response generation unit, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned response generation unit, When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned response generation unit, When generating answers, different answer algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned response generation unit, It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned response generation unit, When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned response generation unit, When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A database unit that stores job and occupation-related data, A graph database section that stores communication information between employees, A learning unit that learns the database unit and the graph database unit, A question reception department that accepts questions from users, The system includes: an answer generation unit that generates an appropriate answer to a question received by the question receiving unit, based on information learned by the learning unit; and an answer generation unit that generates an appropriate answer to a question received by the question receiving unit. A system characterized by the following features.

2. The aforementioned learning unit, Learn about job / occupation-related databases and graph databases. The system according to feature 1.

3. The aforementioned question reception department, We accept questions from users. The system according to feature 1.

4. The aforementioned response generation unit, The learning unit generates appropriate answers to questions received by the question receiving unit based on the information it has learned. The system according to feature 1.

5. The aforementioned response generation unit, Provide information on when specific employees are communicating with experts. The system according to feature 1.

6. The aforementioned response generation unit, Provide information about an employee's previous plans for a specific project. The system according to feature 1.

7. The aforementioned database unit is It estimates the user's emotions and adjusts the data update frequency based on the estimated user emotions. The system according to feature 1.

8. The aforementioned database unit is When acquiring job and occupation-related data, classify the data considering the employee's skill level and years of experience. The system according to feature 1.

9. The aforementioned database unit is When building the database, employee self-assessments and supervisor evaluations should be incorporated. The system according to feature 1.

10. The aforementioned database unit is It estimates the user's emotions and adjusts how data is displayed based on those estimated emotions. The system according to feature 1.

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