system

Abstract

The system according to this embodiment aims to quickly find the appropriate person in charge within the company. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives inquiry information from users. The analysis unit analyzes various tools and history within the company based on the information received by the reception unit. The search unit searches for the appropriate person in charge based on the information analyzed by the analysis unit. The provision unit provides the person in charge information retrieved by the search 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction text related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can quickly find the appropriate person in charge 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 numbered 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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 3o, and a storage 32. The processor 28, the RAM 3o, 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] [[ID=2I]] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The person search system according to an embodiment of the present invention is a system for solving the problem of wasting time and effort within a company because it is difficult to find the right person or contact person for inquiries. This person search system allows users to search for the appropriate person by interacting with an AI agent and inputting information to find the right person or contact person. The user interacts with the AI ​​agent and inputs information such as, "I am looking for someone knowledgeable in the education industry." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and searches various tools and history within the company. The AI ​​agent analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA to search for suitable candidate personnel. For example, when searching for someone knowledgeable in the education industry, it identifies individuals responsible for education-related tasks from these tools and history. The AI ​​agent provides the search results to the user. For example, it provides information such as the contact person's contact details, department, and supervisor, in the form of, "This person is knowledgeable in the education industry." This allows the user to quickly find the appropriate person. This mechanism reduces the time spent searching for the right person and improves work efficiency. Users can find the right person quickly and efficiently through interaction with the AI ​​agent. This improves operational productivity and reduces wasted time and effort. As a result, the personnel search system can efficiently receive, analyze, search, and provide user inquiry information.

[0029] The personnel search system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives inquiry information from users. The reception unit can receive inquiry information in, for example, text format, audio format, image format, etc. The analysis unit analyzes various tools and history within the company based on the information received by the reception unit. The analysis unit analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA, for example. The analysis unit performs analysis based on the algorithm used and the depth of analysis. The search unit searches for the appropriate personnel based on the information analyzed by the analysis unit. The search unit identifies, for example, a person in charge of work related to the education industry. The search unit performs the search based on selection criteria such as the personnel's field of expertise and years of experience. The provision unit provides the personnel information retrieved by the search unit. The provision unit provides, for example, information such as the personnel's contact information, department, and supervisor. The provision unit provides information such as telephone number, email address, department name, and supervisor's name. As a result, the personnel search system according to this embodiment can efficiently receive, analyze, search, and provide user inquiry information.

[0030] The reception department receives inquiry information from users. The reception department can accept inquiry information in various formats, such as text, audio, and image. Specifically, text-based inquiries are received as questions and requests entered by the user, audio inquiries are analyzed using recorded voice messages, and image inquiries are accepted as photos or screenshots taken by the user. This inquiry information is centrally managed by the reception department and stored in a database. When receiving an inquiry, the reception department also collects the user's basic information (name, contact information, department, etc.) and manages it in conjunction with the inquiry content. Furthermore, when receiving an inquiry, the reception department can ask simple questions to determine the user's intent and urgency, and set priorities. For example, high-urgency inquiries can be prioritized for processing by the analysis and search departments. This allows the reception department to efficiently receive and appropriately manage diverse user inquiry information.

[0031] The analytics department analyzes various company tools and histories based on information received by the reception department. For example, the analytics department analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. Specifically, in Salesforce, it analyzes past customer interaction history and sales opportunity information to extract information relevant to the inquiry. In communication tools, it analyzes internal chat history and email exchanges to identify relevant personnel and past response status. In Outsystems, Backlog, and JIRA, it analyzes project management information and task progress to identify projects and tasks related to the inquiry. The analytics department integrates the data collected from these tools and performs analysis using AI. The AI ​​uses natural language processing technology to analyze text data and extract the intent of the inquiry and relevant keywords. It also uses machine learning algorithms to learn patterns for identifying the optimal person in charge based on past inquiry history and response results. This allows the analytics department to quickly and accurately analyze information received from the reception department and provide information to identify the appropriate person in charge.

[0032] The search unit searches for the appropriate person based on the information analyzed by the analysis unit. For example, the search unit identifies individuals who handle tasks related to the education industry. Specifically, it performs searches based on selection criteria such as the person's area of ​​expertise, years of experience, and past handling history, using information provided by the analysis unit. The search unit utilizes the company's HR database and skill matrix to understand each person's area of ​​expertise and skill set. For example, when searching for someone who handles tasks related to the education industry, it identifies individuals with experience working on education-related projects or with a track record of dealing with educational institutions. The search unit also considers the person's current workload and available time slots to select the most suitable person. This allows the search unit to quickly identify the person best suited to the user's inquiry and achieve efficient responses.

[0033] The service provider provides contact information retrieved by the search service provider. This includes information such as the contact person's contact details, department, and supervisor. Specifically, it provides information such as the contact person's phone number, email address, department name, and supervisor's name. The service provider can utilize multiple communication methods to provide this information to users quickly and accurately. For example, it can provide contact person information via email, SMS, or chat tools, depending on the user's preference. The service provider also provides information on the contact person's available times and contact methods to facilitate smooth communication. Furthermore, the service provider can collect user feedback to continuously improve the accuracy and content of the information provided. For example, it can collect evaluations on the appropriateness of the provided contact person information, the speed and quality of the response, and use this feedback to improve the overall system. This allows the service provider to provide users with quick and accurate contact person information and support efficient inquiry handling.

[0034] The analysis unit can analyze tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. For example, the analysis unit can analyze Salesforce data and extract the work history of an employee. It can also analyze the message history of a communication tool and extract the communication history of an employee. Furthermore, it can analyze project data from Outsystems and extract the project history of an employee. For example, the analysis unit can analyze Salesforce data to identify the employee associated with a specific task. It can analyze the message history of a communication tool to identify the employee associated with a specific project. It can analyze project data from Outsystems to identify the employee associated with a specific project. This improves the accuracy of identifying the appropriate employee by analyzing various tools. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input Salesforce data into AI and have the AI ​​perform the extraction of the employee's work history.

[0035] The search unit can identify individuals responsible for tasks related to the education industry. For example, it can identify individuals involved in the development of educational programs. It can also identify individuals involved in the operation of educational institutions. Furthermore, it can identify individuals involved in education-related projects. For example, it can identify individuals involved in the development of educational programs. For example, it can identify individuals involved in the operation of educational institutions. For example, it can identify individuals involved in education-related projects. This allows for the rapid identification of individuals related to the education industry. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can input education-related project data into AI and have the AI ​​perform the identification of individuals.

[0036] The information provision department can provide information such as the contact person's contact details, department, and supervisor. For example, the information provision department can provide the contact person's phone number. It can also provide the contact person's email address. Furthermore, it can provide the contact person's department name. For example, the information provision department can provide the contact person's phone number. It can provide the contact person's email address. It can provide the contact person's department name. This allows for quick contact by providing detailed information about the contact person. Some or all of the above processing in the information provision department may be performed using AI, for example, or not. For example, the information provision department can input the contact person's contact data into AI and have the AI ​​perform the information provision.

[0037] The service provider may include a feedback function to verify the accuracy of search results. For example, the service provider may collect user ratings. The service provider may also set up a method for collecting feedback. Furthermore, the service provider may improve search results based on the feedback. For example, the service provider may collect user ratings, set up a method for collecting feedback, and improve search results based on the feedback. This allows for verification and improvement of the accuracy of search results. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input user feedback data into AI and have the AI ​​improve the search results.

[0038] The analysis unit may have a function to set the information update frequency. For example, the analysis unit can set the daily update frequency. It can also set the weekly update frequency. Furthermore, it can also set the monthly update frequency. For example, the analysis unit can set the daily update frequency. Set the weekly update frequency. Set the monthly update frequency. By setting the information update frequency, the latest information can be used for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information update frequency data into the AI ​​and have the AI ​​perform the setting of the update frequency.

[0039] The reception department can analyze a user's past inquiry history and select the optimal reception method. For example, the reception department can suggest the optimal reception method based on the content of inquiries the user has frequently made in the past. The reception department can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception department can predict and suggest reception methods to be used during specific time periods based on the user's past inquiry history. For example, the reception department can suggest the optimal reception method based on the content of inquiries the user has frequently made in the past. It can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can predict and suggest reception methods to be used during specific time periods based on the user's past inquiry history. This enables efficient response by selecting the optimal reception method based on past inquiry history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the user's past inquiry history data into AI and have the AI ​​select the reception method.

[0040] The reception desk can filter inquiry information based on the user's current work situation and areas of interest. For example, the reception desk can prioritize receiving inquiry information related to a project the user is currently working on. The reception desk can also filter relevant inquiry information based on the user's areas of interest. Furthermore, the reception desk can prioritize receiving inquiry information of high importance depending on the user's work situation. For example, the reception desk can prioritize receiving inquiry information related to a project the user is currently working on. It filters relevant inquiry information based on the user's areas of interest. It prioritizes receiving inquiry information of high importance depending on the user's work situation. In this way, by filtering based on the user's work situation and areas of interest, important inquiry information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user work situation data into AI and have the AI ​​perform the filtering.

[0041] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving inquiry information. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiry information related to that region. The reception desk can also prioritize receiving inquiry information from relevant departments or personnel based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can receive the most relevant inquiry information based on their current location. For example, if the reception desk is in a specific region, it will prioritize receiving inquiry information related to that region. Based on the user's geographical location, it will prioritize receiving inquiry information from relevant departments or personnel. If the user is on the move, it will receive the most relevant inquiry information based on their current location. This enables efficient responses by prioritizing the receipt of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into AI and have the AI ​​perform filtering.

[0042] The reception desk can analyze the user's social media activity and receive relevant information when receiving inquiry information. For example, the reception desk can prioritize receiving inquiry information related to the user's current interests based on the user's social media activity. The reception desk can also filter relevant inquiry information based on the content of the user's social media posts. Furthermore, the reception desk can also receive relevant inquiry information based on the activity of the user's social media followers and friends. For example, the reception desk prioritizes receiving inquiry information related to the user's current interests based on the user's social media activity. It filters relevant inquiry information based on the content of the user's social media posts. It receives relevant inquiry information based on the activity of the user's social media followers and friends. This allows for more appropriate responses by receiving relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​perform the filtering.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the query information during the analysis. For example, the analysis unit will perform a detailed analysis for query information of high importance. The analysis unit can also perform a simplified analysis for query information of low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the query information. For example, the analysis unit will perform a detailed analysis for query information of high importance. For query information of low importance, it will perform a simplified analysis. The priority of the analysis will be adjusted according to the importance of the query information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the query information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query information importance data into AI and have AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the inquiry information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical inquiry information. It can also apply a business-oriented analysis algorithm to business-oriented inquiry information. Furthermore, it can apply a customer support-specific analysis algorithm to customer support-related inquiry information. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of the inquiry information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the inquiry information category data into AI and have AI perform the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the inquiry information during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted inquiry information. It can also postpone the analysis of older inquiry information. Furthermore, the analysis unit can adjust the priority of analysis according to the submission date. For example, the analysis unit may prioritize the analysis of recently submitted inquiry information, postpone the analysis of older inquiry information, and adjust the priority of analysis according to the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the inquiry information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of the inquiry information into the AI ​​and have the AI ​​perform the priority determination.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the query information during analysis. For example, the analysis unit prioritizes analyzing highly relevant query information. It can also postpone analyzing less relevant query information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the query information. For example, the analysis unit prioritizes analyzing highly relevant query information. It postpones analyzing less relevant query information. It adjusts the order of analysis according to the relevance of the query information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the query information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query information relevance data into AI and have AI perform the order adjustment.

[0047] The search unit can improve search accuracy by considering the interrelationships of query information during the search. For example, the search unit provides search results based on related query information. The search unit can also analyze the interrelationships of query information and provide the optimal search results. Furthermore, the search unit can prioritize searching for query information with strong interrelationships. For example, the search unit provides search results based on related query information. It analyzes the interrelationships of query information and provides the optimal search results. It prioritizes searching for query information with strong interrelationships. By considering the interrelationships of query information, it is possible to provide more accurate search results. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the interrelationship data of query information into AI and have the AI ​​perform the search accuracy improvement.

[0048] The search unit can perform searches while considering the attribute information of the person submitting the inquiry. For example, the search unit can provide the most suitable search results based on the submitter's job title and department. The search unit can also provide search results while considering the submitter's past inquiry history. Furthermore, the search unit can provide highly relevant search results based on the submitter's attribute information. For example, the search unit can provide the most suitable search results based on the submitter's job title and department. It can provide search results while considering the submitter's past inquiry history. It can provide highly relevant search results based on the submitter's attribute information. In this way, highly relevant search results can be provided by considering the submitter's attribute information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the submitter's attribute information data into AI and have the AI ​​perform the search.

[0049] The search unit can perform searches while considering the geographical distribution of the inquiry information. For example, the search unit can prioritize searching for personnel who are geographically close. The search unit can also provide highly relevant search results based on geographical distribution. Furthermore, the search unit can provide optimal search results by considering geographical factors. For example, the search unit can prioritize searching for personnel who are geographically close. It can provide highly relevant search results based on geographical distribution. It can provide optimal search results by considering geographical factors. In this way, by considering geographical distribution, highly relevant search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of the inquiry information into AI and have the AI ​​perform the search.

[0050] The search unit can improve the accuracy of its search by referring to related literature in the query information during the search. For example, the search unit provides search results based on related literature. The search unit can also provide the best possible search results by referring to literature related to the query information. Furthermore, the search unit can improve the accuracy of its search by considering related literature. For example, the search unit provides search results based on related literature. It provides the best possible search results by referring to literature related to the query information. It improves the accuracy of the search by considering related literature. As a result, the accuracy of the search is improved by referring to related literature. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data from the query information into AI and have the AI ​​perform the search accuracy improvement.

[0051] The information provider can adjust the level of detail provided based on the importance of the inquiry information at the time of provision. For example, the provider can provide detailed information for highly important inquiries. It can also provide simplified information for less important inquiries. Furthermore, the provider can adjust the level of detail provided according to the importance of the inquiry information. For example, the provider can provide detailed information for highly important inquiries. It can provide simplified information for less important inquiries. The level of detail provided is adjusted according to the importance of the inquiry information. This allows for efficient information provision by adjusting the level of detail according to the importance of the inquiry information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input inquiry importance data into AI and have AI perform the level of detail adjustment.

[0052] The information delivery unit can apply different delivery algorithms depending on the category of the inquiry information at the time of delivery. For example, the information delivery unit can apply a technical delivery algorithm to technical inquiry information. It can also apply a business-oriented delivery algorithm to business-oriented inquiry information. Furthermore, it can apply a customer support-specific delivery algorithm to customer support-related inquiry information. For example, the information delivery unit can apply a technical delivery algorithm to technical inquiry information. It can apply a business-oriented delivery algorithm to business-oriented inquiry information. It can apply a customer support-specific delivery algorithm to customer support-related inquiry information. This improves the accuracy of information delivery by applying the appropriate delivery algorithm according to the category of the inquiry information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the category data of the inquiry information into the AI ​​and have the AI ​​execute the application of the delivery algorithm.

[0053] The information provision department can determine the priority of information provision based on when the inquiry information was submitted. For example, the information provision department can prioritize providing recently submitted inquiry information. It can also postpone providing older inquiry information. Furthermore, the information provision department can adjust the priority of information provision according to the submission date. For example, the information provision department can prioritize providing recently submitted inquiry information, postpone providing older inquiry information, and adjust the priority of information provision according to the submission date. This enables efficient information provision by determining the priority of information provision based on when the inquiry information was submitted. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the submission date data of the inquiry information into AI and have the AI ​​perform the priority determination.

[0054] The information delivery unit can adjust the order of delivery based on the relevance of the inquiry information at the time of delivery. For example, the information delivery unit can prioritize the delivery of highly relevant inquiry information. It can also postpone the delivery of less relevant inquiry information. Furthermore, the information delivery unit can adjust the order of delivery according to the relevance of the inquiry information. For example, the information delivery unit can prioritize the delivery of highly relevant inquiry information. It can postpone the delivery of less relevant inquiry information. It can adjust the order of delivery according to the relevance of the inquiry information. This makes it possible to provide information efficiently by adjusting the order of delivery based on the relevance of the inquiry information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the relevance data of the inquiry information into AI and have AI perform the order adjustment.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception department can suggest the most suitable reception method based on the content of inquiries the user has frequently made in the past. It can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, the reception department can predict and suggest reception methods to be used during specific time periods based on the user's past inquiry history. This allows for efficient handling by selecting the most suitable reception method based on past inquiry history.

[0057] The analysis unit can have a function to set the information update frequency. For example, the analysis unit can set the update frequency daily. It can also set the update frequency weekly. Furthermore, it can also set the update frequency monthly. For example, the analysis unit can set the update frequency daily. It can set the update frequency weekly. It can set the update frequency monthly. By setting the information update frequency, the latest information can be used for analysis.

[0058] The search unit can improve search accuracy by considering the interrelationships of query information during the search process. For example, the search unit provides search results based on relevant query information. It can also analyze the interrelationships of query information and provide optimal search results. Furthermore, the search unit can prioritize searching for query information with strong interrelationships. For example, the search unit provides search results based on relevant query information. It analyzes the interrelationships of query information and provides optimal search results. It prioritizes searching for query information with strong interrelationships. This allows for more accurate search results by considering the interrelationships of query information.

[0059] The information provider can adjust the level of detail provided based on the importance of the inquiry information at the time of provision. For example, the provider will provide detailed information for high-importance inquiries. Conversely, the provider can also provide simplified information for low-importance inquiries. Furthermore, the provider can adjust the level of detail provided according to the importance of the inquiry information. For example, the provider will provide detailed information for high-importance inquiries. For low-importance inquiries, it will provide simplified information. The level of detail provided will be adjusted according to the importance of the inquiry information. This allows for efficient information provision by adjusting the level of detail provided according to the importance of the inquiry information.

[0060] The information delivery unit can apply different delivery algorithms depending on the category of the inquiry information at the time of delivery. For example, the information delivery unit can apply a technical delivery algorithm to technical inquiries. It can also apply a business-oriented delivery algorithm to business inquiries. Furthermore, it can apply a customer support-specific delivery algorithm to customer support inquiries. This improves the accuracy of information delivery by applying the appropriate delivery algorithm according to the category of the inquiry information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives inquiry information from users. The reception desk can accept inquiry information in various formats, such as text, audio, and image. Step 2: The analysis department analyzes various company tools and history based on the information received by the reception department. The analysis department analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. The analysis department performs the analysis based on the algorithms used and the depth of the analysis. Step 3: The search unit searches for the appropriate person based on the information analyzed by the analysis unit. For example, the search unit identifies a person who is in charge of work related to the education industry. The search unit performs the search based on selection criteria such as the person's area of ​​expertise and years of experience. Step 4: The providing department provides the contact person information found by the searching department. The providing department provides information such as the contact person's contact details, department, and supervisor. The providing department provides information such as phone number, email address, department name, and supervisor's name.

[0063] (Example of form 2) The person search system according to an embodiment of the present invention is a system for solving the problem of wasting time and effort within a company because it is difficult to find the right person or contact person for inquiries. This person search system allows users to search for the appropriate person by interacting with an AI agent and inputting information to find the right person or contact person. The user interacts with the AI ​​agent and inputs information such as, "I am looking for someone knowledgeable in the education industry." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and searches various tools and history within the company. The AI ​​agent analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA to search for suitable candidate personnel. For example, when searching for someone knowledgeable in the education industry, it identifies individuals responsible for education-related tasks from these tools and history. The AI ​​agent provides the search results to the user. For example, it provides information such as the contact person's contact details, department, and supervisor, in the form of, "This person is knowledgeable in the education industry." This allows the user to quickly find the appropriate person. This mechanism reduces the time spent searching for the right person and improves work efficiency. Users can find the right person quickly and efficiently through interaction with the AI ​​agent. This improves operational productivity and reduces wasted time and effort. As a result, the personnel search system can efficiently receive, analyze, search, and provide user inquiry information.

[0064] The personnel search system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives inquiry information from users. The reception unit can receive inquiry information in, for example, text format, audio format, image format, etc. The analysis unit analyzes various tools and history within the company based on the information received by the reception unit. The analysis unit analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA, for example. The analysis unit performs analysis based on the algorithm used and the depth of analysis. The search unit searches for the appropriate personnel based on the information analyzed by the analysis unit. The search unit identifies, for example, a person in charge of work related to the education industry. The search unit performs the search based on selection criteria such as the personnel's field of expertise and years of experience. The provision unit provides the personnel information retrieved by the search unit. The provision unit provides, for example, information such as the personnel's contact information, department, and supervisor. The provision unit provides information such as telephone number, email address, department name, and supervisor's name. As a result, the personnel search system according to this embodiment can efficiently receive, analyze, search, and provide user inquiry information.

[0065] The reception department receives inquiry information from users. The reception department can accept inquiry information in various formats, such as text, audio, and image. Specifically, text-based inquiries are received as questions and requests entered by the user, audio inquiries are analyzed using recorded voice messages, and image inquiries are accepted as photos or screenshots taken by the user. This inquiry information is centrally managed by the reception department and stored in a database. When receiving an inquiry, the reception department also collects the user's basic information (name, contact information, department, etc.) and manages it in conjunction with the inquiry content. Furthermore, when receiving an inquiry, the reception department can ask simple questions to determine the user's intent and urgency, and set priorities. For example, high-urgency inquiries can be prioritized for processing by the analysis and search departments. This allows the reception department to efficiently receive and appropriately manage diverse user inquiry information.

[0066] The analytics department analyzes various company tools and histories based on information received by the reception department. For example, the analytics department analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. Specifically, in Salesforce, it analyzes past customer interaction history and sales opportunity information to extract information relevant to the inquiry. In communication tools, it analyzes internal chat history and email exchanges to identify relevant personnel and past response status. In Outsystems, Backlog, and JIRA, it analyzes project management information and task progress to identify projects and tasks related to the inquiry. The analytics department integrates the data collected from these tools and performs analysis using AI. The AI ​​uses natural language processing technology to analyze text data and extract the intent of the inquiry and relevant keywords. It also uses machine learning algorithms to learn patterns for identifying the optimal person in charge based on past inquiry history and response results. This allows the analytics department to quickly and accurately analyze information received from the reception department and provide information to identify the appropriate person in charge.

[0067] The search unit searches for the appropriate person based on the information analyzed by the analysis unit. For example, the search unit identifies individuals who handle tasks related to the education industry. Specifically, it performs searches based on selection criteria such as the person's area of ​​expertise, years of experience, and past handling history, using information provided by the analysis unit. The search unit utilizes the company's HR database and skill matrix to understand each person's area of ​​expertise and skill set. For example, when searching for someone who handles tasks related to the education industry, it identifies individuals with experience working on education-related projects or with a track record of dealing with educational institutions. The search unit also considers the person's current workload and available time slots to select the most suitable person. This allows the search unit to quickly identify the person best suited to the user's inquiry and achieve efficient responses.

[0068] The service provider provides contact information retrieved by the search service provider. This includes information such as the contact person's contact details, department, and supervisor. Specifically, it provides information such as the contact person's phone number, email address, department name, and supervisor's name. The service provider can utilize multiple communication methods to provide this information to users quickly and accurately. For example, it can provide contact person information via email, SMS, or chat tools, depending on the user's preference. The service provider also provides information on the contact person's available times and contact methods to facilitate smooth communication. Furthermore, the service provider can collect user feedback to continuously improve the accuracy and content of the information provided. For example, it can collect evaluations on the appropriateness of the provided contact person information, the speed and quality of the response, and use this feedback to improve the overall system. This allows the service provider to provide users with quick and accurate contact person information and support efficient inquiry handling.

[0069] The analysis unit can analyze tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. For example, the analysis unit can analyze Salesforce data and extract the work history of an employee. It can also analyze the message history of a communication tool and extract the communication history of an employee. Furthermore, it can analyze project data from Outsystems and extract the project history of an employee. For example, the analysis unit can analyze Salesforce data to identify the employee associated with a specific task. It can analyze the message history of a communication tool to identify the employee associated with a specific project. It can analyze project data from Outsystems to identify the employee associated with a specific project. This improves the accuracy of identifying the appropriate employee by analyzing various tools. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input Salesforce data into AI and have the AI ​​perform the extraction of the employee's work history.

[0070] The search unit can identify individuals responsible for tasks related to the education industry. For example, it can identify individuals involved in the development of educational programs. It can also identify individuals involved in the operation of educational institutions. Furthermore, it can identify individuals involved in education-related projects. For example, it can identify individuals involved in the development of educational programs. For example, it can identify individuals involved in the operation of educational institutions. For example, it can identify individuals involved in education-related projects. This allows for the rapid identification of individuals related to the education industry. Some or all of the above processing in the search unit may be performed using AI, for example, or not. For example, the search unit can input education-related project data into AI and have the AI ​​perform the identification of individuals.

[0071] The information provision department can provide information such as the contact person's contact details, department, and supervisor. For example, the information provision department can provide the contact person's phone number. It can also provide the contact person's email address. Furthermore, it can provide the contact person's department name. For example, the information provision department can provide the contact person's phone number. It can provide the contact person's email address. It can provide the contact person's department name. This allows for quick contact by providing detailed information about the contact person. Some or all of the above processing in the information provision department may be performed using AI, for example, or not. For example, the information provision department can input the contact person's contact data into AI and have the AI ​​perform the information provision.

[0072] The service provider may include a feedback function to verify the accuracy of search results. For example, the service provider may collect user ratings. The service provider may also set up a method for collecting feedback. Furthermore, the service provider may improve search results based on the feedback. For example, the service provider may collect user ratings, set up a method for collecting feedback, and improve search results based on the feedback. This allows for verification and improvement of the accuracy of search results. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input user feedback data into AI and have the AI ​​improve the search results.

[0073] The analysis unit may have a function to set the information update frequency. For example, the analysis unit can set the daily update frequency. It can also set the weekly update frequency. Furthermore, it can also set the monthly update frequency. For example, the analysis unit can set the daily update frequency. Set the weekly update frequency. Set the monthly update frequency. By setting the information update frequency, the latest information can be used for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information update frequency data into the AI ​​and have the AI ​​perform the setting of the update frequency.

[0074] The reception desk can estimate the user's emotions and adjust how inquiry information is received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of inquiry information. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick input of inquiry information. This allows for a more appropriate response by adjusting how inquiry information is received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into the AI ​​and have the AI ​​adjust the reception process accordingly.

[0075] The reception department can analyze a user's past inquiry history and select the optimal reception method. For example, the reception department can suggest the optimal reception method based on the content of inquiries the user has frequently made in the past. The reception department can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception department can predict and suggest reception methods to be used during specific time periods based on the user's past inquiry history. For example, the reception department can suggest the optimal reception method based on the content of inquiries the user has frequently made in the past. It can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can predict and suggest reception methods to be used during specific time periods based on the user's past inquiry history. This enables efficient response by selecting the optimal reception method based on past inquiry history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the user's past inquiry history data into AI and have the AI ​​select the reception method.

[0076] The reception desk can filter inquiry information based on the user's current work situation and areas of interest. For example, the reception desk can prioritize receiving inquiry information related to a project the user is currently working on. The reception desk can also filter relevant inquiry information based on the user's areas of interest. Furthermore, the reception desk can prioritize receiving inquiry information of high importance depending on the user's work situation. For example, the reception desk can prioritize receiving inquiry information related to a project the user is currently working on. It filters relevant inquiry information based on the user's areas of interest. It prioritizes receiving inquiry information of high importance depending on the user's work situation. In this way, by filtering based on the user's work situation and areas of interest, important inquiry information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user work situation data into AI and have the AI ​​perform the filtering.

[0077] The reception desk can estimate the user's emotions and determine the priority of inquiry information to accept based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize accepting high-priority inquiries. If the user is relaxed, the reception desk may also prioritize accepting detailed inquiries. Furthermore, if the user is in a hurry, the reception desk may also prioritize accepting inquiries that require a quick response. For example, if the user is stressed, the reception desk will prioritize accepting high-priority inquiries. If the user is relaxed, it will prioritize accepting detailed inquiries. If the user is in a hurry, it will prioritize accepting inquiries that require a quick response. This allows for a quick and appropriate response by prioritizing inquiry information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into the AI ​​and have the AI ​​determine priorities.

[0078] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving inquiry information. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiry information related to that region. The reception desk can also prioritize receiving inquiry information from relevant departments or personnel based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can receive the most relevant inquiry information based on their current location. For example, if the reception desk is in a specific region, it will prioritize receiving inquiry information related to that region. Based on the user's geographical location, it will prioritize receiving inquiry information from relevant departments or personnel. If the user is on the move, it will receive the most relevant inquiry information based on their current location. This enables efficient responses by prioritizing the receipt of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into AI and have the AI ​​perform filtering.

[0079] The reception desk can analyze the user's social media activity and receive relevant information when receiving inquiry information. For example, the reception desk can prioritize receiving inquiry information related to the user's current interests based on the user's social media activity. The reception desk can also filter relevant inquiry information based on the content of the user's social media posts. Furthermore, the reception desk can also receive relevant inquiry information based on the activity of the user's social media followers and friends. For example, the reception desk prioritizes receiving inquiry information related to the user's current interests based on the user's social media activity. It filters relevant inquiry information based on the content of the user's social media posts. It receives relevant inquiry information based on the activity of the user's social media followers and friends. This allows for more appropriate responses by receiving relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​perform the filtering.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a rapid analysis and provide results. If the user is relaxed, the analysis unit can also perform a detailed analysis and provide results. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing information of high importance. For example, if the user is stressed, the analysis unit can perform a rapid analysis and provide results. If the user is relaxed, it can perform a detailed analysis and provide results. If the user is in a hurry, it prioritizes analyzing information of high importance. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the query information during the analysis. For example, the analysis unit will perform a detailed analysis for query information of high importance. The analysis unit can also perform a simplified analysis for query information of low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the query information. For example, the analysis unit will perform a detailed analysis for query information of high importance. For query information of low importance, it will perform a simplified analysis. The priority of the analysis will be adjusted according to the importance of the query information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the query information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query information importance data into AI and have AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the inquiry information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical inquiry information. It can also apply a business-oriented analysis algorithm to business-oriented inquiry information. Furthermore, it can apply a customer support-specific analysis algorithm to customer support-related inquiry information. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of the inquiry information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the inquiry information category data into AI and have AI perform the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing high-priority inquiry information. The analysis unit can also perform a detailed analysis if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis. This allows for rapid and appropriate analysis by determining the priority of analysis 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​determine the priorities.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the inquiry information during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted inquiry information. It can also postpone the analysis of older inquiry information. Furthermore, the analysis unit can adjust the priority of analysis according to the submission date. For example, the analysis unit may prioritize the analysis of recently submitted inquiry information, postpone the analysis of older inquiry information, and adjust the priority of analysis according to the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the inquiry information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of the inquiry information into the AI ​​and have the AI ​​perform the priority determination.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the query information during analysis. For example, the analysis unit prioritizes analyzing highly relevant query information. It can also postpone analyzing less relevant query information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the query information. For example, the analysis unit prioritizes analyzing highly relevant query information. It postpones analyzing less relevant query information. It adjusts the order of analysis according to the relevance of the query information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the query information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input query information relevance data into AI and have AI perform the order adjustment.

[0086] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is stressed, the search unit can provide search results quickly. If the user is relaxed, the search unit can also provide detailed search results. Furthermore, if the user is in a hurry, the search unit can prioritize providing high-priority search results. For example, if the user is stressed, the search unit provides search results quickly. If the user is relaxed, it provides detailed search results. If the user is in a hurry, it prioritizes providing high-priority search results. This allows for more appropriate search results by adjusting the search criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into AI and have the AI ​​adjust the search criteria.

[0087] The search unit can improve search accuracy by considering the interrelationships of query information during the search. For example, the search unit provides search results based on related query information. The search unit can also analyze the interrelationships of query information and provide the optimal search results. Furthermore, the search unit can prioritize searching for query information with strong interrelationships. For example, the search unit provides search results based on related query information. It analyzes the interrelationships of query information and provides the optimal search results. It prioritizes searching for query information with strong interrelationships. By considering the interrelationships of query information, it is possible to provide more accurate search results. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the interrelationship data of query information into AI and have the AI ​​perform the search accuracy improvement.

[0088] The search unit can perform searches while considering the attribute information of the person submitting the inquiry. For example, the search unit can provide the most suitable search results based on the submitter's job title and department. The search unit can also provide search results while considering the submitter's past inquiry history. Furthermore, the search unit can provide highly relevant search results based on the submitter's attribute information. For example, the search unit can provide the most suitable search results based on the submitter's job title and department. It can provide search results while considering the submitter's past inquiry history. It can provide highly relevant search results based on the submitter's attribute information. In this way, highly relevant search results can be provided by considering the submitter's attribute information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the submitter's attribute information data into AI and have the AI ​​perform the search.

[0089] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit will prioritize displaying high-importance search results. It can also display detailed search results if the user is relaxed. Furthermore, if the user is in a hurry, the search unit can display search results quickly. This allows for the provision of more relevant information by adjusting the display order of search results 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, or not. For example, the search unit can input user emotion data into an AI and have the AI ​​adjust the display order.

[0090] The search unit can perform searches while considering the geographical distribution of the inquiry information. For example, the search unit can prioritize searching for personnel who are geographically close. The search unit can also provide highly relevant search results based on geographical distribution. Furthermore, the search unit can provide optimal search results by considering geographical factors. For example, the search unit can prioritize searching for personnel who are geographically close. It can provide highly relevant search results based on geographical distribution. It can provide optimal search results by considering geographical factors. In this way, by considering geographical distribution, highly relevant search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of the inquiry information into AI and have the AI ​​perform the search.

[0091] The search unit can improve the accuracy of its search by referring to related literature in the query information during the search. For example, the search unit provides search results based on related literature. The search unit can also provide the best possible search results by referring to literature related to the query information. Furthermore, the search unit can improve the accuracy of its search by considering related literature. For example, the search unit provides search results based on related literature. It provides the best possible search results by referring to literature related to the query information. It improves the accuracy of the search by considering related literature. As a result, the accuracy of the search is improved by referring to related literature. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data from the query information into AI and have the AI ​​perform the search accuracy improvement.

[0092] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is stressed, the provider can provide simple and easily understandable information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can provide concise information. For example, if the user is stressed, the provider can provide simple and easily understandable information. If the user is relaxed, it can provide detailed information. If the user is in a hurry, it can provide concise information. This allows for the provision of more appropriate information by adjusting the way the information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into AI and have the AI ​​adjust the presentation method.

[0093] The information provider can adjust the level of detail provided based on the importance of the inquiry information at the time of provision. For example, the provider can provide detailed information for highly important inquiries. It can also provide simplified information for less important inquiries. Furthermore, the provider can adjust the level of detail provided according to the importance of the inquiry information. For example, the provider can provide detailed information for highly important inquiries. It can provide simplified information for less important inquiries. The level of detail provided is adjusted according to the importance of the inquiry information. This allows for efficient information provision by adjusting the level of detail according to the importance of the inquiry information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input inquiry importance data into AI and have AI perform the level of detail adjustment.

[0094] The information delivery unit can apply different delivery algorithms depending on the category of the inquiry information at the time of delivery. For example, the information delivery unit can apply a technical delivery algorithm to technical inquiry information. It can also apply a business-oriented delivery algorithm to business-oriented inquiry information. Furthermore, it can apply a customer support-specific delivery algorithm to customer support-related inquiry information. For example, the information delivery unit can apply a technical delivery algorithm to technical inquiry information. It can apply a business-oriented delivery algorithm to business-oriented inquiry information. It can apply a customer support-specific delivery algorithm to customer support-related inquiry information. This improves the accuracy of information delivery by applying the appropriate delivery algorithm according to the category of the inquiry information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the category data of the inquiry information into the AI ​​and have the AI ​​execute the application of the delivery algorithm.

[0095] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is stressed, the provider will prioritize providing information of high importance. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can prioritize providing information that needs to be provided quickly. For example, if the user is stressed, the provider will prioritize providing information of high importance. If the user is relaxed, it will provide detailed information. If the user is in a hurry, it will prioritize providing information that needs to be provided quickly. This allows for the provision of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into an AI and have the AI ​​perform the priority determination.

[0096] The information provision department can determine the priority of information provision based on when the inquiry information was submitted. For example, the information provision department can prioritize providing recently submitted inquiry information. It can also postpone providing older inquiry information. Furthermore, the information provision department can adjust the priority of information provision according to the submission date. For example, the information provision department can prioritize providing recently submitted inquiry information, postpone providing older inquiry information, and adjust the priority of information provision according to the submission date. This enables efficient information provision by determining the priority of information provision based on when the inquiry information was submitted. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the submission date data of the inquiry information into AI and have the AI ​​perform the priority determination.

[0097] The information delivery unit can adjust the order of delivery based on the relevance of the inquiry information at the time of delivery. For example, the information delivery unit can prioritize the delivery of highly relevant inquiry information. It can also postpone the delivery of less relevant inquiry information. Furthermore, the information delivery unit can adjust the order of delivery according to the relevance of the inquiry information. For example, the information delivery unit can prioritize the delivery of highly relevant inquiry information. It can postpone the delivery of less relevant inquiry information. It can adjust the order of delivery according to the relevance of the inquiry information. This makes it possible to provide information efficiently by adjusting the order of delivery based on the relevance of the inquiry information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the relevance data of the inquiry information into AI and have AI perform the order adjustment.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception department can suggest the most suitable reception method based on the content of inquiries the user has frequently made in the past. It can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, the reception department can predict and suggest reception methods to be used during specific time periods based on the user's past inquiry history. This allows for efficient handling by selecting the most suitable reception method based on past inquiry history.

[0100] The analysis unit can have a function to set the information update frequency. For example, the analysis unit can set the update frequency daily. It can also set the update frequency weekly. Furthermore, it can also set the update frequency monthly. For example, the analysis unit can set the update frequency daily. It can set the update frequency weekly. It can set the update frequency monthly. By setting the information update frequency, the latest information can be used for analysis.

[0101] The search unit can improve search accuracy by considering the interrelationships of query information during the search process. For example, the search unit provides search results based on relevant query information. It can also analyze the interrelationships of query information and provide optimal search results. Furthermore, the search unit can prioritize searching for query information with strong interrelationships. For example, the search unit provides search results based on relevant query information. It analyzes the interrelationships of query information and provides optimal search results. It prioritizes searching for query information with strong interrelationships. This allows for more accurate search results by considering the interrelationships of query information.

[0102] The information provider can adjust the level of detail provided based on the importance of the inquiry information at the time of provision. For example, the provider will provide detailed information for high-importance inquiries. Conversely, the provider can also provide simplified information for low-importance inquiries. Furthermore, the provider can adjust the level of detail provided according to the importance of the inquiry information. For example, the provider will provide detailed information for high-importance inquiries. For low-importance inquiries, it will provide simplified information. The level of detail provided will be adjusted according to the importance of the inquiry information. This allows for efficient information provision by adjusting the level of detail provided according to the importance of the inquiry information.

[0103] The information delivery unit can apply different delivery algorithms depending on the category of the inquiry information at the time of delivery. For example, the information delivery unit can apply a technical delivery algorithm to technical inquiries. It can also apply a business-oriented delivery algorithm to business inquiries. Furthermore, it can apply a customer support-specific delivery algorithm to customer support inquiries. This improves the accuracy of information delivery by applying the appropriate delivery algorithm according to the category of the inquiry information.

[0104] The reception desk can estimate the user's emotions and adjust how inquiry information is received based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of inquiry information. In this way, by adjusting how inquiry information is received according to the user's emotions, a more appropriate response can be provided.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a rapid analysis and provide results. If the user is relaxed, the analysis unit can also perform a detailed analysis and provide results. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing information of high importance. For example, if the user is stressed, the analysis unit can perform a rapid analysis and provide results. If the user is relaxed, it can perform a detailed analysis and provide results. If the user is in a hurry, it can prioritize analyzing information of high importance. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided.

[0106] The search engine can estimate the user's emotions and adjust search criteria based on those emotions. For example, if the user is stressed, the search engine can provide search results quickly. If the user is relaxed, it can provide detailed search results. Furthermore, if the user is in a hurry, it can prioritize high-priority search results. For example, if the user is stressed, the search engine can provide search results quickly. If the user is relaxed, it can provide detailed search results. If the user is in a hurry, it can prioritize high-priority search results. By adjusting search criteria according to the user's emotions, more relevant search results can be provided.

[0107] The search engine can estimate the user's emotions and adjust the order in which search results are displayed based on that estimation. For example, if the user is stressed, the search engine will prioritize displaying high-priority search results. It can also display detailed search results if the user is relaxed. Furthermore, if the user is in a hurry, the search engine can display search results quickly. This allows the system to provide more relevant information by adjusting the order of search results according to the user's emotions.

[0108] The information provider can estimate the user's emotions and adjust the way the information is presented based on those emotions. For example, if the user is stressed, the provider can provide simple and easily visible information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can provide concise information. In this way, by adjusting the way information is presented according to the user's emotions, more appropriate information can be provided.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives inquiry information from users. The reception desk can accept inquiry information in various formats, such as text, audio, and image. Step 2: The analysis department analyzes various company tools and history based on the information received by the reception department. The analysis department analyzes tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. The analysis department performs the analysis based on the algorithms used and the depth of the analysis. Step 3: The search unit searches for the appropriate person based on the information analyzed by the analysis unit. For example, the search unit identifies a person who is in charge of work related to the education industry. The search unit performs the search based on selection criteria such as the person's area of ​​expertise and years of experience. Step 4: The providing department provides the contact person information found by the searching department. The providing department provides information such as the contact person's contact details, department, and supervisor. The providing department provides information such as phone number, email address, department name, and supervisor's name.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives inquiry information from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes various tools and history within the company. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the appropriate person in charge. The provision unit is implemented by, for example, the output device 40 of the smart device 14 and provides the retrieved person in charge information. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives inquiry information from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes various tools and history within the company. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the appropriate person in charge. The provision unit is implemented by, for example, the speaker 240 of the smart glasses 214 and provides the retrieved person in charge information. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives inquiry information from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes various tools and history within the company. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the appropriate person in charge. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the retrieved person in charge information. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives inquiry information from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes various tools and history within the company. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the appropriate person in charge. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the retrieved person in charge information. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives inquiries from users, Based on the information received by the aforementioned reception unit, an analysis unit analyzes various tools and history within the company. A search unit searches for the appropriate person in charge based on the information analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the person in charge information retrieved by the search unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze tools such as Salesforce, communication tools, Outsystems, Backlog, and JIRA. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Identify individuals responsible for tasks related to the education industry. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide contact information for the person in charge, including their department and supervisor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It includes a feedback function to verify the accuracy of search results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It includes a function to set the frequency of information updates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust how we accept inquiry information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving inquiry information, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inquiry information to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving inquiry information, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving inquiry information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the query information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the query information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the inquiry information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the query information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When performing a search, the system improves search accuracy by considering the interrelationships between the query information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When performing a search, the system takes into account the attribute information of the person who submitted the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When performing a search, the search should take into account the geographical distribution of the inquiry information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, refer to related literature for the inquiry information to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the level of detail provided will be adjusted based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of the inquiry information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the inquiry information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the order of delivery will be adjusted based on the relevance of the inquiry information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives inquiries from users, Based on the information received by the aforementioned reception unit, an analysis unit analyzes various tools and history within the company. A search unit searches for the appropriate person in charge based on the information analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the person in charge information retrieved by the search unit. A system characterized by the following features.

2. The aforementioned search unit, Identify individuals responsible for tasks related to the education industry. The system according to feature 1.

3. The aforementioned supply unit is, Provide contact information, department, supervisor, and other details of the person in charge. The system according to feature 1.

4. The aforementioned supply unit is, It includes a feedback function to verify the accuracy of search results. The system according to feature 1.

5. The aforementioned analysis unit, It includes a function to set the frequency of information updates. The system according to feature 1.

6. The aforementioned reception unit is We estimate the user's emotions and adjust how we receive inquiry information based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable method of handling inquiries. The system according to feature 1.

8. The aforementioned reception unit is When receiving inquiry information, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inquiry information to accept based on the estimated user emotions. The system according to feature 1.

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