system

Abstract

The system according to this embodiment aims to provide quick and accurate solutions to labor-related questions and issues, and to refer them to specialists as needed. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a reference unit, a provision unit, and a handover unit. The reception unit receives input of labor-related questions and cases. The analysis unit analyzes the information received by the reception unit. The reference unit allows the analysis unit to refer to past case precedents and legal databases. The provision unit provides solutions and advice based on the results analyzed by the analysis unit. The handover unit hands over serious problems to experts based on the results provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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

[0004] In the conventional technology, it is difficult to provide quick and accurate solutions to labor-related questions and cases, and there is a problem that the handover to experts is not smoothly carried out.

[0005] The system according to the embodiment aims to provide quick and accurate solutions to labor-related questions and cases and hand over to experts as needed.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a reference unit, a provision unit, and a handover unit. The reception unit receives input of labor-related questions and cases. The analysis unit analyzes the information received by the reception unit. The reference unit allows the analysis unit to refer to past case precedents and legal databases. The provision unit provides solutions and advice based on the results analyzed by the analysis unit. The handover unit hands over serious problems to experts based on the results provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment provides quick and accurate solutions to labor-related questions and issues, and can refer them to specialists as needed. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The labor consultation system according to an embodiment of the present invention is a tool that allows employees and HR personnel to consult with AI regarding labor-related questions and issues. The labor consultation system allows employees and HR personnel to input labor-related questions and issues, and the AI ​​provides appropriate solutions and advice by referring to past case precedents and legal databases. For example, it provides quick and accurate answers to questions about labor standards law, consultations regarding working hours and leave, and harassment issues. Furthermore, it includes a function to refer serious issues to specialists. The purpose of this tool is to enable employees to quickly resolve labor-related issues and provide a secure working environment. For example, employees and HR personnel input labor-related questions and issues. In this case, the user only needs to input the specific question or consultation content, such as "question about labor standards law" or "consultation regarding working hours." This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​refers to past case precedents and legal databases and provides appropriate solutions and advice. For example, in response to a question about labor standards law, the AI ​​refers to past case precedents and legal databases and provides an appropriate solution. Furthermore, based on the solutions and advice provided by the AI, employees and HR personnel resolve the problem. For example, labor-related issues can be resolved by following the solutions provided by the AI. Finally, it also includes a function to refer serious issues to experts. For example, serious issues that the AI ​​cannot handle, such as harassment issues, can be referred to experts. This allows employees and HR personnel to consult about labor-related issues with peace of mind. In this way, the labor consultation system enables employees and HR personnel to resolve labor-related questions and cases quickly and accurately, and serious issues can be referred to experts.

[0029] The labor consultation system according to this embodiment comprises a reception unit, an analysis unit, a reference unit, a provision unit, and a handover unit. The reception unit receives input of labor-related questions and cases. The reception unit provides, for example, an interface for users to input labor-related questions and cases. The reception unit can support multiple input methods, such as text input, voice input, and image input. For example, the reception unit enables users to input labor-related questions and cases in text format. The reception unit also enables users to input labor-related questions and cases by voice using speech recognition technology. Furthermore, the reception unit enables users to input labor-related questions and cases in image format using image recognition technology. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text entered by the user, for example, using natural language processing technology. The analysis unit can analyze the user's input using technologies such as text analysis, data mining, and statistical analysis. For example, the analysis unit analyzes the text entered by the user to identify the content of the labor-related questions and cases. Furthermore, the analysis unit can extract relevant information from user input using data mining techniques. In addition, the analysis unit can analyze user input using statistical analysis techniques to understand trends in labor-related questions and cases. The reference unit allows the analysis unit to access past case law and legal databases. The reference unit can access multiple databases, such as legal databases, case law databases, and industry databases. Based on user input, the reference unit selects the appropriate database and retrieves the necessary information. For example, the reference unit can access legal databases for questions regarding labor standards law. It can also access past case law databases for consultations regarding working hours and leave. Furthermore, it can access databases containing expert opinions regarding harassment issues. The provision unit provides solutions and advice based on the results analyzed by the analysis unit. The provision unit can provide solutions and advice in multiple ways, such as in written form, verbal explanations, and video tutorials.The service provider selects and provides appropriate solutions and advice based on the user's input. For example, the service provider provides solutions to questions regarding labor standards law based on a legal database. It can also provide advice on working hours and leave based on past case precedents. Furthermore, it can provide solutions to harassment issues by referencing expert opinions. The handover service provider then forwards serious issues to experts based on the results provided by the service provider. For example, the handover service can forward issues with high legal risk or high urgency to appropriate experts. The handover service provider selects and forwards appropriate experts based on the user's input. For example, it can forward harassment issues to a psychological counselor. It can also forward questions regarding labor standards law to legal experts. Furthermore, it can forward consultations regarding working hours and leave to labor management experts. As a result, the labor consultation system according to this embodiment allows employees and HR personnel to quickly and accurately resolve labor-related questions and issues, and to refer serious problems to specialists.

[0030] The reception desk accepts input of labor-related questions and requests. For example, the reception desk provides an interface for users to input labor-related questions and requests. The reception desk can support multiple input methods, including text input, voice input, and image input. For instance, the reception desk allows users to input labor-related questions and requests in text format. It also uses voice recognition technology to allow users to input labor-related questions and requests by voice. Furthermore, it uses image recognition technology to allow users to input labor-related questions and requests in image format. Specifically, for text input, users enter their questions or requests into a dedicated input form and send the information to the reception desk by pressing a submit button. For voice input, users speak into a microphone, and voice recognition technology converts their speech into text, which is then sent to the reception desk. For image input, users upload relevant documents or images, and image recognition technology analyzes their content and sends it to the reception desk. This allows the reception desk to offer diverse input methods, enabling users to input labor-related questions and requests in the most convenient way for them. Furthermore, the reception department can centrally manage the entered information and quickly transfer it to the analysis department or other departments. This allows the reception department to enhance user convenience and improve the overall efficiency of the labor consultation system.

[0031] The analysis unit analyzes information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze text entered by users. The analysis unit can analyze user input using technologies such as text analysis, data mining, and statistical analysis. For instance, the analysis unit analyzes user input to identify labor-related questions and issues. Furthermore, the analysis unit can extract relevant information from user input using data mining technology. Additionally, the analysis unit can analyze user input using statistical analysis technology to understand trends in labor-related questions and issues. Specifically, it uses natural language processing technology to grammatically analyze user input and extract keywords and important phrases. This allows the analysis unit to identify the subject of user questions and issues and provide basic data for finding appropriate solutions. It also uses data mining technology to extract similar cases and related information from past databases, providing reference information for user questions and issues. Finally, it uses statistical analysis technology to analyze user input and understand trends and patterns related to specific labor issues. This allows the analysis unit to perform quick and accurate analyses of user questions and cases, thereby enhancing the overall effectiveness of the labor consultation system.

[0032] The reference unit allows the analysis unit to access past case law and legal databases. The reference unit can access multiple databases, such as legal databases, case law databases, and industry databases. Based on user input, the reference unit selects the appropriate database and retrieves the necessary information. For example, it can access legal databases to address questions about labor standards law. It can also access past case law databases for inquiries regarding working hours and leave. Furthermore, it can access databases containing expert opinions regarding harassment issues. Specifically, the reference unit receives user input from the analysis unit and selects the database most relevant to that content. For example, if a question about labor standards law is entered, the reference unit searches legal databases to retrieve the relevant articles and interpretations. If inquiries about working hours and leave are entered, the reference unit searches past case law databases to retrieve similar cases and court precedents. For harassment issues, it accesses databases containing expert opinions and guidelines to find appropriate countermeasures. This allows the reference section to quickly provide reliable information in response to user questions and issues, thereby improving the overall reliability and effectiveness of the labor consultation system.

[0033] The service provider will provide solutions and advice based on the results analyzed by the analysis provider. The service provider can provide solutions and advice in multiple ways, such as in written form, verbal explanations, and video tutorials. The service provider will select and provide appropriate solutions and advice based on the user's input. For example, the service provider will provide solutions based on a legal database for questions regarding labor standards law. The service provider can also provide advice based on past case precedents for consultations regarding working hours and leave. Furthermore, the service provider can provide solutions for harassment issues by referring to expert opinions. Specifically, the service provider will select the optimal solution for the user's questions and cases based on the analysis results received from the analysis provider. For example, for questions regarding labor standards law, it will provide specific countermeasures in written form based on articles and interpretations obtained from a legal database. For consultations regarding working hours and leave, it will provide verbal explanations of advice based on past case precedents and provide video tutorials as needed. For harassment issues, it will provide solutions by referring to expert opinions and guidelines to help users take appropriate action. This allows the service provider to quickly and appropriately provide solutions and advice to users' questions and issues, thereby enhancing the overall effectiveness of the labor consultation system.

[0034] The handover department will transfer serious issues to specialists based on the results provided by the service provider. For example, the handover department can transfer issues with high legal risk or high urgency to appropriate specialists. The handover department will select and transfer the appropriate specialist based on the user's input. For example, the handover department will transfer harassment issues to a psychological counselor. The handover department can also transfer questions regarding labor standards law to a legal specialist. Furthermore, the handover department can transfer consultations regarding working hours and leave to a labor management specialist. Specifically, the handover department will determine whether the user's problem is serious based on the results of the solutions and advice received from the service provider. For issues with high legal risk or high urgency, it will select and transfer the appropriate specialist. For example, harassment issues will be transferred to a psychological counselor so that the user can receive appropriate counseling. Questions regarding labor standards law will be transferred to a legal specialist so that the user can receive legal advice. For inquiries regarding working hours and leave, the user will be referred to a labor management specialist, ensuring they receive appropriate labor management advice. This allows the referral department to respond quickly and appropriately to serious user issues, improving the overall reliability and effectiveness of the labor consultation system.

[0035] The reception desk can analyze the user's past consultation history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions labor-related questions and cases that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest labor-related questions and cases to be used during specific time periods based on the user's past consultation history. This improves input efficiency by suggesting the optimal input method based on the user's past consultation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past consultation history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception desk can filter input content based on the user's job title or position when they enter labor-related questions or cases. For example, if the user is in a management position, the reception desk will prioritize displaying labor-related questions or cases related to management. If the user is a new employee, the reception desk can also prioritize displaying labor-related questions or cases related to new employees. If the user belongs to a specific department, the reception desk can also prioritize displaying labor-related questions or cases related to that department. This allows users to enter more appropriate information by providing input content tailored to their job title or position. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's job title or position data into a generating AI and have the generating AI perform the filtering of the input content.

[0037] The reception desk can prioritize the input of highly relevant information when users enter labor-related questions or cases, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize displaying labor-related questions or cases related to that region. If a user is on a business trip, the reception desk can also prioritize displaying labor-related questions or cases related to their business trip destination. If a user is working remotely, the reception desk can also prioritize displaying labor-related questions or cases related to remote work. This allows users to input more appropriate information by providing highly relevant information based on their 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 a generating AI and have the generating AI prioritize the display of highly relevant information.

[0038] The reception desk can analyze the user's social media activity and prompt them to input relevant information when they enter labor-related questions or cases. For example, the reception desk can automatically input labor-related questions or cases that the user has shared on social media. The reception desk can also predict relevant labor-related questions or cases from the user's social media activity and prompt them to input them. The reception desk can also input labor-related questions or cases by referring to the opinions of experts that the user follows on social media. This allows for the input of more appropriate information by providing relevant information based on the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. For example, the analysis unit can perform a detailed analysis on labor-related questions or cases of high importance. The analysis unit can also perform a concise analysis on labor-related questions or cases of low importance. The analysis unit can also perform an analysis of an appropriate level of detail on labor-related questions or cases of moderate importance. This improves the efficiency of the analysis by performing the analysis according to the importance of the input 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 importance data of the input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of labor-related questions and cases during analysis. For example, the analysis unit can apply an analysis algorithm based on a legal database to questions concerning labor standards law. The analysis unit can also apply an analysis algorithm based on past case precedents to consultations regarding working hours and leave. The analysis unit can also apply an analysis algorithm that takes expert opinions into account to harassment issues. This improves the accuracy of the analysis by performing analysis according to the category of labor-related questions and cases. 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 category data of labor-related questions and cases into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the input information during the analysis process. For example, the analysis unit can prioritize the analysis of urgent labor-related questions and cases. The analysis unit can also postpone the analysis of older labor-related questions and cases. The analysis unit can also prioritize the analysis of newer labor-related questions and cases. This improves the efficiency of the analysis by performing the analysis based on the submission date of the input 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 input information into a generating AI and have the generating AI determine the priority of the analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the input information during the analysis process. For example, the analysis unit can prioritize the analysis of labor-related questions and cases that are highly relevant. The analysis unit can also postpone the analysis of labor-related questions and cases that are less relevant. The analysis unit can also moderately analyze labor-related questions and cases that are of moderate relevance. This improves the efficiency of the analysis by performing the analysis based on the relevance of the input 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 relevance data of the input information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The reference unit can select the most suitable data by referring to past case precedents and the update history of legal databases during the reference process. For example, the reference unit may prioritize referencing databases containing the most recent case precedents. The reference unit may also prioritize referencing databases containing the most recent laws. The reference unit may also prioritize referencing databases with frequent update histories. This improves the accuracy of the reference by selecting data based on the update history of past case precedents and legal databases. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input database update history data into a generating AI and have the generating AI select the most suitable data.

[0044] The reference unit can access different databases depending on the category of the labor-related question or case. For example, the reference unit can access a legal database for questions regarding labor standards law. It can also access a database of past case precedents for consultations regarding working hours or leave. It can also access a database containing expert opinions for harassment issues. This improves the accuracy of the reference by accessing databases appropriate to the category of the labor-related question or case. Some or all of the above processing in the reference unit may be performed using AI, for example, or not. For example, the reference unit can input category data of labor-related questions and cases into a generating AI and have the generating AI access different databases.

[0045] The reference unit can prioritize the retrieval of highly relevant data by considering the geographical distribution of the input information during the retrieval process. For example, if the user is in a specific region, the reference unit will prioritize the retrieval of data related to that region. If the user is on a business trip, the reference unit can also prioritize the retrieval of data related to the business trip destination. If the user is working remotely, the reference unit can also prioritize the retrieval of data related to remote work. This improves the accuracy of the retrieval by performing data retrieval based on the geographical distribution of the input information. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input geographical distribution data of the input information into a generating AI and have the generating AI perform priority retrieval of highly relevant data.

[0046] The reference unit can improve the accuracy of the reference results by referring to related literature during the referencing process. For example, the reference unit can improve the accuracy of the reference results by referring to related academic papers. The reference unit can also improve the accuracy of the reference results by referring to related professional books. The reference unit can also improve the accuracy of the reference results by referring to related industry reports. In this way, the accuracy of the reference results is improved by referencing related literature. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input related literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the reference results.

[0047] The delivery unit can adjust the level of detail provided based on the importance of the solutions and advice at the time of delivery. For example, the delivery unit can provide detailed explanations for highly important solutions and advice. The delivery unit can also provide concise explanations for less important solutions and advice. The delivery unit can also provide explanations of a moderate level of detail for solutions and advice of moderate importance. This allows for a deeper understanding by providing solutions and advice according to their importance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance data of solutions and advice into a generating AI and have the generating AI adjust the level of detail provided.

[0048] The service provider can apply different service provision algorithms depending on the category of labor-related questions and cases when providing information. For example, the service provider can apply a service provision algorithm based on a legal database to questions concerning labor standards law. The service provider can also apply a service provision algorithm based on past case precedents to consultations regarding working hours and leave. The service provider can also apply a service provision algorithm that takes expert opinions into account to address harassment issues. This improves the accuracy of the service provision by providing information according to the category of labor-related questions and cases. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input category data of labor-related questions and cases into a generating AI and have the generating AI execute the application of different service provision algorithms.

[0049] The service provider can determine the priority of solutions and advice based on when they are submitted. For example, the service provider can prioritize providing urgent solutions and advice. The service provider can also postpone providing older solutions and advice. The service provider can also prioritize providing newer solutions and advice. This improves the efficiency of the service by providing solutions and advice based on when they are submitted. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on the submission dates of solutions and advice into a generating AI and have the generating AI determine the priority of the services.

[0050] The delivery unit can adjust the order in which solutions and advice are delivered based on their relevance. For example, the delivery unit may prioritize highly relevant solutions and advice. It may also postpone less relevant solutions and advice. It may also provide solutions and advice of moderate relevance at appropriate intervals. This improves the efficiency of delivery by providing solutions and advice based on their relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input relevance data of solutions and advice into a generating AI and have the generating AI adjust the order in which they are delivered.

[0051] The handover unit can select the most suitable expert by referring to the user's past consultation history during the handover process. For example, the handover unit may prioritize selecting experts the user has consulted with in the past. The handover unit can also predict and select the most suitable expert based on the user's past consultation history. The handover unit can also select relevant experts based on the content of past consultations the user has had. This improves the accuracy of the handover process by selecting experts based on the user's past consultation history. Some or all of the above-described processes in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's past consultation history data into a generating AI and have the generating AI perform the selection of the most suitable expert.

[0052] The handover unit can apply different handover methods depending on the category of labor-related questions and cases during the handover process. For example, the handover unit can hand over questions regarding labor standards law to a legal expert. It can also hand over consultations regarding working hours and leave to a labor management expert. It can also hand over harassment issues to a psychological counselor. This improves the accuracy of the handover by performing handovers according to the category of labor-related questions and cases. Some or all of the above processing in the handover unit may be performed using AI, for example, or not using AI. For example, the handover unit can input category data of labor-related questions and cases into a generating AI and have the generating AI execute the application of different handover methods.

[0053] The handover unit can select the most suitable expert during the handover process, taking into account the user's geographical location. For example, if the user is in a specific region, the handover unit can prioritize selecting experts related to that region. If the user is on a business trip, the handover unit can also prioritize selecting experts related to the destination of the business trip. If the user is working remotely, the handover unit can also prioritize selecting experts related to remote work. This improves the accuracy of the handover process by selecting experts based on the user's geographical location. Some or all of the above processing in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable expert.

[0054] The handover unit can analyze the user's social media activity during the handover process to select the most suitable expert. For example, the handover unit can select a relevant expert based on labor-related questions or issues shared by the user on social media. The handover unit can also predict and select the most suitable expert based on the user's social media activity. The handover unit can also select a relevant expert by referring to the opinions of experts the user follows on social media. This improves the accuracy of the handover process by selecting experts based on the user's social media activity. Some or all of the above processes in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's social media activity data into a generating AI and have the generating AI select the most suitable expert.

[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 desk can analyze the user's past consultation history and suggest the optimal input method. For example, it can automatically display labor-related questions and cases that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest labor-related questions and cases that will be used during specific time periods based on the user's past consultation history. This improves input efficiency by suggesting the optimal input method based on the user's past consultation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past consultation history data into a generating AI and have the generating AI suggest the optimal input method.

[0057] The reception desk can filter input content based on the user's job title or position when they enter labor-related questions or cases. For example, if the user is a manager, labor-related questions or cases related to management can be displayed preferentially. Similarly, if the user is a new employee, labor-related questions or cases related to new employees can be displayed preferentially. Furthermore, if the user belongs to a specific department, labor-related questions or cases related to that department can be displayed preferentially. This allows for the input of more appropriate information by providing input content tailored to the user's job title or position. 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 job title or position data into a generating AI and have the generating AI perform the filtering of the input content.

[0058] The reception desk can prioritize the input of highly relevant information when users enter labor-related questions or cases, taking into account their geographical location. For example, if a user is in a specific region, labor-related questions or cases related to that region can be displayed preferentially. Similarly, if a user is on a business trip, labor-related questions or cases related to their destination can be displayed preferentially. Furthermore, if a user is working remotely, labor-related questions or cases related to remote work can be displayed preferentially. This allows users to input more appropriate information by providing highly relevant information based on their 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 a generating AI and have the generating AI prioritize the display of highly relevant information.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. For example, it can perform a detailed analysis on labor-related questions or cases of high importance. It can also perform a concise analysis on labor-related questions or cases of low importance. Furthermore, it can perform an analysis with an appropriate level of detail on labor-related questions or cases of moderate importance. This improves the efficiency of the analysis by performing the analysis according to the importance of the input 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 importance data of the input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0060] The reference unit can select the most suitable data by referring to past case precedents and the update history of legal databases during a lookup. For example, it can prioritize referring to databases containing the latest case precedents. It can also prioritize referring to databases containing the latest laws. Furthermore, it can prioritize referring to databases with frequent update histories. This improves the accuracy of the lookup by selecting data based on the update history of past case precedents and legal databases. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input database update history data into a generating AI and have the generating AI perform the selection of the most suitable data.

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

[0062] Step 1: The reception desk accepts input of labor-related questions and requests. The reception desk provides an interface for users to input labor-related questions and requests, supporting multiple input methods such as text input, voice input, and image input. For example, users can input labor-related questions and requests in text format, and can also input via voice using speech recognition technology. Furthermore, input in image format is also possible using image recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses technologies such as natural language processing, text analysis, data mining, and statistical analysis to analyze the text entered by the user and identify the content of labor-related questions and cases. Furthermore, it extracts relevant information and identifies trends. Step 3: The reference unit consults past case law and legal databases. The reference unit consults multiple databases, such as legal databases, case law databases, and industry databases, and selects the appropriate database based on the user's input to obtain the necessary information. For example, it might consult a legal database for questions regarding labor standards law, and a database of past case law for consultations regarding working hours and leave. Step 4: The service provider provides solutions and advice based on the results analyzed by the analysis provider. The service provider provides solutions and advice in multiple ways, such as in written form, verbal explanations, and video tutorials, and selects the appropriate solutions and advice based on the user's input. For example, it provides solutions based on a legal database for questions regarding labor standards law, and provides advice based on past case precedents for consultations regarding working hours and leave. Step 5: The handover department transfers serious issues to specialists based on the results provided by the service provider. The handover department transfers issues with high legal risk or urgency to appropriate specialists. For example, harassment issues are transferred to psychological counselors, and questions regarding labor standards law are transferred to legal experts.

[0063] (Example of form 2) The labor consultation system according to an embodiment of the present invention is a tool that allows employees and HR personnel to consult with AI regarding labor-related questions and issues. The labor consultation system allows employees and HR personnel to input labor-related questions and issues, and the AI ​​provides appropriate solutions and advice by referring to past case precedents and legal databases. For example, it provides quick and accurate answers to questions about labor standards law, consultations regarding working hours and leave, and harassment issues. Furthermore, it includes a function to refer serious issues to specialists. The purpose of this tool is to enable employees to quickly resolve labor-related issues and provide a secure working environment. For example, employees and HR personnel input labor-related questions and issues. In this case, the user only needs to input the specific question or consultation content, such as "question about labor standards law" or "consultation regarding working hours." This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​refers to past case precedents and legal databases and provides appropriate solutions and advice. For example, in response to a question about labor standards law, the AI ​​refers to past case precedents and legal databases and provides an appropriate solution. Furthermore, based on the solutions and advice provided by the AI, employees and HR personnel resolve the problem. For example, labor-related issues can be resolved by following the solutions provided by the AI. Finally, it also includes a function to refer serious issues to experts. For example, serious issues that the AI ​​cannot handle, such as harassment issues, can be referred to experts. This allows employees and HR personnel to consult about labor-related issues with peace of mind. In this way, the labor consultation system enables employees and HR personnel to resolve labor-related questions and cases quickly and accurately, and serious issues can be referred to experts.

[0064] The labor consultation system according to this embodiment comprises a reception unit, an analysis unit, a reference unit, a provision unit, and a handover unit. The reception unit receives input of labor-related questions and cases. The reception unit provides, for example, an interface for users to input labor-related questions and cases. The reception unit can support multiple input methods, such as text input, voice input, and image input. For example, the reception unit enables users to input labor-related questions and cases in text format. The reception unit also enables users to input labor-related questions and cases by voice using speech recognition technology. Furthermore, the reception unit enables users to input labor-related questions and cases in image format using image recognition technology. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text entered by the user, for example, using natural language processing technology. The analysis unit can analyze the user's input using technologies such as text analysis, data mining, and statistical analysis. For example, the analysis unit analyzes the text entered by the user to identify the content of the labor-related questions and cases. Furthermore, the analysis unit can extract relevant information from user input using data mining techniques. In addition, the analysis unit can analyze user input using statistical analysis techniques to understand trends in labor-related questions and cases. The reference unit allows the analysis unit to access past case law and legal databases. The reference unit can access multiple databases, such as legal databases, case law databases, and industry databases. Based on user input, the reference unit selects the appropriate database and retrieves the necessary information. For example, the reference unit can access legal databases for questions regarding labor standards law. It can also access past case law databases for consultations regarding working hours and leave. Furthermore, it can access databases containing expert opinions regarding harassment issues. The provision unit provides solutions and advice based on the results analyzed by the analysis unit. The provision unit can provide solutions and advice in multiple ways, such as in written form, verbal explanations, and video tutorials.The service provider selects and provides appropriate solutions and advice based on the user's input. For example, the service provider provides solutions to questions regarding labor standards law based on a legal database. It can also provide advice on working hours and leave based on past case precedents. Furthermore, it can provide solutions to harassment issues by referencing expert opinions. The handover service provider then forwards serious issues to experts based on the results provided by the service provider. For example, the handover service can forward issues with high legal risk or high urgency to appropriate experts. The handover service provider selects and forwards appropriate experts based on the user's input. For example, it can forward harassment issues to a psychological counselor. It can also forward questions regarding labor standards law to legal experts. Furthermore, it can forward consultations regarding working hours and leave to labor management experts. As a result, the labor consultation system according to this embodiment allows employees and HR personnel to quickly and accurately resolve labor-related questions and issues, and to refer serious problems to specialists.

[0065] The reception desk accepts input of labor-related questions and requests. For example, the reception desk provides an interface for users to input labor-related questions and requests. The reception desk can support multiple input methods, including text input, voice input, and image input. For instance, the reception desk allows users to input labor-related questions and requests in text format. It also uses voice recognition technology to allow users to input labor-related questions and requests by voice. Furthermore, it uses image recognition technology to allow users to input labor-related questions and requests in image format. Specifically, for text input, users enter their questions or requests into a dedicated input form and send the information to the reception desk by pressing a submit button. For voice input, users speak into a microphone, and voice recognition technology converts their speech into text, which is then sent to the reception desk. For image input, users upload relevant documents or images, and image recognition technology analyzes their content and sends it to the reception desk. This allows the reception desk to offer diverse input methods, enabling users to input labor-related questions and requests in the most convenient way for them. Furthermore, the reception department can centrally manage the entered information and quickly transfer it to the analysis department or other departments. This allows the reception department to enhance user convenience and improve the overall efficiency of the labor consultation system.

[0066] The analysis unit analyzes information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze text entered by users. The analysis unit can analyze user input using technologies such as text analysis, data mining, and statistical analysis. For instance, the analysis unit analyzes user input to identify labor-related questions and issues. Furthermore, the analysis unit can extract relevant information from user input using data mining technology. Additionally, the analysis unit can analyze user input using statistical analysis technology to understand trends in labor-related questions and issues. Specifically, it uses natural language processing technology to grammatically analyze user input and extract keywords and important phrases. This allows the analysis unit to identify the subject of user questions and issues and provide basic data for finding appropriate solutions. It also uses data mining technology to extract similar cases and related information from past databases, providing reference information for user questions and issues. Finally, it uses statistical analysis technology to analyze user input and understand trends and patterns related to specific labor issues. This allows the analysis unit to perform quick and accurate analyses of user questions and cases, thereby enhancing the overall effectiveness of the labor consultation system.

[0067] The reference unit allows the analysis unit to access past case law and legal databases. The reference unit can access multiple databases, such as legal databases, case law databases, and industry databases. Based on user input, the reference unit selects the appropriate database and retrieves the necessary information. For example, it can access legal databases to address questions about labor standards law. It can also access past case law databases for inquiries regarding working hours and leave. Furthermore, it can access databases containing expert opinions regarding harassment issues. Specifically, the reference unit receives user input from the analysis unit and selects the database most relevant to that content. For example, if a question about labor standards law is entered, the reference unit searches legal databases to retrieve the relevant articles and interpretations. If inquiries about working hours and leave are entered, the reference unit searches past case law databases to retrieve similar cases and court precedents. For harassment issues, it accesses databases containing expert opinions and guidelines to find appropriate countermeasures. This allows the reference section to quickly provide reliable information in response to user questions and issues, thereby improving the overall reliability and effectiveness of the labor consultation system.

[0068] The service provider will provide solutions and advice based on the results analyzed by the analysis provider. The service provider can provide solutions and advice in multiple ways, such as in written form, verbal explanations, and video tutorials. The service provider will select and provide appropriate solutions and advice based on the user's input. For example, the service provider will provide solutions based on a legal database for questions regarding labor standards law. The service provider can also provide advice based on past case precedents for consultations regarding working hours and leave. Furthermore, the service provider can provide solutions for harassment issues by referring to expert opinions. Specifically, the service provider will select the optimal solution for the user's questions and cases based on the analysis results received from the analysis provider. For example, for questions regarding labor standards law, it will provide specific countermeasures in written form based on articles and interpretations obtained from a legal database. For consultations regarding working hours and leave, it will provide verbal explanations of advice based on past case precedents and provide video tutorials as needed. For harassment issues, it will provide solutions by referring to expert opinions and guidelines to help users take appropriate action. This allows the service provider to quickly and appropriately provide solutions and advice to users' questions and issues, thereby enhancing the overall effectiveness of the labor consultation system.

[0069] The handover department will transfer serious issues to specialists based on the results provided by the service provider. For example, the handover department can transfer issues with high legal risk or high urgency to appropriate specialists. The handover department will select and transfer the appropriate specialist based on the user's input. For example, the handover department will transfer harassment issues to a psychological counselor. The handover department can also transfer questions regarding labor standards law to a legal specialist. Furthermore, the handover department can transfer consultations regarding working hours and leave to a labor management specialist. Specifically, the handover department will determine whether the user's problem is serious based on the results of the solutions and advice received from the service provider. For issues with high legal risk or high urgency, it will select and transfer the appropriate specialist. For example, harassment issues will be transferred to a psychological counselor so that the user can receive appropriate counseling. Questions regarding labor standards law will be transferred to a legal specialist so that the user can receive legal advice. For inquiries regarding working hours and leave, the user will be referred to a labor management specialist, ensuring they receive appropriate labor management advice. This allows the referral department to respond quickly and appropriately to serious user issues, improving the overall reliability and effectiveness of the labor consultation system.

[0070] The reception desk can estimate the user's emotions and adjust the display of the input interface 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. If the user is in a hurry, the reception desk can prioritize voice input, allowing for quick input of labor-related questions or matters. This improves user convenience by providing an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze the user's past consultation history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions labor-related questions and cases that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest labor-related questions and cases to be used during specific time periods based on the user's past consultation history. This improves input efficiency by suggesting the optimal input method based on the user's past consultation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past consultation history data into a generating AI and have the generating AI suggest the optimal input method.

[0072] The reception desk can filter input content based on the user's job title or position when they enter labor-related questions or cases. For example, if the user is in a management position, the reception desk will prioritize displaying labor-related questions or cases related to management. If the user is a new employee, the reception desk can also prioritize displaying labor-related questions or cases related to new employees. If the user belongs to a specific department, the reception desk can also prioritize displaying labor-related questions or cases related to that department. This allows users to enter more appropriate information by providing input content tailored to their job title or position. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's job title or position data into a generating AI and have the generating AI perform the filtering of the input content.

[0073] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception desk will prioritize urgent labor-related questions and cases. If the user is relaxed, the reception desk may prioritize detailed input. If the user is in a hurry, the reception desk may prioritize concise input. This allows for more appropriate responses by prioritizing input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can prioritize the input of highly relevant information when users enter labor-related questions or cases, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize displaying labor-related questions or cases related to that region. If a user is on a business trip, the reception desk can also prioritize displaying labor-related questions or cases related to their business trip destination. If a user is working remotely, the reception desk can also prioritize displaying labor-related questions or cases related to remote work. This allows users to input more appropriate information by providing highly relevant information based on their 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 a generating AI and have the generating AI prioritize the display of highly relevant information.

[0075] The reception desk can analyze the user's social media activity and prompt them to input relevant information when they enter labor-related questions or cases. For example, the reception desk can automatically input labor-related questions or cases that the user has shared on social media. The reception desk can also predict relevant labor-related questions or cases from the user's social media activity and prompt them to input them. The reception desk can also input labor-related questions or cases by referring to the opinions of experts that the user follows on social media. This allows for the input of more appropriate information by providing relevant information based on the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input relevant information.

[0076] 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 quick and concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is in a hurry, the analysis unit can also focus on the most important information. This improves the accuracy of the analysis by providing an analysis method that is tailored 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 not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. For example, the analysis unit can perform a detailed analysis on labor-related questions or cases of high importance. The analysis unit can also perform a concise analysis on labor-related questions or cases of low importance. The analysis unit can also perform an analysis of an appropriate level of detail on labor-related questions or cases of moderate importance. This improves the efficiency of the analysis by performing the analysis according to the importance of the input 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 importance data of the input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of labor-related questions and cases during analysis. For example, the analysis unit can apply an analysis algorithm based on a legal database to questions concerning labor standards law. The analysis unit can also apply an analysis algorithm based on past case precedents to consultations regarding working hours and leave. The analysis unit can also apply an analysis algorithm that takes expert opinions into account to harassment issues. This improves the accuracy of the analysis by performing analysis according to the category of labor-related questions and cases. 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 category data of labor-related questions and cases into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This deepens the user's understanding by providing a display method of analysis results that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can determine the priority of analysis based on the submission date of the input information during the analysis process. For example, the analysis unit can prioritize the analysis of urgent labor-related questions and cases. The analysis unit can also postpone the analysis of older labor-related questions and cases. The analysis unit can also prioritize the analysis of newer labor-related questions and cases. This improves the efficiency of the analysis by performing the analysis based on the submission date of the input 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 input information into a generating AI and have the generating AI determine the priority of the analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the input information during the analysis process. For example, the analysis unit can prioritize the analysis of labor-related questions and cases that are highly relevant. The analysis unit can also postpone the analysis of labor-related questions and cases that are less relevant. The analysis unit can also moderately analyze labor-related questions and cases that are of moderate relevance. This improves the efficiency of the analysis by performing the analysis based on the relevance of the input 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 relevance data of the input information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0082] The reference unit can estimate the user's emotions and select a database to refer to based on the estimated emotions. For example, if the user is stressed, the reference unit will prioritize referring to a database that can provide a quick response. If the user is relaxed, the reference unit may also refer to a database containing detailed information. If the user is in a hurry, the reference unit may also refer to a database that provides concise information. This improves the accuracy of the referral by selecting a database according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using AI or not using AI. For example, the reference unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reference unit can select the most suitable data by referring to past case precedents and the update history of legal databases during the reference process. For example, the reference unit may prioritize referencing databases containing the most recent case precedents. The reference unit may also prioritize referencing databases containing the most recent laws. The reference unit may also prioritize referencing databases with frequent update histories. This improves the accuracy of the reference by selecting data based on the update history of past case precedents and legal databases. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input database update history data into a generating AI and have the generating AI select the most suitable data.

[0084] The reference unit can access different databases depending on the category of the labor-related question or case. For example, the reference unit can access a legal database for questions regarding labor standards law. It can also access a database of past case precedents for consultations regarding working hours or leave. It can also access a database containing expert opinions for harassment issues. This improves the accuracy of the reference by accessing databases appropriate to the category of the labor-related question or case. Some or all of the above processing in the reference unit may be performed using AI, for example, or not. For example, the reference unit can input category data of labor-related questions and cases into a generating AI and have the generating AI access different databases.

[0085] The reference unit can estimate the user's emotions and adjust the display method of the reference results based on the estimated user emotions. For example, if the user is stressed, the reference unit can provide a simple and highly visible display method. If the user is relaxed, the reference unit can also provide a display method that includes detailed information. If the user is in a hurry, the reference unit can also provide a display method that gets straight to the point. This deepens the user's understanding by providing a display method of reference results that is tailored 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 reference unit may be performed using AI, for example, or not using AI. For example, the reference unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The reference unit can prioritize the retrieval of highly relevant data by considering the geographical distribution of the input information during the retrieval process. For example, if the user is in a specific region, the reference unit will prioritize the retrieval of data related to that region. If the user is on a business trip, the reference unit can also prioritize the retrieval of data related to the business trip destination. If the user is working remotely, the reference unit can also prioritize the retrieval of data related to remote work. This improves the accuracy of the retrieval by performing data retrieval based on the geographical distribution of the input information. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input geographical distribution data of the input information into a generating AI and have the generating AI perform priority retrieval of highly relevant data.

[0087] The reference unit can improve the accuracy of the reference results by referring to related literature during the referencing process. For example, the reference unit can improve the accuracy of the reference results by referring to related academic papers. The reference unit can also improve the accuracy of the reference results by referring to related professional books. The reference unit can also improve the accuracy of the reference results by referring to related industry reports. In this way, the accuracy of the reference results is improved by referencing related literature. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input related literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the reference results.

[0088] The service provider can estimate the user's emotions and adjust the way solutions and advice are expressed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a concise and easy-to-understand expression. If the user is relaxed, the service provider can also provide an expression that includes detailed explanations. If the user is in a hurry, the service provider can also provide a concise and rapid expression. This deepens the user's understanding by providing solutions and advice expressed in a way that matches their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The delivery unit can adjust the level of detail provided based on the importance of the solutions and advice at the time of delivery. For example, the delivery unit can provide detailed explanations for highly important solutions and advice. The delivery unit can also provide concise explanations for less important solutions and advice. The delivery unit can also provide explanations of a moderate level of detail for solutions and advice of moderate importance. This allows for a deeper understanding by providing solutions and advice according to their importance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance data of solutions and advice into a generating AI and have the generating AI adjust the level of detail provided.

[0090] The service provider can apply different service provision algorithms depending on the category of labor-related questions and cases when providing information. For example, the service provider can apply a service provision algorithm based on a legal database to questions concerning labor standards law. The service provider can also apply a service provision algorithm based on past case precedents to consultations regarding working hours and leave. The service provider can also apply a service provision algorithm that takes expert opinions into account to address harassment issues. This improves the accuracy of the service provision by providing information according to the category of labor-related questions and cases. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input category data of labor-related questions and cases into a generating AI and have the generating AI execute the application of different service provision algorithms.

[0091] The service provider can estimate the user's emotions and adjust the length of solutions and advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise solutions and advice. If the user is relaxed, the service provider can also provide longer solutions and advice with more detailed explanations. If the user is in a hurry, the service provider can provide quick and concise solutions and advice. This allows for a deeper understanding of the user by providing solutions and advice of appropriate length based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can determine the priority of solutions and advice based on when they are submitted. For example, the service provider can prioritize providing urgent solutions and advice. The service provider can also postpone providing older solutions and advice. The service provider can also prioritize providing newer solutions and advice. This improves the efficiency of the service by providing solutions and advice based on when they are submitted. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on the submission dates of solutions and advice into a generating AI and have the generating AI determine the priority of the services.

[0093] The delivery unit can adjust the order in which solutions and advice are delivered based on their relevance. For example, the delivery unit may prioritize highly relevant solutions and advice. It may also postpone less relevant solutions and advice. It may also provide solutions and advice of moderate relevance at appropriate intervals. This improves the efficiency of delivery by providing solutions and advice based on their relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input relevance data of solutions and advice into a generating AI and have the generating AI adjust the order in which they are delivered.

[0094] The handover unit can estimate the user's emotions and adjust the handover method based on the estimated emotions. For example, if the user is stressed, the handover unit can provide a quick and concise handover method. If the user is relaxed, the handover unit can also provide a handover method that includes detailed explanations. If the user is in a hurry, the handover unit can also provide a concise and quick handover method. This improves the efficiency of the handover by providing a handover method that is tailored 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 handover unit may be performed using AI, for example, or not using AI. For example, the handover unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The handover unit can select the most suitable expert by referring to the user's past consultation history during the handover process. For example, the handover unit may prioritize selecting experts the user has consulted with in the past. The handover unit can also predict and select the most suitable expert based on the user's past consultation history. The handover unit can also select relevant experts based on the content of past consultations the user has had. This improves the accuracy of the handover process by selecting experts based on the user's past consultation history. Some or all of the above-described processes in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's past consultation history data into a generating AI and have the generating AI perform the selection of the most suitable expert.

[0096] The handover unit can apply different handover methods depending on the category of labor-related questions and cases during the handover process. For example, the handover unit can hand over questions regarding labor standards law to a legal expert. It can also hand over consultations regarding working hours and leave to a labor management expert. It can also hand over harassment issues to a psychological counselor. This improves the accuracy of the handover by performing handovers according to the category of labor-related questions and cases. Some or all of the above processing in the handover unit may be performed using AI, for example, or not using AI. For example, the handover unit can input category data of labor-related questions and cases into a generating AI and have the generating AI execute the application of different handover methods.

[0097] The handover unit can estimate the user's emotions and determine the priority of handovers based on the estimated emotions. For example, if the user is stressed, the handover unit will prioritize urgent handovers. If the user is relaxed, the handover unit may also prioritize detailed handovers. If the user is in a hurry, the handover unit may also prioritize quick handovers. This improves the efficiency of handovers by providing handover priorities 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 handover unit may be performed using AI or not using AI. For example, the handover unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The handover unit can select the most suitable expert during the handover process, taking into account the user's geographical location. For example, if the user is in a specific region, the handover unit can prioritize selecting experts related to that region. If the user is on a business trip, the handover unit can also prioritize selecting experts related to the destination of the business trip. If the user is working remotely, the handover unit can also prioritize selecting experts related to remote work. This improves the accuracy of the handover process by selecting experts based on the user's geographical location. Some or all of the above processing in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable expert.

[0099] The handover unit can analyze the user's social media activity during the handover process to select the most suitable expert. For example, the handover unit can select a relevant expert based on labor-related questions or issues shared by the user on social media. The handover unit can also predict and select the most suitable expert based on the user's social media activity. The handover unit can also select a relevant expert by referring to the opinions of experts the user follows on social media. This improves the accuracy of the handover process by selecting experts based on the user's social media activity. Some or all of the above processes in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's social media activity data into a generating AI and have the generating AI select the most suitable expert.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of labor-related questions or requests. This improves user convenience by providing an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] 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, a quick and concise analysis can be performed. If the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is in a hurry, an analysis focusing on the most important information can be performed. This improves the accuracy of the analysis by providing an analysis method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0103] The service provider can estimate the user's emotions and adjust the way solutions and advice are presented based on the estimated emotions. For example, if the user is stressed, it can provide concise and easy-to-understand solutions. If the user is relaxed, it can provide solutions that include detailed explanations. Furthermore, if the user is in a hurry, it can provide concise and rapid solutions. This allows for a deeper understanding of the user by providing solutions and advice tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The handover unit can estimate the user's emotions and adjust the handover method based on the estimated emotions. For example, if the user is stressed, a quick and concise handover method can be provided. If the user is relaxed, a handover method including detailed explanations can be provided. Furthermore, if the user is in a hurry, a concise and efficient handover method can be provided. This improves the efficiency of the handover by providing a handover method that is tailored 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 handover unit may be performed using AI or not using AI. For example, the handover unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The reference unit can estimate the user's emotions and select a database to reference based on the estimated emotions. For example, if the user is stressed, it can prioritize referencing a database that can provide a quick response. If the user is relaxed, it can also refer to a database containing detailed information. Furthermore, if the user is in a hurry, it can refer to a database that provides concise information. This improves the accuracy of the reference by selecting a database 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 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 reference unit may be performed using AI or not. For example, the reference unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The reception desk can analyze the user's past consultation history and suggest the optimal input method. For example, it can automatically display labor-related questions and cases that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest labor-related questions and cases that will be used during specific time periods based on the user's past consultation history. This improves input efficiency by suggesting the optimal input method based on the user's past consultation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past consultation history data into a generating AI and have the generating AI suggest the optimal input method.

[0107] The reception desk can filter input content based on the user's job title or position when they enter labor-related questions or cases. For example, if the user is a manager, labor-related questions or cases related to management can be displayed preferentially. Similarly, if the user is a new employee, labor-related questions or cases related to new employees can be displayed preferentially. Furthermore, if the user belongs to a specific department, labor-related questions or cases related to that department can be displayed preferentially. This allows for the input of more appropriate information by providing input content tailored to the user's job title or position. 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 job title or position data into a generating AI and have the generating AI perform the filtering of the input content.

[0108] The reception desk can prioritize the input of highly relevant information when users enter labor-related questions or cases, taking into account their geographical location. For example, if a user is in a specific region, labor-related questions or cases related to that region can be displayed preferentially. Similarly, if a user is on a business trip, labor-related questions or cases related to their destination can be displayed preferentially. Furthermore, if a user is working remotely, labor-related questions or cases related to remote work can be displayed preferentially. This allows users to input more appropriate information by providing highly relevant information based on their 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 a generating AI and have the generating AI prioritize the display of highly relevant information.

[0109] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. For example, it can perform a detailed analysis on labor-related questions or cases of high importance. It can also perform a concise analysis on labor-related questions or cases of low importance. Furthermore, it can perform an analysis with an appropriate level of detail on labor-related questions or cases of moderate importance. This improves the efficiency of the analysis by performing the analysis according to the importance of the input 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 importance data of the input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0110] The reference unit can select the most suitable data by referring to past case precedents and the update history of legal databases during a lookup. For example, it can prioritize referring to databases containing the latest case precedents. It can also prioritize referring to databases containing the latest laws. Furthermore, it can prioritize referring to databases with frequent update histories. This improves the accuracy of the lookup by selecting data based on the update history of past case precedents and legal databases. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input database update history data into a generating AI and have the generating AI perform the selection of the most suitable data.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk accepts input of labor-related questions and requests. The reception desk provides an interface for users to input labor-related questions and requests, supporting multiple input methods such as text input, voice input, and image input. For example, users can input labor-related questions and requests in text format, and can also input via voice using speech recognition technology. Furthermore, input in image format is also possible using image recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses technologies such as natural language processing, text analysis, data mining, and statistical analysis to analyze the text entered by the user and identify the content of labor-related questions and cases. Furthermore, it extracts relevant information and identifies trends. Step 3: The reference unit consults past case law and legal databases. The reference unit consults multiple databases, such as legal databases, case law databases, and industry databases, and selects the appropriate database based on the user's input to obtain the necessary information. For example, it might consult a legal database for questions regarding labor standards law, and a database of past case law for consultations regarding working hours and leave. Step 4: The service provider provides solutions and advice based on the results analyzed by the analysis provider. The service provider provides solutions and advice in multiple ways, such as in written form, verbal explanations, and video tutorials, and selects the appropriate solutions and advice based on the user's input. For example, it provides solutions based on a legal database for questions regarding labor standards law, and provides advice based on past case precedents for consultations regarding working hours and leave. Step 5: The handover department transfers serious issues to specialists based on the results provided by the service provider. The handover department transfers issues with high legal risk or urgency to appropriate specialists. For example, harassment issues are transferred to psychological counselors, and questions regarding labor standards law are transferred to legal experts.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, reference unit, provision unit, and handover unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input labor-related questions and cases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information entered by the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to past case precedents and legal databases. The provision unit is implemented by the control unit 46A of the smart device 14 and provides solutions and advice based on the analysis results. The handover unit is implemented by the specific processing unit 290 of the data processing unit 12 and hands over serious problems to experts. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, reference unit, provision unit, and handover unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input labor-related questions and cases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information entered by the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to past case precedents and legal databases. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides solutions and advice based on the analysis results. The handover unit is implemented by the specific processing unit 290 of the data processing unit 12 and hands over serious problems to experts. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, reference unit, provision unit, and handover unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input labor-related questions and cases. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information entered by the user. The reference unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and refers to past precedents and legal databases. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides solutions and advice based on the analysis results. The handover unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hands over serious problems to experts. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, reference unit, provision unit, and handover unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input labor-related questions and cases. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information entered by the user. The reference unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and refers to past case precedents and legal databases. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides solutions and advice based on the analysis results. The handover unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hands over serious problems to experts. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception desk that accepts inquiries and requests regarding labor matters, An analysis unit that analyzes the information received by the reception unit, The aforementioned analysis unit includes a reference unit that refers to past case precedents and legal databases, A provision unit provides solutions and advice based on the results of the analysis performed by the aforementioned analysis unit, The system includes a handover unit that, based on the results provided by the aforementioned provisioning unit, transfers serious problems to experts. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We analyze the user's past consultation history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter questions or requests regarding labor-related matters, the system filters the input based on their job title or position. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter questions or requests regarding labor-related matters, the system prioritizes input of highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter questions or requests regarding labor-related matters, the system analyzes their social media activity and prompts them to input relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of labor-related questions and cases. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the input information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reference section is, The system estimates the user's emotions and selects a database to reference based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reference section is, When referencing data, the system selects the most suitable data by referring to past case precedents and the update history of legal databases. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reference section is, When referencing, different databases are consulted depending on the category of labor-related questions or cases. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reference section is, It estimates the user's emotions and adjusts how the referral results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reference section is, When referencing data, the system prioritizes retrieving highly relevant data, taking into account the geographical distribution of the entered information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reference section is, When referencing, we improve the accuracy of the reference results by referring to related literature. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way solutions and advice are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing solutions or advice, adjust the level of detail based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, different provision algorithms are applied depending on the category of labor-related questions or cases. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of solutions and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing solutions or advice, we will prioritize their delivery based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing solutions and advice, we adjust the order of delivery based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned handover section is, It estimates the user's emotions and adjusts the handover method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned handover section is, During the handover process, the system selects the most suitable expert by referring to the user's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned handover section is, During the handover process, different handover methods will be applied depending on the type of labor-related questions and issues. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned handover section is, It estimates the user's emotions and determines the priority of the handover based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned handover section is, During the handover process, the most suitable expert will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned handover section is, During the handover process, we analyze the user's social media activity to select the most suitable expert. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 accepts inquiries and requests regarding labor matters, An analysis unit that analyzes the information received by the reception unit, The aforementioned analysis unit includes a reference unit that refers to past case precedents and legal databases, A provision unit provides solutions and advice based on the results of the analysis performed by the aforementioned analysis unit, The system includes a handover unit that, based on the results provided by the aforementioned provisioning unit, transfers serious problems to experts. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is We analyze the user's past consultation history and suggest the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter questions or requests regarding labor-related matters, the system filters the input based on their job title or position. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter questions or requests regarding labor-related matters, the system prioritizes input of highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter questions or requests regarding labor-related matters, the system analyzes their social media activity and prompts them to input relevant information. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the input information. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of labor-related questions and cases. The system according to feature 1.

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