system

Abstract

The system according to this embodiment aims to provide immediate responses to user questions and optimized multilingual guidance. [Solution] The system according to the embodiment comprises a reception unit, a response unit, a setting unit, an optimization unit, and a multilingual support unit. The reception unit receives user questions. The response unit immediately responds to questions received by the reception unit. The setting unit sets facility-specific information. The optimization unit optimizes guidance based on the information set by the setting unit. The multilingual support unit provides guidance in multiple languages.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction 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, there is a problem that the waiting time for guidance is long and there are limitations in mechanical voice guidance, resulting in low user satisfaction.

[0005] The system according to the embodiment aims to immediately respond to a user's question and provide optimized multilingual guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a response unit, a setting unit, an optimization unit, and a multilingual support unit. The reception unit receives user questions. The response unit immediately responds to questions received by the reception unit. The setting unit sets facility-specific information. The optimization unit optimizes guidance based on the information set by the setting unit. The multilingual support unit provides guidance in multiple languages. [Effects of the Invention]

[0007] The system according to this embodiment can respond immediately to user questions and provide optimized multilingual guidance. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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 smart reception AI agent system according to an embodiment of the present invention is a system for automating reception work and reducing the workload of reception staff. This smart reception AI agent system has components that support natural language, a customizable system, and multilingual support. As a component that supports natural language, the AI ​​enables natural dialogue and responds immediately to user questions. This overcomes the limitations of conventional mechanical voice guidance and realizes smooth communication with users. For example, if a user says, "I would like to make an appointment for a medical examination," the AI ​​will respond immediately and guide the user through the appointment procedure. As a customizable system, facility-specific information can be set and guidance can be optimized. This enables guidance tailored to each facility and improves user convenience. For example, hospitals can provide guidance by medical department, and companies can provide guidance by department to be visited. As a component that supports multilingual support, the AI ​​provides guidance in multiple languages. This improves visitor convenience and can accommodate international use. For example, guidance can be provided in multiple languages ​​such as English, Chinese, and Spanish. This system is expected to reduce waiting times, improve the accuracy and efficiency of guidance, and increase user satisfaction. Specifically, users can receive guidance smoothly without having to wait long hours at the reception desk, thereby improving the efficiency of reception operations. In addition, the AI's natural conversational skills reduce user stress and improve satisfaction. This smart reception AI agent will be used in various industries, including hospitals, government offices, and businesses. For example, it will enable smoother patient guidance in hospitals, more efficient resident services in government offices, and quick guidance for visitors in businesses. As a result, the smart reception AI agent system can respond immediately to user questions, optimize guidance based on facility-specific information, and provide guidance in multiple languages, thereby reducing waiting times, improving the accuracy and efficiency of guidance, and increasing user satisfaction.

[0029] The smart reception AI agent system according to this embodiment comprises a reception unit, a response unit, a setting unit, an optimization unit, and a multilingual support unit. The reception unit receives user questions. User questions include, but are not limited to, examples of making appointments for medical consultations, providing information about the facility, and explaining how to use services. The reception unit can receive questions by methods such as voice input, text input, and touch panel input. The response unit provides immediate responses to questions received by the reception unit. The response unit provides appropriate answers to user questions by, for example, using AI to engage in natural conversation. The response unit can provide responses such as guiding users through the appointment booking process, providing directions to the facility, and explaining how to use services. The setting unit sets facility-specific information. The setting unit can set, for example, the facility's name, address, service details, and information for each medical department. The optimization unit optimizes the guidance based on the information set by the setting unit. The optimization unit provides optimal guidance information according to the content of the user's question. The optimization unit can optimize, for example, guidance for each medical department, guidance for each department to be visited, and guidance on how to use services. The multilingual support unit provides guidance in multiple languages. The multilingual support unit can provide guidance in multiple languages, for example, English, Chinese, and Spanish. The multilingual support unit provides guidance in the appropriate language according to the user's language settings. As a result, the smart reception AI agent system according to the embodiment can respond immediately to user questions, optimize guidance based on facility-specific information, and provide guidance in multiple languages.

[0030] The reception desk receives user inquiries. User inquiries include, but are not limited to, requests for appointment scheduling, facility information, and service usage. The reception desk can accept inquiries using methods such as voice input, text input, and touch panel input. Specifically, in the case of voice input, the user speaks their question into a microphone, and speech recognition technology is used to convert the content into text data. In the case of text input, the user enters their question using a keyboard or touch panel. In the case of touch panel input, the user can select a question by tapping buttons or icons on the screen. This allows the reception desk to support diverse input methods and improve user convenience. Furthermore, the reception desk analyzes the user's input in real time and prepares to provide an appropriate response. For example, in the case of voice input, a speech recognition engine analyzes the user's speech and understands the intent of the question. In the case of text input, natural language processing technology is used to analyze the input and understand the content of the question. This allows the reception desk to respond to user inquiries quickly and accurately.

[0031] The response unit provides immediate responses to questions received by the reception unit. For example, the response unit uses AI to engage in natural conversation and provide appropriate answers to user questions. Specifically, the AI ​​analyzes the user's question using natural language processing technology and generates appropriate responses. For instance, when guiding users through the appointment booking process, the AI ​​confirms the user's preferred date, time, and department, and presents available time slots. When guiding users to a facility, the AI ​​understands the user's current location and destination, and guides them along the optimal route. When explaining how to use a service, the AI ​​explains specific procedures and points to note according to the user's question. This allows the response unit to respond quickly and appropriately to user questions. Furthermore, the response unit records the history of conversations with users and can utilize this information for future responses. For example, for users who have previously booked appointments, the response unit can refer to their previous booking details to guide them through a smoother booking process. The response unit can also collect user feedback and continuously improve the accuracy and quality of its responses. This allows the response unit to improve user satisfaction and enhance the overall system performance.

[0032] The settings unit is used to configure facility-specific information. For example, it can configure the facility's name, address, services offered, and information for each medical department. Specifically, facility administrators input basic and detailed facility information through the settings screen. For example, they can input the facility's name, address, and contact information, and set the consultation hours and assigned physician information for each medical department. They can also set specific usage instructions and points to note regarding services. This allows the settings unit to accurately register facility-specific information, enabling other departments to utilize that information. Furthermore, the settings unit can handle periodic information updates and changes. For example, if there are changes to consultation hours or the addition of new services, administrators can update the information through the settings screen to provide users with the latest information. In addition, the settings unit can centrally manage information from multiple facilities and departments. This allows the settings unit to efficiently manage information for the entire facility and provide users with accurate and up-to-date information.

[0033] The optimization unit optimizes guidance based on information set by the settings unit. For example, the optimization unit provides optimal guidance information according to the user's question. Specifically, it uses AI to analyze the user's question and generate the optimal answer. For example, when providing guidance for each medical department, it confirms the user's symptoms and desired department and provides information on the optimal consultation time and doctor. When providing guidance for each department to visit, it understands the user's destination and current location and guides them on the optimal route. When providing guidance on how to use the service, it explains specific procedures and points to note according to the user's question. This allows the optimization unit to respond to user questions quickly and appropriately. Furthermore, the optimization unit can continuously improve the accuracy and quality of the guidance content based on the user's usage history and feedback. For example, for users who have previously made a medical appointment, it refers to the details of the previous appointment and guides them through a smoother appointment process. In addition, the optimization unit can analyze the user's usage status and behavior patterns and provide personalized guidance that meets individual needs. This allows the optimization unit to improve user satisfaction and the overall performance of the system.

[0034] The multilingual support unit provides guidance in multiple languages. For example, it can provide guidance in multiple languages ​​such as English, Chinese, and Spanish. Specifically, it provides guidance in the appropriate language according to the user's language settings. For example, if the user selects English, all guidance will be provided in English. Similarly, if the user selects Chinese or Spanish, guidance will be provided in the respective language. This allows the multilingual support unit to accommodate users who speak different languages, improving convenience. Furthermore, the multilingual support unit can perform real-time translation using AI. For example, if a user enters a question in Japanese, the AI ​​will translate the content into another language and generate an appropriate answer. Conversely, it can also translate questions entered in other languages ​​into Japanese and process them within the system. This allows the multilingual support unit to provide consistent service to users, transcending language barriers. Additionally, the multilingual support unit can perform translations that take into account the cultural background and nuances of each language. This allows users to engage in natural conversations in their native language, making the system more comfortable to use.

[0035] The response unit can engage in natural dialogue with the user's questions. For example, the response unit can use AI to provide appropriate answers to the user's questions. The response unit can provide responses such as guiding the user through the appointment scheduling process, guiding them to the location of a facility, or explaining how to use a service. For example, the response unit enables smooth communication with the user by engaging in natural dialogue with the user's questions. Some or all of the above-described processes in the response unit may be performed using, for example, generative AI, or without generative AI. For example, the response unit inputs the user's question into the generative AI, and the generative AI generates an appropriate answer. This allows the response unit to engage in natural dialogue with the user's questions.

[0036] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display frequently asked questions as candidates. For example, the reception desk can prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest questions to be asked at a specific time based on the user's past question history. In this way, the reception desk can provide the user with the optimal reception method by analyzing past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's past question history data into a generating AI, and the generating AI selects the optimal reception method.

[0037] The reception unit can filter questions based on the user's current situation and areas of interest when receiving them. For example, the reception unit can prioritize receiving relevant questions based on the user's current location. For example, the reception unit can filter and display relevant questions based on the user's areas of interest. For example, the reception unit can suggest the most relevant questions based on the user's current situation (time of day, location, etc.). In this way, the reception unit can prioritize receiving highly relevant questions by filtering them based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit inputs the user's current situation data into a generating AI, and the generating AI filters the relevant questions.

[0038] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving relevant questions based on the user's current location. For example, the reception desk can suggest the most relevant questions based on the user's geographical location information. For example, the reception desk can update the user's current location information in real time and prioritize receiving relevant questions. This allows the reception desk to prioritize receiving highly relevant questions by taking into account the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's geographical location data into a generating AI, and the generating AI selects relevant questions.

[0039] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can prioritize questions related to topics of interest based on the user's social media activity. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant questions. For example, the reception desk can prioritize questions by referring to the activities of the user's social media followers and friends. In this way, the reception desk can prioritize questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media activity data into a generating AI, and the generating AI selects relevant questions.

[0040] The response unit can adjust the level of detail in its response based on the importance of the question. For example, it can provide a detailed response to an important question, a concise response to a general question, and a quick response to an urgent question. This allows the response unit to provide a more appropriate response by adjusting the level of detail based on the importance of the question. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question importance data into a generating AI, which then adjusts the level of detail in the response.

[0041] The response unit can apply different response algorithms depending on the category of the question when responding. For example, the response unit can apply a specialized response algorithm to medical-related questions. For example, the response unit can apply a business-oriented response algorithm to corporate-related questions. For example, the response unit can apply a general-purpose response algorithm to general questions. This allows the response unit to provide more appropriate responses by applying different response algorithms depending on the category of the question. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question category data into a generating AI, and the generating AI applies different response algorithms.

[0042] The response unit can determine the priority of responses based on when the questions were submitted. For example, the response unit will prioritize responses to recently submitted questions. For example, the response unit may postpone responses to questions submitted in the past. For example, the response unit can respond immediately to urgent questions. This allows the response unit to respond in a more appropriate order by determining the priority of responses based on when the questions were submitted. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question submission time data into a generating AI, and the generating AI determines the priority of responses.

[0043] The response unit can adjust the order of responses based on the relevance of the questions during the response process. For example, the response unit will prioritize responses to highly relevant questions. For example, the response unit can postpone responses to less relevant questions. For example, the response unit can analyze the relevance of questions in real time and respond in the optimal order. This allows the response unit to respond in a more appropriate order by adjusting the order of responses based on the relevance of the questions. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question relevance data into a generating AI, and the generating AI adjusts the order of responses.

[0044] The configuration unit can optimize its configuration algorithm by referring to the facility's past configuration data during configuration. For example, the configuration unit provides optimal configuration information based on the facility's past configuration data. For example, the configuration unit can analyze the facility's past configuration data and optimize its configuration algorithm. For example, the configuration unit can improve the accuracy of configuration by referring to the facility's past configuration data. As a result, the configuration unit improves the accuracy of its configuration algorithm by referring to the facility's past configuration data. Some or all of the above processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit inputs the facility's past configuration data into a generating AI, and the generating AI optimizes the configuration algorithm.

[0045] The configuration unit can apply different configuration methods to each facility category during configuration. For example, the configuration unit can apply medical-related configuration methods to medical facilities. For example, the configuration unit can apply business-related configuration methods to companies. For example, the configuration unit can apply general-purpose configuration methods to general facilities. This allows the configuration unit to perform more appropriate configurations by applying different configuration methods to each facility category. Some or all of the above-described processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit inputs facility category data into a generating AI, and the generating AI applies different configuration methods.

[0046] The configuration unit can select configuration information while considering the facility's geographical location information. The configuration unit can, for example, provide optimal configuration information based on the facility's geographical location information. The configuration unit can, for example, improve the accuracy of the configuration by referring to the facility's geographical location information. The configuration unit can, for example, adjust the frequency of configuration by considering the facility's geographical location information. As a result, the configuration unit can provide more appropriate configuration information by considering the facility's geographical location information. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit inputs the facility's geographical location information data into a generating AI, and the generating AI selects the configuration information.

[0047] The configuration unit can improve the accuracy of its configuration by referring to relevant literature on the facility during the configuration process. For example, the configuration unit provides optimal configuration information based on relevant literature on the facility. For example, the configuration unit can optimize its configuration algorithm by referring to relevant literature on the facility. For example, the configuration unit can improve the accuracy of its configuration by referring to relevant literature on the facility. As a result, the configuration unit improves the accuracy of its configuration by referring to relevant literature on the facility. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without using AI. For example, the configuration unit inputs relevant literature data on the facility into a generating AI, and the generating AI improves the accuracy of the configuration.

[0048] The optimization unit can optimize the optimization algorithm by referring to past facility guidance data during optimization. For example, the optimization unit provides the optimal optimization method based on past facility guidance data. For example, the optimization unit can analyze past facility guidance data and optimize the optimization algorithm. For example, the optimization unit can improve the accuracy of optimization by referring to past facility guidance data. As a result, the accuracy of the optimization algorithm is improved by the optimization unit referring to past facility guidance data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit inputs past facility guidance data into a generating AI, and the generating AI optimizes the optimization algorithm.

[0049] The optimization unit can apply different optimization methods to each facility category during optimization. For example, the optimization unit can apply medical-related optimization methods to medical facilities. For example, the optimization unit can apply business-related optimization methods to companies. For example, the optimization unit can apply general-purpose optimization methods to general facilities. This allows the optimization unit to achieve more appropriate optimization by applying different optimization methods to each facility category. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs facility category data into a generating AI, and the generating AI applies different optimization methods.

[0050] The optimization unit can select optimization information while considering the geographical location information of the facility. The optimization unit provides optimal optimization information based on the geographical location information of the facility, for example. The optimization unit can improve the accuracy of optimization by referring to the geographical location information of the facility, for example. The optimization unit can adjust the frequency of optimization by considering the geographical location information of the facility, for example. This allows the optimization unit to provide more appropriate optimization information by considering the geographical location information of the facility. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs the geographical location information data of the facility to a generating AI, and the generating AI selects the optimization information.

[0051] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the facility during the optimization process. For example, the optimization unit provides optimal optimization information based on relevant literature on the facility. For example, the optimization unit can optimize the optimization algorithm by referring to relevant literature on the facility. For example, the optimization unit can improve the accuracy of optimization by referring to relevant literature on the facility. As a result, the optimization unit improves the accuracy of optimization by referring to relevant literature on the facility. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit inputs the relevant literature data on the facility into a generating AI, and the generating AI improves the accuracy of optimization.

[0052] The multilingual support unit can optimize its multilingual support algorithm by referring to past multilingual support data during multilingual support. For example, the multilingual support unit can provide the optimal multilingual support method based on past multilingual support data. For example, the multilingual support unit can analyze past multilingual support data and optimize its multilingual support algorithm. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to past multilingual support data. As a result, the multilingual support unit improves the accuracy of its multilingual support algorithm by referring to past multilingual support data. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit inputs past multilingual support data into a generating AI, and the generating AI optimizes the multilingual support algorithm.

[0053] The multilingual support unit can apply different multilingual support methods to each language category when handling multilingual support. For example, the multilingual support unit can apply English-related multilingual support methods to English. For example, the multilingual support unit can apply Chinese-related multilingual support methods to Chinese. For example, the multilingual support unit can apply Spanish-related multilingual support methods to Spanish. By applying different multilingual support methods to each language category, the multilingual support unit can achieve more appropriate multilingual support. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit inputs language category data into a generating AI, and the generating AI applies different multilingual support methods.

[0054] The multilingual support unit can select multilingual information while considering the user's geographical location information. For example, the multilingual support unit provides optimal multilingual information based on the user's geographical location information. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to the user's geographical location information. For example, the multilingual support unit can adjust the frequency of multilingual support by considering the user's geographical location information. As a result, the multilingual support unit can provide more appropriate multilingual information by considering the user's geographical location information. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit inputs the user's geographical location information data into a generating AI, and the generating AI selects multilingual information.

[0055] The multilingual support unit can improve the accuracy of multilingual support by referring to relevant literature during the multilingual support process. For example, the multilingual support unit provides optimal multilingual support information based on relevant literature. For example, the multilingual support unit can optimize the multilingual support algorithm by referring to relevant literature. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to relevant literature. As a result, the multilingual support unit improves the accuracy of multilingual support by referring to relevant literature. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit inputs relevant literature data into a generating AI, and the generating AI improves the accuracy of multilingual support.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can analyze a user's past behavior history and suggest the most suitable reception method. For example, it can automatically display frequently asked questions as suggestions. It can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions that might be asked at specific times based on the user's past behavior history. In this way, the reception desk can provide users with the most suitable reception method by analyzing their past behavior history.

[0058] The configuration unit can optimize its configuration algorithm by referring to the facility's past configuration data. For example, it can provide optimal configuration information based on the facility's past configuration data. It can also analyze the facility's past configuration data and optimize its configuration algorithm. Furthermore, it can improve the accuracy of the configuration by referring to the facility's past configuration data. As a result, the configuration unit improves the accuracy of its configuration algorithm by referring to the facility's past configuration data.

[0059] The multilingual support unit can optimize its multilingual support algorithm by referring to past multilingual support data. For example, it can provide the optimal multilingual support method based on past multilingual support data. It can also analyze past multilingual support data and optimize the multilingual support algorithm. Furthermore, it can improve the accuracy of multilingual support by referring to past multilingual support data. In this way, the multilingual support unit improves the accuracy of its multilingual support algorithm by referring to past multilingual support data.

[0060] The reception desk can filter questions based on the user's current situation and areas of interest. For example, it can prioritize receiving relevant questions based on the user's current location. It can also filter and display relevant questions based on the user's areas of interest. Furthermore, it can suggest the most appropriate questions based on the user's current situation (time of day, location, etc.). As a result, the reception desk can prioritize receiving highly relevant questions by filtering them based on the user's current situation and areas of interest.

[0061] The response unit can adjust the level of detail in its response based on the importance of the question. For example, it can provide a detailed response to important questions, a concise response to general questions, and a rapid response to urgent questions. This allows the response unit to provide more appropriate responses by adjusting the level of detail based on the importance of the question.

[0062] The configuration unit can apply different configuration methods depending on the facility category. For example, medical facilities can be configured using medical-related configuration methods. Businesses can be configured using business-related configuration methods. Furthermore, general facilities can be configured using general-purpose configuration methods. This allows the configuration unit to apply different configuration methods to each facility category, enabling more appropriate settings.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives user inquiries. These inquiries may include questions about making appointments, facility information, and how to use services. The reception desk can accept inquiries via voice input, text input, touch panel input, etc. Step 2: The response unit immediately responds to questions received by the reception unit. The response unit uses AI to engage in natural conversation and provide appropriate answers to user questions. For example, it can provide guidance on scheduling appointments, directions to facilities, and explanations on how to use services. Step 3: The settings unit configures the facility's unique information. The settings unit can configure the facility's name, location, services offered, and information for each medical department. Step 4: The optimization unit optimizes the guidance based on the information set by the settings unit. The optimization unit provides the most suitable guidance information according to the user's question. For example, it can optimize guidance for each medical department, guidance for each department to visit, and guidance on how to use services. Step 5: The multilingual support unit provides guidance in multiple languages. The multilingual support unit can provide guidance in multiple languages, such as English, Chinese, and Spanish. Guidance is provided in the appropriate language according to the user's language settings.

[0065] (Example of form 2) The smart reception AI agent system according to an embodiment of the present invention is a system for automating reception work and reducing the workload of reception staff. This smart reception AI agent system has components that support natural language, a customizable system, and multilingual support. As a component that supports natural language, the AI ​​enables natural dialogue and responds immediately to user questions. This overcomes the limitations of conventional mechanical voice guidance and realizes smooth communication with users. For example, if a user says, "I would like to make an appointment for a medical examination," the AI ​​will respond immediately and guide the user through the appointment procedure. As a customizable system, facility-specific information can be set and guidance can be optimized. This enables guidance tailored to each facility and improves user convenience. For example, hospitals can provide guidance by medical department, and companies can provide guidance by department to be visited. As a component that supports multilingual support, the AI ​​provides guidance in multiple languages. This improves visitor convenience and can accommodate international use. For example, guidance can be provided in multiple languages ​​such as English, Chinese, and Spanish. This system is expected to reduce waiting times, improve the accuracy and efficiency of guidance, and increase user satisfaction. Specifically, users can receive guidance smoothly without having to wait long hours at the reception desk, thereby improving the efficiency of reception operations. In addition, the AI's natural conversational skills reduce user stress and improve satisfaction. This smart reception AI agent will be used in various industries, including hospitals, government offices, and businesses. For example, it will enable smoother patient guidance in hospitals, more efficient resident services in government offices, and quick guidance for visitors in businesses. As a result, the smart reception AI agent system can respond immediately to user questions, optimize guidance based on facility-specific information, and provide guidance in multiple languages, thereby reducing waiting times, improving the accuracy and efficiency of guidance, and increasing user satisfaction.

[0066] The smart reception AI agent system according to this embodiment comprises a reception unit, a response unit, a setting unit, an optimization unit, and a multilingual support unit. The reception unit receives user questions. User questions include, but are not limited to, examples of making appointments for medical consultations, providing information about the facility, and explaining how to use services. The reception unit can receive questions by methods such as voice input, text input, and touch panel input. The response unit provides immediate responses to questions received by the reception unit. The response unit provides appropriate answers to user questions by, for example, using AI to engage in natural conversation. The response unit can provide responses such as guiding users through the appointment booking process, providing directions to the facility, and explaining how to use services. The setting unit sets facility-specific information. The setting unit can set, for example, the facility's name, address, service details, and information for each medical department. The optimization unit optimizes the guidance based on the information set by the setting unit. The optimization unit provides optimal guidance information according to the content of the user's question. The optimization unit can optimize, for example, guidance for each medical department, guidance for each department to be visited, and guidance on how to use services. The multilingual support unit provides guidance in multiple languages. The multilingual support unit can provide guidance in multiple languages, for example, English, Chinese, and Spanish. The multilingual support unit provides guidance in the appropriate language according to the user's language settings. As a result, the smart reception AI agent system according to the embodiment can respond immediately to user questions, optimize guidance based on facility-specific information, and provide guidance in multiple languages.

[0067] The reception desk receives user inquiries. User inquiries include, but are not limited to, requests for appointment scheduling, facility information, and service usage. The reception desk can accept inquiries using methods such as voice input, text input, and touch panel input. Specifically, in the case of voice input, the user speaks their question into a microphone, and speech recognition technology is used to convert the content into text data. In the case of text input, the user enters their question using a keyboard or touch panel. In the case of touch panel input, the user can select a question by tapping buttons or icons on the screen. This allows the reception desk to support diverse input methods and improve user convenience. Furthermore, the reception desk analyzes the user's input in real time and prepares to provide an appropriate response. For example, in the case of voice input, a speech recognition engine analyzes the user's speech and understands the intent of the question. In the case of text input, natural language processing technology is used to analyze the input and understand the content of the question. This allows the reception desk to respond to user inquiries quickly and accurately.

[0068] The response unit provides immediate responses to questions received by the reception unit. For example, the response unit uses AI to engage in natural conversation and provide appropriate answers to user questions. Specifically, the AI ​​analyzes the user's question using natural language processing technology and generates appropriate responses. For instance, when guiding users through the appointment booking process, the AI ​​confirms the user's preferred date, time, and department, and presents available time slots. When guiding users to a facility, the AI ​​understands the user's current location and destination, and guides them along the optimal route. When explaining how to use a service, the AI ​​explains specific procedures and points to note according to the user's question. This allows the response unit to respond quickly and appropriately to user questions. Furthermore, the response unit records the history of conversations with users and can utilize this information for future responses. For example, for users who have previously booked appointments, the response unit can refer to their previous booking details to guide them through a smoother booking process. The response unit can also collect user feedback and continuously improve the accuracy and quality of its responses. This allows the response unit to improve user satisfaction and enhance the overall system performance.

[0069] The settings unit is used to configure facility-specific information. For example, it can configure the facility's name, address, services offered, and information for each medical department. Specifically, facility administrators input basic and detailed facility information through the settings screen. For example, they can input the facility's name, address, and contact information, and set the consultation hours and assigned physician information for each medical department. They can also set specific usage instructions and points to note regarding services. This allows the settings unit to accurately register facility-specific information, enabling other departments to utilize that information. Furthermore, the settings unit can handle periodic information updates and changes. For example, if there are changes to consultation hours or the addition of new services, administrators can update the information through the settings screen to provide users with the latest information. In addition, the settings unit can centrally manage information from multiple facilities and departments. This allows the settings unit to efficiently manage information for the entire facility and provide users with accurate and up-to-date information.

[0070] The optimization unit optimizes guidance based on information set by the settings unit. For example, the optimization unit provides optimal guidance information according to the user's question. Specifically, it uses AI to analyze the user's question and generate the optimal answer. For example, when providing guidance for each medical department, it confirms the user's symptoms and desired department and provides information on the optimal consultation time and doctor. When providing guidance for each department to visit, it understands the user's destination and current location and guides them on the optimal route. When providing guidance on how to use the service, it explains specific procedures and points to note according to the user's question. This allows the optimization unit to respond to user questions quickly and appropriately. Furthermore, the optimization unit can continuously improve the accuracy and quality of the guidance content based on the user's usage history and feedback. For example, for users who have previously made a medical appointment, it refers to the details of the previous appointment and guides them through a smoother appointment process. In addition, the optimization unit can analyze the user's usage status and behavior patterns and provide personalized guidance that meets individual needs. This allows the optimization unit to improve user satisfaction and the overall performance of the system.

[0071] The multilingual support unit provides guidance in multiple languages. For example, it can provide guidance in multiple languages ​​such as English, Chinese, and Spanish. Specifically, it provides guidance in the appropriate language according to the user's language settings. For example, if the user selects English, all guidance will be provided in English. Similarly, if the user selects Chinese or Spanish, guidance will be provided in the respective language. This allows the multilingual support unit to accommodate users who speak different languages, improving convenience. Furthermore, the multilingual support unit can perform real-time translation using AI. For example, if a user enters a question in Japanese, the AI ​​will translate the content into another language and generate an appropriate answer. Conversely, it can also translate questions entered in other languages ​​into Japanese and process them within the system. This allows the multilingual support unit to provide consistent service to users, transcending language barriers. Additionally, the multilingual support unit can perform translations that take into account the cultural background and nuances of each language. This allows users to engage in natural conversations in their native language, making the system more comfortable to use.

[0072] The response unit can engage in natural dialogue with the user's questions. For example, the response unit can use AI to provide appropriate answers to the user's questions. The response unit can provide responses such as guiding the user through the appointment scheduling process, guiding them to the location of a facility, or explaining how to use a service. For example, the response unit enables smooth communication with the user by engaging in natural dialogue with the user's questions. Some or all of the above-described processes in the response unit may be performed using, for example, generative AI, or without generative AI. For example, the response unit inputs the user's question into the generative AI, and the generative AI generates an appropriate answer. This allows the response unit to engage in natural dialogue with the user's questions.

[0073] The reception desk can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, if the user is stressed, the AI ​​can immediately accept the question and respond quickly. If the user is relaxed, the AI ​​can accept the question at an appropriate time and provide a detailed response. If the user is in a hurry, the AI ​​can prioritize the question and process it quickly. This allows the reception desk to accept questions at a more appropriate time by adjusting the timing of question reception 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk inputs user emotion data into a generative AI, and the generative AI estimates the emotion. This allows the reception desk to adjust the timing of question reception based on the user's emotions.

[0074] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display frequently asked questions as candidates. For example, the reception desk can prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest questions to be asked at a specific time based on the user's past question history. In this way, the reception desk can provide the user with the optimal reception method by analyzing past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's past question history data into a generating AI, and the generating AI selects the optimal reception method.

[0075] The reception unit can filter questions based on the user's current situation and areas of interest when receiving them. For example, the reception unit can prioritize receiving relevant questions based on the user's current location. For example, the reception unit can filter and display relevant questions based on the user's areas of interest. For example, the reception unit can suggest the most relevant questions based on the user's current situation (time of day, location, etc.). In this way, the reception unit can prioritize receiving highly relevant questions by filtering them based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit inputs the user's current situation data into a generating AI, and the generating AI filters the relevant questions.

[0076] The reception desk can estimate the user's emotions and determine the priority of questions to be received based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize receiving questions and respond quickly. For example, if the user is relaxed, the AI ​​will receive questions at an appropriate time and provide detailed responses. For example, if the user is in a hurry, the AI ​​will prioritize receiving questions and process them quickly. This allows the reception desk to receive questions in a more appropriate order by determining the priority of questions 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk inputs user emotion data into a generative AI, and the generative AI estimates the emotions. This allows the reception desk to determine the priority of questions based on the user's emotions.

[0077] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving relevant questions based on the user's current location. For example, the reception desk can suggest the most relevant questions based on the user's geographical location information. For example, the reception desk can update the user's current location information in real time and prioritize receiving relevant questions. This allows the reception desk to prioritize receiving highly relevant questions by taking into account the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's geographical location data into a generating AI, and the generating AI selects relevant questions.

[0078] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can prioritize questions related to topics of interest based on the user's social media activity. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant questions. For example, the reception desk can prioritize questions by referring to the activities of the user's social media followers and friends. In this way, the reception desk can prioritize questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media activity data into a generating AI, and the generating AI selects relevant questions.

[0079] The response unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is tense, the response unit may respond in a calm tone. For example, if the user is relaxed, the response unit may respond in a bright tone. For example, if the user is in a hurry, the response unit may provide a quick and concise response. This allows the response unit to provide a more appropriate response by adjusting the way it expresses its response 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response unit may be performed using AI, or not using AI. For example, the response unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the way it expresses its response.

[0080] The response unit can adjust the level of detail in its response based on the importance of the question. For example, it can provide a detailed response to an important question, a concise response to a general question, and a quick response to an urgent question. This allows the response unit to provide a more appropriate response by adjusting the level of detail based on the importance of the question. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question importance data into a generating AI, which then adjusts the level of detail in the response.

[0081] The response unit can apply different response algorithms depending on the category of the question when responding. For example, the response unit can apply a specialized response algorithm to medical-related questions. For example, the response unit can apply a business-oriented response algorithm to corporate-related questions. For example, the response unit can apply a general-purpose response algorithm to general questions. This allows the response unit to provide more appropriate responses by applying different response algorithms depending on the category of the question. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question category data into a generating AI, and the generating AI applies different response algorithms.

[0082] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the response unit can provide a short, to-the-point response. If the user is relaxed, the response unit can provide a longer response that includes detailed explanations. If the user is excited, the response unit can provide a response with visually stimulating effects. This allows the response unit to provide a more appropriate response by adjusting the length of the response 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 response unit may be performed using AI or not using AI. For example, the response unit inputs user emotion data into the generative AI, and the generative AI adjusts the length of the response.

[0083] The response unit can determine the priority of responses based on when the questions were submitted. For example, the response unit will prioritize responses to recently submitted questions. For example, the response unit may postpone responses to questions submitted in the past. For example, the response unit can respond immediately to urgent questions. This allows the response unit to respond in a more appropriate order by determining the priority of responses based on when the questions were submitted. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question submission time data into a generating AI, and the generating AI determines the priority of responses.

[0084] The response unit can adjust the order of responses based on the relevance of the questions during the response process. For example, the response unit will prioritize responses to highly relevant questions. For example, the response unit can postpone responses to less relevant questions. For example, the response unit can analyze the relevance of questions in real time and respond in the optimal order. This allows the response unit to respond in a more appropriate order by adjusting the order of responses based on the relevance of the questions. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit inputs question relevance data into a generating AI, and the generating AI adjusts the order of responses.

[0085] The settings unit can estimate the user's emotions and select settings information based on the estimated emotions. For example, if the user is stressed, the settings unit can provide simple settings information. For example, if the user is relaxed, the settings unit can provide detailed settings information. For example, if the user is in a hurry, the settings unit can provide settings information quickly. This allows the settings unit to select settings information according to the user's emotions, enabling more appropriate settings. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit inputs the user's emotion data into the generative AI, and the generative AI selects the settings information.

[0086] The configuration unit can optimize its configuration algorithm by referring to the facility's past configuration data during configuration. For example, the configuration unit provides optimal configuration information based on the facility's past configuration data. For example, the configuration unit can analyze the facility's past configuration data and optimize its configuration algorithm. For example, the configuration unit can improve the accuracy of configuration by referring to the facility's past configuration data. As a result, the configuration unit improves the accuracy of its configuration algorithm by referring to the facility's past configuration data. Some or all of the above processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit inputs the facility's past configuration data into a generating AI, and the generating AI optimizes the configuration algorithm.

[0087] The configuration unit can apply different configuration methods to each facility category during configuration. For example, the configuration unit can apply medical-related configuration methods to medical facilities. For example, the configuration unit can apply business-related configuration methods to companies. For example, the configuration unit can apply general-purpose configuration methods to general facilities. This allows the configuration unit to perform more appropriate configurations by applying different configuration methods to each facility category. Some or all of the above-described processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit inputs facility category data into a generating AI, and the generating AI applies different configuration methods.

[0088] The settings unit can estimate the user's emotions and adjust the frequency of settings based on the estimated emotions. For example, if the user is stressed, the settings unit can reduce the frequency of settings. For example, if the user is relaxed, the settings unit can increase the frequency of settings. For example, if the user is in a hurry, the settings unit can make settings quickly. In this way, the settings unit can make more appropriate settings by adjusting the frequency of settings 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit inputs user emotion data into the generative AI, and the generative AI adjusts the frequency of settings.

[0089] The configuration unit can select configuration information while considering the facility's geographical location information. The configuration unit can, for example, provide optimal configuration information based on the facility's geographical location information. The configuration unit can, for example, improve the accuracy of the configuration by referring to the facility's geographical location information. The configuration unit can, for example, adjust the frequency of configuration by considering the facility's geographical location information. As a result, the configuration unit can provide more appropriate configuration information by considering the facility's geographical location information. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit inputs the facility's geographical location information data into a generating AI, and the generating AI selects the configuration information.

[0090] The configuration unit can improve the accuracy of its configuration by referring to relevant literature on the facility during the configuration process. For example, the configuration unit provides optimal configuration information based on relevant literature on the facility. For example, the configuration unit can optimize its configuration algorithm by referring to relevant literature on the facility. For example, the configuration unit can improve the accuracy of its configuration by referring to relevant literature on the facility. As a result, the configuration unit improves the accuracy of its configuration by referring to relevant literature on the facility. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without using AI. For example, the configuration unit inputs relevant literature data on the facility into a generating AI, and the generating AI improves the accuracy of the configuration.

[0091] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is stressed, the optimization unit can provide a simple optimization method. For example, if the user is relaxed, the optimization unit can provide a detailed optimization method. For example, if the user is in a hurry, the optimization unit can perform optimization quickly. This allows the optimization unit to perform more appropriate optimization by adjusting the optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the optimization method.

[0092] The optimization unit can optimize the optimization algorithm by referring to past facility guidance data during optimization. For example, the optimization unit provides the optimal optimization method based on past facility guidance data. For example, the optimization unit can analyze past facility guidance data and optimize the optimization algorithm. For example, the optimization unit can improve the accuracy of optimization by referring to past facility guidance data. As a result, the accuracy of the optimization algorithm is improved by the optimization unit referring to past facility guidance data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit inputs past facility guidance data into a generating AI, and the generating AI optimizes the optimization algorithm.

[0093] The optimization unit can apply different optimization methods to each facility category during optimization. For example, the optimization unit can apply medical-related optimization methods to medical facilities. For example, the optimization unit can apply business-related optimization methods to companies. For example, the optimization unit can apply general-purpose optimization methods to general facilities. This allows the optimization unit to achieve more appropriate optimization by applying different optimization methods to each facility category. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs facility category data into a generating AI, and the generating AI applies different optimization methods.

[0094] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit can prioritize optimization higher. For example, if the user is relaxed, the optimization unit can prioritize optimization lower. For example, if the user is in a hurry, the optimization unit can perform optimization quickly. This allows the optimization unit to optimize in a more appropriate order by determining optimization 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs user emotion data into the generative AI, and the generative AI determines the optimization priorities.

[0095] The optimization unit can select optimization information while considering the geographical location information of the facility. The optimization unit provides optimal optimization information based on the geographical location information of the facility, for example. The optimization unit can improve the accuracy of optimization by referring to the geographical location information of the facility, for example. The optimization unit can adjust the frequency of optimization by considering the geographical location information of the facility, for example. This allows the optimization unit to provide more appropriate optimization information by considering the geographical location information of the facility. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs the geographical location information data of the facility to a generating AI, and the generating AI selects the optimization information.

[0096] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the facility during the optimization process. For example, the optimization unit provides optimal optimization information based on relevant literature on the facility. For example, the optimization unit can optimize the optimization algorithm by referring to relevant literature on the facility. For example, the optimization unit can improve the accuracy of optimization by referring to relevant literature on the facility. As a result, the optimization unit improves the accuracy of optimization by referring to relevant literature on the facility. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit inputs the relevant literature data on the facility into a generating AI, and the generating AI improves the accuracy of optimization.

[0097] The multilingual support unit can estimate the user's emotions and adjust its multilingual support method based on the estimated emotions. For example, if the user is stressed, the multilingual support unit can provide a simple multilingual support method. For example, if the user is relaxed, the multilingual support unit can provide a detailed multilingual support method. For example, if the user is in a hurry, the multilingual support unit can provide rapid multilingual support. In this way, the multilingual support unit can provide more appropriate multilingual support by adjusting its multilingual support method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit inputs user emotion data into the generative AI, and the generative AI adjusts the multilingual support method.

[0098] The multilingual support unit can optimize its multilingual support algorithm by referring to past multilingual support data during multilingual support. For example, the multilingual support unit can provide the optimal multilingual support method based on past multilingual support data. For example, the multilingual support unit can analyze past multilingual support data and optimize its multilingual support algorithm. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to past multilingual support data. As a result, the multilingual support unit improves the accuracy of its multilingual support algorithm by referring to past multilingual support data. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit inputs past multilingual support data into a generating AI, and the generating AI optimizes the multilingual support algorithm.

[0099] The multilingual support unit can apply different multilingual support methods to each language category when handling multilingual support. For example, the multilingual support unit can apply English-related multilingual support methods to English. For example, the multilingual support unit can apply Chinese-related multilingual support methods to Chinese. For example, the multilingual support unit can apply Spanish-related multilingual support methods to Spanish. By applying different multilingual support methods to each language category, the multilingual support unit can achieve more appropriate multilingual support. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit inputs language category data into a generating AI, and the generating AI applies different multilingual support methods.

[0100] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated user emotions. For example, if the user is stressed, the multilingual support unit can prioritize multilingual support higher. For example, if the user is relaxed, the multilingual support unit can prioritize multilingual support lower. For example, if the user is in a hurry, the multilingual support unit can provide multilingual support quickly. In this way, the multilingual support unit can provide multilingual support in a more appropriate order by determining the priority of multilingual support 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit inputs user emotion data into the generative AI, and the generative AI determines the priority of multilingual support.

[0101] The multilingual support unit can select multilingual information while considering the user's geographical location information. For example, the multilingual support unit provides optimal multilingual information based on the user's geographical location information. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to the user's geographical location information. For example, the multilingual support unit can adjust the frequency of multilingual support by considering the user's geographical location information. As a result, the multilingual support unit can provide more appropriate multilingual information by considering the user's geographical location information. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit inputs the user's geographical location information data into a generating AI, and the generating AI selects multilingual information.

[0102] The multilingual support unit can improve the accuracy of multilingual support by referring to relevant literature during the multilingual support process. For example, the multilingual support unit provides optimal multilingual support information based on relevant literature. For example, the multilingual support unit can optimize the multilingual support algorithm by referring to relevant literature. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to relevant literature. As a result, the multilingual support unit improves the accuracy of multilingual support by referring to relevant literature. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit inputs relevant literature data into a generating AI, and the generating AI improves the accuracy of multilingual support.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The reception desk can analyze a user's past behavior history and suggest the most suitable reception method. For example, it can automatically display frequently asked questions as suggestions. It can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions that might be asked at specific times based on the user's past behavior history. In this way, the reception desk can provide users with the most suitable reception method by analyzing their past behavior history.

[0105] The response unit can estimate the user's emotions and adjust the tone of its response based on those emotions. For example, if the user is tense, it can respond in a calm tone. If the user is relaxed, it can respond in a bright tone. Furthermore, if the user is in a hurry, it can provide a quick and concise response. In this way, the response unit can provide a more appropriate response by adjusting the tone of its response according to the user's emotions.

[0106] The configuration unit can optimize its configuration algorithm by referring to the facility's past configuration data. For example, it can provide optimal configuration information based on the facility's past configuration data. It can also analyze the facility's past configuration data and optimize its configuration algorithm. Furthermore, it can improve the accuracy of the configuration by referring to the facility's past configuration data. As a result, the configuration unit improves the accuracy of its configuration algorithm by referring to the facility's past configuration data.

[0107] The optimization unit can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is stressed, it can provide a simple optimization method. If the user is relaxed, it can provide a more detailed optimization method. Furthermore, if the user is in a hurry, it can perform optimization quickly. In this way, the optimization unit can perform more appropriate optimization by adjusting the optimization method according to the user's emotions.

[0108] The multilingual support unit can optimize its multilingual support algorithm by referring to past multilingual support data. For example, it can provide the optimal multilingual support method based on past multilingual support data. It can also analyze past multilingual support data and optimize the multilingual support algorithm. Furthermore, it can improve the accuracy of multilingual support by referring to past multilingual support data. In this way, the multilingual support unit improves the accuracy of its multilingual support algorithm by referring to past multilingual support data.

[0109] The reception desk can filter questions based on the user's current situation and areas of interest. For example, it can prioritize receiving relevant questions based on the user's current location. It can also filter and display relevant questions based on the user's areas of interest. Furthermore, it can suggest the most appropriate questions based on the user's current situation (time of day, location, etc.). As a result, the reception desk can prioritize receiving highly relevant questions by filtering them based on the user's current situation and areas of interest.

[0110] The response unit can adjust the level of detail in its response based on the importance of the question. For example, it can provide a detailed response to important questions, a concise response to general questions, and a rapid response to urgent questions. This allows the response unit to provide more appropriate responses by adjusting the level of detail based on the importance of the question.

[0111] The reception desk can estimate the user's emotions and adjust the timing of question reception based on that estimation. For example, if the user is stressed, the AI ​​can immediately accept the question and respond quickly. If the user is relaxed, the AI ​​can accept the question at an appropriate time and provide a detailed response. Furthermore, if the user is in a hurry, the AI ​​can prioritize the question and process it quickly. In this way, the reception desk can accept questions at a more appropriate time by adjusting the timing of question reception according to the user's emotions.

[0112] The configuration unit can apply different configuration methods depending on the facility category. For example, medical facilities can be configured using medical-related configuration methods. Businesses can be configured using business-related configuration methods. Furthermore, general facilities can be configured using general-purpose configuration methods. This allows the configuration unit to apply different configuration methods to each facility category, enabling more appropriate settings.

[0113] The response unit can estimate the user's emotions and adjust the length of the response based on that estimation. For example, if the user is in a hurry, it can provide a short, to-the-point response. If the user is relaxed, it can provide a longer response that includes detailed explanations. Furthermore, if the user is excited, it can provide a response with visually stimulating effects. In this way, the response unit can provide more appropriate responses by adjusting the length of the response according to the user's emotions.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The reception desk receives user inquiries. These inquiries may include questions about making appointments, facility information, and how to use services. The reception desk can accept inquiries via voice input, text input, touch panel input, etc. Step 2: The response unit immediately responds to questions received by the reception unit. The response unit uses AI to engage in natural conversation and provide appropriate answers to user questions. For example, it can provide guidance on scheduling appointments, directions to facilities, and explanations on how to use services. Step 3: The settings unit configures the facility's unique information. The settings unit can configure the facility's name, location, services offered, and information for each medical department. Step 4: The optimization unit optimizes the guidance based on the information set by the settings unit. The optimization unit provides the most suitable guidance information according to the user's question. For example, it can optimize guidance for each medical department, guidance for each department to visit, and guidance on how to use services. Step 5: The multilingual support unit provides guidance in multiple languages. The multilingual support unit can provide guidance in multiple languages, such as English, Chinese, and Spanish. Guidance is provided in the appropriate language according to the user's language settings.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reception unit, response unit, setting unit, optimization unit, and multilingual support 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 reception device 38 of the smart device 14 and receives voice and text input from the user. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to engage in natural conversation and respond immediately to the user's questions. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets facility-specific information. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes guidance based on the set information. The multilingual support unit is implemented by the control unit 46A of the smart device 14 and provides guidance in multiple languages. 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.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, response unit, setting unit, optimization unit, and multilingual support 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 microphone 238 of the smart glasses 214 and receives voice input from the user. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to engage in natural conversation and respond immediately to the user's questions. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets facility-specific information. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes guidance based on the set information. The multilingual support unit is implemented by the control unit 46A of the smart glasses 214 and provides guidance in multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, response unit, setting unit, optimization unit, and multilingual support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to engage in natural conversation and respond immediately to the user's questions. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets facility-specific information. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes guidance based on the set information. The multilingual support unit is implemented by the control unit 46A of the headset terminal 314 and provides guidance in multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, response unit, setting unit, optimization unit, and multilingual support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to engage in natural conversation and respond immediately to the user's questions. The setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets facility-specific information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes guidance based on the set information. The multilingual support unit is implemented by, for example, the control unit 46A of the robot 414 and provides guidance in multiple languages. 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A reception desk that handles user inquiries, A response unit that immediately responds to questions received by the reception unit, A setting unit for setting facility-specific information, An optimization unit that optimizes guidance based on the information set by the setting unit, It includes a multilingual support unit that provides guidance in multiple languages. A system characterized by the following features. (Note 2) The response unit is Engage in natural conversations with users regarding their questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The response unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The response unit is When responding, adjust the level of detail in your response based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 11) The response unit is When responding, apply a different response algorithm depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 12) The response unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The response unit is When responding, we will prioritize responses based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The response unit is When responding, adjust the order of responses based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned setting unit is, The system estimates the user's emotions and selects setting information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned setting unit is, During setup, the configuration algorithm is optimized by referencing the facility's past configuration data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned setting unit is, During setup, different setup methods are applied depending on the facility category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned setting unit is, It estimates the user's emotions and adjusts the frequency of settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned setting unit is, During setup, the configuration information is selected taking into account the facility's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned setting unit is, During setup, refer to relevant literature for the facility to improve the accuracy of the settings. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the optimization algorithm is optimized by referring to the facility's past visitor data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, different optimization methods are applied to each facility category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, the optimization information is selected while considering the geographical location of the facility. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature on the facility. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the multilingual support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned multilingual support unit is When implementing multilingual support, the multilingual support algorithm is optimized by referring to past multilingual support data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned multilingual support unit is When implementing multilingual support, different multilingual support methods are applied for each language category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned multilingual support unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned multilingual support unit is When providing multilingual support, the selection of multilingual information takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned multilingual support unit is When implementing multilingual support, refer to relevant literature to improve the accuracy of multilingual support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 handles user inquiries, A response unit that immediately responds to questions received by the reception unit, A setting unit for setting facility-specific information, An optimization unit that optimizes guidance based on the information set by the setting unit, It includes a multilingual support unit that provides guidance in multiple languages. A system characterized by the following features.

2. The response unit is Engage in natural conversations with users regarding their questions. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

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

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

6. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system according to feature 1.

Images