system

Abstract

The system according to this embodiment aims to automate and streamline reception operations. [Solution] The system according to the embodiment comprises a recognition unit, a dialogue unit, an analysis unit, a reservation unit, and a linkage unit. The recognition unit recognizes the visitor's statements. The dialogue unit engages in dialogue with the visitor based on the statements recognized by the recognition unit. The analysis unit analyzes the information collected by the dialogue unit. The reservation unit makes reservations for meeting rooms based on the information analyzed by the analysis unit. The linkage unit provides personalized responses in conjunction with a customer database based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction 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 prior art, reception tasks such as greeting visitors and reserving meeting rooms have not been fully automated, and there is room for improvement.

[0005] The system according to the embodiment aims to automate and improve reception tasks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a dialogue unit, an analysis unit, a reservation unit, and a linkage unit. The recognition unit recognizes the visitor's statements. The dialogue unit engages in dialogue with the visitor based on the statements recognized by the recognition unit. The analysis unit analyzes the information collected by the dialogue unit. The reservation unit makes reservations for meeting rooms based on the information analyzed by the analysis unit. The linkage unit provides personalized responses by linking with a customer database based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and streamline reception operations. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 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 reception support agent system according to an embodiment of the present invention is a system that automates corporate reception operations by utilizing speech recognition, voice dialogue, and natural language processing technologies. This reception support agent system handles everything from greeting visitors to booking meeting rooms using AI. For example, when a visitor arrives at the reception, the reception support agent system uses speech recognition technology to recognize what the visitor is saying. Next, the reception support agent system uses voice dialogue technology to engage in a natural conversation with the visitor and collect necessary information. For example, the reception support agent system confirms the visitor's name and the name of the person they are visiting. The collected information is analyzed using natural language processing technology to provide an appropriate response. For example, if a meeting room reservation is needed, the AI ​​automatically checks the availability of the meeting room and makes the reservation. Furthermore, the reception support agent system links visitor information with a customer database, enabling personalized service. For example, the reception support agent system provides the most suitable service to the visitor based on their past visit history and specific requests. In addition, the reception support agent system is multilingual and can accommodate foreign visitors. This system improves the efficiency of reception operations, resulting in reduced labor costs and shorter waiting times. In the future, further service improvements are expected through AI learning capabilities. For example, the reception support agent system will be able to respond more quickly and accurately by learning from past data. In this way, the reception support agent system will revolutionize corporate reception operations, achieving efficient business operations and improved customer experience. As a result, the reception support agent system can automate corporate reception operations, achieving efficient business operations and improved customer experience.

[0029] The reception support agent system according to this embodiment comprises a recognition unit, a dialogue unit, an analysis unit, a reservation unit, and a coordination unit. The recognition unit recognizes the visitor's statements. The recognition unit recognizes the visitor's statements using, for example, speech recognition technology. The recognition unit can, for example, recognize the visitor's statements in real time and convert them into text data. The recognition unit can, for example, record the visitor's statements and save them for later analysis. The dialogue unit engages in dialogue with the visitor based on the statements recognized by the recognition unit. The dialogue unit engages in natural dialogue with the visitor using, for example, speech dialogue technology. The dialogue unit confirms, for example, the visitor's name and the name of the person in charge at the destination. The dialogue unit can, for example, provide appropriate answers to the visitor's questions. The analysis unit analyzes the information collected by the dialogue unit. The analysis unit analyzes the collected information using, for example, natural language processing technology. The analysis unit can, for example, classify the collected information and extract the necessary information. The analysis unit can, for example, save the collected information in a database and refer to it later. The reservation unit makes reservations for meeting rooms based on information analyzed by the analysis unit. The reservation unit, for example, checks the availability of meeting rooms and makes reservations. The reservation unit, for example, can link with a meeting room reservation system to check availability in real time. The reservation unit can also, for example, automatically make reservations for meeting rooms and send reservation confirmation notifications. The integration unit provides personalized responses by linking with the customer database based on information analyzed by the analysis unit. The integration unit, for example, links with the customer database to provide the best possible service to visitors based on their past visit history and specific requests. The integration unit can, for example, update the information in the customer database in real time and respond based on the latest information. The integration unit can also, for example, analyze the information in the customer database and provide services that meet the specific requests of visitors. As a result, the reception support agent system according to the embodiment can recognize visitors' statements, engage in dialogue, analyze information, make meeting room reservations, and provide personalized responses by linking with the customer database.

[0030] The recognition unit recognizes what visitors say. For example, it uses speech recognition technology to recognize visitors' speech. Specifically, the recognition unit is equipped with a high-precision speech recognition engine that can convert visitors' speech into text data in real time. This speech recognition engine uses a combination of noise cancellation and speech filtering technologies to maintain high recognition accuracy even in noisy environments. The recognition unit can also record visitors' speech and save it for later analysis. The recorded audio data is securely stored in cloud storage and accessible as needed. Furthermore, the recognition unit supports multiple languages ​​and can accurately recognize what visitors say, even if they speak a foreign language. This allows the recognition unit to accurately and quickly recognize diverse visitor speech, improving the overall efficiency of the system.

[0031] The dialogue unit engages in conversation with visitors based on statements recognized by the recognition unit. The dialogue unit uses, for example, voice dialogue technology to engage in natural conversations with visitors. Specifically, the dialogue unit utilizes natural language processing technology to understand the content of visitors' statements and generate appropriate responses. The dialogue unit can ask pre-set questions to confirm the visitor's name and the name of the person they are visiting. It can also refer to internal databases and external information sources to provide appropriate answers to visitors' questions. For example, if a visitor wishes to meet a specific person, the dialogue unit can check that person's schedule and immediately respond regarding the availability of the meeting. Furthermore, the dialogue unit can engage in contextually balanced conversations to accurately grasp the intent behind the visitor's statements. This enables smooth communication with visitors and improves visitor satisfaction.

[0032] The analysis unit analyzes the information collected by the dialogue unit. For example, the analysis unit uses natural language processing techniques to analyze the collected information. Specifically, the analysis unit uses text mining techniques to classify the content of visitor statements and extract necessary information. For instance, it can extract the purpose of the visit and requests from visitor statements and respond appropriately based on that information. The analysis unit can also store the collected information in a database for later reference. The stored data is used to analyze visitor visit history and request trends. Furthermore, the analysis unit can use machine learning algorithms to learn visitor statements and behavior patterns, enabling it to predict future visits and optimize responses. This allows the analysis unit to efficiently analyze visitor information and improve the overall system performance.

[0033] The reservation department makes meeting room reservations based on information analyzed by the analysis department. For example, the reservation department checks the availability of meeting rooms and makes reservations. Specifically, the reservation department can check availability in real time by linking with the meeting room reservation system. The reservation department selects the most suitable meeting room according to the purpose of the visit and the number of people, and makes the reservation automatically. Once the reservation is complete, a reservation confirmation notification is sent to the visitor and the relevant staff. Notifications are made through multiple means such as email, SMS, and app notifications to ensure that the information is reliably conveyed. Furthermore, the reservation department also manages information on the equipment and layout of meeting rooms, and if specific equipment is required, it can select the appropriate meeting room based on that information. In this way, the reservation department can make meeting room reservations efficiently and accurately, improving the convenience of visitors.

[0034] The Integration Department integrates with the customer database based on information analyzed by the Analysis Department to provide personalized service. Specifically, the Integration Department integrates with the customer database to provide the best possible service to visitors based on their past visit history and specific requests. For example, the Integration Department can refer to a visitor's past visit history and provide consistent service during the current visit based on the services and responses provided during previous visits. The Integration Department can update the customer database information in real time and respond based on the latest information. For example, visitor requests and feedback can be immediately reflected in the database and used for future visits. Furthermore, the Integration Department can analyze the customer database information and provide services tailored to the visitor's specific requests. For example, if a particular visitor prefers a specific beverage, this information can be used to prepare it before the visitor arrives. This allows the Integration Department to provide personalized service to visitors and improve their satisfaction.

[0035] The integration department can provide optimal service to visitors based on their past visit history and specific requests by integrating with the customer database. For example, the integration department can refer to past visit history in conjunction with the customer database to provide optimal service to visitors. The integration department can also provide optimal service to visitors based on specific requests in conjunction with the customer database. For example, the integration department can analyze information in the customer database to provide services tailored to the visitor's specific requests. This allows the integration department to provide optimal service to visitors based on their past visit history and specific requests by integrating with the customer database.

[0036] The recognition unit can handle multiple languages. For example, the recognition unit recognizes a visitor's speech using speech recognition technology that supports multiple languages. For example, the recognition unit can translate a visitor's speech in real time and respond in multiple languages. For example, the recognition unit can record a visitor's speech and translate it into multiple languages ​​later. This multilingual support allows the system to accommodate foreign visitors.

[0037] The analysis unit can analyze information collected using natural language processing techniques. For example, the analysis unit can analyze information collected using morphological analysis. The analysis unit can also analyze information collected using grammatical analysis. Furthermore, the analysis unit can analyze information collected using semantic analysis. This allows for the provision of more accurate information by analyzing information collected using natural language processing techniques.

[0038] The reservation department can check the availability of meeting rooms and make reservations. For example, the reservation department can check the availability of meeting rooms in real time and make reservations. For example, the reservation department can also link with meeting room reservation systems to check availability. For example, the reservation department can check the availability of meeting rooms and send reservation confirmation notifications. This allows for efficient use of meeting rooms by checking their availability and making reservations.

[0039] The integration department can provide personalized service. For example, it can integrate with the customer database to provide personalized service based on the specific requests of visitors. For example, it can analyze information in the customer database to provide services that meet the specific requests of visitors. For example, it can update information in the customer database in real time and provide personalized service based on the latest information. This allows for personalized service, enabling the provision of the best possible service to visitors.

[0040] The analysis unit can improve services using AI learning capabilities. For example, the analysis unit can improve services by learning from collected data using machine learning algorithms. The analysis unit can also improve service accuracy by training AI models using training data. The analysis unit can also identify areas for service improvement by analyzing historical data. This enables faster and more accurate responses by improving services using AI learning capabilities.

[0041] The recognition unit can analyze the tone and speed of a visitor's voice and determine the importance of what they say. For example, the recognition unit can analyze the tone of a visitor's voice and emphasize important statements. For example, the recognition unit can analyze the speed of a visitor's voice and prioritize processing urgent statements. For example, the recognition unit can comprehensively analyze the tone and speed of a visitor's voice and determine the importance of what they say. In this way, by analyzing the tone and speed of a visitor's voice, the importance of what they say can be determined.

[0042] The recognition unit can analyze the visitor's gestures and body language, and improve recognition accuracy by combining this with the content of their speech. For example, the recognition unit can analyze the visitor's hand movements to recognize the emphasized parts of their speech. The recognition unit can also analyze the visitor's facial expressions to recognize the emotional nuances of their speech. For example, the recognition unit can analyze the visitor's whole-body movements to more accurately understand the intent behind their speech. As a result, by analyzing the visitor's gestures and body language, recognition accuracy can be improved by combining this with the content of their speech.

[0043] The recognition unit can filter out background noise from visitors to improve the accuracy of recognizing their speech. For example, the recognition unit can filter out ambient noise surrounding the visitor to clearly recognize their speech. The recognition unit can also analyze the visitor's background noise and highlight important statements. Furthermore, the recognition unit can filter out background noise in real time to improve the accuracy of recognizing their speech. This means that by filtering out background noise, the accuracy of recognizing speech can be improved.

[0044] The recognition unit can improve the accuracy of recognizing the content of a statement by referring to the visitor's past statement history. For example, the recognition unit can refer to the visitor's past statement history to understand the context of the statement. For example, the recognition unit can analyze the visitor's past statement history to recognize important parts of the statement. For example, the recognition unit can improve the accuracy of recognizing the content of a statement based on the visitor's past statement history. In this way, the accuracy of recognizing the content of a statement can be improved by referring to the visitor's past statement history.

[0045] The dialogue unit can generate appropriate questions based on what the visitor says, thereby streamlining information gathering. For example, the dialogue unit generates questions to collect necessary information based on what the visitor says. The dialogue unit can also analyze what the visitor says and generate additional questions necessary for information gathering. For example, the dialogue unit can generate questions to efficiently gather information based on what the visitor says. In this way, by generating appropriate questions based on what the visitor says, information gathering can be made more efficient.

[0046] The dialogue unit can translate what visitors say in real time, enhancing multilingual support. For example, the dialogue unit can translate what visitors say in real time and engage in conversation. The dialogue unit can also translate what visitors say into multiple languages ​​and respond appropriately. The dialogue unit can also translate what visitors say in real time, enhancing multilingual support. This allows for enhanced multilingual support by translating what visitors say in real time.

[0047] The dialogue unit can refer to the visitor's past dialogue history and conduct personalized conversations. For example, the dialogue unit can refer to the visitor's past dialogue history and conduct conversations tailored to the visitor's preferences. For example, the dialogue unit can analyze the visitor's past dialogue history and conduct conversations that meet the visitor's requests. For example, the dialogue unit can conduct personalized conversations based on the visitor's past dialogue history. This makes personalized conversations possible by referring to the visitor's past dialogue history.

[0048] The dialogue unit can summarize what the visitor says and send only the important information to the analysis unit. For example, the dialogue unit can summarize what the visitor says, extract the important information, and send it to the analysis unit. The dialogue unit can also analyze what the visitor says and send only the important information to the analysis unit. For example, the dialogue unit can summarize what the visitor says and transmit information efficiently. By summarizing what the visitor says, only the important information can be sent to the analysis unit.

[0049] The analysis unit can analyze the content of a visitor's statements based on context and extract more accurate information. For example, the analysis unit can analyze the content of a visitor's statements based on context and extract important information. The analysis unit can also, for example, analyze the content of a visitor's statements based on context and provide accurate information. The analysis unit can also, for example, analyze the content of a visitor's statements based on context and improve the accuracy of the information. This allows for the extraction of more accurate information by analyzing the content of a visitor's statements based on context.

[0050] The analysis unit can classify the content of visitors' statements into categories and perform efficient information analysis. For example, the analysis unit can classify the content of visitors' statements into categories and perform efficient information analysis. The analysis unit can also, for example, analyze the content of visitors' statements into categories and organize the information. The analysis unit can also, for example, classify the content of visitors' statements into categories and streamline information management. This allows for efficient information analysis by classifying the content of visitors' statements into categories.

[0051] The analysis unit can improve the accuracy of information analysis by referring to the visitor's past visit history. For example, the analysis unit can improve the accuracy of information analysis by referring to the visitor's past visit history. For example, the analysis unit can also improve the accuracy of information by analyzing the visitor's past visit history. The analysis unit can also improve the accuracy of information analysis based on the visitor's past visit history. This allows for improved accuracy of information analysis by referring to the visitor's past visit history.

[0052] The analysis unit can improve the reliability of information by comparing the content of visitor statements with other data sources. For example, the analysis unit can improve the reliability of information by comparing the content of visitor statements with other data sources. The analysis unit can also verify the accuracy of information by comparing the content of visitor statements with other data sources. In this way, the reliability of information can be improved by comparing the content of visitor statements with other data sources.

[0053] The reservation department can refer to the meeting room usage history and suggest the optimal reservation time. For example, the reservation department can refer to the meeting room usage history and suggest the optimal reservation time. For example, the reservation department can analyze the meeting room usage history and suggest less frequently used time slots. For example, the reservation department can suggest a reservation time tailored to the user's preferences based on the meeting room usage history. In this way, by referring to the meeting room usage history, the optimal reservation time can be suggested.

[0054] The reservation department can select an appropriate meeting room by considering the meeting room's equipment information. For example, the reservation department can refer to the meeting room's equipment information and select a meeting room that has the necessary equipment. For example, the reservation department can analyze the meeting room's equipment information and select a meeting room that is best suited to the purpose of use. For example, the reservation department can select a meeting room that meets the user's requests based on the meeting room's equipment information. In this way, by considering the meeting room's equipment information, an appropriate meeting room can be selected.

[0055] The reservation department can monitor meeting room usage in real time and automatically modify or cancel reservations. For example, the reservation department can monitor meeting room usage in real time and respond to user changes and cancellations. For example, the reservation department can analyze meeting room usage and suggest the most suitable reservation changes or cancellations. For example, the reservation department can automatically modify or cancel reservations based on meeting room usage and user requests. In this way, by monitoring meeting room usage in real time, reservation changes and cancellations can be automated.

[0056] The reservation department can efficiently manage reservations by linking the reservation status of meeting rooms with other systems. For example, the reservation department can efficiently manage reservations by linking the reservation status of meeting rooms with other systems. For example, the reservation department can improve user convenience by sharing the reservation status of meeting rooms with other systems. For example, the reservation department can prevent duplicate reservations by linking the reservation status of meeting rooms with other systems. This enables efficient reservation management by linking the reservation status of meeting rooms with other systems.

[0057] The liaison department can update customer database information in real time and respond based on the latest information. For example, the liaison department can update customer database information in real time and respond based on the latest information. The liaison department can also analyze customer database information and respond based on the latest information. For example, the liaison department can provide services based on the latest information using customer database information. This enables responses based on the latest information by updating customer database information in real time.

[0058] The Collaboration Department can analyze information from the customer database and provide services tailored to the specific requests of visitors. For example, the Collaboration Department can analyze information from the customer database and provide services tailored to the specific requests of visitors. The Collaboration Department can also, for example, provide services tailored to visitor requests based on information from the customer database. The Collaboration Department can also, for example, analyze information from the customer database and provide services tailored to the specific needs of visitors. This allows for the provision of services tailored to the specific requests of visitors by analyzing information from the customer database.

[0059] The integration unit can integrate with other systems to centrally manage visitor information. For example, the integration unit can integrate with other systems to centrally manage visitor information. The integration unit can also share information with other systems to efficiently manage visitor information. For example, the integration unit can integrate with other systems to manage visitor information in a unified manner. This allows for centralized management of visitor information through integration with other systems.

[0060] The integration unit can improve the reliability of information by comparing customer database information with other data sources. For example, the integration unit can improve the reliability of information by comparing customer database information with other data sources. The integration unit can also verify the accuracy of information by comparing customer database information with other data sources. In this way, the reliability of information can be improved by comparing customer database information with other data sources.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The dialogue unit can generate appropriate questions based on what the visitor says, thereby streamlining information gathering. For example, the dialogue unit generates questions to collect necessary information based on what the visitor says. The dialogue unit can also analyze what the visitor says and generate additional questions necessary for information gathering. The dialogue unit can also generate questions to facilitate efficient information gathering based on what the visitor says. In this way, by generating appropriate questions based on what the visitor says, information gathering can be made more efficient.

[0063] The analysis unit can analyze the content of a visitor's statements based on context and extract more accurate information. For example, the analysis unit can analyze the content of a visitor's statements based on context and extract important information. The analysis unit can also analyze the content of a visitor's statements based on context and provide accurate information. The analysis unit can also analyze the content of a visitor's statements based on context and improve the accuracy of the information. As a result, by analyzing the content of a visitor's statements based on context, more accurate information can be extracted.

[0064] The reservation department can refer to the meeting room usage history and suggest the optimal reservation time. For example, the reservation department can refer to the meeting room usage history and suggest the optimal reservation time. The reservation department can also analyze the meeting room usage history and suggest less frequently used time slots. Based on the meeting room usage history, the reservation department can also suggest reservation times that match the user's preferences. In this way, by referring to the meeting room usage history, the optimal reservation time can be suggested.

[0065] The integration unit can compare information from the customer database with other data sources to improve the reliability of the information. For example, the integration unit can compare information from the customer database with other data sources to improve the reliability of the information. The integration unit can also compare information from the customer database with other data sources to verify the accuracy of the information. The integration unit can also compare information from the customer database with other data sources to verify the consistency of the information. In this way, the reliability of the information can be improved by comparing information from the customer database with other data sources.

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

[0067] Step 1: The recognition unit recognizes the visitor's speech. The recognition unit can, for example, use speech recognition technology to recognize the visitor's speech in real time and convert it into text data. It can also record the visitor's speech and save it for later analysis. Step 2: The dialogue unit engages in conversation with the visitor based on the utterances recognized by the recognition unit. The dialogue unit, for example, uses voice dialogue technology to engage in a natural conversation with the visitor and confirm the visitor's name and the name of the person they are visiting. It can also provide appropriate answers to the visitor's questions. Step 3: The analysis unit analyzes the information collected by the dialogue unit. The analysis unit can, for example, use natural language processing techniques to analyze, classify, and extract the necessary information. It can also store the collected information in a database for later reference. Step 4: The reservation department makes meeting room reservations based on the information analyzed by the analysis department. For example, the reservation department checks the availability of meeting rooms and makes reservations. It can also integrate with the meeting room reservation system to check availability in real time, make reservations automatically, and send reservation confirmation notifications. Step 5: The Integration Unit integrates with the customer database based on the information analyzed by the Analysis Unit to provide personalized service. For example, the Integration Unit integrates with the customer database to provide the best possible service to visitors based on their past visit history and specific requests. It can update the customer database information in real time and respond based on the latest information. It can also analyze the customer database information and provide services that meet the specific requests of visitors.

[0068] (Example of form 2) The reception support agent system according to an embodiment of the present invention is a system that automates corporate reception operations by utilizing speech recognition, voice dialogue, and natural language processing technologies. This reception support agent system handles everything from greeting visitors to booking meeting rooms using AI. For example, when a visitor arrives at the reception, the reception support agent system uses speech recognition technology to recognize what the visitor is saying. Next, the reception support agent system uses voice dialogue technology to engage in a natural conversation with the visitor and collect necessary information. For example, the reception support agent system confirms the visitor's name and the name of the person they are visiting. The collected information is analyzed using natural language processing technology to provide an appropriate response. For example, if a meeting room reservation is needed, the AI ​​automatically checks the availability of the meeting room and makes the reservation. Furthermore, the reception support agent system links visitor information with a customer database, enabling personalized service. For example, the reception support agent system provides the most suitable service to the visitor based on their past visit history and specific requests. In addition, the reception support agent system is multilingual and can accommodate foreign visitors. This system improves the efficiency of reception operations, resulting in reduced labor costs and shorter waiting times. In the future, further service improvements are expected through AI learning capabilities. For example, the reception support agent system will be able to respond more quickly and accurately by learning from past data. In this way, the reception support agent system will revolutionize corporate reception operations, achieving efficient business operations and improved customer experience. As a result, the reception support agent system can automate corporate reception operations, achieving efficient business operations and improved customer experience.

[0069] The reception support agent system according to this embodiment comprises a recognition unit, a dialogue unit, an analysis unit, a reservation unit, and a coordination unit. The recognition unit recognizes the visitor's statements. The recognition unit recognizes the visitor's statements using, for example, speech recognition technology. The recognition unit can, for example, recognize the visitor's statements in real time and convert them into text data. The recognition unit can, for example, record the visitor's statements and save them for later analysis. The dialogue unit engages in dialogue with the visitor based on the statements recognized by the recognition unit. The dialogue unit engages in natural dialogue with the visitor using, for example, speech dialogue technology. The dialogue unit confirms, for example, the visitor's name and the name of the person in charge at the destination. The dialogue unit can, for example, provide appropriate answers to the visitor's questions. The analysis unit analyzes the information collected by the dialogue unit. The analysis unit analyzes the collected information using, for example, natural language processing technology. The analysis unit can, for example, classify the collected information and extract the necessary information. The analysis unit can, for example, save the collected information in a database and refer to it later. The reservation unit makes reservations for meeting rooms based on information analyzed by the analysis unit. The reservation unit, for example, checks the availability of meeting rooms and makes reservations. The reservation unit, for example, can link with a meeting room reservation system to check availability in real time. The reservation unit can also, for example, automatically make reservations for meeting rooms and send reservation confirmation notifications. The integration unit provides personalized responses by linking with the customer database based on information analyzed by the analysis unit. The integration unit, for example, links with the customer database to provide the best possible service to visitors based on their past visit history and specific requests. The integration unit can, for example, update the information in the customer database in real time and respond based on the latest information. The integration unit can also, for example, analyze the information in the customer database and provide services that meet the specific requests of visitors. As a result, the reception support agent system according to the embodiment can recognize visitors' statements, engage in dialogue, analyze information, make meeting room reservations, and provide personalized responses by linking with the customer database.

[0070] The recognition unit recognizes what visitors say. For example, it uses speech recognition technology to recognize visitors' speech. Specifically, the recognition unit is equipped with a high-precision speech recognition engine that can convert visitors' speech into text data in real time. This speech recognition engine uses a combination of noise cancellation and speech filtering technologies to maintain high recognition accuracy even in noisy environments. The recognition unit can also record visitors' speech and save it for later analysis. The recorded audio data is securely stored in cloud storage and accessible as needed. Furthermore, the recognition unit supports multiple languages ​​and can accurately recognize what visitors say, even if they speak a foreign language. This allows the recognition unit to accurately and quickly recognize diverse visitor speech, improving the overall efficiency of the system.

[0071] The dialogue unit engages in conversation with visitors based on statements recognized by the recognition unit. The dialogue unit uses, for example, voice dialogue technology to engage in natural conversations with visitors. Specifically, the dialogue unit utilizes natural language processing technology to understand the content of visitors' statements and generate appropriate responses. The dialogue unit can ask pre-set questions to confirm the visitor's name and the name of the person they are visiting. It can also refer to internal databases and external information sources to provide appropriate answers to visitors' questions. For example, if a visitor wishes to meet a specific person, the dialogue unit can check that person's schedule and immediately respond regarding the availability of the meeting. Furthermore, the dialogue unit can engage in contextually balanced conversations to accurately grasp the intent behind the visitor's statements. This enables smooth communication with visitors and improves visitor satisfaction.

[0072] The analysis unit analyzes the information collected by the dialogue unit. For example, the analysis unit uses natural language processing techniques to analyze the collected information. Specifically, the analysis unit uses text mining techniques to classify the content of visitor statements and extract necessary information. For instance, it can extract the purpose of the visit and requests from visitor statements and respond appropriately based on that information. The analysis unit can also store the collected information in a database for later reference. The stored data is used to analyze visitor visit history and request trends. Furthermore, the analysis unit can use machine learning algorithms to learn visitor statements and behavior patterns, enabling it to predict future visits and optimize responses. This allows the analysis unit to efficiently analyze visitor information and improve the overall system performance.

[0073] The reservation department makes meeting room reservations based on information analyzed by the analysis department. For example, the reservation department checks the availability of meeting rooms and makes reservations. Specifically, the reservation department can check availability in real time by linking with the meeting room reservation system. The reservation department selects the most suitable meeting room according to the purpose of the visit and the number of people, and makes the reservation automatically. Once the reservation is complete, a reservation confirmation notification is sent to the visitor and the relevant staff. Notifications are made through multiple means such as email, SMS, and app notifications to ensure that the information is reliably conveyed. Furthermore, the reservation department also manages information on the equipment and layout of meeting rooms, and if specific equipment is required, it can select the appropriate meeting room based on that information. In this way, the reservation department can make meeting room reservations efficiently and accurately, improving the convenience of visitors.

[0074] The Integration Department integrates with the customer database based on information analyzed by the Analysis Department to provide personalized service. Specifically, the Integration Department integrates with the customer database to provide the best possible service to visitors based on their past visit history and specific requests. For example, the Integration Department can refer to a visitor's past visit history and provide consistent service during the current visit based on the services and responses provided during previous visits. The Integration Department can update the customer database information in real time and respond based on the latest information. For example, visitor requests and feedback can be immediately reflected in the database and used for future visits. Furthermore, the Integration Department can analyze the customer database information and provide services tailored to the visitor's specific requests. For example, if a particular visitor prefers a specific beverage, this information can be used to prepare it before the visitor arrives. This allows the Integration Department to provide personalized service to visitors and improve their satisfaction.

[0075] The integration department can provide optimal service to visitors based on their past visit history and specific requests by integrating with the customer database. For example, the integration department can refer to past visit history in conjunction with the customer database to provide optimal service to visitors. The integration department can also provide optimal service to visitors based on specific requests in conjunction with the customer database. For example, the integration department can analyze information in the customer database to provide services tailored to the visitor's specific requests. This allows the integration department to provide optimal service to visitors based on their past visit history and specific requests by integrating with the customer database.

[0076] The recognition unit can handle multiple languages. For example, the recognition unit recognizes a visitor's speech using speech recognition technology that supports multiple languages. For example, the recognition unit can translate a visitor's speech in real time and respond in multiple languages. For example, the recognition unit can record a visitor's speech and translate it into multiple languages ​​later. This multilingual support allows the system to accommodate foreign visitors.

[0077] The analysis unit can analyze information collected using natural language processing techniques. For example, the analysis unit can analyze information collected using morphological analysis. The analysis unit can also analyze information collected using grammatical analysis. Furthermore, the analysis unit can analyze information collected using semantic analysis. This allows for the provision of more accurate information by analyzing information collected using natural language processing techniques.

[0078] The reservation department can check the availability of meeting rooms and make reservations. For example, the reservation department can check the availability of meeting rooms in real time and make reservations. For example, the reservation department can also link with meeting room reservation systems to check availability. For example, the reservation department can check the availability of meeting rooms and send reservation confirmation notifications. This allows for efficient use of meeting rooms by checking their availability and making reservations.

[0079] The integration department can provide personalized service. For example, it can integrate with the customer database to provide personalized service based on the specific requests of visitors. For example, it can analyze information in the customer database to provide services that meet the specific requests of visitors. For example, it can update information in the customer database in real time and provide personalized service based on the latest information. This allows for personalized service, enabling the provision of the best possible service to visitors.

[0080] The analysis unit can improve services using AI learning capabilities. For example, the analysis unit can improve services by learning from collected data using machine learning algorithms. The analysis unit can also improve service accuracy by training AI models using training data. The analysis unit can also identify areas for service improvement by analyzing historical data. This enables faster and more accurate responses by improving services using AI learning capabilities.

[0081] The recognition unit can estimate the visitor's emotions and adjust the accuracy of speech recognition based on the estimated emotions. For example, if the visitor is nervous, the recognition unit adjusts the accuracy of recognition by considering the tone and speed of speech. For example, if the visitor is relaxed, the recognition unit can adjust to recognize the content of speech in detail. For example, if the visitor is in a hurry, the recognition unit can adjust to prioritize the recognition of important parts of speech. This allows for more accurate speech recognition by adjusting the accuracy of speech recognition based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The recognition unit can analyze the tone and speed of a visitor's voice and determine the importance of what they say. For example, the recognition unit can analyze the tone of a visitor's voice and emphasize important statements. For example, the recognition unit can analyze the speed of a visitor's voice and prioritize processing urgent statements. For example, the recognition unit can comprehensively analyze the tone and speed of a visitor's voice and determine the importance of what they say. In this way, by analyzing the tone and speed of a visitor's voice, the importance of what they say can be determined.

[0083] The recognition unit can analyze the visitor's gestures and body language, and improve recognition accuracy by combining this with the content of their speech. For example, the recognition unit can analyze the visitor's hand movements to recognize the emphasized parts of their speech. The recognition unit can also analyze the visitor's facial expressions to recognize the emotional nuances of their speech. For example, the recognition unit can analyze the visitor's whole-body movements to more accurately understand the intent behind their speech. As a result, by analyzing the visitor's gestures and body language, recognition accuracy can be improved by combining this with the content of their speech.

[0084] The recognition unit can estimate the visitor's emotions and adjust the timing of speech recognition based on the estimated emotions. For example, if the visitor is nervous, the recognition unit may delay the timing of speech recognition. For example, if the visitor is relaxed, the recognition unit may recognize speech at the normal timing. For example, if the visitor is in a hurry, the recognition unit may advance the timing of speech recognition. In this way, by adjusting the timing of speech recognition based on the visitor's emotions, speech can be recognized at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The recognition unit can filter out background noise from visitors to improve the accuracy of recognizing their speech. For example, the recognition unit can filter out ambient noise surrounding the visitor to clearly recognize their speech. The recognition unit can also analyze the visitor's background noise and highlight important statements. Furthermore, the recognition unit can filter out background noise in real time to improve the accuracy of recognizing their speech. This means that by filtering out background noise, the accuracy of recognizing speech can be improved.

[0086] The recognition unit can improve the accuracy of recognizing the content of a statement by referring to the visitor's past statement history. For example, the recognition unit can refer to the visitor's past statement history to understand the context of the statement. For example, the recognition unit can analyze the visitor's past statement history to recognize important parts of the statement. For example, the recognition unit can improve the accuracy of recognizing the content of a statement based on the visitor's past statement history. In this way, the accuracy of recognizing the content of a statement can be improved by referring to the visitor's past statement history.

[0087] The dialogue unit can estimate the visitor's emotions and adjust the tone and content of the dialogue based on the estimated emotions. For example, if the visitor is nervous, the dialogue unit will use a calm tone. If the visitor is relaxed, the dialogue unit can also use a cheerful tone. If the visitor is in a hurry, the dialogue unit can also use a quick and concise tone. By adjusting the tone and content of the dialogue based on the visitor's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The dialogue unit can generate appropriate questions based on what the visitor says, thereby streamlining information gathering. For example, the dialogue unit generates questions to collect necessary information based on what the visitor says. The dialogue unit can also analyze what the visitor says and generate additional questions necessary for information gathering. For example, the dialogue unit can generate questions to efficiently gather information based on what the visitor says. In this way, by generating appropriate questions based on what the visitor says, information gathering can be made more efficient.

[0089] The dialogue unit can translate what visitors say in real time, enhancing multilingual support. For example, the dialogue unit can translate what visitors say in real time and engage in conversation. The dialogue unit can also translate what visitors say into multiple languages ​​and respond appropriately. The dialogue unit can also translate what visitors say in real time, enhancing multilingual support. This allows for enhanced multilingual support by translating what visitors say in real time.

[0090] The dialogue unit can estimate the visitor's emotions and adjust the pace of the dialogue based on the estimated emotions. For example, if the visitor is nervous, the dialogue unit will proceed at a slow pace. If the visitor is relaxed, the dialogue unit can proceed at a normal pace. If the visitor is in a hurry, the dialogue unit can proceed at a rapid pace. By adjusting the pace of the dialogue based on the visitor's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The dialogue unit can refer to the visitor's past dialogue history and conduct personalized conversations. For example, the dialogue unit can refer to the visitor's past dialogue history and conduct conversations tailored to the visitor's preferences. For example, the dialogue unit can analyze the visitor's past dialogue history and conduct conversations that meet the visitor's requests. For example, the dialogue unit can conduct personalized conversations based on the visitor's past dialogue history. This makes personalized conversations possible by referring to the visitor's past dialogue history.

[0092] The dialogue unit can summarize what the visitor says and send only the important information to the analysis unit. For example, the dialogue unit can summarize what the visitor says, extract the important information, and send it to the analysis unit. The dialogue unit can also analyze what the visitor says and send only the important information to the analysis unit. For example, the dialogue unit can summarize what the visitor says and transmit information efficiently. By summarizing what the visitor says, only the important information can be sent to the analysis unit.

[0093] The analysis unit can estimate the visitor's emotions and determine the priority of information analysis based on the estimated emotions. For example, if the visitor is nervous, the analysis unit will prioritize the analysis of important information. If the visitor is relaxed, the analysis unit can also analyze information with normal priority. If the visitor is in a hurry, the analysis unit can also quickly analyze information. This allows for the prioritization of important information by determining the priority of information analysis based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analysis unit can analyze the content of a visitor's statements based on context and extract more accurate information. For example, the analysis unit can analyze the content of a visitor's statements based on context and extract important information. The analysis unit can also, for example, analyze the content of a visitor's statements based on context and provide accurate information. The analysis unit can also, for example, analyze the content of a visitor's statements based on context and improve the accuracy of the information. This allows for the extraction of more accurate information by analyzing the content of a visitor's statements based on context.

[0095] The analysis unit can classify the content of visitors' statements into categories and perform efficient information analysis. For example, the analysis unit can classify the content of visitors' statements into categories and perform efficient information analysis. The analysis unit can also, for example, analyze the content of visitors' statements into categories and organize the information. The analysis unit can also, for example, classify the content of visitors' statements into categories and streamline information management. This allows for efficient information analysis by classifying the content of visitors' statements into categories.

[0096] The analysis unit can estimate the visitor's emotions and adjust the information analysis method based on the estimated emotions. For example, if the visitor is nervous, the analysis unit can perform a concise information analysis. For example, if the visitor is relaxed, the analysis unit can also perform a detailed information analysis. For example, if the visitor is in a hurry, the analysis unit can also perform a rapid information analysis. By adjusting the information analysis method based on the visitor's emotions, more appropriate information analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The analysis unit can improve the accuracy of information analysis by referring to the visitor's past visit history. For example, the analysis unit can improve the accuracy of information analysis by referring to the visitor's past visit history. For example, the analysis unit can also improve the accuracy of information by analyzing the visitor's past visit history. The analysis unit can also improve the accuracy of information analysis based on the visitor's past visit history. This allows for improved accuracy of information analysis by referring to the visitor's past visit history.

[0098] The analysis unit can improve the reliability of information by comparing the content of visitor statements with other data sources. For example, the analysis unit can improve the reliability of information by comparing the content of visitor statements with other data sources. The analysis unit can also verify the accuracy of information by comparing the content of visitor statements with other data sources. In this way, the reliability of information can be improved by comparing the content of visitor statements with other data sources.

[0099] The reservation system can estimate a visitor's emotions and prioritize reservations based on those emotions. For example, if a visitor is nervous, the reservation system will prioritize important reservations. If a visitor is relaxed, the reservation system can also prioritize reservations at normal priority. If a visitor is in a hurry, the reservation system can also process reservations quickly. This allows for prioritizing important reservations based on the visitor's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The reservation department can refer to the meeting room usage history and suggest the optimal reservation time. For example, the reservation department can refer to the meeting room usage history and suggest the optimal reservation time. For example, the reservation department can analyze the meeting room usage history and suggest less frequently used time slots. For example, the reservation department can suggest a reservation time tailored to the user's preferences based on the meeting room usage history. In this way, by referring to the meeting room usage history, the optimal reservation time can be suggested.

[0101] The reservation department can select an appropriate meeting room by considering the meeting room's equipment information. For example, the reservation department can refer to the meeting room's equipment information and select a meeting room that has the necessary equipment. For example, the reservation department can analyze the meeting room's equipment information and select a meeting room that is best suited to the purpose of use. For example, the reservation department can select a meeting room that meets the user's requests based on the meeting room's equipment information. In this way, by considering the meeting room's equipment information, an appropriate meeting room can be selected.

[0102] The reservation department can estimate the visitor's emotions and adjust the reservation confirmation method based on the estimated emotions. For example, if the visitor is nervous, the reservation department can provide a concise confirmation method. For example, if the visitor is relaxed, the reservation department can also provide a detailed confirmation method. For example, if the visitor is in a hurry, the reservation department can also provide a quick confirmation method. In this way, by adjusting the reservation confirmation method based on the visitor's emotions, a more appropriate confirmation method can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The reservation department can monitor meeting room usage in real time and automatically modify or cancel reservations. For example, the reservation department can monitor meeting room usage in real time and respond to user changes and cancellations. For example, the reservation department can analyze meeting room usage and suggest the most suitable reservation changes or cancellations. For example, the reservation department can automatically modify or cancel reservations based on meeting room usage and user requests. In this way, by monitoring meeting room usage in real time, reservation changes and cancellations can be automated.

[0104] The reservation department can efficiently manage reservations by linking the reservation status of meeting rooms with other systems. For example, the reservation department can efficiently manage reservations by linking the reservation status of meeting rooms with other systems. For example, the reservation department can improve user convenience by sharing the reservation status of meeting rooms with other systems. For example, the reservation department can prevent duplicate reservations by linking the reservation status of meeting rooms with other systems. This enables efficient reservation management by linking the reservation status of meeting rooms with other systems.

[0105] The interaction unit can estimate the emotions of visitors and provide personalized service based on those estimated emotions. For example, if a visitor is nervous, the interaction unit can provide a calm response. If a visitor is relaxed, the interaction unit can provide a cheerful response. If a visitor is in a hurry, the interaction unit can provide a quick response. This allows for more appropriate service by providing personalized service based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The liaison department can update customer database information in real time and respond based on the latest information. For example, the liaison department can update customer database information in real time and respond based on the latest information. The liaison department can also analyze customer database information and respond based on the latest information. For example, the liaison department can provide services based on the latest information using customer database information. This enables responses based on the latest information by updating customer database information in real time.

[0107] The Collaboration Department can analyze information from the customer database and provide services tailored to the specific requests of visitors. For example, the Collaboration Department can analyze information from the customer database and provide services tailored to the specific requests of visitors. The Collaboration Department can also, for example, provide services tailored to visitor requests based on information from the customer database. The Collaboration Department can also, for example, analyze information from the customer database and provide services tailored to the specific needs of visitors. This allows for the provision of services tailored to the specific requests of visitors by analyzing information from the customer database.

[0108] The interaction unit can estimate the visitor's emotions and determine the priority of responses based on the estimated emotions. For example, if the visitor is nervous, the interaction unit will prioritize important responses. If the visitor is relaxed, the interaction unit can also respond with normal priority. If the visitor is in a hurry, the interaction unit can also respond quickly. In this way, by determining the priority of responses based on the visitor's emotions, important responses can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The integration unit can integrate with other systems to centrally manage visitor information. For example, the integration unit can integrate with other systems to centrally manage visitor information. The integration unit can also share information with other systems to efficiently manage visitor information. For example, the integration unit can integrate with other systems to manage visitor information in a unified manner. This allows for centralized management of visitor information through integration with other systems.

[0110] The integration unit can improve the reliability of information by comparing customer database information with other data sources. For example, the integration unit can improve the reliability of information by comparing customer database information with other data sources. The integration unit can also verify the accuracy of information by comparing customer database information with other data sources. In this way, the reliability of information can be improved by comparing customer database information with other data sources.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The recognition unit can not only recognize what the visitor says, but also analyze their facial expressions and gestures. For example, the recognition unit can analyze the visitor's facial expressions and estimate their emotions. The recognition unit can also analyze the visitor's hand movements and recognize the emphasized parts of what they say. The recognition unit can also analyze the visitor's whole body movements and more accurately recognize the intent behind what they say. As a result, by analyzing the visitor's facial expressions and gestures, the accuracy of recognizing what they say can be improved.

[0113] The dialogue unit can generate appropriate questions based on what the visitor says, thereby streamlining information gathering. For example, the dialogue unit generates questions to collect necessary information based on what the visitor says. The dialogue unit can also analyze what the visitor says and generate additional questions necessary for information gathering. The dialogue unit can also generate questions to facilitate efficient information gathering based on what the visitor says. In this way, by generating appropriate questions based on what the visitor says, information gathering can be made more efficient.

[0114] The analysis unit can analyze the content of a visitor's statements based on context and extract more accurate information. For example, the analysis unit can analyze the content of a visitor's statements based on context and extract important information. The analysis unit can also analyze the content of a visitor's statements based on context and provide accurate information. The analysis unit can also analyze the content of a visitor's statements based on context and improve the accuracy of the information. As a result, by analyzing the content of a visitor's statements based on context, more accurate information can be extracted.

[0115] The reservation department can refer to the meeting room usage history and suggest the optimal reservation time. For example, the reservation department can refer to the meeting room usage history and suggest the optimal reservation time. The reservation department can also analyze the meeting room usage history and suggest less frequently used time slots. Based on the meeting room usage history, the reservation department can also suggest reservation times that match the user's preferences. In this way, by referring to the meeting room usage history, the optimal reservation time can be suggested.

[0116] The integration unit can compare information from the customer database with other data sources to improve the reliability of the information. For example, the integration unit can compare information from the customer database with other data sources to improve the reliability of the information. The integration unit can also compare information from the customer database with other data sources to verify the accuracy of the information. The integration unit can also compare information from the customer database with other data sources to verify the consistency of the information. In this way, the reliability of the information can be improved by comparing information from the customer database with other data sources.

[0117] The recognition unit can estimate the visitor's emotions and adjust the accuracy of speech recognition based on the estimated emotions. For example, if the visitor is nervous, the recognition unit adjusts the accuracy of recognition by considering the tone and speed of speech. If the visitor is relaxed, the recognition unit can also adjust to recognize the content of the speech in detail. If the visitor is in a hurry, the recognition unit can also adjust to prioritize recognizing the important parts of the speech. By adjusting the accuracy of speech recognition based on the visitor's emotions, more accurate speech recognition becomes possible.

[0118] The dialogue unit can estimate the visitor's emotions and adjust the tone and content of the dialogue based on those estimates. For example, if the visitor is nervous, the dialogue unit will use a calm tone. If the visitor is relaxed, the dialogue unit can use a cheerful tone. If the visitor is in a hurry, the dialogue unit can use a quick and concise tone. By adjusting the tone and content of the dialogue based on the visitor's emotions, a more appropriate dialogue becomes possible.

[0119] The analysis unit can estimate the visitor's emotions and determine the priority of information analysis based on those emotions. For example, if the visitor is nervous, the analysis unit will prioritize analyzing important information. If the visitor is relaxed, the analysis unit can also analyze information with normal priorities. If the visitor is in a hurry, the analysis unit can also analyze information quickly. This allows for the prioritization of information analysis based on the visitor's emotions, ensuring that important information is analyzed first.

[0120] The reservations department can estimate a visitor's emotions and prioritize reservations based on those emotions. For example, if a visitor is nervous, the reservations department will prioritize important reservations. If a visitor is relaxed, the reservations department can also process reservations with normal priority. If a visitor is in a hurry, the reservations department can process reservations quickly. This allows for prioritizing important reservations by determining reservation priorities based on the visitor's emotions.

[0121] The liaison department can estimate the emotions of visitors and provide personalized service based on those estimates. For example, if a visitor is nervous, the liaison department will provide a calm response. If a visitor is relaxed, the liaison department can provide a cheerful response. If a visitor is in a hurry, the liaison department can provide a quick response. In this way, more appropriate service can be provided by providing personalized service based on the visitor's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The recognition unit recognizes the visitor's speech. The recognition unit can, for example, use speech recognition technology to recognize the visitor's speech in real time and convert it into text data. It can also record the visitor's speech and save it for later analysis. Step 2: The dialogue unit engages in conversation with the visitor based on the utterances recognized by the recognition unit. The dialogue unit, for example, uses voice dialogue technology to engage in a natural conversation with the visitor and confirm the visitor's name and the name of the person they are visiting. It can also provide appropriate answers to the visitor's questions. Step 3: The analysis unit analyzes the information collected by the dialogue unit. The analysis unit can, for example, use natural language processing techniques to analyze, classify, and extract the necessary information. It can also store the collected information in a database for later reference. Step 4: The reservation department makes meeting room reservations based on the information analyzed by the analysis department. For example, the reservation department checks the availability of meeting rooms and makes reservations. It can also integrate with the meeting room reservation system to check availability in real time, make reservations automatically, and send reservation confirmation notifications. Step 5: The Integration Unit integrates with the customer database based on the information analyzed by the Analysis Unit to provide personalized service. For example, the Integration Unit integrates with the customer database to provide the best possible service to visitors based on their past visit history and specific requests. It can update the customer database information in real time and respond based on the latest information. It can also analyze the customer database information and provide services that meet the specific requests of visitors.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the recognition unit, dialogue unit, analysis unit, reservation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 38B and control unit 46A of the smart device 14, which recognizes the visitor's speech in real time and converts it into text data. The dialogue unit is implemented by the control unit 46A of the smart device 14, which engages in natural dialogue with the visitor using voice dialogue technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12, which checks the availability of meeting rooms and makes reservations. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which collaborates with the customer database to provide personalized service. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the recognition unit, dialogue unit, analysis unit, reservation unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214, which recognizes the visitor's speech in real time and converts it into text data. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, which engages in natural conversation with the visitor using voice dialogue technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12, which checks the availability of meeting rooms and makes reservations. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which collaborates with the customer database to provide personalized service. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the recognition unit, dialogue unit, analysis unit, reservation unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314, which recognizes the visitor's speech in real time and converts it into text data. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, which engages in natural conversation with the visitor using voice dialogue technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12, which checks the availability of meeting rooms and makes reservations. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which collaborates with the customer database to provide personalized service. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The 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.

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

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the recognition unit, dialogue unit, analysis unit, reservation unit, and collaboration unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 238 and control unit 46A of the robot 414, which recognizes the visitor's speech in real time and converts it into text data. The dialogue unit is implemented by the control unit 46A of the robot 414, which engages in natural dialogue with the visitor using voice dialogue technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12, which checks the availability of meeting rooms and makes reservations. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which collaborates with the customer database to provide personalized service. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A recognition unit that recognizes what the visitor says, A dialogue unit that engages in conversation with the visitor based on the statements recognized by the recognition unit, An analysis unit that analyzes the information collected by the aforementioned dialogue unit, A reservation unit that reserves meeting rooms based on the information analyzed by the aforementioned analysis unit, Based on the information analyzed by the aforementioned analysis unit, the integration unit works in conjunction with the customer database to provide personalized responses. Equipped with A system characterized by the following features. (Note 2) The aforementioned linkage unit is, We provide optimal service to visitors by linking with our customer database and based on their past visit history and specific requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recognition unit, Multilingual support The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze the collected information using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reservation section is, Check the availability of the meeting room and make a reservation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Provide personalized service The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Using AI learning capabilities to improve services The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, The system estimates the visitor's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, The tone and speed of the visitor's voice are analyzed to determine the importance of what they are saying. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, Analyzing visitors' gestures and body language, combined with their speech, improves recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, The system estimates the visitor's emotions and adjusts the timing of speech recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, Filtering background noise from visitors improves the accuracy of speech recognition. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, We improve the accuracy of recognizing what visitors have said by referring to their past statements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, The system estimates the visitor's emotions and adjusts the tone and content of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, Based on what visitors say, the system generates appropriate questions to streamline information gathering. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, We will translate what visitors say in real time to enhance multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, The system estimates the visitor's emotions and adjusts the pace of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned dialogue unit, Referencing the visitor's past interaction history allows for personalized conversations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, Summarize what the visitor said and send only the important information to the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates the emotions of visitors and determines the priority of information analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, Analyze the content of visitors' statements based on their context to extract more accurate information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, The content of visitors' statements is categorized to enable efficient information analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, We estimate the emotions of visitors and adjust the information analysis method based on the estimated emotions of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, Referencing visitors' past visit history improves the accuracy of information analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, By cross-referencing visitor statements with other data sources, the reliability of the information is improved. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, The system estimates the emotions of visitors and prioritizes reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, We refer to the meeting room usage history and suggest the optimal reservation time. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, Considering the facilities and equipment of the meeting rooms, select the appropriate meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, The system estimates the visitor's emotions and adjusts the reservation confirmation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, The system monitors meeting room usage in real time and automatically handles changes and cancellations to reservations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, The system integrates meeting room reservation status with other systems for efficient reservation management. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, It estimates the emotions of visitors and provides personalized service based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, We update customer database information in real time and respond based on the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, We analyze information from our customer database and provide services tailored to the specific needs of each visitor. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, The system estimates the visitor's emotions and determines the priority of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, It integrates with other systems to centrally manage visitor information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, We compare customer database information with other data sources to improve the reliability of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 recognition unit that recognizes what the visitor says, A dialogue unit that engages in conversation with the visitor based on the statements recognized by the recognition unit, An analysis unit that analyzes the information collected by the aforementioned dialogue unit, A reservation unit that reserves meeting rooms based on the information analyzed by the aforementioned analysis unit, The system includes a linking unit that performs personalized responses in conjunction with a customer database based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned linkage unit is, We provide optimal service to visitors by linking with our customer database and based on their past visit history and specific requests. The system according to feature 1.

3. The aforementioned recognition unit, Multilingual support The system according to feature 1.

4. The aforementioned analysis unit, We analyze the collected information using natural language processing technology. The system according to feature 1.

5. The aforementioned reservation section is, Check the availability of the meeting room and make a reservation. The system according to feature 1.

6. The aforementioned linkage unit is, Provide personalized service The system according to feature 1.

7. The aforementioned analysis unit, We will use AI's learning capabilities to improve the service. The system according to feature 1.

8. The aforementioned recognition unit, The system estimates the visitor's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system according to feature 1.

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