system

Abstract

The system according to this embodiment aims to facilitate the smooth reception and guidance of visitors and prevent confusion at the site. [Solution] The system according to the embodiment comprises a registration unit, a face recognition unit, a display unit, and an information sharing unit. The registration unit registers visitor information. The face recognition unit recognizes the visitor's face based on the information registered by the registration unit. The display unit displays the visitor's information recognized by the face recognition unit to the attendant. The information sharing unit shares the information displayed by the display unit with the members of the organizing committee.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is not possible to smoothly receive and guide visitors during ceremonies or events, and there is a risk of chaos and waiting time at the scene.

[0005] The system according to the embodiment aims to smoothly receive and guide visitors and prevent chaos at the scene.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a face recognition unit, a display unit, and an information sharing unit. The registration unit registers visitor information. The face recognition unit recognizes the visitor's face based on the information registered by the registration unit. The display unit displays the visitor's information recognized by the face recognition unit to the attendant. The information sharing unit shares the information displayed by the display unit with the members of the organizing committee. [Effects of the Invention]

[0007] The system according to this embodiment can facilitate the smooth reception and guidance of visitors, preventing confusion at the site. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 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 system according to an embodiment of the present invention is a system for smoothly receiving and guiding visitors during ceremonies and events. This system allows for smooth operation with a small number of people without making visitors wait by pre-registering visitor information, performing facial recognition with a camera, identifying arriving visitors to the attendant, and sharing the information with the organizing committee members. This system allows for courteous service even without knowing the visitor's face, eliminating confusion on site and improving efficiency. It also enables smart operation. For example, visitor information is registered in advance. At this time, detailed information such as the visitor's facial photograph, name, company name, position, in-house attendant, expected arrival time, vehicle information, and secretary information is entered. For example, by registering the visitor's facial photograph, facial recognition by the camera becomes possible. Next, when a visitor arrives, facial recognition is performed by the camera. The camera recognizes the visitor's face and compares it with the pre-registered information. For example, when a visitor arrives, the camera recognizes their face and checks if it matches the registered information. If facial recognition is successful, the arrival information is identified to the attendant. Attendants can verify arrival information and provide smooth service. For example, they can check information such as the arrival person's name, company name, and position, and provide appropriate assistance. Furthermore, arrival information is shared with the operations office members. This allows the operations office members to understand the arrival status of visitors and ensure smooth operations. For example, they can check a list of those who have not yet arrived and take necessary action. This system allows for smooth operation with a small number of staff without making visitors wait. In addition, since courteous service can be provided even without knowing the visitor's face, on-site confusion is eliminated and efficiency is improved. For example, when a visitor arrives, facial recognition is performed using a camera, and the arrival person's information is displayed to the attendant, enabling smooth service. Furthermore, operations can be made smarter. For example, by utilizing facial recognition and vehicle license plate recognition using cameras, the arrival status of visitors can be understood in real time, enabling smooth operations. Also, by knowing the location information of attendants, efficient operations are possible. In this way, the system allows for smooth reception and guidance of visitors, and efficient operation with a small number of staff.

[0029] The system according to the embodiment comprises a registration unit, a face recognition unit, a display unit, and an information sharing unit. The registration unit registers visitor information. The registration unit registers detailed information such as the visitor's facial photograph, name, company name, job title, in-house attendant, expected arrival time, vehicle information, and secretary information. By registering the visitor's facial photograph, for example, the registration unit enables face recognition by camera. The face recognition unit recognizes the visitor's face based on the information registered by the registration unit. The face recognition unit recognizes the visitor's face with a camera and compares it with previously registered information. For example, when a visitor arrives, the face recognition unit checks if the camera recognizes their face and if it matches the registered information. The display unit displays the visitor's information recognized by the face recognition unit to the attendant. For example, the display unit displays information such as the visitor's name, company name, and job title to the attendant. The display unit can confirm the visitor's information and respond smoothly. The Information Sharing Department shares the information specified by the Specification Department with the members of the organizing committee. For example, the Information Sharing Department shares information about arriving visitors with the organizing committee members. For example, the Information Sharing Department provides the organizing committee members with a list of those who have not yet arrived. This allows the system to smoothly handle visitor registration and guidance, enabling efficient operation with a small number of staff.

[0030] The registration department registers visitor information. This includes detailed information such as the visitor's photo, name, company name, job title, internal attendant, estimated arrival time, vehicle information, and secretary information. Specifically, visitors enter their information in advance through online forms or a dedicated app, and this information is stored in the system. Visitor photos are stored as high-resolution images to ensure accurate recognition by facial recognition algorithms. Text information such as names, company names, and job titles is organized in a database and structured for easy searching and matching. Internal attendant information includes the visitor's purpose and the person they will be meeting, while estimated arrival time and vehicle information are used to support smooth visitor check-in. Secretary information is useful if the visitor is an important person and special attention is needed. This allows the registration department to collect detailed visitor information in advance, supporting the efficient operation of the entire system.

[0031] The facial recognition unit recognizes visitors' faces based on information registered by the registration unit. For example, the facial recognition unit recognizes visitors' faces using cameras and compares them with pre-registered information. Specifically, high-performance cameras installed at entrances and reception counters capture visitors' faces, and the facial recognition algorithm operates in real time. The facial recognition algorithm compares registered facial photographs with captured facial images and calculates the degree of match. If a high degree of match is obtained, the visitor is determined to be a person who was registered in advance. The facial recognition unit immediately transmits the matching result to the system to verify the visitor's information. This allows the facial recognition unit to quickly and accurately recognize visitors upon arrival and verify that they match the registered information. Furthermore, by coordinating multiple cameras, the facial recognition unit can achieve high-precision recognition even under different angles and lighting conditions. This improves the accuracy of visitor recognition and enhances the overall reliability of the system.

[0032] The display unit displays information about visitors recognized by the facial recognition unit to the attendant. For example, the display unit displays information such as the visitor's name, company name, and job title to the attendant. Specifically, the visitor's information is displayed in real time on the tablet or smartphone held by the attendant. The displayed information includes the visitor's photo, name, company name, job title, purpose of visit, and the attendant's name. This allows the attendant to immediately check the visitor's information and take appropriate action. For example, if an important visitor arrives, the attendant can respond quickly and guide them smoothly. In addition, the display unit updates the visitor's arrival status in real time, so that the attendant can always have the latest information. As a result, the display unit can provide accurate and timely visitor information to the attendant, improving the efficiency of reception operations.

[0033] The Information Sharing Department shares information specified by the Specification Department with the organizing committee members. For example, the Information Sharing Department shares information about arriving visitors with the organizing committee members. Specifically, information about arriving visitors is displayed in real time through a dedicated dashboard or application accessible to the organizing committee members. The displayed information includes the visitor's name, company name, job title, arrival time, and purpose of visit. This allows the organizing committee members to grasp the status of visitors at a glance and take necessary actions quickly. For example, if an important visitor arrives, the organizing committee members can respond immediately and make the necessary preparations. The Information Sharing Department can also provide the organizing committee members with a list of those who have not yet arrived and follow up with visitors who have missed their scheduled arrival time. In this way, the Information Sharing Department can efficiently share visitor information and support the work of the organizing committee. Furthermore, the Information Sharing Department securely manages visitor information and implements appropriate access controls to prevent information leaks and unauthorized access. In this way, the Information Sharing Department can ensure the security of the entire system and achieve highly reliable information sharing.

[0034] The registration unit can register specific information about visitors, such as their facial photograph, name, company name, job title, company attendant, estimated arrival time, vehicle information, and secretary information. For example, the registration unit can register the visitor's facial photograph. For example, the registration unit can register information such as the visitor's name, company name, and job title. For example, the registration unit can register the visitor's estimated arrival time and vehicle information. This allows for smoother reception and guidance by registering detailed visitor information in advance. Detailed information includes, but is not limited to, a facial photograph, name, company name, job title, and estimated arrival time. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the visitor's facial photograph into a generating AI and have the generating AI perform facial photograph analysis.

[0035] The face recognition unit can recognize a visitor's face using a camera and compare it with pre-registered information. For example, the face recognition unit recognizes a visitor's face using a camera. The face recognition unit compares it with pre-registered information. For example, when a visitor arrives, the camera recognizes their face and checks if it matches the registered information. This allows for quick confirmation of a visitor's arrival by performing face recognition with a camera. The comparison includes, but is not limited to, a comparison algorithm and the accuracy of the comparison. Some or all of the above-described processes in the face recognition unit may be performed using, for example, AI, or not using AI. For example, the face recognition unit can input a face image acquired by the camera into a generating AI and have the generating AI perform face image comparison.

[0036] The disclosure unit can disclose specific information such as the name, company name, and job title of the arrival to the attendant. For example, the disclosure unit can disclose the arrival's name to the attendant. For example, the disclosure unit can disclose the arrival's company name to the attendant. For example, the disclosure unit can disclose the arrival's job title to the attendant. By disclosing the arrival's information to the attendant, appropriate action can be taken. Specific information includes, but is not limited to, names, company names, and job titles. Some or all of the processing described above in the disclosure unit may be performed using AI, for example, or without AI. For example, the disclosure unit can input the arrival's information into a generating AI and have the generating AI perform the disclosure of the information.

[0037] The information sharing unit can share information about arrivals with the organizing committee members. For example, the information sharing unit can share information about arrivals with the organizing committee members. For example, the information sharing unit can provide information about arrivals to the organizing committee members. For example, the information sharing unit can notify the organizing committee members of information about arrivals. This allows for smoother operation by sharing information with the organizing committee members. Information sharing includes, but is not limited to, the types of information to be shared and the methods of sharing. Some or all of the above-described processes in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input information about arrivals into a generating AI and have the generating AI perform the information sharing.

[0038] The information sharing unit can provide a list of unarrived passengers to the organizing committee members. The information sharing unit can, for example, provide a list of unarrived passengers to the organizing committee members. The information sharing unit can, for example, notify the organizing committee members of the list of unarrived passengers. The information sharing unit can, for example, display the list of unarrived passengers to the organizing committee members. This allows the organizing committee members to take appropriate action by providing a list of unarrived passengers. The list of unarrived passengers may include, for example, the criteria for unarrived passengers and the frequency of the list update, but is not limited to such examples. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit can input a list of unarrived passengers into a generating AI and have the generating AI create the list.

[0039] The information sharing unit can grasp the attendant's location information and provide it to the operations office members. For example, the information sharing unit grasps the attendant's location information. For example, the information sharing unit provides the attendant's location information to the operations office members. For example, the information sharing unit notifies the operations office members of the attendant's location information. By providing the attendant's location information, efficient operation becomes possible. Location information includes, but is not limited to, GPS data and the frequency of location information updates. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit can input the attendant's location information into a generating AI and have the generating AI perform location information acquisition.

[0040] The registration unit can analyze a visitor's past event participation history during registration and select the optimal registration method. For example, the registration unit can automatically generate a registration form suitable for the visitor based on information from past events. For example, the registration unit can automatically fill in frequently entered information from past participation history. For example, the registration unit can analyze past participation history and propose the most suitable registration procedure for the visitor. In this way, by analyzing past participation history, the optimal registration method can be provided. The optimal registration method includes, but is not limited to, the method of analyzing past participation history and the type of registration method. Some or all of the above processes in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input past event participation history into a generating AI and have the generating AI select the optimal registration method.

[0041] The registration unit can filter visitors based on their current occupation and areas of interest during registration. For example, the registration unit can simplify the registration process by displaying only information relevant to the visitor's occupation. For example, the registration unit can prioritize displaying relevant event information based on the visitor's areas of interest. For example, the registration unit can suggest appropriate registration options based on the visitor's occupation and areas of interest. This allows for the provision of appropriate information by filtering based on the visitor's occupation and areas of interest. Filtering includes, but is not limited to, methods for classifying occupations and areas of interest, and filtering algorithms. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input visitor occupation and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0042] The registration unit can prioritize registering highly relevant information based on the visitor's geographical location information during registration. For example, the registration unit can prioritize displaying information about the nearest event venue based on the visitor's current location. For example, the registration unit can automatically register relevant transportation information based on the visitor's geographical location information. For example, the registration unit can suggest the optimal registration option considering the visitor's geographical location information. This enables the provision of appropriate information by prioritizing the registration of highly relevant information based on geographical location information. Geographical location information includes, but is not limited to, GPS data and location information update frequency. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the visitor's geographical location information into a generating AI and have the generating AI perform the registration of highly relevant information.

[0043] The registration unit can analyze visitors' social media activity and register relevant information during registration. For example, the registration unit can analyze the content of visitors' social media posts and automatically register relevant event information. For example, the registration unit can prioritize displaying relevant information based on visitors' social media follower information. For example, the registration unit can analyze visitors' social media activity history and suggest the optimal registration option. This allows for the appropriate registration of relevant information by analyzing social media activity. Social media activity includes, but is not limited to, the analysis of post content and methods for extracting relevant information. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input visitors' social media activity data into a generating AI and have the generating AI perform the registration of relevant information.

[0044] The face recognition unit can optimize its recognition algorithm by referring to the visitor's past face recognition history during face recognition. For example, the face recognition unit optimizes the recognition algorithm based on data of faces that have been recognized in the past. For example, the face recognition unit improves recognition accuracy by referring to past face recognition history. For example, the face recognition unit analyzes past face recognition history and applies the optimal recognition algorithm. This optimizes the recognition algorithm and improves accuracy by referring to past face recognition history. Optimization of the recognition algorithm includes, but is not limited to, how past history is used and the optimization algorithm. Some or all of the above processing in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input past face recognition history data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0045] The facial recognition unit can perform recognition while considering the visitor's attribute information. For example, the facial recognition unit can improve the accuracy of facial recognition based on the visitor's attribute information, such as age and gender. For example, the facial recognition unit can perform facial recognition while considering the visitor's attribute information, such as job title and position. For example, the facial recognition unit can apply the optimal recognition algorithm based on the visitor's attribute information. This improves recognition accuracy by considering the visitor's attribute information. Attribute information includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the facial recognition unit may be performed using, for example, AI, or without AI. For example, the facial recognition unit can input the visitor's attribute information into a generating AI and have the generating AI perform the recognition.

[0046] The face recognition unit can perform recognition while considering the geographical distribution of visitors. For example, the face recognition unit can improve the accuracy of face recognition based on the geographical distribution of visitors. For example, the face recognition unit can apply the optimal recognition algorithm while considering the geographical distribution of visitors. For example, the face recognition unit can analyze the geographical distribution of visitors and improve the recognition accuracy. In this way, recognition accuracy is improved by considering geographical distribution. Geographical distribution includes, but is not limited to, regional distribution data and distribution analysis methods. Some or all of the above processing in the face recognition unit may be performed using, for example, AI, or without AI. For example, the face recognition unit can input the geographical distribution data of visitors into a generating AI and have the generating AI perform the recognition.

[0047] The face recognition unit can improve the accuracy of face recognition by referring to the visitor's relevant literature during face recognition. For example, the face recognition unit improves the accuracy of face recognition based on the visitor's relevant literature. For example, the face recognition unit applies the optimal recognition algorithm by referring to the visitor's relevant literature. For example, the face recognition unit analyzes the visitor's relevant literature to improve recognition accuracy. In this way, recognition accuracy is improved by referring to relevant literature. Relevant literature includes, but is not limited to, the type of literature and the method of referring to literature. Some or all of the above processing in the face recognition unit may be performed using, for example, AI, or not using AI. For example, the face recognition unit can input the visitor's relevant literature data into a generating AI and have the generating AI perform the recognition.

[0048] The explicit information unit can adjust the level of detail of the explicit information based on the importance of the visitor during the explicit information display process. For example, the explicit information unit displays detailed information for important visitors. For example, the explicit information unit displays only the minimum necessary information for general visitors. The explicit information unit adjusts the level of detail of the information displayed according to the importance of the visitor. This allows for the provision of appropriate information by adjusting the level of detail according to the importance of the visitor. The adjustment of level of detail includes, but is not limited to, importance evaluation criteria and level of detail adjustment algorithms. Some or all of the above processing in the explicit information unit may be performed using, for example, AI, or not using AI. For example, the explicit information unit can input visitor importance data into a generating AI and have the generating AI perform the level of detail adjustment.

[0049] The expliciting unit can apply different expliciting algorithms depending on the visitor's category at the time of expliciting. For example, the expliciting unit may apply a special expliciting algorithm to VIP visitors. For example, the expliciting unit may apply a normal expliciting algorithm to general visitors. For example, the expliciting unit may apply the most suitable expliciting algorithm depending on the visitor's category. This makes it possible to provide appropriate information by applying the most suitable expliciting algorithm according to the visitor's category. The expliciting algorithm includes, but is not limited to, the type of algorithm and application criteria for each category. Some or all of the processing described above in the expliciting unit may be performed using, for example, AI, or not using AI. For example, the expliciting unit can input visitor category data into a generating AI and have the generating AI execute the application of the expliciting algorithm.

[0050] The display unit can determine the priority of displays based on the arrival time of visitors. For example, the display unit may prioritize displaying information of visitors who arrive early. For example, the display unit may postpone displaying information of visitors who arrive late. The display unit determines the priority of the information to display based on the arrival time of visitors. This enables the provision of appropriate information by prioritizing information based on the arrival time of visitors. The determination of priority includes, but is not limited to, evaluation criteria for arrival time and a priority determination algorithm. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit may input visitor arrival time data into a generating AI and have the generating AI perform the priority determination.

[0051] The explicit unit can adjust the order of explicit information based on the relevance of visitors during the explicit process. For example, the explicit unit may prioritize displaying information about important visitors. For example, the explicit unit may postpone displaying information about general visitors. The explicit unit adjusts the order of displayed information based on the relevance of visitors. This allows for the provision of appropriate information by adjusting the order of information based on the relevance of visitors. The order adjustment includes, but is not limited to, relevance evaluation criteria and order adjustment algorithms. Some or all of the above processing in the explicit unit may be performed using, for example, AI, or not using AI. For example, the explicit unit can input visitor relevance data into a generating AI and have the generating AI perform the order adjustment.

[0052] The information sharing unit can optimize the current sharing method by referring to past shared data when sharing information. For example, the information sharing unit can propose the optimal sharing method based on previously shared data. For example, the information sharing unit can improve the accuracy of sharing by referring to past shared data. For example, the information sharing unit can analyze past shared data and apply the optimal sharing method. In this way, the optimal sharing method can be provided by referring to past shared data. Optimization of the sharing method includes, but is not limited to, examples of how to use past data and optimization algorithms. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or without AI. For example, the information sharing unit can input past shared data into a generating AI and have the generating AI perform the optimization of the sharing method.

[0053] The information sharing unit can apply different sharing methods to different visitor categories when sharing information. For example, the information sharing unit may apply a special sharing method to VIP visitors. For example, it may apply a normal sharing method to general visitors. The information sharing unit may apply the most suitable sharing method according to the visitor category. This enables appropriate information sharing by applying the most suitable sharing method according to the visitor category. Sharing methods include, but are not limited to, the type of method and application criteria for each category. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input visitor category data into a generating AI and have the generating AI execute the application of sharing methods.

[0054] The information sharing unit can analyze changes in information sharing based on the arrival times of visitors. For example, the information sharing unit may prioritize sharing information of visitors who arrive early. For example, it may postpone sharing information of visitors who arrive late. For example, the information sharing unit may determine the priority of information to share based on the arrival times of visitors. This enables appropriate information sharing by determining the priority of information based on the arrival times of visitors. The analysis of changes in sharing may include, but is not limited to, evaluation criteria for arrival times and algorithms for analyzing changes. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit may input visitor arrival time data into a generating AI and have the generating AI perform the analysis of changes in sharing.

[0055] The information sharing unit can analyze the sharing process by referring to relevant market data of visitors during information sharing. For example, the information sharing unit can propose the optimal sharing method based on the relevant market data of visitors. For example, the information sharing unit can improve the accuracy of sharing by referring to relevant market data of visitors. For example, the information sharing unit can analyze the relevant market data of visitors and apply the optimal sharing method. In this way, the optimal sharing method can be provided by referring to relevant market data. Relevant market data includes, but is not limited to, the type of market data and the method of data reference. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit can input relevant market data of visitors into a generating AI and have the generating AI perform the sharing analysis.

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

[0057] The system can analyze visitors' past event participation history and select the most suitable guidance method. For example, it can automatically generate a suitable guidance route based on information from past events attended. It can automatically suggest frequently used routes based on past participation history. By analyzing past participation history, it can suggest the most suitable guidance procedure for visitors. In this way, it can provide the most optimal guidance method by analyzing past participation history.

[0058] The system can prioritize providing highly relevant guidance information based on the visitor's geographical location. For example, it can prioritize displaying the nearest route based on the visitor's current location. It can automatically provide relevant traffic information based on the visitor's geographical location. It can suggest the optimal guidance option considering the visitor's geographical location. This enables appropriate guidance by prioritizing the provision of highly relevant guidance information based on geographical location.

[0059] The system can analyze visitors' social media activity and provide relevant information. For example, it can analyze visitors' social media posts and automatically provide relevant event information. It can prioritize displaying relevant information based on visitors' social media follower information. It can analyze visitors' social media activity history and suggest the most suitable guidance options. In this way, by analyzing social media activity, it can appropriately provide relevant information.

[0060] The system can optimize guidance methods by referring to visitors' past guidance history. For example, it can suggest the optimal guidance method based on previously used routes. It improves the accuracy of guidance by referring to past guidance history. It analyzes past guidance history and applies the optimal guidance method. In this way, it can provide the most optimal guidance method by referring to past guidance history.

[0061] The system can analyze guidance by referring to relevant market data for visitors. For example, it can suggest the optimal guidance method based on relevant market data for visitors. It can improve the accuracy of guidance by referring to relevant market data for visitors. It analyzes relevant market data for visitors and applies the optimal guidance method. In this way, it can provide the optimal guidance method by referring to relevant market data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The registration section registers visitor information. For example, it registers detailed information such as the visitor's photo, name, company name, job title, company attendant, estimated arrival time, vehicle information, and secretary information. This enables facial recognition by the camera. Step 2: The facial recognition unit recognizes the visitor's face based on the information registered by the registration unit. For example, the camera recognizes the visitor's face and compares it with the previously registered information. When a visitor arrives, the camera recognizes their face and checks if it matches the registered information. Step 3: The display unit displays the visitor's information, recognized by the facial recognition unit, to the attendant. For example, it displays information such as the visitor's name, company name, and job title to the attendant, allowing them to confirm the visitor's information and provide smooth service. Step 4: The Information Sharing Department shares the information specified by the Specification Department with the members of the organizing committee. For example, it shares information on arriving visitors with the organizing committee members and provides a list of those who have not yet arrived. This allows the system to smoothly handle visitor registration and guidance, enabling efficient operation with a small number of staff.

[0064] (Example of form 2) The system according to an embodiment of the present invention is a system for smoothly receiving and guiding visitors during ceremonies and events. This system allows for smooth operation with a small number of people without making visitors wait by pre-registering visitor information, performing facial recognition with a camera, identifying arriving visitors to the attendant, and sharing the information with the organizing committee members. This system allows for courteous service even without knowing the visitor's face, eliminating confusion on site and improving efficiency. It also enables smart operation. For example, visitor information is registered in advance. At this time, detailed information such as the visitor's facial photograph, name, company name, position, in-house attendant, expected arrival time, vehicle information, and secretary information is entered. For example, by registering the visitor's facial photograph, facial recognition by the camera becomes possible. Next, when a visitor arrives, facial recognition is performed by the camera. The camera recognizes the visitor's face and compares it with the pre-registered information. For example, when a visitor arrives, the camera recognizes their face and checks if it matches the registered information. If facial recognition is successful, the arrival information is identified to the attendant. Attendants can verify arrival information and provide smooth service. For example, they can check information such as the arrival person's name, company name, and position, and provide appropriate assistance. Furthermore, arrival information is shared with the operations office members. This allows the operations office members to understand the arrival status of visitors and ensure smooth operations. For example, they can check a list of those who have not yet arrived and take necessary action. This system allows for smooth operation with a small number of staff without making visitors wait. In addition, since courteous service can be provided even without knowing the visitor's face, on-site confusion is eliminated and efficiency is improved. For example, when a visitor arrives, facial recognition is performed using a camera, and the arrival person's information is displayed to the attendant, enabling smooth service. Furthermore, operations can be made smarter. For example, by utilizing facial recognition and vehicle license plate recognition using cameras, the arrival status of visitors can be understood in real time, enabling smooth operations. Also, by knowing the location information of attendants, efficient operations are possible. In this way, the system allows for smooth reception and guidance of visitors, and efficient operation with a small number of staff.

[0065] The system according to the embodiment comprises a registration unit, a face recognition unit, a display unit, and an information sharing unit. The registration unit registers visitor information. The registration unit registers detailed information such as the visitor's facial photograph, name, company name, job title, in-house attendant, expected arrival time, vehicle information, and secretary information. By registering the visitor's facial photograph, for example, the registration unit enables face recognition by camera. The face recognition unit recognizes the visitor's face based on the information registered by the registration unit. The face recognition unit recognizes the visitor's face with a camera and compares it with previously registered information. For example, when a visitor arrives, the face recognition unit checks if the camera recognizes their face and if it matches the registered information. The display unit displays the visitor's information recognized by the face recognition unit to the attendant. For example, the display unit displays information such as the visitor's name, company name, and job title to the attendant. The display unit can confirm the visitor's information and respond smoothly. The Information Sharing Department shares the information specified by the Specification Department with the members of the organizing committee. For example, the Information Sharing Department shares information about arriving visitors with the organizing committee members. For example, the Information Sharing Department provides the organizing committee members with a list of those who have not yet arrived. This allows the system to smoothly handle visitor registration and guidance, enabling efficient operation with a small number of staff.

[0066] The registration department registers visitor information. This includes detailed information such as the visitor's photo, name, company name, job title, internal attendant, estimated arrival time, vehicle information, and secretary information. Specifically, visitors enter their information in advance through online forms or a dedicated app, and this information is stored in the system. Visitor photos are stored as high-resolution images to ensure accurate recognition by facial recognition algorithms. Text information such as names, company names, and job titles is organized in a database and structured for easy searching and matching. Internal attendant information includes the visitor's purpose and the person they will be meeting, while estimated arrival time and vehicle information are used to support smooth visitor check-in. Secretary information is useful if the visitor is an important person and special attention is needed. This allows the registration department to collect detailed visitor information in advance, supporting the efficient operation of the entire system.

[0067] The facial recognition unit recognizes visitors' faces based on information registered by the registration unit. For example, the facial recognition unit recognizes visitors' faces using cameras and compares them with pre-registered information. Specifically, high-performance cameras installed at entrances and reception counters capture visitors' faces, and the facial recognition algorithm operates in real time. The facial recognition algorithm compares registered facial photographs with captured facial images and calculates the degree of match. If a high degree of match is obtained, the visitor is determined to be a person who was registered in advance. The facial recognition unit immediately transmits the matching result to the system to verify the visitor's information. This allows the facial recognition unit to quickly and accurately recognize visitors upon arrival and verify that they match the registered information. Furthermore, by coordinating multiple cameras, the facial recognition unit can achieve high-precision recognition even under different angles and lighting conditions. This improves the accuracy of visitor recognition and enhances the overall reliability of the system.

[0068] The display unit displays information about visitors recognized by the facial recognition unit to the attendant. For example, the display unit displays information such as the visitor's name, company name, and job title to the attendant. Specifically, the visitor's information is displayed in real time on the tablet or smartphone held by the attendant. The displayed information includes the visitor's photo, name, company name, job title, purpose of visit, and the attendant's name. This allows the attendant to immediately check the visitor's information and take appropriate action. For example, if an important visitor arrives, the attendant can respond quickly and guide them smoothly. In addition, the display unit updates the visitor's arrival status in real time, so that the attendant can always have the latest information. As a result, the display unit can provide accurate and timely visitor information to the attendant, improving the efficiency of reception operations.

[0069] The Information Sharing Department shares information specified by the Specification Department with the organizing committee members. For example, the Information Sharing Department shares information about arriving visitors with the organizing committee members. Specifically, information about arriving visitors is displayed in real time through a dedicated dashboard or application accessible to the organizing committee members. The displayed information includes the visitor's name, company name, job title, arrival time, and purpose of visit. This allows the organizing committee members to grasp the status of visitors at a glance and take necessary actions quickly. For example, if an important visitor arrives, the organizing committee members can respond immediately and make the necessary preparations. The Information Sharing Department can also provide the organizing committee members with a list of those who have not yet arrived and follow up with visitors who have missed their scheduled arrival time. In this way, the Information Sharing Department can efficiently share visitor information and support the work of the organizing committee. Furthermore, the Information Sharing Department securely manages visitor information and implements appropriate access controls to prevent information leaks and unauthorized access. In this way, the Information Sharing Department can ensure the security of the entire system and achieve highly reliable information sharing.

[0070] The registration unit can register specific information about visitors, such as their facial photograph, name, company name, job title, company attendant, estimated arrival time, vehicle information, and secretary information. For example, the registration unit can register the visitor's facial photograph. For example, the registration unit can register information such as the visitor's name, company name, and job title. For example, the registration unit can register the visitor's estimated arrival time and vehicle information. This allows for smoother reception and guidance by registering detailed visitor information in advance. Detailed information includes, but is not limited to, a facial photograph, name, company name, job title, and estimated arrival time. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the visitor's facial photograph into a generating AI and have the generating AI perform facial photograph analysis.

[0071] The face recognition unit can recognize a visitor's face using a camera and compare it with pre-registered information. For example, the face recognition unit recognizes a visitor's face using a camera. The face recognition unit compares it with pre-registered information. For example, when a visitor arrives, the camera recognizes their face and checks if it matches the registered information. This allows for quick confirmation of a visitor's arrival by performing face recognition with a camera. The comparison includes, but is not limited to, a comparison algorithm and the accuracy of the comparison. Some or all of the above-described processes in the face recognition unit may be performed using, for example, AI, or not using AI. For example, the face recognition unit can input a face image acquired by the camera into a generating AI and have the generating AI perform face image comparison.

[0072] The disclosure unit can disclose specific information such as the name, company name, and job title of the arrival to the attendant. For example, the disclosure unit can disclose the arrival's name to the attendant. For example, the disclosure unit can disclose the arrival's company name to the attendant. For example, the disclosure unit can disclose the arrival's job title to the attendant. By disclosing the arrival's information to the attendant, appropriate action can be taken. Specific information includes, but is not limited to, names, company names, and job titles. Some or all of the processing described above in the disclosure unit may be performed using AI, for example, or without AI. For example, the disclosure unit can input the arrival's information into a generating AI and have the generating AI perform the disclosure of the information.

[0073] The information sharing unit can share information about arrivals with the organizing committee members. For example, the information sharing unit can share information about arrivals with the organizing committee members. For example, the information sharing unit can provide information about arrivals to the organizing committee members. For example, the information sharing unit can notify the organizing committee members of information about arrivals. This allows for smoother operation by sharing information with the organizing committee members. Information sharing includes, but is not limited to, the types of information to be shared and the methods of sharing. Some or all of the above-described processes in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input information about arrivals into a generating AI and have the generating AI perform the information sharing.

[0074] The information sharing unit can provide a list of unarrived passengers to the organizing committee members. The information sharing unit can, for example, provide a list of unarrived passengers to the organizing committee members. The information sharing unit can, for example, notify the organizing committee members of the list of unarrived passengers. The information sharing unit can, for example, display the list of unarrived passengers to the organizing committee members. This allows the organizing committee members to take appropriate action by providing a list of unarrived passengers. The list of unarrived passengers may include, for example, the criteria for unarrived passengers and the frequency of the list update, but is not limited to such examples. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit can input a list of unarrived passengers into a generating AI and have the generating AI create the list.

[0075] The information sharing unit can grasp the attendant's location information and provide it to the operations office members. For example, the information sharing unit grasps the attendant's location information. For example, the information sharing unit provides the attendant's location information to the operations office members. For example, the information sharing unit notifies the operations office members of the attendant's location information. By providing the attendant's location information, efficient operation becomes possible. Location information includes, but is not limited to, GPS data and the frequency of location information updates. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit can input the attendant's location information into a generating AI and have the generating AI perform location information acquisition.

[0076] The registration unit can estimate the visitor's emotions and adjust the level of detail in the registration information based on the estimated emotions. For example, if a visitor is nervous, the registration unit will require only the minimum necessary information to be entered, simplifying the registration process. For example, if a visitor is relaxed, the registration unit will require detailed information to be entered, collecting more data. For example, if a visitor is in a hurry, the registration unit will allow them to quickly enter registration information using voice input. This allows for appropriate information collection by adjusting the level of detail in the registration information according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit can input visitor emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The registration unit can analyze a visitor's past event participation history during registration and select the optimal registration method. For example, the registration unit can automatically generate a registration form suitable for the visitor based on information from past events. For example, the registration unit can automatically fill in frequently entered information from past participation history. For example, the registration unit can analyze past participation history and propose the most suitable registration procedure for the visitor. In this way, by analyzing past participation history, the optimal registration method can be provided. The optimal registration method includes, but is not limited to, the method of analyzing past participation history and the type of registration method. Some or all of the above processes in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input past event participation history into a generating AI and have the generating AI select the optimal registration method.

[0078] The registration unit can filter visitors based on their current occupation and areas of interest during registration. For example, the registration unit can simplify the registration process by displaying only information relevant to the visitor's occupation. For example, the registration unit can prioritize displaying relevant event information based on the visitor's areas of interest. For example, the registration unit can suggest appropriate registration options based on the visitor's occupation and areas of interest. This allows for the provision of appropriate information by filtering based on the visitor's occupation and areas of interest. Filtering includes, but is not limited to, methods for classifying occupations and areas of interest, and filtering algorithms. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input visitor occupation and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0079] The registration unit can estimate the visitor's emotions and prioritize registration information based on the estimated visitor's emotions. For example, if the visitor is nervous, the registration unit may prioritize inputting important information. If the visitor is relaxed, the registration unit may prioritize inputting detailed information later. If the visitor is in a hurry, the registration unit may prioritize inputting only the most important information. In this way, by prioritizing registration information according to the visitor's emotions, important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit may input visitor emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The registration unit can prioritize registering highly relevant information based on the visitor's geographical location information during registration. For example, the registration unit can prioritize displaying information about the nearest event venue based on the visitor's current location. For example, the registration unit can automatically register relevant transportation information based on the visitor's geographical location information. For example, the registration unit can suggest the optimal registration option considering the visitor's geographical location information. This enables the provision of appropriate information by prioritizing the registration of highly relevant information based on geographical location information. Geographical location information includes, but is not limited to, GPS data and location information update frequency. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the visitor's geographical location information into a generating AI and have the generating AI perform the registration of highly relevant information.

[0081] The registration unit can analyze visitors' social media activity and register relevant information during registration. For example, the registration unit can analyze the content of visitors' social media posts and automatically register relevant event information. For example, the registration unit can prioritize displaying relevant information based on visitors' social media follower information. For example, the registration unit can analyze visitors' social media activity history and suggest the optimal registration option. This allows for the appropriate registration of relevant information by analyzing social media activity. Social media activity includes, but is not limited to, the analysis of post content and methods for extracting relevant information. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input visitors' social media activity data into a generating AI and have the generating AI perform the registration of relevant information.

[0082] The facial recognition unit can estimate the visitor's emotions and adjust the accuracy of facial recognition based on the estimated emotions. For example, if the visitor is nervous, the facial recognition unit can increase the accuracy of facial recognition to recognize them quickly. For example, if the visitor is relaxed, the facial recognition unit can maintain normal accuracy. For example, if the visitor is in a hurry, the facial recognition unit can increase the accuracy of facial recognition to recognize them quickly. This allows for quick and accurate recognition by adjusting the accuracy of facial recognition according to 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. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input visitor emotion data into the generative AI and have the generative AI perform the adjustment of facial recognition accuracy.

[0083] The face recognition unit can optimize its recognition algorithm by referring to the visitor's past face recognition history during face recognition. For example, the face recognition unit optimizes the recognition algorithm based on data of faces that have been recognized in the past. For example, the face recognition unit improves recognition accuracy by referring to past face recognition history. For example, the face recognition unit analyzes past face recognition history and applies the optimal recognition algorithm. This optimizes the recognition algorithm and improves accuracy by referring to past face recognition history. Optimization of the recognition algorithm includes, but is not limited to, how past history is used and the optimization algorithm. Some or all of the above processing in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input past face recognition history data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0084] The facial recognition unit can perform recognition while considering the visitor's attribute information. For example, the facial recognition unit can improve the accuracy of facial recognition based on the visitor's attribute information, such as age and gender. For example, the facial recognition unit can perform facial recognition while considering the visitor's attribute information, such as job title and position. For example, the facial recognition unit can apply the optimal recognition algorithm based on the visitor's attribute information. This improves recognition accuracy by considering the visitor's attribute information. Attribute information includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the facial recognition unit may be performed using, for example, AI, or without AI. For example, the facial recognition unit can input the visitor's attribute information into a generating AI and have the generating AI perform the recognition.

[0085] The facial recognition unit can estimate the visitor's emotions and adjust the order in which the facial recognition results are displayed based on the estimated emotions. For example, if the visitor is nervous, the facial recognition unit will prioritize displaying important information. For example, if the visitor is relaxed, the facial recognition unit will postpone displaying detailed information. For example, if the visitor is in a hurry, the facial recognition unit will prioritize displaying only the most important information. In this way, important information can be prioritized by adjusting the order in which the facial recognition results are displayed according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facial recognition unit may be performed using AI or not using AI. For example, the facial recognition unit can input visitor emotion data into the generative AI and have the generative AI perform the adjustment of the display order.

[0086] The face recognition unit can perform recognition while considering the geographical distribution of visitors. For example, the face recognition unit can improve the accuracy of face recognition based on the geographical distribution of visitors. For example, the face recognition unit can apply the optimal recognition algorithm while considering the geographical distribution of visitors. For example, the face recognition unit can analyze the geographical distribution of visitors and improve the recognition accuracy. In this way, recognition accuracy is improved by considering geographical distribution. Geographical distribution includes, but is not limited to, regional distribution data and distribution analysis methods. Some or all of the above processing in the face recognition unit may be performed using, for example, AI, or without AI. For example, the face recognition unit can input the geographical distribution data of visitors into a generating AI and have the generating AI perform the recognition.

[0087] The face recognition unit can improve the accuracy of face recognition by referring to the visitor's relevant literature during face recognition. For example, the face recognition unit improves the accuracy of face recognition based on the visitor's relevant literature. For example, the face recognition unit applies the optimal recognition algorithm by referring to the visitor's relevant literature. For example, the face recognition unit analyzes the visitor's relevant literature to improve recognition accuracy. In this way, recognition accuracy is improved by referring to relevant literature. Relevant literature includes, but is not limited to, the type of literature and the method of referring to literature. Some or all of the above processing in the face recognition unit may be performed using, for example, AI, or not using AI. For example, the face recognition unit can input the visitor's relevant literature data into a generating AI and have the generating AI perform the recognition.

[0088] The explicit unit can estimate the visitor's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the visitor is nervous, the explicit unit displays simple and easily visible information. For example, if the visitor is relaxed, the explicit unit displays detailed information. For example, if the visitor is in a hurry, the explicit unit displays concise information. This allows for the provision of appropriate information by adjusting the way the information is presented according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explicit unit may be performed using AI or not using AI. For example, the explicit unit can input visitor emotion data into the generative AI and have the generative AI adjust the presentation method.

[0089] The explicit information unit can adjust the level of detail of the explicit information based on the importance of the visitor during the explicit information display process. For example, the explicit information unit displays detailed information for important visitors. For example, the explicit information unit displays only the minimum necessary information for general visitors. The explicit information unit adjusts the level of detail of the information displayed according to the importance of the visitor. This allows for the provision of appropriate information by adjusting the level of detail according to the importance of the visitor. The adjustment of level of detail includes, but is not limited to, importance evaluation criteria and level of detail adjustment algorithms. Some or all of the above processing in the explicit information unit may be performed using, for example, AI, or not using AI. For example, the explicit information unit can input visitor importance data into a generating AI and have the generating AI perform the level of detail adjustment.

[0090] The expliciting unit can apply different expliciting algorithms depending on the visitor's category at the time of expliciting. For example, the expliciting unit may apply a special expliciting algorithm to VIP visitors. For example, the expliciting unit may apply a normal expliciting algorithm to general visitors. For example, the expliciting unit may apply the most suitable expliciting algorithm depending on the visitor's category. This makes it possible to provide appropriate information by applying the most suitable expliciting algorithm according to the visitor's category. The expliciting algorithm includes, but is not limited to, the type of algorithm and application criteria for each category. Some or all of the processing described above in the expliciting unit may be performed using, for example, AI, or not using AI. For example, the expliciting unit can input visitor category data into a generating AI and have the generating AI execute the application of the expliciting algorithm.

[0091] The explicit section can estimate the visitor's emotions and adjust the length of the information it displays based on the estimated emotions. For example, if the visitor is nervous, the explicit section displays short, concise information. For example, if the visitor is relaxed, the explicit section displays detailed information. For example, if the visitor is in a hurry, the explicit section displays short, concise information. This allows for the provision of appropriate information by adjusting the length of the information according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the explicit section may be performed using AI or not using AI. For example, the explicit section can input visitor emotion data into a generative AI and have the generative AI adjust the length of the information.

[0092] The display unit can determine the priority of displays based on the arrival time of visitors. For example, the display unit may prioritize displaying information of visitors who arrive early. For example, the display unit may postpone displaying information of visitors who arrive late. The display unit determines the priority of the information to display based on the arrival time of visitors. This enables the provision of appropriate information by prioritizing information based on the arrival time of visitors. The determination of priority includes, but is not limited to, evaluation criteria for arrival time and a priority determination algorithm. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit may input visitor arrival time data into a generating AI and have the generating AI perform the priority determination.

[0093] The explicit unit can adjust the order of explicit information based on the relevance of visitors during the explicit process. For example, the explicit unit may prioritize displaying information about important visitors. For example, the explicit unit may postpone displaying information about general visitors. The explicit unit adjusts the order of displayed information based on the relevance of visitors. This allows for the provision of appropriate information by adjusting the order of information based on the relevance of visitors. The order adjustment includes, but is not limited to, relevance evaluation criteria and order adjustment algorithms. Some or all of the above processing in the explicit unit may be performed using, for example, AI, or not using AI. For example, the explicit unit can input visitor relevance data into a generating AI and have the generating AI perform the order adjustment.

[0094] The information sharing unit can estimate the emotions of visitors and adjust the importance of the information it shares based on the estimated emotions. For example, if a visitor is nervous, the information sharing unit will prioritize sharing important information. For example, if a visitor is relaxed, the information sharing unit will share detailed information. For example, if a visitor is in a hurry, the information sharing unit will prioritize sharing only the most important information. This allows for appropriate information sharing by adjusting the importance of information according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information sharing unit may be performed using AI, or not using AI. For example, the information sharing unit can input visitor emotion data into the generative AI and have the generative AI perform the importance adjustment.

[0095] The information sharing unit can optimize the current sharing method by referring to past shared data when sharing information. For example, the information sharing unit can propose the optimal sharing method based on previously shared data. For example, the information sharing unit can improve the accuracy of sharing by referring to past shared data. For example, the information sharing unit can analyze past shared data and apply the optimal sharing method. In this way, the optimal sharing method can be provided by referring to past shared data. Optimization of the sharing method includes, but is not limited to, examples of how to use past data and optimization algorithms. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or without AI. For example, the information sharing unit can input past shared data into a generating AI and have the generating AI perform the optimization of the sharing method.

[0096] The information sharing unit can apply different sharing methods to different visitor categories when sharing information. For example, the information sharing unit may apply a special sharing method to VIP visitors. For example, it may apply a normal sharing method to general visitors. The information sharing unit may apply the most suitable sharing method according to the visitor category. This enables appropriate information sharing by applying the most suitable sharing method according to the visitor category. Sharing methods include, but are not limited to, the type of method and application criteria for each category. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input visitor category data into a generating AI and have the generating AI execute the application of sharing methods.

[0097] The information sharing unit can estimate the emotions of visitors and adjust the display method of the information being shared based on the estimated emotions. For example, if a visitor is nervous, the information sharing unit provides a simple and highly visible display method. For example, if a visitor is relaxed, the information sharing unit provides a display method that includes detailed information. For example, if a visitor is in a hurry, the information sharing unit provides a display method that gets straight to the point. This allows for appropriate information sharing by adjusting the display method of information according to the emotions of visitors. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information sharing unit may be performed using AI, for example, or without AI. For example, the information sharing unit can input visitor emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0098] The information sharing unit can analyze changes in information sharing based on the arrival times of visitors. For example, the information sharing unit may prioritize sharing information of visitors who arrive early. For example, it may postpone sharing information of visitors who arrive late. For example, the information sharing unit may determine the priority of information to share based on the arrival times of visitors. This enables appropriate information sharing by determining the priority of information based on the arrival times of visitors. The analysis of changes in sharing may include, but is not limited to, evaluation criteria for arrival times and algorithms for analyzing changes. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit may input visitor arrival time data into a generating AI and have the generating AI perform the analysis of changes in sharing.

[0099] The information sharing unit can analyze the sharing process by referring to relevant market data of visitors during information sharing. For example, the information sharing unit can propose the optimal sharing method based on the relevant market data of visitors. For example, the information sharing unit can improve the accuracy of sharing by referring to relevant market data of visitors. For example, the information sharing unit can analyze the relevant market data of visitors and apply the optimal sharing method. In this way, the optimal sharing method can be provided by referring to relevant market data. Relevant market data includes, but is not limited to, the type of market data and the method of data reference. Some or all of the above processing in the information sharing unit may be performed using, for example, AI, or not using AI. For example, the information sharing unit can input relevant market data of visitors into a generating AI and have the generating AI perform the sharing analysis.

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

[0101] The system can estimate visitors' emotions and adjust the way it guides them based on those emotions. For example, if a visitor is nervous, the attendant can provide gentle and courteous service. If a visitor is relaxed, the attendant can provide quick and efficient service. If a visitor is in a hurry, the attendant can guide them along the shortest route. This enables optimal guidance tailored to each visitor's emotions.

[0102] The system can analyze visitors' past event participation history and select the most suitable guidance method. For example, it can automatically generate a suitable guidance route based on information from past events attended. It can automatically suggest frequently used routes based on past participation history. By analyzing past participation history, it can suggest the most suitable guidance procedure for visitors. In this way, it can provide the most optimal guidance method by analyzing past participation history.

[0103] The system can prioritize providing highly relevant guidance information based on the visitor's geographical location. For example, it can prioritize displaying the nearest route based on the visitor's current location. It can automatically provide relevant traffic information based on the visitor's geographical location. It can suggest the optimal guidance option considering the visitor's geographical location. This enables appropriate guidance by prioritizing the provision of highly relevant guidance information based on geographical location.

[0104] The system can analyze visitors' social media activity and provide relevant information. For example, it can analyze visitors' social media posts and automatically provide relevant event information. It can prioritize displaying relevant information based on visitors' social media follower information. It can analyze visitors' social media activity history and suggest the most suitable guidance options. In this way, by analyzing social media activity, it can appropriately provide relevant information.

[0105] The system can estimate visitors' emotions and adjust how information is displayed based on those emotions. For example, if a visitor is nervous, it provides a simple and highly visible display. If a visitor is relaxed, it provides a display that includes detailed information. If a visitor is in a hurry, it provides a display that gets straight to the point. By adjusting how information is displayed according to the visitor's emotions, it enables appropriate guidance.

[0106] The system can estimate visitors' emotions and prioritize information based on those emotions. For example, if a visitor is nervous, important information will be given first. If a visitor is relaxed, detailed information will be given later. If a visitor is in a hurry, only the most important information will be given first. In this way, by prioritizing information according to the visitor's emotions, important information can be given priority.

[0107] The system can estimate the visitor's emotions and adjust the level of detail in the guidance based on those emotions. For example, if a visitor is nervous, only the bare minimum of information is provided. If a visitor is relaxed, detailed information is provided. If a visitor is in a hurry, only the most important information is provided. This allows for the provision of appropriate information by adjusting the level of detail in the guidance according to the visitor's emotions.

[0108] The system can optimize guidance methods by referring to visitors' past guidance history. For example, it can suggest the optimal guidance method based on previously used routes. It improves the accuracy of guidance by referring to past guidance history. It analyzes past guidance history and applies the optimal guidance method. In this way, it can provide the most optimal guidance method by referring to past guidance history.

[0109] The system can analyze guidance by referring to relevant market data for visitors. For example, it can suggest the optimal guidance method based on relevant market data for visitors. It can improve the accuracy of guidance by referring to relevant market data for visitors. It analyzes relevant market data for visitors and applies the optimal guidance method. In this way, it can provide the optimal guidance method by referring to relevant market data.

[0110] The system can estimate visitors' emotions and adjust the order of information based on those emotions. For example, if a visitor is nervous, important information will be prioritized. If a visitor is relaxed, detailed information will be given later. If a visitor is in a hurry, only the most important information will be prioritized. In this way, important information can be prioritized by adjusting the order of information according to the visitor's emotions.

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

[0112] Step 1: The registration section registers visitor information. For example, it registers detailed information such as the visitor's photo, name, company name, job title, company attendant, estimated arrival time, vehicle information, and secretary information. This enables facial recognition by the camera. Step 2: The facial recognition unit recognizes the visitor's face based on the information registered by the registration unit. For example, the camera recognizes the visitor's face and compares it with the previously registered information. When a visitor arrives, the camera recognizes their face and checks if it matches the registered information. Step 3: The display unit displays the visitor's information, recognized by the facial recognition unit, to the attendant. For example, it displays information such as the visitor's name, company name, and job title to the attendant, allowing them to confirm the visitor's information and provide smooth service. Step 4: The Information Sharing Department shares the information specified by the Specification Department with the members of the organizing committee. For example, it shares information on arriving visitors with the organizing committee members and provides a list of those who have not yet arrived. This allows the system to smoothly handle visitor registration and guidance, enabling efficient operation with a small number of staff.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the registration unit, face recognition unit, identification unit, and information sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and registers the visitor's face photograph and detailed information. The face recognition unit is implemented by the camera 42 and control unit 46A of the smart device 14 and recognizes the visitor's face and compares it with the registered information. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and displays the recognized visitor's information to the attendant. The information sharing unit is implemented by the identification processing unit 290 of the data processing unit 12 and shares the arrival information with the operating office members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the registration unit, face recognition unit, identification unit, and information sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and registers the visitor's face photograph and detailed information. The face recognition unit is implemented by the camera 42 and control unit 46A of the smart glasses 214 and recognizes the visitor's face and compares it with the registered information. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and displays the recognized visitor's information to the attendant. The information sharing unit is implemented by the identification processing unit 290 of the data processing unit 12 and shares the arrival information with the operating office members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the registration unit, face recognition unit, identification unit, and information sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and registers the visitor's face photograph and detailed information. The face recognition unit is implemented by the camera 42 and control unit 46A of the headset terminal 314 and recognizes the visitor's face and compares it with the registered information. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and displays the recognized visitor's information to the attendant. The information sharing unit is implemented by the identification processing unit 290 of the data processing unit 12 and shares the arrival information with the operating office members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the registration unit, face recognition unit, identification unit, and information sharing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and registers the visitor's face photograph and detailed information. The face recognition unit is implemented by, for example, the camera 42 and control unit 46A of the robot 414 and recognizes the visitor's face and compares it with the registered information. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and displays the recognized visitor's information to the attendant. The information sharing unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and shares the arrival information with the operating office members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A registration area for registering visitor information, A face recognition unit recognizes the face of a visitor based on the information registered by the registration unit, A display unit that displays the visitor's information recognized by the facial recognition unit to the attendant, The system includes an information sharing unit that shares the information explicitly stated by the explicit unit with members of the operating secretariat. A system characterized by the following features. (Note 2) The aforementioned registration unit is Register specific information about the visitor, including their photo, name, company name, job title, internal attendant, estimated arrival time, vehicle information, and secretary information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned face recognition unit, The system uses cameras to recognize visitors' faces and compares them with pre-registered information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned explicit section is, Provide the attendant with specific information about the arrival, including their name, company name, and job title. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information sharing unit is Share arrival information with the organizing committee members. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned information sharing unit is Provide a list of those who have not yet arrived to the organizing committee members. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information sharing unit is The location information of the attendant will be tracked and provided to the organizing committee members. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system estimates the emotions of visitors and adjusts the level of detail in registration information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is During registration, the system analyzes the attendee's past event participation history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is During registration, the system filters attendees based on their current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is The system estimates the emotions of visitors and prioritizes registration information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is During registration, the system prioritizes registering highly relevant information based on the visitor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned registration unit is During registration, the system analyzes visitors' social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned face recognition unit, The system estimates the emotions of visitors and adjusts the accuracy of facial recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned face recognition unit, During face recognition, the recognition algorithm is optimized by referring to the visitor's past face recognition history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned face recognition unit, During facial recognition, the system takes into account the visitor's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned face recognition unit, The system estimates the emotions of visitors and adjusts the order in which the facial recognition results are displayed based on the estimated emotions of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned face recognition unit, When performing facial recognition, the recognition process takes into account the geographical distribution of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned face recognition unit, During facial recognition, the system improves recognition accuracy by referencing relevant literature of the visitor. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned explicit section is, We estimate the emotions of visitors and adjust the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned explicit section is, When highlighting, adjust the level of detail based on the importance of the visitor. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned explicit section is, During explicit notification, different explicit notification algorithms are applied depending on the visitor's category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned explicit section is, The system estimates the visitor's emotions and adjusts the length of the information displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned explicit section is, When displaying information, the priority of displaying information will be determined based on the arrival time of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned explicit section is, When displaying information, adjust the order of display based on the relevance of the visitor. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned information sharing unit is It estimates the emotions of visitors and adjusts the importance of the information shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned information sharing unit is When sharing information, refer to past shared data to optimize the current sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned information sharing unit is When sharing information, different sharing methods will be applied depending on the category of the visitor. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information sharing unit is The system estimates the emotions of visitors and adjusts how shared information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned information sharing unit is When sharing information, analyze how the information changes based on the arrival time of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned information sharing unit is When sharing information, we analyze the shared information by referring to relevant market data of the attendees. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration area for registering visitor information, A face recognition unit recognizes the face of a visitor based on the information registered by the registration unit, A display unit that displays the visitor's information recognized by the facial recognition unit to the attendant, The system includes an information sharing unit that shares the information explicitly stated by the explicit unit with members of the operating secretariat. A system characterized by the following features.

2. The aforementioned registration unit is Register specific information about the visitor, including their photo, name, company name, job title, internal attendant, estimated arrival time, vehicle information, and secretary information. The system according to feature 1.

3. The aforementioned face recognition unit, The system uses cameras to recognize visitors' faces and compares them with pre-registered information. The system according to feature 1.

4. The aforementioned explicit section is, Provide the attendant with specific information about the arrival, including their name, company name, and job title. The system according to feature 1.

5. The aforementioned information sharing unit is: Share arrival information with the organizing committee members. The system according to feature 1.

6. The aforementioned information sharing unit is: Provide a list of those who have not yet arrived to the organizing committee members. The system according to feature 1.

7. The aforementioned information sharing unit is: The location information of the attendant will be tracked and provided to the organizing committee members. The system according to feature 1.

8. The aforementioned registration unit is The system estimates the emotions of visitors and adjusts the level of detail in registration information based on those estimated emotions. The system according to feature 1.

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