system

Abstract

The system according to this embodiment aims to enable exhibition visitors to efficiently navigate booths and obtain optimal information tailored to their specific needs. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a guidance unit. The collection unit collects information based on the visitor's interests and schedule. The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. The guidance unit guides the visitor based on the navigation plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for exhibition visitors to efficiently visit booths and obtain optimal information according to their purposes.

[0005] The system according to the embodiment aims to enable exhibition visitors to efficiently visit booths and obtain optimal information according to their purposes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and a guidance unit. The collection unit collects information based on the visitor's interests and schedule. The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. The guidance unit guides the visitor based on the navigation plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows exhibition visitors to efficiently navigate booths and obtain optimal information tailored to their needs. [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 manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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) An AI agent system according to an embodiment of the present invention is a system that helps exhibition visitors efficiently navigate booths and obtain optimal information tailored to their purpose. This AI agent system maximizes the exhibition experience by providing personalized navigation based on the visitor's interests and schedule. For example, the AI ​​agent system receives information about the visitor's interests and schedule. For instance, it receives information such as the areas the visitor is interested in, the booths they want to visit, and the times of lectures and demonstrations they want to attend. This information is analyzed by the AI ​​agent system, and an optimal navigation plan is generated for the visitor. Next, the AI ​​agent system provides personalized navigation based on the visitor's interests and schedule. For example, it guides the visitor along the optimal route, tailored to the location of booths they are interested in and the times of lectures and demonstrations they want to attend. It also considers factors such as crowd levels and reservation status to support an efficient exhibition experience. Furthermore, the AI ​​agent system also helps visitors organize the information they obtain at the exhibition and streamlines the review process. For example, it records information about the booths visited and lectures attended by the visitor, making it easy to refer to later. This mechanism allows visitors to efficiently navigate the exhibition and obtain optimal information tailored to their purpose. Furthermore, exhibitors and organizers can also improve the efficiency of matching with participants and providing information by utilizing the AI ​​agent system, thereby enhancing the overall value of the event. For example, exhibitors can use the AI ​​agent system to provide appropriate information to participants with potential interest, increasing business opportunities. Organizers can also use the AI ​​agent system to smoothly manage the event and increase participant and exhibitor satisfaction. In this way, the AI ​​agent system provides a "win-win-win" AI platform that supports participants, exhibitors, and organizers, realizing a better exhibition experience. As a result, the AI ​​agent system can help exhibition visitors efficiently navigate booths and obtain the most suitable information according to their objectives.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a generation unit, and a guidance unit. The collection unit collects information based on the visitor's interests and schedule. For example, the collection unit collects information such as the fields the visitor is interested in, the booths they want to visit, and the times of lectures or demonstrations they want to attend. For example, the collection unit can prioritize the collection of information on fields the visitor is interested in. The collection unit can also collect information on events the visitor can attend according to their schedule. Furthermore, the collection unit can collect the most suitable information considering the visitor's areas of interest and schedule. The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. For example, the generation unit can generate the optimal route for the visitor based on the collected information. Furthermore, the generation unit can generate a personalized navigation plan based on the visitor's interests and schedule. Furthermore, the generation unit can also generate an efficient navigation plan considering factors such as congestion and reservation status. The guidance unit guides the visitor based on the navigation plan generated by the generation unit. The information desk can, for example, guide visitors to the optimal route based on the location of booths they are interested in or the times of lectures and demonstrations they wish to attend. The information desk can also support an efficient exhibition experience by considering factors such as crowd levels and reservation status. Furthermore, the information desk can help visitors organize the information they gather at the exhibition, streamlining their review process. As a result, the AI ​​agent system according to this embodiment can efficiently navigate the exhibition based on visitors' interests and schedules, and obtain the most relevant information.

[0030] The information gathering team collects information based on visitors' interests and schedules. Specifically, it collects information such as areas of interest registered in advance by visitors, booths they wish to visit, and times for lectures and demonstrations they wish to attend. For example, if a visitor uses the exhibition's official app to input their areas of interest and schedule, the information gathering team can prioritize collecting information on relevant booths and events based on that information. Furthermore, the information gathering team can also collect real-time visitor behavior data. For example, it can collect data on booths visited and lectures attended by visitors to understand changes in visitors' interests and new interests. The information gathering team can also collect information on events that visitors can attend according to their schedules. For example, if a visitor is free during a specific time slot, it can collect information on events and demonstrations held during that time and provide it to the visitor. In addition, the information gathering team can collect the most optimal information considering the visitor's areas of interest and schedule. For example, if booths in an area of ​​interest to a visitor are crowded, the information gathering team can collect that information and predict times when the crowds will subside. This allows the information gathering team to efficiently collect and provide the most useful information to visitors.

[0031] The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. Specifically, it can generate the best route for visitors based on the collected information. For example, it analyzes the location information of booths that visitors are interested in and lectures they want to attend, and calculates the shortest route and the most efficient order of visits. The generation unit can also generate personalized navigation plans based on visitors' interests and schedules. For example, if a visitor wants to attend a lecture at a specific time, it can optimize the order of booths to visit before and after that lecture. Furthermore, the generation unit can generate efficient navigation plans by considering factors such as congestion and reservation status. For example, if a particular booth is crowded, the generation unit can use that information to adjust the navigation plan so that visitors can visit during less crowded times. In addition, the generation unit can analyze visitors' past behavior data and the behavior patterns of other visitors to generate more accurate navigation plans. As a result, the generation unit can provide visitors with the most efficient and comfortable exhibition experience.

[0032] The guidance unit directs visitors based on navigation plans generated by the generation unit. Specifically, it can guide visitors along the optimal route based on the location of booths they are interested in and the times of lectures and demonstrations they wish to attend. For example, when visitors move within the exhibition hall, the guidance unit uses real-time updated map information to guide them along the shortest route or routes that avoid congestion. The guidance unit can also support an efficient exhibition experience by considering congestion levels and reservation status. For example, if a particular booth is crowded, the guidance unit can use this information to guide visitors to visit during less crowded times. Furthermore, the guidance unit can help visitors organize the information they gained at the exhibition and streamline their review process. For example, it can automatically record information about booths visited and lectures attended by visitors, making it easy to review later. The guidance unit can also collect visitor feedback and continuously improve the accuracy and effectiveness of the guidance. For example, it can improve the accuracy of navigation plans based on the results of visitors following the guidance. In this way, the guidance unit can provide visitors with quick and reliable guidance, optimizing their exhibition experience.

[0033] The organization unit can organize visitor information and streamline the review process. For example, the organization unit can record information about booths visited and lectures attended by visitors, making it easy to refer to later. For example, the organization unit can categorize and organize information about booths visited by visitors. It can also tag and organize information about lectures attended by visitors. Furthermore, the organization unit can organize the information obtained by visitors in chronological order. This allows visitors to efficiently organize the information they obtained at the exhibition and easily refer to it later. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the organization unit can input the information obtained by visitors into a generative AI and have the generative AI perform the organization of the information.

[0034] The support department can support exhibitors or organizers. For example, the support department can help exhibitors provide appropriate information to potential interested participants. For example, the support department can help exhibitors match with participants. The support department can also help organizers run the event smoothly. Furthermore, the support department can help organizers increase participant and exhibitor satisfaction. This allows exhibitors and organizers to improve the efficiency of participant matching and information provision. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input information provided by exhibitors into a generative AI and have the generative AI perform the information provision.

[0035] The data collection unit can analyze visitors' past exhibition visit history and select the most suitable information collection method. For example, the data collection unit can prioritize collecting information on relevant booths based on information about booths the visitor has visited in the past. For example, the data collection unit can collect information on relevant events based on information about lectures and demonstrations the visitor has attended in the past. The data collection unit can also collect information on new booths that might be of interest to the visitor based on their past visit history. This enables efficient information provision by prioritizing the collection of relevant information based on past visit history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's past visit history data into a generative AI and have the generative AI select the information collection method.

[0036] The data collection unit can filter information based on the visitor's current areas of interest and schedule during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that the visitor is currently interested in. For example, the data collection unit can filter and provide information on events that the visitor can attend according to their schedule. The data collection unit can also select and provide the most relevant information, taking into account the visitor's areas of interest and schedule. This allows the system to provide the most relevant information based on the visitor's areas of interest and schedule. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input visitor's areas of interest and schedule data into a generative AI and have the generative AI perform the information filtering.

[0037] The data collection unit can prioritize collecting highly relevant information by considering the visitor's geographical location during information gathering. For example, the data collection unit can prioritize collecting information about relevant booths near the booth the visitor is currently at. For example, the data collection unit can prioritize collecting information related to the area the visitor is moving through. The data collection unit can also collect information related to the area the visitor plans to visit in advance. This allows the system to provide highly relevant information by considering the visitor's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's geographical location information into a generative AI and have the generative AI collect highly relevant information.

[0038] The data collection unit can analyze visitors' social media activity and collect relevant information during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that visitors have shown on social media. For example, the data collection unit can collect information on relevant booths and events based on information about accounts that visitors follow. The data collection unit can also analyze visitors' social media activity history and collect information that they might be interested in. This allows the system to provide visitors with information tailored to their interests by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input visitors' social media activity data into a generative AI and have the generative AI collect relevant information.

[0039] The generation unit can adjust the level of detail in the navigation plan based on the visitor's level of interest when generating the plan. For example, the generation unit can provide a detailed navigation plan for areas of high interest to the visitor. For example, the generation unit can provide a simplified navigation plan for areas of low interest to the visitor. The generation unit can also dynamically adjust the level of detail in the navigation plan according to the visitor's level of interest. This allows for more appropriate guidance by adjusting the level of detail in the navigation plan according to the visitor's level of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input visitor interest data into the generation AI and have the generation AI perform the adjustment of the level of detail in the navigation plan.

[0040] The generation unit can apply different generation algorithms depending on the visitor's schedule when generating a navigation plan. For example, if a visitor wants to efficiently navigate within a limited time, the generation unit can apply a generation algorithm that prioritizes the shortest route. If a visitor wants to explore at a leisurely pace, the generation unit can apply a generation algorithm that prioritizes booths of interest. The generation unit can also dynamically select the algorithm that generates the optimal navigation plan according to the visitor's schedule. This allows the generation unit to provide the optimal navigation plan according to the visitor's schedule. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input visitor schedule data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0041] The generation unit can determine the priority of navigation plans based on the visitor's visit timing when generating them. For example, if a visitor visits on the first day of the exhibition, the generation unit can provide a navigation plan that prioritizes major booths. For example, if a visitor visits on the last day of the exhibition, the generation unit can provide a navigation plan that avoids congestion. The generation unit can also dynamically generate the optimal navigation plan according to the visitor's visit timing. This allows the generation unit to provide the optimal navigation plan according to the visitor's visit timing. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input visitor visit timing data into the generation AI and have the generation AI perform the determination of plan priorities.

[0042] The generation unit can adjust the order of navigation plans based on visitor relevance when generating them. For example, the generation unit can provide a navigation plan that prioritizes visits to booths of high interest to the visitor. For example, the generation unit can adjust the order of visits to match the times of lectures and demonstrations that the visitor wishes to attend. The generation unit can also dynamically adjust the optimal order of visits based on the visitor's areas of interest. This allows the generation unit to provide an optimal order of visits based on visitor relevance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input visitor relevance data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0043] The guidance system can select the most suitable guidance method by referring to the visitor's past visit history. For example, the guidance system can prioritize guiding visitors to relevant booths based on information about booths they have visited in the past. For example, the guidance system can prioritize guiding visitors to relevant events based on information about lectures and demonstrations they have attended in the past. The guidance system can also prioritize guiding visitors to new booths that are likely to be of interest to them based on their past visit history. This enables efficient guidance by prioritizing relevant guidance based on past visit history. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance system can input the visitor's past visit history data into a generative AI and have the generative AI select the guidance method.

[0044] The guidance unit can customize the guidance methods based on the visitor's current location information. For example, the guidance unit can prioritize guidance to relevant booths near the booth the visitor is currently in. For example, the guidance unit can prioritize guidance related to the area the visitor is moving through. The guidance unit can also provide guidance in advance related to the area the visitor plans to visit. This allows for the provision of optimal guidance based on the current location information. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the visitor's current location information into a generative AI and have the generative AI perform the customization of the guidance methods.

[0045] The guidance system can select the most appropriate guidance method when providing directions, taking into account the visitor's geographical location. For example, the guidance system may prioritize guiding visitors to related booths near the booth they are currently at. For example, it may prioritize guidance related to the area the visitor is currently moving through. The guidance system can also provide guidance in advance to areas the visitor plans to visit. This allows for more appropriate guidance by considering geographical location information. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance system can input the visitor's geographical location information into a generative AI and have the generative AI select the guidance method.

[0046] The information desk can analyze visitors' social media activity and suggest guidance methods when providing information. For example, the information desk can prioritize guidance on areas that visitors have shown interest in on social media. For example, the information desk can prioritize guidance on relevant booths and events based on information about accounts that visitors follow. The information desk can also analyze visitors' social media activity history and prioritize guidance that they are likely to be interested in. In this way, by analyzing social media activity, it becomes possible to provide guidance based on visitors' interests. Some or all of the above processing in the information desk may be performed using, for example, generative AI, or not using generative AI. For example, the information desk can input visitors' social media activity data into generative AI and have the generative AI suggest guidance methods.

[0047] The organization unit can select the optimal organization method by referring to the visitor's past visit history when organizing information. For example, the organization unit can prioritize organizing relevant information based on information about booths the visitor has visited in the past. For example, the organization unit can organize relevant information based on information about lectures and demonstrations the visitor has attended in the past. The organization unit can also organize new information that is likely to be of interest to the visitor based on their past visit history. This enables efficient information organization by prioritizing relevant information based on past visit history. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input the visitor's past visit history data into a generative AI and have the generative AI select the organization method.

[0048] The organization unit can select the optimal organization method when organizing information, taking into account the visitor's device information. For example, if a visitor is using a smartphone, the organization unit can provide an organization method that matches the screen size. For example, if a visitor is using a tablet, the organization unit can provide an organization method optimized for a larger screen. Furthermore, if a visitor is using a personal computer, the organization unit can provide a method for organizing detailed information. This allows for more appropriate information organization by considering device information. Some or all of the above processing in the organization unit may be performed using, for example, a generating AI, or without a generating AI. For example, the organization unit can input the visitor's device information into a generating AI and have the generating AI select an organization method.

[0049] The support department can select the most appropriate support method by referring to the visitor's past visit history during support sessions. For example, the support department can prioritize relevant support based on information about booths the visitor has previously visited. For example, the support department can prioritize relevant support based on information about lectures and demonstrations the visitor has previously attended. The support department can also prioritize new support that is likely to be of interest to the visitor based on their past visit history. This enables efficient support by prioritizing relevant support based on past visit history. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the visitor's past visit history data into a generative AI and have the generative AI select the appropriate support method.

[0050] The support department can select the most appropriate support method when providing support, taking into account the visitor's geographical location. For example, the support department may prioritize support for related booths near the visitor's current booth. For example, the support department may prioritize support related to the area the visitor is moving through. The support department can also provide support in advance for areas the visitor plans to visit. This allows for more appropriate support by considering geographical location. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input the visitor's geographical location information into a generative AI and have the generative AI select the support method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can analyze visitors' past exhibition visit history and select the most suitable information collection method. For example, the data collection unit can prioritize collecting information on relevant booths based on information about booths the visitor has visited in the past. For example, the data collection unit can collect information on relevant events based on information about lectures and demonstrations the visitor has attended in the past. The data collection unit can also collect information on new booths that might be of interest to the visitor based on their past visit history. This enables efficient information provision by prioritizing the collection of relevant information based on past visit history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's past visit history data into a generative AI and have the generative AI select the information collection method.

[0053] The data collection unit can filter information based on the visitor's current areas of interest and schedule during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that the visitor is currently interested in. For example, the data collection unit can filter and provide information on events that the visitor can attend according to their schedule. The data collection unit can also select and provide the most relevant information, taking into account the visitor's areas of interest and schedule. This allows the system to provide the most relevant information based on the visitor's areas of interest and schedule. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input visitor's areas of interest and schedule data into a generative AI and have the generative AI perform the information filtering.

[0054] The data collection unit can prioritize collecting highly relevant information by considering the visitor's geographical location during information gathering. For example, the data collection unit can prioritize collecting information about relevant booths near the booth the visitor is currently at. For example, the data collection unit can prioritize collecting information related to the area the visitor is moving through. The data collection unit can also collect information related to the area the visitor plans to visit in advance. This allows the system to provide highly relevant information by considering the visitor's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's geographical location information into a generative AI and have the generative AI collect highly relevant information.

[0055] The data collection unit can analyze visitors' social media activity and collect relevant information during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that visitors have shown on social media. For example, the data collection unit can collect information on relevant booths and events based on information about accounts that visitors follow. The data collection unit can also analyze visitors' social media activity history and collect information that they might be interested in. This allows the system to provide visitors with information tailored to their interests by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input visitors' social media activity data into a generative AI and have the generative AI collect relevant information.

[0056] The generation unit can apply different generation algorithms depending on the visitor's schedule when generating a navigation plan. For example, if a visitor wants to efficiently navigate within a limited time, the generation unit can apply a generation algorithm that prioritizes the shortest route. If a visitor wants to explore at a leisurely pace, the generation unit can apply a generation algorithm that prioritizes booths of interest. The generation unit can also dynamically select the algorithm that generates the optimal navigation plan according to the visitor's schedule. This allows the generation unit to provide the optimal navigation plan according to the visitor's schedule. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input visitor schedule data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0057] The information desk can analyze visitors' social media activity and suggest guidance methods when providing information. For example, the information desk can prioritize guidance on areas that visitors have shown interest in on social media. For example, the information desk can prioritize guidance on relevant booths and events based on information about accounts that visitors follow. The information desk can also analyze visitors' social media activity history and prioritize guidance that they are likely to be interested in. In this way, by analyzing social media activity, it becomes possible to provide guidance based on visitors' interests. Some or all of the above processing in the information desk may be performed using, for example, generative AI, or not using generative AI. For example, the information desk can input visitors' social media activity data into generative AI and have the generative AI suggest guidance methods.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The information gathering team collects information based on visitors' interests and schedules. For example, they collect information on areas of interest, booths visitors want to visit, and times for lectures and demonstrations visitors want to attend. The information gathering team can prioritize collecting information on visitors' areas of interest and collect information on events that visitors can attend according to their schedules. Furthermore, the information gathering team can also collect the most relevant information by considering visitors' areas of interest and schedules. Step 2: The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. For example, based on the collected information, it generates the optimal route for visitors and a personalized navigation plan based on the visitor's interests and schedule. Furthermore, the generation unit can also generate an efficient navigation plan by taking into account factors such as congestion and reservation status. Step 3: The guidance unit guides visitors based on the navigation plan generated by the generation unit. For example, it guides visitors to the optimal route based on the location of booths they are interested in and the times of lectures and demonstrations they wish to attend. Furthermore, the guidance unit can also support an efficient exhibition experience by considering factors such as crowd levels and reservation status, and can help visitors organize the information they gained at the exhibition and streamline their review process.

[0060] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that helps exhibition visitors efficiently navigate booths and obtain optimal information tailored to their purpose. This AI agent system maximizes the exhibition experience by providing personalized navigation based on the visitor's interests and schedule. For example, the AI ​​agent system receives information about the visitor's interests and schedule. For instance, it receives information such as the areas the visitor is interested in, the booths they want to visit, and the times of lectures and demonstrations they want to attend. This information is analyzed by the AI ​​agent system, and an optimal navigation plan is generated for the visitor. Next, the AI ​​agent system provides personalized navigation based on the visitor's interests and schedule. For example, it guides the visitor along the optimal route, tailored to the location of booths they are interested in and the times of lectures and demonstrations they want to attend. It also considers factors such as crowd levels and reservation status to support an efficient exhibition experience. Furthermore, the AI ​​agent system also helps visitors organize the information they obtain at the exhibition and streamlines the review process. For example, it records information about the booths visited and lectures attended by the visitor, making it easy to refer to later. This mechanism allows visitors to efficiently navigate the exhibition and obtain optimal information tailored to their purpose. Furthermore, exhibitors and organizers can also improve the efficiency of matching with participants and providing information by utilizing the AI ​​agent system, thereby enhancing the overall value of the event. For example, exhibitors can use the AI ​​agent system to provide appropriate information to participants with potential interest, increasing business opportunities. Organizers can also use the AI ​​agent system to smoothly manage the event and increase participant and exhibitor satisfaction. In this way, the AI ​​agent system provides a "win-win-win" AI platform that supports participants, exhibitors, and organizers, realizing a better exhibition experience. As a result, the AI ​​agent system can help exhibition visitors efficiently navigate booths and obtain the most suitable information according to their objectives.

[0061] The AI ​​agent system according to this embodiment comprises a collection unit, a generation unit, and a guidance unit. The collection unit collects information based on the visitor's interests and schedule. For example, the collection unit collects information such as the fields the visitor is interested in, the booths they want to visit, and the times of lectures or demonstrations they want to attend. For example, the collection unit can prioritize the collection of information on fields the visitor is interested in. The collection unit can also collect information on events the visitor can attend according to their schedule. Furthermore, the collection unit can collect the most suitable information considering the visitor's areas of interest and schedule. The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. For example, the generation unit can generate the optimal route for the visitor based on the collected information. Furthermore, the generation unit can generate a personalized navigation plan based on the visitor's interests and schedule. Furthermore, the generation unit can also generate an efficient navigation plan considering factors such as congestion and reservation status. The guidance unit guides the visitor based on the navigation plan generated by the generation unit. The information desk can, for example, guide visitors to the optimal route based on the location of booths they are interested in or the times of lectures and demonstrations they wish to attend. The information desk can also support an efficient exhibition experience by considering factors such as crowd levels and reservation status. Furthermore, the information desk can help visitors organize the information they gather at the exhibition, streamlining their review process. As a result, the AI ​​agent system according to this embodiment can efficiently navigate the exhibition based on visitors' interests and schedules, and obtain the most relevant information.

[0062] The information gathering team collects information based on visitors' interests and schedules. Specifically, it collects information such as areas of interest registered in advance by visitors, booths they wish to visit, and times for lectures and demonstrations they wish to attend. For example, if a visitor uses the exhibition's official app to input their areas of interest and schedule, the information gathering team can prioritize collecting information on relevant booths and events based on that information. Furthermore, the information gathering team can also collect real-time visitor behavior data. For example, it can collect data on booths visited and lectures attended by visitors to understand changes in visitors' interests and new interests. The information gathering team can also collect information on events that visitors can attend according to their schedules. For example, if a visitor is free during a specific time slot, it can collect information on events and demonstrations held during that time and provide it to the visitor. In addition, the information gathering team can collect the most optimal information considering the visitor's areas of interest and schedule. For example, if booths in an area of ​​interest to a visitor are crowded, the information gathering team can collect that information and predict times when the crowds will subside. This allows the information gathering team to efficiently collect and provide the most useful information to visitors.

[0063] The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. Specifically, it can generate the best route for visitors based on the collected information. For example, it analyzes the location information of booths that visitors are interested in and lectures they want to attend, and calculates the shortest route and the most efficient order of visits. The generation unit can also generate personalized navigation plans based on visitors' interests and schedules. For example, if a visitor wants to attend a lecture at a specific time, it can optimize the order of booths to visit before and after that lecture. Furthermore, the generation unit can generate efficient navigation plans by considering factors such as congestion and reservation status. For example, if a particular booth is crowded, the generation unit can use that information to adjust the navigation plan so that visitors can visit during less crowded times. In addition, the generation unit can analyze visitors' past behavior data and the behavior patterns of other visitors to generate more accurate navigation plans. As a result, the generation unit can provide visitors with the most efficient and comfortable exhibition experience.

[0064] The guidance unit directs visitors based on navigation plans generated by the generation unit. Specifically, it can guide visitors along the optimal route based on the location of booths they are interested in and the times of lectures and demonstrations they wish to attend. For example, when visitors move within the exhibition hall, the guidance unit uses real-time updated map information to guide them along the shortest route or routes that avoid congestion. The guidance unit can also support an efficient exhibition experience by considering congestion levels and reservation status. For example, if a particular booth is crowded, the guidance unit can use this information to guide visitors to visit during less crowded times. Furthermore, the guidance unit can help visitors organize the information they gained at the exhibition and streamline their review process. For example, it can automatically record information about booths visited and lectures attended by visitors, making it easy to review later. The guidance unit can also collect visitor feedback and continuously improve the accuracy and effectiveness of the guidance. For example, it can improve the accuracy of navigation plans based on the results of visitors following the guidance. In this way, the guidance unit can provide visitors with quick and reliable guidance, optimizing their exhibition experience.

[0065] The organization unit can organize visitor information and streamline the review process. For example, the organization unit can record information about booths visited and lectures attended by visitors, making it easy to refer to later. For example, the organization unit can categorize and organize information about booths visited by visitors. It can also tag and organize information about lectures attended by visitors. Furthermore, the organization unit can organize the information obtained by visitors in chronological order. This allows visitors to efficiently organize the information they obtained at the exhibition and easily refer to it later. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the organization unit can input the information obtained by visitors into a generative AI and have the generative AI perform the organization of the information.

[0066] The support department can support exhibitors or organizers. For example, the support department can help exhibitors provide appropriate information to potential interested participants. For example, the support department can help exhibitors match with participants. The support department can also help organizers run the event smoothly. Furthermore, the support department can help organizers increase participant and exhibitor satisfaction. This allows exhibitors and organizers to improve the efficiency of participant matching and information provision. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input information provided by exhibitors into a generative AI and have the generative AI perform the information provision.

[0067] The data collection unit can estimate the emotions of visitors and adjust the timing of information collection based on the estimated emotions. For example, if a visitor is excited, the data collection unit can speed up the timing of information collection and quickly provide information on booths that interest them. For example, if a visitor is tired, the data collection unit can delay the timing of information collection and provide information after a break. Also, if a visitor is relaxed, the data collection unit can appropriately adjust the timing of information collection and provide information at a comfortable pace. In this way, by adjusting the timing of information collection according to the emotions of visitors, more appropriate information can be provided. 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 data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input visitor emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can analyze visitors' past exhibition visit history and select the most suitable information collection method. For example, the data collection unit can prioritize collecting information on relevant booths based on information about booths the visitor has visited in the past. For example, the data collection unit can collect information on relevant events based on information about lectures and demonstrations the visitor has attended in the past. The data collection unit can also collect information on new booths that might be of interest to the visitor based on their past visit history. This enables efficient information provision by prioritizing the collection of relevant information based on past visit history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's past visit history data into a generative AI and have the generative AI select the information collection method.

[0069] The data collection unit can filter information based on the visitor's current areas of interest and schedule during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that the visitor is currently interested in. For example, the data collection unit can filter and provide information on events that the visitor can attend according to their schedule. The data collection unit can also select and provide the most relevant information, taking into account the visitor's areas of interest and schedule. This allows the system to provide the most relevant information based on the visitor's areas of interest and schedule. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input visitor's areas of interest and schedule data into a generative AI and have the generative AI perform the information filtering.

[0070] The data collection unit can estimate the emotions of visitors and determine the priority of information to collect based on the estimated emotions. For example, if a visitor is excited, the data collection unit will prioritize collecting information in areas of interest. If a visitor is tired, the data collection unit can prioritize collecting information on relaxing events and booths. If a visitor is relaxed, the data collection unit can also collect information in a balanced manner across a wide range of fields. This allows for the provision of more appropriate information by prioritizing 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-described processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input visitor emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can prioritize collecting highly relevant information by considering the visitor's geographical location during information gathering. For example, the data collection unit can prioritize collecting information about relevant booths near the booth the visitor is currently at. For example, the data collection unit can prioritize collecting information related to the area the visitor is moving through. The data collection unit can also collect information related to the area the visitor plans to visit in advance. This allows the system to provide highly relevant information by considering the visitor's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's geographical location information into a generative AI and have the generative AI collect highly relevant information.

[0072] The data collection unit can analyze visitors' social media activity and collect relevant information during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that visitors have shown on social media. For example, the data collection unit can collect information on relevant booths and events based on information about accounts that visitors follow. The data collection unit can also analyze visitors' social media activity history and collect information that they might be interested in. This allows the system to provide visitors with information tailored to their interests by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input visitors' social media activity data into a generative AI and have the generative AI collect relevant information.

[0073] The generation unit can estimate the visitor's emotions and adjust the presentation of the navigation plan based on the estimated emotions. For example, if the visitor is excited, the generation unit can provide a visually stimulating navigation plan. If the visitor is relaxed, the generation unit can provide a calmly designed navigation plan. Furthermore, if the visitor is tired, the generation unit can provide a simple and easy-to-understand navigation plan. By adjusting the presentation of the navigation plan according to the visitor's emotions, more appropriate guidance becomes possible. 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 generation unit may be performed using a generative AI, or not. For example, the generation unit can input visitor emotion data into a generative AI and have the generative AI adjust the presentation of the navigation plan.

[0074] The generation unit can adjust the level of detail in the navigation plan based on the visitor's level of interest when generating the plan. For example, the generation unit can provide a detailed navigation plan for areas of high interest to the visitor. For example, the generation unit can provide a simplified navigation plan for areas of low interest to the visitor. The generation unit can also dynamically adjust the level of detail in the navigation plan according to the visitor's level of interest. This allows for more appropriate guidance by adjusting the level of detail in the navigation plan according to the visitor's level of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input visitor interest data into the generation AI and have the generation AI perform the adjustment of the level of detail in the navigation plan.

[0075] The generation unit can apply different generation algorithms depending on the visitor's schedule when generating a navigation plan. For example, if a visitor wants to efficiently navigate within a limited time, the generation unit can apply a generation algorithm that prioritizes the shortest route. If a visitor wants to explore at a leisurely pace, the generation unit can apply a generation algorithm that prioritizes booths of interest. The generation unit can also dynamically select the algorithm that generates the optimal navigation plan according to the visitor's schedule. This allows the generation unit to provide the optimal navigation plan according to the visitor's schedule. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input visitor schedule data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0076] The generation unit can estimate the visitor's emotions and adjust the length of the navigation plan based on the estimated emotions. For example, if the visitor is excited, the generation unit can provide a longer navigation plan. For example, if the visitor is tired, the generation unit can provide a shorter navigation plan. The generation unit can also provide a navigation plan of appropriate length if the visitor is relaxed. By adjusting the length of the navigation plan according to the visitor's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input visitor emotion data into a generation AI and have the generation AI adjust the length of the navigation plan.

[0077] The generation unit can determine the priority of navigation plans based on the visitor's visit timing when generating them. For example, if a visitor visits on the first day of the exhibition, the generation unit can provide a navigation plan that prioritizes major booths. For example, if a visitor visits on the last day of the exhibition, the generation unit can provide a navigation plan that avoids congestion. The generation unit can also dynamically generate the optimal navigation plan according to the visitor's visit timing. This allows the generation unit to provide the optimal navigation plan according to the visitor's visit timing. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input visitor visit timing data into the generation AI and have the generation AI perform the determination of plan priorities.

[0078] The generation unit can adjust the order of navigation plans based on visitor relevance when generating them. For example, the generation unit can provide a navigation plan that prioritizes visits to booths of high interest to the visitor. For example, the generation unit can adjust the order of visits to match the times of lectures and demonstrations that the visitor wishes to attend. The generation unit can also dynamically adjust the optimal order of visits based on the visitor's areas of interest. This allows the generation unit to provide an optimal order of visits based on visitor relevance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input visitor relevance data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0079] The guidance system can estimate the visitor's emotions and adjust the guidance method based on the estimated emotions. For example, if a visitor is excited, the guidance system can provide a visually stimulating guidance method. If a visitor is relaxed, the guidance system can provide a calmly designed guidance method. Furthermore, if a visitor is tired, the guidance system can provide a simple and easy-to-understand guidance method. By adjusting the guidance method according to the visitor's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using a generative AI, or not. For example, the guidance system can input visitor emotion data into a generative AI and have the generative AI adjust the guidance method.

[0080] The guidance system can select the most suitable guidance method by referring to the visitor's past visit history. For example, the guidance system can prioritize guiding visitors to relevant booths based on information about booths they have visited in the past. For example, the guidance system can prioritize guiding visitors to relevant events based on information about lectures and demonstrations they have attended in the past. The guidance system can also prioritize guiding visitors to new booths that are likely to be of interest to them based on their past visit history. This enables efficient guidance by prioritizing relevant guidance based on past visit history. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance system can input the visitor's past visit history data into a generative AI and have the generative AI select the guidance method.

[0081] The guidance unit can customize the guidance methods based on the visitor's current location information. For example, the guidance unit can prioritize guidance to relevant booths near the booth the visitor is currently in. For example, the guidance unit can prioritize guidance related to the area the visitor is moving through. The guidance unit can also provide guidance in advance related to the area the visitor plans to visit. This allows for the provision of optimal guidance based on the current location information. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the visitor's current location information into a generative AI and have the generative AI perform the customization of the guidance methods.

[0082] The information desk can estimate visitors' emotions and determine the priority of guidance based on those emotions. For example, if a visitor is excited, the information desk can prioritize guidance in areas of interest. If a visitor is tired, the information desk can prioritize guidance to relaxing events or booths. Alternatively, if a visitor is relaxed, the information desk can provide a balanced range of guidance across various fields. By prioritizing guidance according to the visitor's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information desk may be performed using generative AI, or not. For example, the information desk can input visitor emotion data into a generative AI and have the generative AI determine the priority of guidance.

[0083] The guidance system can select the most appropriate guidance method when providing directions, taking into account the visitor's geographical location. For example, the guidance system may prioritize guiding visitors to related booths near the booth they are currently at. For example, it may prioritize guidance related to the area the visitor is currently moving through. The guidance system can also provide guidance in advance to areas the visitor plans to visit. This allows for more appropriate guidance by considering geographical location information. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance system can input the visitor's geographical location information into a generative AI and have the generative AI select the guidance method.

[0084] The information desk can analyze visitors' social media activity and suggest guidance methods when providing information. For example, the information desk can prioritize guidance on areas that visitors have shown interest in on social media. For example, the information desk can prioritize guidance on relevant booths and events based on information about accounts that visitors follow. The information desk can also analyze visitors' social media activity history and prioritize guidance that they are likely to be interested in. In this way, by analyzing social media activity, it becomes possible to provide guidance based on visitors' interests. Some or all of the above processing in the information desk may be performed using, for example, generative AI, or not using generative AI. For example, the information desk can input visitors' social media activity data into generative AI and have the generative AI suggest guidance methods.

[0085] The information organization unit can estimate the visitor's emotions and adjust the information organization method based on the estimated visitor's emotions. For example, if a visitor is excited, the information organization unit can provide a visually stimulating organization method. For example, if a visitor is relaxed, the information organization unit can provide a calmly designed organization method. Furthermore, if a visitor is tired, the information organization unit can provide a simple and easy-to-understand organization method. This allows for more appropriate information organization by adjusting the information organization method 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 information organization unit may be performed using a generative AI, or not. For example, the information organization unit can input visitor emotion data into a generative AI and have the generative AI adjust the information organization method.

[0086] The organization unit can select the optimal organization method by referring to the visitor's past visit history when organizing information. For example, the organization unit can prioritize organizing relevant information based on information about booths the visitor has visited in the past. For example, the organization unit can organize relevant information based on information about lectures and demonstrations the visitor has attended in the past. The organization unit can also organize new information that is likely to be of interest to the visitor based on their past visit history. This enables efficient information organization by prioritizing relevant information based on past visit history. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input the visitor's past visit history data into a generative AI and have the generative AI select the organization method.

[0087] The information processing unit can estimate the visitor's emotions and determine the priority of information organization based on the estimated visitor's emotions. For example, if a visitor is excited, the information processing unit will prioritize information in areas of interest. For example, if a visitor is tired, the information processing unit can prioritize information that helps them relax. Also, if a visitor is relaxed, the information processing unit can organize information from a wide range of fields in a balanced manner. This allows for more appropriate information organization by determining the priority of information organization 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 information processing unit may be performed using a generative AI, or not. For example, the information processing unit can input visitor emotion data into a generative AI and have the generative AI determine the priority of information organization.

[0088] The organization unit can select the optimal organization method when organizing information, taking into account the visitor's device information. For example, if a visitor is using a smartphone, the organization unit can provide an organization method that matches the screen size. For example, if a visitor is using a tablet, the organization unit can provide an organization method optimized for a larger screen. Furthermore, if a visitor is using a personal computer, the organization unit can provide a method for organizing detailed information. This allows for more appropriate information organization by considering device information. Some or all of the above processing in the organization unit may be performed using, for example, a generating AI, or without a generating AI. For example, the organization unit can input the visitor's device information into a generating AI and have the generating AI select an organization method.

[0089] The support unit can estimate the visitor's emotions and adjust its support methods based on the estimated emotions. For example, if a visitor is excited, the support unit can provide visually stimulating support methods. If a visitor is relaxed, the support unit can provide support methods with a calming design. Furthermore, if a visitor is tired, the support unit can provide simple and easy-to-understand support methods. By adjusting the support methods according to the visitor's emotions, more appropriate support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, the support unit can input visitor emotion data into the generative AI and have the generative AI adjust the support methods.

[0090] The support department can select the most appropriate support method by referring to the visitor's past visit history during support sessions. For example, the support department can prioritize relevant support based on information about booths the visitor has previously visited. For example, the support department can prioritize relevant support based on information about lectures and demonstrations the visitor has previously attended. The support department can also prioritize new support that is likely to be of interest to the visitor based on their past visit history. This enables efficient support by prioritizing relevant support based on past visit history. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the visitor's past visit history data into a generative AI and have the generative AI select the appropriate support method.

[0091] The support unit can estimate the emotions of visitors and determine the priority of support based on the estimated emotions. For example, if a visitor is excited, the support unit can prioritize support in areas of interest. If a visitor is tired, the support unit can prioritize support that helps them relax. Alternatively, if a visitor is relaxed, the support unit can provide a balanced range of support across various areas. This allows for more appropriate support by prioritizing support 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 support unit may be performed using, for example, generative AI, or not. For example, the support unit can input visitor emotion data into a generative AI and have the generative AI determine the priority of support.

[0092] The support department can select the most appropriate support method when providing support, taking into account the visitor's geographical location. For example, the support department may prioritize support for related booths near the visitor's current booth. For example, the support department may prioritize support related to the area the visitor is moving through. The support department can also provide support in advance for areas the visitor plans to visit. This allows for more appropriate support by considering geographical location. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input the visitor's geographical location information into a generative AI and have the generative AI select the support method.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The data collection unit can analyze visitors' past exhibition visit history and select the most suitable information collection method. For example, the data collection unit can prioritize collecting information on relevant booths based on information about booths the visitor has visited in the past. For example, the data collection unit can collect information on relevant events based on information about lectures and demonstrations the visitor has attended in the past. The data collection unit can also collect information on new booths that might be of interest to the visitor based on their past visit history. This enables efficient information provision by prioritizing the collection of relevant information based on past visit history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's past visit history data into a generative AI and have the generative AI select the information collection method.

[0095] The generation unit can estimate the visitor's emotions and adjust the presentation of the navigation plan based on the estimated emotions. For example, if the visitor is excited, the generation unit can provide a visually stimulating navigation plan. If the visitor is relaxed, the generation unit can provide a calmly designed navigation plan. Furthermore, if the visitor is tired, the generation unit can provide a simple and easy-to-understand navigation plan. By adjusting the presentation of the navigation plan according to the visitor's emotions, more appropriate guidance becomes possible. 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 generation unit may be performed using a generative AI, or not. For example, the generation unit can input visitor emotion data into a generative AI and have the generative AI adjust the presentation of the navigation plan.

[0096] The guidance system can estimate the visitor's emotions and adjust the guidance method based on the estimated emotions. For example, if a visitor is excited, the guidance system can provide a visually stimulating guidance method. If a visitor is relaxed, the guidance system can provide a calmly designed guidance method. Furthermore, if a visitor is tired, the guidance system can provide a simple and easy-to-understand guidance method. By adjusting the guidance method according to the visitor's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using a generative AI, or not. For example, the guidance system can input visitor emotion data into a generative AI and have the generative AI adjust the guidance method.

[0097] The information organization unit can estimate the visitor's emotions and adjust the information organization method based on the estimated visitor's emotions. For example, if a visitor is excited, the information organization unit can provide a visually stimulating organization method. For example, if a visitor is relaxed, the information organization unit can provide a calmly designed organization method. Furthermore, if a visitor is tired, the information organization unit can provide a simple and easy-to-understand organization method. This allows for more appropriate information organization by adjusting the information organization method 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 information organization unit may be performed using a generative AI, or not. For example, the information organization unit can input visitor emotion data into a generative AI and have the generative AI adjust the information organization method.

[0098] The support unit can estimate the visitor's emotions and adjust its support methods based on the estimated emotions. For example, if a visitor is excited, the support unit can provide visually stimulating support methods. If a visitor is relaxed, the support unit can provide support methods with a calming design. Furthermore, if a visitor is tired, the support unit can provide simple and easy-to-understand support methods. By adjusting the support methods according to the visitor's emotions, more appropriate support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, the support unit can input visitor emotion data into the generative AI and have the generative AI adjust the support methods.

[0099] The data collection unit can filter information based on the visitor's current areas of interest and schedule during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that the visitor is currently interested in. For example, the data collection unit can filter and provide information on events that the visitor can attend according to their schedule. The data collection unit can also select and provide the most relevant information, taking into account the visitor's areas of interest and schedule. This allows the system to provide the most relevant information based on the visitor's areas of interest and schedule. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input visitor's areas of interest and schedule data into a generative AI and have the generative AI perform the information filtering.

[0100] The data collection unit can prioritize collecting highly relevant information by considering the visitor's geographical location during information gathering. For example, the data collection unit can prioritize collecting information about relevant booths near the booth the visitor is currently at. For example, the data collection unit can prioritize collecting information related to the area the visitor is moving through. The data collection unit can also collect information related to the area the visitor plans to visit in advance. This allows the system to provide highly relevant information by considering the visitor's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the visitor's geographical location information into a generative AI and have the generative AI collect highly relevant information.

[0101] The data collection unit can analyze visitors' social media activity and collect relevant information during the information gathering process. For example, the data collection unit can prioritize collecting information on areas of interest that visitors have shown on social media. For example, the data collection unit can collect information on relevant booths and events based on information about accounts that visitors follow. The data collection unit can also analyze visitors' social media activity history and collect information that they might be interested in. This allows the system to provide visitors with information tailored to their interests by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input visitors' social media activity data into a generative AI and have the generative AI collect relevant information.

[0102] The generation unit can apply different generation algorithms depending on the visitor's schedule when generating a navigation plan. For example, if a visitor wants to efficiently navigate within a limited time, the generation unit can apply a generation algorithm that prioritizes the shortest route. If a visitor wants to explore at a leisurely pace, the generation unit can apply a generation algorithm that prioritizes booths of interest. The generation unit can also dynamically select the algorithm that generates the optimal navigation plan according to the visitor's schedule. This allows the generation unit to provide the optimal navigation plan according to the visitor's schedule. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input visitor schedule data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0103] The information desk can analyze visitors' social media activity and suggest guidance methods when providing information. For example, the information desk can prioritize guidance on areas that visitors have shown interest in on social media. For example, the information desk can prioritize guidance on relevant booths and events based on information about accounts that visitors follow. The information desk can also analyze visitors' social media activity history and prioritize guidance that they are likely to be interested in. In this way, by analyzing social media activity, it becomes possible to provide guidance based on visitors' interests. Some or all of the above processing in the information desk may be performed using, for example, generative AI, or not using generative AI. For example, the information desk can input visitors' social media activity data into generative AI and have the generative AI suggest guidance methods.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The information gathering team collects information based on visitors' interests and schedules. For example, they collect information on areas of interest, booths visitors want to visit, and times for lectures and demonstrations visitors want to attend. The information gathering team can prioritize collecting information on visitors' areas of interest and collect information on events that visitors can attend according to their schedules. Furthermore, the information gathering team can also collect the most relevant information by considering visitors' areas of interest and schedules. Step 2: The generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan. For example, based on the collected information, it generates the optimal route for visitors and a personalized navigation plan based on the visitor's interests and schedule. Furthermore, the generation unit can also generate an efficient navigation plan by taking into account factors such as congestion and reservation status. Step 3: The guidance unit guides visitors based on the navigation plan generated by the generation unit. For example, it guides visitors to the optimal route based on the location of booths they are interested in and the times of lectures and demonstrations they wish to attend. Furthermore, the guidance unit can also support an efficient exhibition experience by considering factors such as crowd levels and reservation status, and can help visitors organize the information they gained at the exhibition and streamline their review process.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the collection unit, generation unit, guidance unit, organization unit, and support unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information based on visitors' interests and schedules by the control unit 46A of the smart device 14. The generation unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 and generates an optimal navigation plan. The guidance unit guides visitors based on the navigation plan generated by the control unit 46A of the smart device 14. The organization unit organizes visitor information by the specific processing unit 290 of the data processing unit 12 to streamline the review process. The support unit supports exhibitors and organizers by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the collection unit, generation unit, guidance unit, organization unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information based on visitors' interests and schedules using the control unit 46A of the smart glasses 214. The generation unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12 and generates an optimal navigation plan. The guidance unit guides visitors based on the navigation plan generated by the control unit 46A of the smart glasses 214. The organization unit organizes visitor information using the specific processing unit 290 of the data processing unit 12 to streamline the review process. The support unit supports exhibitors and organizers using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the collection unit, generation unit, guidance unit, organization unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information based on visitors' interests and schedules by the control unit 46A of the headset terminal 314. The generation unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 and generates an optimal navigation plan. The guidance unit guides visitors based on the navigation plan generated by the control unit 46A of the headset terminal 314. The organization unit organizes visitor information by the specific processing unit 290 of the data processing unit 12 to streamline the review process. The support unit supports exhibitors and organizers by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, generation unit, guidance unit, organization unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information based on visitors' interests and schedules by the control unit 46A of the robot 414. The generation unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 and generates an optimal navigation plan. The guidance unit guides visitors based on the navigation plan generated by the control unit 46A of the robot 414. The organization unit organizes visitor information by the specific processing unit 290 of the data processing unit 12 to streamline the review process. The support unit supports exhibitors and organizers by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) The collection department collects information based on the interests and schedules of visitors, A generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan, The system includes a guidance unit that guides visitors based on a navigation plan generated by the generation unit. A system characterized by the following features. (Note 2) It includes a data processing department to organize visitor information and streamline the review process. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes a support department to assist exhibitors or organizers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system estimates the emotions of visitors and adjusts the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze visitors' past exhibition visit history to select appropriate information gathering methods. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When gathering information, filtering is performed based on the visitor's current areas of interest and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of visitors and prioritizes the information to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the visitor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, we analyze visitors' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the emotions of visitors and adjusts the presentation of the navigation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a navigation plan, adjust the level of detail in the plan based on the visitor's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating navigation plans, different generation algorithms are applied depending on the visitor's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the emotions of visitors and adjusts the length of the navigation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating navigation plans, the plan priorities are determined based on the visitor's arrival time. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a navigation plan, adjust the order of the plan based on the relevance of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is The system estimates the emotions of visitors and adjusts the guidance method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is When providing guidance, the appropriate guidance method is selected by referring to the visitor's past visit history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is When providing directions, the guidance method will be customized based on the visitor's current location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates the emotions of visitors and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is When providing directions, select the appropriate guidance method considering the visitor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is During the tour, we analyze visitors' social media activity and propose guidance methods. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned editing unit, We estimate the emotions of visitors and adjust the information organization method based on the estimated emotions of the visitors. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned editing unit, When organizing information, refer to the visitor's past visit history to select the appropriate organization method. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned editing unit, The system estimates the emotions of visitors and determines the priority of information organization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned editing unit, When organizing information, select an appropriate organization method that takes into account the visitor's device information. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned support unit is We estimate the emotions of visitors and adjust the support methods based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, the appropriate support method is selected by referring to the visitor's past visit history. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the emotions of visitors and determines support priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing support, the appropriate support method will be selected considering the visitor's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0178] 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. The collection department collects information based on the interests and schedules of visitors, A generation unit analyzes the information collected by the collection unit and generates an optimal navigation plan, The system includes a guidance unit that guides visitors based on a navigation plan generated by the generation unit. A system characterized by the following features.

2. It includes a data processing department to organize visitor information and streamline the review process. The system according to feature 1.

3. The facility includes a support department to assist exhibitors or organizers. The system according to feature 1.

4. The aforementioned collection unit is The system estimates the emotions of visitors and adjusts the timing of information gathering based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze visitors' past exhibition visit history to select appropriate information gathering methods. The system according to feature 1.

6. The aforementioned collection unit is When gathering information, filtering is performed based on the visitor's current areas of interest and schedule. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the emotions of visitors and prioritizes the information to be collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the visitor's geographical location. The system according to feature 1.

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