system

Abstract

The system according to this embodiment aims to provide real-time information on the congestion status of tourist destinations and facilities, enabling users to enjoy sightseeing efficiently and comfortably. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a prediction unit, and a suggestion unit. The collection unit collects congestion information for tourist destinations and facilities. The analysis unit analyzes the data collected by the collection unit. The provision unit provides the results analyzed by the analysis unit. The prediction unit makes congestion predictions based on the data obtained by the analysis unit. The suggestion unit proposes less crowded spots and optimal visiting times based on the results obtained by the prediction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, there is a problem that information for grasping the congestion status of tourist spots and facilities in real time and enjoying tourism efficiently is not sufficiently provided.

[0005] The system according to the embodiment aims to provide the congestion status of tourist spots and facilities in real time so that users can enjoy tourism efficiently and comfortably.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a prediction unit, and a proposal unit. The data collection unit collects information on the congestion status of tourist destinations and facilities. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides the results analyzed by the analysis unit. The prediction unit makes congestion predictions based on the data obtained by the analysis unit. The proposal unit proposes less crowded spots and optimal visiting times based on the results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment provides real-time information on the congestion status of tourist destinations and facilities, enabling users to enjoy sightseeing efficiently and comfortably. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tourism support system according to an embodiment of the present invention is a system that provides real-time information on the congestion status of tourist destinations and facilities, helping users to enjoy sightseeing efficiently and comfortably. This tourism support system is a mechanism that provides real-time information on the congestion status of tourist destinations and facilities, helping users to enjoy sightseeing efficiently and comfortably. Specifically, it consists of the following steps. First, the congestion status of tourist destinations and facilities is collected in real time using sensors, image acquisition devices, and location information acquisition means. Next, the collected data is analyzed by AI and the current congestion status is provided to the user. Furthermore, based on past data, the AI ​​makes congestion predictions and suggests less crowded spots and optimal visiting times to the user. This allows users to enjoy sightseeing efficiently and comfortably. First, the congestion status of tourist destinations and facilities is collected in real time using sensors, image acquisition devices, and location information acquisition means. For example, cameras installed at tourist destinations monitor the flow of people and determine the degree of congestion. In addition, location information data from smartphones is used to track the movements of tourists. This makes it possible to accurately grasp the congestion status of tourist destinations and facilities. Next, the collected data is analyzed by AI and the current congestion status is provided to the user. For example, users can check the current congestion status through an app that displays the congestion level of tourist destinations in real time. This allows users to enjoy sightseeing while avoiding crowds. Furthermore, the AI ​​predicts crowd levels based on past data and suggests less crowded spots and optimal visiting times. For example, by analyzing past data, if there is a tendency for crowds to occur at certain times or days of the week, the system will suggest the optimal visiting time to the user based on that information. In addition, by suggesting less crowded spots, users can enjoy sightseeing more efficiently. This system allows users to enjoy sightseeing efficiently and comfortably. For example, users can check the crowd levels of tourist destinations in real time and enjoy sightseeing while avoiding crowds. In addition, by being suggested optimal visiting times based on crowd predictions, users can enjoy sightseeing more efficiently. Furthermore, by receiving suggestions for less crowded spots, users can discover new tourist destinations. In this way, the tourism support system allows users to enjoy sightseeing efficiently and comfortably.

[0029] The tourism support system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a prediction unit, and a proposal unit. The data collection unit collects congestion information for tourist destinations and facilities. The data collection unit collects congestion information for tourist destinations and facilities in real time, for example, using sensors, image acquisition devices, and location information acquisition means. For example, cameras installed at tourist destinations monitor the flow of people and determine the degree of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. This allows for accurate understanding of the congestion status of tourist destinations and facilities. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using AI and determines the current congestion status, for example. For example, the AI ​​uses technologies such as machine learning and deep learning to analyze the collected data and calculate the degree of congestion. The data provision unit provides the results analyzed by the analysis unit. The data provision unit provides the analysis results to the user, for example. For example, the user can check the current congestion status through a smartphone app or website. The prediction unit makes congestion predictions based on the data obtained by the analysis unit. The prediction unit makes congestion predictions using AI based on past data, for example. For example, by analyzing past data, if there is a tendency for congestion during specific times of day or on certain days of the week, the system suggests the optimal time to visit the user based on that information. The suggestion unit then suggests less crowded spots and optimal visiting times based on the results obtained by the prediction unit. For example, by suggesting less crowded spots, the user can enjoy sightseeing more efficiently. In this way, the tourism support system according to the embodiment allows users to enjoy sightseeing efficiently and comfortably.

[0030] The data collection unit collects information on the congestion levels of tourist destinations and facilities. For example, it uses sensors, image acquisition devices, and location information acquisition methods to collect real-time information on congestion levels at tourist destinations and facilities. Specifically, cameras installed at tourist destinations monitor the flow of people and determine the degree of congestion. The cameras cover a wide area with high resolution and analyze the number and movement of people using image processing technology. This allows for an accurate understanding of how crowded a particular area is. The data collection unit can also track the movements of tourists using smartphone location data. It utilizes the GPS function of smartphones to acquire the current location and travel routes of tourists in real time. Furthermore, it can acquire highly accurate location information indoors using Wi-Fi and Bluetooth® beacons. This allows for a detailed understanding of the congestion level of the entire tourist destination. The data collection unit centrally manages this data and stores it in a database that is updated in real time. The database is built on a cloud server and accessible to the analysis and provisioning units. This allows the data collection unit to collect data efficiently and accurately, improving the overall system performance. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, increasing the frequency of data collection in accordance with specific events or seasons allows for a more detailed understanding of congestion levels. This enables the data collection unit to accurately grasp congestion levels at tourist destinations and facilities, and build a foundation for providing appropriate information to users.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and determine the current congestion level. Specifically, the AI ​​uses technologies such as machine learning and deep learning to analyze the collected data and calculate the degree of congestion. For example, it uses image recognition technology to analyze camera footage and detect the number and movement of people. This makes it possible to understand the degree of congestion in a particular area in real time. It also analyzes location data to analyze the movement patterns and length of stay of tourists. This makes it possible to predict the degree of congestion in specific time periods and areas. Furthermore, the analysis unit can also analyze long-term congestion trends by utilizing past data and statistical information. For example, based on past congestion data, it can predict congestion trends during specific seasons or events and plan future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term congestion management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis unit can visualize the analysis results and provide them to users and administrators in an easy-to-understand manner. For example, congestion levels can be displayed using color-coded maps or graphs to make them intuitively understandable. This allows the analysis unit to quickly and accurately analyze the collected data and provide useful information to the user.

[0032] The service provider provides the results analyzed by the analysis provider. For example, the service provider provides the analysis results to users. Specifically, users can check the current congestion status through a smartphone app or website. The smartphone app displays the congestion status of nearby tourist attractions and facilities in real time based on the user's current location. Through the app, users can find less crowded spots or plan routes to avoid crowds. The website visually displays the congestion status of tourist attractions and facilities on a map, making it easy for users to understand intuitively. Furthermore, the service provider can also provide the analysis results to users through a notification function. For example, if a particular tourist attraction is crowded, the service provider can notify the user via push notification to encourage them to take action to avoid the crowd. The service provider updates this information in real time, always providing users with the latest information. The service provider can also collect user feedback and continuously improve the accuracy and usefulness of the information provided. For example, based on feedback on the congestion status of tourist attractions that users have actually visited, the service provider can adjust the analysis algorithm to provide more accurate information. In this way, the service provider can provide users with quick and accurate information and support them in planning and executing their travels. Furthermore, the service provider can integrate with other systems and services to offer users comprehensive tourism support. For example, by integrating with traffic and weather information, it can provide users with more comprehensive information, allowing them to plan their trips more efficiently.

[0033] The prediction unit performs congestion predictions based on data obtained by the analysis unit. For example, the prediction unit uses AI to predict congestion based on past data. Specifically, it analyzes past data and, if there is a tendency for congestion during certain times of the day or on certain days of the week, it uses that information to suggest the optimal time to visit the user. The AI ​​uses machine learning algorithms to learn from past congestion data and predict future congestion levels. For example, if a particular tourist destination tends to be crowded on weekends and holidays, it uses that information to suggest that the user visit on a weekday. It can also perform congestion predictions based on specific events or seasons. For example, for tourist destinations that are crowded during certain seasons, such as cherry blossom season or autumn foliage season, it performs congestion predictions based on past data and suggests the optimal time to visit the user. Furthermore, the prediction unit can continuously revise its prediction results based on data that is updated in real time, allowing it to respond to the latest situation. For example, it can update congestion predictions in real time in response to changes in weather or traffic conditions, providing users with the latest information. In addition, the prediction unit can perform more accurate risk assessments by considering the characteristics of each region and past disaster history. This allows the prediction unit to provide highly accurate congestion predictions based on the latest information at all times, and to suggest optimal visit times and routes to users. Furthermore, the prediction unit can learn the user's behavioral history and preferences, and provide individually customized prediction results. This enables the prediction unit to provide users with more personalized tourism support and improve their satisfaction with their trip.

[0034] The suggestion unit proposes less crowded spots and optimal visiting times based on the results obtained by the prediction unit. For example, the suggestion unit suggests less crowded spots and optimal visiting times to the user based on the prediction results. Specifically, by suggesting less crowded spots, the user can enjoy sightseeing efficiently. The suggestion unit proposes the optimal sightseeing route considering the user's current location and interests. For example, when a user visits a particular tourist destination, it suggests less crowded spots nearby, allowing the user to enjoy sightseeing efficiently. In addition, the suggestion unit can provide personalized suggestions based on the user's preferences and past behavior history. For example, it suggests similar tourist destinations and new spots based on tourist destinations the user has visited in the past or spots they were interested in. Furthermore, the suggestion unit can continuously revise its suggestions based on real-time updated data to respond to the latest situation. For example, if a particular tourist destination suddenly becomes crowded, it can immediately update its suggestions and provide new suggestions to the user. The suggestion unit can also collect user feedback and continuously improve the accuracy and usefulness of its suggestions. As a result, the suggestion unit can propose optimal sightseeing routes and spots to users, improving their satisfaction with their sightseeing. Furthermore, the proposal department can integrate with other systems and services to provide users with comprehensive tourism support. For example, by integrating with traffic and weather information, it can provide users with more comprehensive information and enable them to plan their trips more efficiently. In this way, the proposal department can help users enjoy their trips efficiently and comfortably.

[0035] The data collection unit can collect real-time information on the congestion status of tourist destinations and facilities using sensors, image acquisition devices, and location information acquisition means. For example, the data collection unit can monitor the flow of people using cameras installed in tourist destinations and determine the degree of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. For example, cameras installed in tourist destinations can monitor the flow of people and determine the degree of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. This allows for an accurate understanding of the congestion status of tourist destinations and facilities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data from cameras installed in tourist destinations into a generating AI and have the generating AI perform a congestion determination.

[0036] The analysis unit can analyze the collected data using AI to determine the current congestion level. For example, the analysis unit can analyze the collected data using AI to determine the current congestion level. For example, the AI ​​uses techniques such as machine learning and deep learning to analyze the collected data and calculate the congestion level. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the congestion level determination.

[0037] The service provider can provide the analysis results to the user. For example, the service provider can provide the analysis results to the user. For example, the user can check the current congestion status through a smartphone app or website. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate information to be provided to the user.

[0038] The prediction unit can use AI to predict congestion based on past data. For example, the prediction unit analyzes past data and, if there is a tendency for congestion during specific times or days of the week, it uses that information to suggest the optimal time for the user to visit. Some or all of the above-described processes in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input past data into a generating AI and have the generating AI perform congestion prediction.

[0039] The suggestion unit can suggest available spots and optimal visiting times to the user based on the prediction results. For example, by suggesting less crowded spots, the user can enjoy sightseeing more efficiently. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the prediction results into a generating AI and have the generating AI perform the task of suggesting available spots and optimal visiting times.

[0040] The data collection unit can collect detailed congestion data for specific areas of a tourist destination and provide congestion information for each area. For example, the data collection unit can collect congestion data for the main area and surrounding areas of a tourist destination separately and provide detailed congestion information. The data collection unit can also collect congestion data for specific spots within a tourist destination (e.g., observation decks, restaurants) individually and provide this information to the user. Furthermore, the data collection unit can update the congestion data for each area of ​​the tourist destination in real time, providing the user with the latest information. This allows users to obtain more specific information by providing detailed congestion information for each area. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input congestion data for specific areas of a tourist destination into a generating AI and have the generating AI collect detailed congestion information.

[0041] The data collection unit can correct the data based on weather and event information when collecting congestion data. For example, the data collection unit can correct the congestion level during rainy weather based on weather information and provide it to the user. The data collection unit can also correct the congestion level during specific events based on event information and provide it to the user. Furthermore, the data collection unit can combine weather and event information to correct the congestion level and provide more accurate information to the user. This allows for the provision of more accurate congestion information by considering weather and event information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information and event information into a generating AI and have the generating AI perform the data correction.

[0042] The data collection unit can analyze audio data from tourist destinations to estimate the degree of congestion when collecting congestion information. For example, the data collection unit can analyze audio data from tourist destinations in real time to estimate the degree of congestion. The data collection unit can also identify areas with high congestion levels based on the results of the audio data analysis. Furthermore, the data collection unit can combine the results of the audio data analysis with other data to estimate the degree of congestion more accurately. This allows for a more accurate estimation of congestion levels by analyzing audio data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input audio data from tourist destinations into a generating AI and have the generating AI perform the congestion level estimation.

[0043] The data collection unit can estimate congestion levels by utilizing Wi-Fi connection information from tourist destinations when collecting congestion data. For example, the data collection unit can collect Wi-Fi connection information from tourist destinations in real time and estimate congestion levels. The data collection unit can also identify areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, the data collection unit can combine the analysis results of the Wi-Fi connection information with other data to estimate congestion levels more accurately. This allows for more accurate congestion level estimation by utilizing Wi-Fi connection information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input Wi-Fi connection information from tourist destinations into a generating AI and have the generating AI perform congestion level estimation.

[0044] The analysis unit can perform a detailed analysis of congestion levels in specific areas of a tourist destination. For example, the analysis unit can analyze congestion levels separately for the main area and surrounding areas of the tourist destination. Furthermore, the analysis unit can individually analyze congestion levels at specific spots within the tourist destination (e.g., observation decks, restaurants). In addition, the analysis unit can analyze congestion levels for each area of ​​the tourist destination in real time and provide this information to the user. This allows users to obtain more specific information by analyzing detailed congestion levels for each area. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input congestion data for specific areas of the tourist destination into a generating AI and have the generating AI perform a detailed analysis of the congestion levels.

[0045] The analysis unit can correct the current congestion status by referring to past congestion data of the tourist destination during the analysis. For example, the analysis unit corrects the current congestion status based on past congestion data. The analysis unit can also compare past congestion data with current data and correct outliers. Furthermore, the analysis unit can correct the congestion status for specific time periods or days of the week based on past congestion data. This allows for a more accurate correction of the current congestion status by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past congestion data of the tourist destination into a generating AI and have the generating AI perform the correction of the current congestion status.

[0046] The analysis unit can estimate the degree of congestion by analyzing audio data from tourist destinations during the analysis process. For example, the analysis unit can analyze audio data from tourist destinations in real time and estimate the degree of congestion. The analysis unit can also identify areas with high congestion levels based on the results of the audio data analysis. Furthermore, the analysis unit can combine the results of the audio data analysis with other data to estimate the degree of congestion more accurately. This allows for a more accurate estimation of congestion levels by analyzing audio data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data from tourist destinations into a generating AI and have the generating AI perform the congestion level estimation.

[0047] The analysis unit can estimate congestion levels using Wi-Fi connection information from tourist destinations during analysis. For example, the analysis unit can analyze Wi-Fi connection information from tourist destinations in real time and estimate congestion levels. The analysis unit can also identify areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, the analysis unit can combine the analysis results of the Wi-Fi connection information with other data to estimate congestion levels more accurately. This allows for more accurate congestion estimation by utilizing Wi-Fi connection information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input Wi-Fi connection information from tourist destinations into a generating AI and have the generating AI perform congestion level estimation.

[0048] The service provider can suggest the optimal sightseeing route based on the user's current location at the time of service provision. For example, the service provider can suggest the nearest tourist spot from the user's current location. The service provider can also suggest a sightseeing route that can be efficiently visited from the user's current location. Furthermore, the service provider can suggest the optimal sightseeing route considering the user's current location and congestion levels. This allows users to enjoy sightseeing efficiently by suggesting the optimal sightseeing route based on their current location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location data into a generating AI and have the generating AI suggest the optimal sightseeing route.

[0049] The information provider can provide customized information by referring to the user's past travel history at the time of delivery. For example, the information provider can provide customized information based on tourist spots the user has visited in the past. The information provider can also suggest spots of interest based on the user's past travel history. Furthermore, the information provider can analyze the user's past travel history and suggest the optimal travel route. This allows for the provision of more customized information by referring to the user's past travel history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing customized information.

[0050] The service provider can select the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This improves visibility by providing the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0051] The service provider can analyze the user's social media activity and provide relevant tourism information at the time of delivery. For example, the service provider can analyze the user's social media posts and suggest tourist spots of interest. The service provider can also provide relevant tourism information based on the user's social media check-in information. Furthermore, the service provider can analyze the activity of the user's social media followers and provide relevant tourism information. This allows for the provision of more relevant tourism information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant tourism information.

[0052] The prediction unit can perform detailed congestion predictions for specific areas of a tourist destination during the prediction process. For example, the prediction unit can perform congestion predictions separately for the main area and surrounding areas of the tourist destination. The prediction unit can also perform congestion predictions individually for specific spots within the tourist destination (e.g., observation decks, restaurants). Furthermore, the prediction unit can perform real-time congestion predictions for each area of ​​the tourist destination and provide them to the user. This allows users to obtain more specific information by providing detailed congestion predictions for each area. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input congestion data for specific areas of the tourist destination into a generating AI and have the generating AI perform detailed congestion predictions.

[0053] The prediction unit can improve prediction accuracy by referring to past congestion data of tourist destinations during the prediction process. For example, the prediction unit makes a current congestion prediction based on past congestion data. The prediction unit can also improve prediction accuracy by comparing past congestion data with current data. Furthermore, the prediction unit can make congestion predictions for specific time periods or days of the week based on past congestion data. This allows for more accurate current congestion predictions by referring to past data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past congestion data of tourist destinations into a generating AI and have the generating AI perform the task of improving prediction accuracy.

[0054] The prediction unit can predict the degree of congestion by analyzing audio data of tourist destinations during the prediction process. For example, the prediction unit can analyze audio data of tourist destinations in real time and predict the degree of congestion. The prediction unit can also predict areas with high congestion based on the results of the audio data analysis. Furthermore, the prediction unit can combine the results of the audio data analysis with other data to predict the degree of congestion more accurately. This allows for a more accurate prediction of congestion by analyzing audio data. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input audio data of tourist destinations into a generating AI and have the generating AI perform the congestion prediction.

[0055] The prediction unit can predict congestion levels using Wi-Fi connection information of tourist destinations during the prediction process. For example, the prediction unit analyzes Wi-Fi connection information of tourist destinations in real time to predict congestion levels. The prediction unit can also predict areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, the prediction unit can combine the analysis results of the Wi-Fi connection information with other data to predict congestion levels more accurately. This allows for more accurate congestion predictions by utilizing Wi-Fi connection information. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input Wi-Fi connection information of tourist destinations into a generating AI and have the generating AI perform congestion level predictions.

[0056] The suggestion unit can provide detailed suggestions of available spots in specific areas of a tourist destination. For example, it can suggest available spots in the main area and surrounding areas of the tourist destination separately. The suggestion unit can also suggest available times for specific spots within the tourist destination (e.g., observation decks, restaurants). Furthermore, the suggestion unit can provide real-time suggestions of available spots in each area of ​​the tourist destination. This allows users to obtain more specific information by suggesting detailed available spots in each area. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on available spots in specific areas of the tourist destination into a generating AI and have the generating AI execute detailed suggestions.

[0057] The suggestion unit can propose the optimal visit time by referring to past congestion data of the tourist destination when making a suggestion. For example, the suggestion unit can propose the optimal visit time for the current time based on past congestion data. The suggestion unit can also propose the optimal visit time by comparing past congestion data with current data. Furthermore, the suggestion unit can propose the optimal visit time for a specific time of day or day of the week based on past congestion data. In this way, the optimal visit time for the current time can be proposed by referring to past data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input past congestion data of the tourist destination into a generating AI and have the generating AI execute the proposal of the optimal visit time.

[0058] The suggestion unit can propose the optimal sightseeing route based on the user's current location. For example, the suggestion unit can suggest the nearest tourist spot from the user's current location. It can also propose a sightseeing route that can be efficiently visited from the user's current location. Furthermore, the suggestion unit can propose the optimal sightseeing route considering the user's current location and congestion levels. This allows users to enjoy sightseeing efficiently by suggesting the optimal sightseeing route based on their current location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current location data into a generating AI and have the generating AI propose the optimal sightseeing route.

[0059] The suggestion unit can analyze the user's social media activity and suggest relevant tourist spots when making suggestions. For example, the suggestion unit can analyze the user's social media posts and suggest tourist spots of interest. The suggestion unit can also suggest relevant tourist spots based on the user's social media check-in information. Furthermore, the suggestion unit can analyze the activity of the user's social media followers and suggest relevant tourist spots. In this way, by analyzing the user's social media activity, it is possible to suggest more relevant tourist spots. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant tourist spots.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The tourism support system can estimate congestion levels at tourist destinations by analyzing audio data collected from those locations. For example, it can analyze audio data in real time to estimate congestion levels. It can also identify areas with high congestion levels based on the audio data analysis results. Furthermore, it can combine the audio data analysis results with other data to estimate congestion levels more accurately. This allows for more accurate congestion estimation by analyzing audio data.

[0062] The tourism support system can estimate congestion levels using Wi-Fi connection information at tourist destinations. For example, it can collect Wi-Fi connection information at tourist destinations in real time and estimate congestion levels. It can also identify areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, it can combine the analysis results of the Wi-Fi connection information with other data to estimate congestion levels more accurately. In this way, it is possible to estimate congestion levels more accurately by using Wi-Fi connection information.

[0063] The tourism support system can collect detailed congestion data for specific areas within a tourist destination and provide information on congestion levels for each area. For example, it can collect congestion data separately for the main area and surrounding areas of a tourist destination and provide detailed congestion information. It can also collect congestion data for specific spots within a tourist destination (e.g., observation decks, restaurants) individually and provide this information to users. Furthermore, it can update congestion data for each area of ​​a tourist destination in real time, providing users with the latest information. This allows users to obtain more specific information by providing detailed congestion information for each area.

[0064] Tourism support systems can improve prediction accuracy by referencing past congestion data for tourist destinations. For example, they can make current congestion predictions based on past congestion data. They can also improve prediction accuracy by comparing past congestion data with current data. Furthermore, they can make congestion predictions for specific time periods or days of the week based on past congestion data. This allows for more accurate current congestion predictions by referencing past data.

[0065] The tourism support system can provide detailed suggestions for available spots within specific areas of a tourist destination. For example, it can suggest available spots in the main area and surrounding areas of a tourist destination separately. It can also suggest available times for specific spots within a tourist destination (e.g., observation decks, restaurants). Furthermore, it can provide real-time suggestions for available spots within each area of ​​a tourist destination. This allows users to obtain more specific information by suggesting detailed available spots in each area.

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

[0067] Step 1: The data collection unit collects information on the congestion levels of tourist destinations and facilities. The data collection unit uses sensors, image acquisition devices, and location information acquisition methods to collect information on the congestion levels of tourist destinations and facilities in real time. For example, cameras installed at tourist destinations monitor the flow of people and determine the level of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the collected data and determine the current congestion level. For example, the AI ​​uses technologies such as machine learning and deep learning to analyze the collected data and calculate the degree of congestion. Step 3: The service provider provides the results analyzed by the analysis unit. The service provider then provides the analysis results to the user. For example, the user can check the current congestion status through a smartphone app or website. Step 4: The prediction unit performs congestion prediction based on the data obtained by the analysis unit. The prediction unit uses AI to predict congestion based on past data. For example, if past data is analyzed and there is a tendency for congestion during specific times of day or days of the week, the prediction unit will use that information to suggest the optimal time for the user to visit. Step 5: The suggestion unit proposes less crowded spots and optimal visiting times based on the results obtained by the prediction unit. The suggestion unit proposes less crowded spots and optimal visiting times to the user based on the prediction results. For example, by suggesting less crowded spots, the user can enjoy sightseeing more efficiently.

[0068] (Example of form 2) The tourism support system according to an embodiment of the present invention is a system that provides real-time information on the congestion status of tourist destinations and facilities, helping users to enjoy sightseeing efficiently and comfortably. This tourism support system is a mechanism that provides real-time information on the congestion status of tourist destinations and facilities, helping users to enjoy sightseeing efficiently and comfortably. Specifically, it consists of the following steps. First, the congestion status of tourist destinations and facilities is collected in real time using sensors, image acquisition devices, and location information acquisition means. Next, the collected data is analyzed by AI and the current congestion status is provided to the user. Furthermore, based on past data, the AI ​​makes congestion predictions and suggests less crowded spots and optimal visiting times to the user. This allows users to enjoy sightseeing efficiently and comfortably. First, the congestion status of tourist destinations and facilities is collected in real time using sensors, image acquisition devices, and location information acquisition means. For example, cameras installed at tourist destinations monitor the flow of people and determine the degree of congestion. In addition, location information data from smartphones is used to track the movements of tourists. This makes it possible to accurately grasp the congestion status of tourist destinations and facilities. Next, the collected data is analyzed by AI and the current congestion status is provided to the user. For example, users can check the current congestion status through an app that displays the congestion level of tourist destinations in real time. This allows users to enjoy sightseeing while avoiding crowds. Furthermore, the AI ​​predicts crowd levels based on past data and suggests less crowded spots and optimal visiting times. For example, by analyzing past data, if there is a tendency for crowds to occur at certain times or days of the week, the system will suggest the optimal visiting time to the user based on that information. In addition, by suggesting less crowded spots, users can enjoy sightseeing more efficiently. This system allows users to enjoy sightseeing efficiently and comfortably. For example, users can check the crowd levels of tourist destinations in real time and enjoy sightseeing while avoiding crowds. In addition, by being suggested optimal visiting times based on crowd predictions, users can enjoy sightseeing more efficiently. Furthermore, by receiving suggestions for less crowded spots, users can discover new tourist destinations. In this way, the tourism support system allows users to enjoy sightseeing efficiently and comfortably.

[0069] The tourism support system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a prediction unit, and a proposal unit. The data collection unit collects congestion information for tourist destinations and facilities. The data collection unit collects congestion information for tourist destinations and facilities in real time, for example, using sensors, image acquisition devices, and location information acquisition means. For example, cameras installed at tourist destinations monitor the flow of people and determine the degree of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. This allows for accurate understanding of the congestion status of tourist destinations and facilities. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using AI and determines the current congestion status, for example. For example, the AI ​​uses technologies such as machine learning and deep learning to analyze the collected data and calculate the degree of congestion. The data provision unit provides the results analyzed by the analysis unit. The data provision unit provides the analysis results to the user, for example. For example, the user can check the current congestion status through a smartphone app or website. The prediction unit makes congestion predictions based on the data obtained by the analysis unit. The prediction unit makes congestion predictions using AI based on past data, for example. For example, by analyzing past data, if there is a tendency for congestion during specific times of day or on certain days of the week, the system suggests the optimal time to visit the user based on that information. The suggestion unit then suggests less crowded spots and optimal visiting times based on the results obtained by the prediction unit. For example, by suggesting less crowded spots, the user can enjoy sightseeing more efficiently. In this way, the tourism support system according to the embodiment allows users to enjoy sightseeing efficiently and comfortably.

[0070] The data collection unit collects information on the congestion levels of tourist destinations and facilities. For example, it uses sensors, image acquisition devices, and location information acquisition methods to collect real-time information on congestion levels at tourist destinations and facilities. Specifically, cameras installed at tourist destinations monitor the flow of people and determine the degree of congestion. The cameras cover a wide area with high resolution and analyze the number and movement of people using image processing technology. This allows for an accurate understanding of how crowded a particular area is. The data collection unit can also track the movements of tourists using smartphone location data. It utilizes the GPS function of smartphones to acquire the current location and travel routes of tourists in real time. Furthermore, it can acquire highly accurate location information even indoors using Wi-Fi and Bluetooth beacons. This allows for a detailed understanding of the congestion level of the entire tourist destination. The data collection unit centrally manages this data and stores it in a database that is updated in real time. The database is built on a cloud server and accessible to the analysis and provisioning units. This allows the data collection unit to collect data efficiently and accurately, improving the overall system performance. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by increasing the frequency of data collection in accordance with specific events or seasons, it becomes possible to grasp congestion levels in more detail. This allows the data collection unit to accurately understand the congestion levels of tourist destinations and facilities and build a foundation for providing appropriate information to users.

[0071] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and determine the current congestion level. Specifically, the AI ​​uses technologies such as machine learning and deep learning to analyze the collected data and calculate the degree of congestion. For example, it uses image recognition technology to analyze camera footage and detect the number and movement of people. This makes it possible to understand the degree of congestion in a particular area in real time. It also analyzes location data to analyze the movement patterns and length of stay of tourists. This makes it possible to predict the degree of congestion in specific time periods and areas. Furthermore, the analysis unit can also analyze long-term congestion trends by utilizing past data and statistical information. For example, based on past congestion data, it can predict congestion trends during specific seasons or events and plan future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term congestion management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis unit can visualize the analysis results and provide them to users and administrators in an easy-to-understand manner. For example, congestion levels can be displayed using color-coded maps or graphs to make them intuitively understandable. This allows the analysis unit to quickly and accurately analyze the collected data and provide useful information to the user.

[0072] The service provider provides the results analyzed by the analysis provider. For example, the service provider provides the analysis results to users. Specifically, users can check the current congestion status through a smartphone app or website. The smartphone app displays the congestion status of nearby tourist attractions and facilities in real time based on the user's current location. Through the app, users can find less crowded spots or plan routes to avoid crowds. The website visually displays the congestion status of tourist attractions and facilities on a map, making it easy for users to understand intuitively. Furthermore, the service provider can also provide the analysis results to users through a notification function. For example, if a particular tourist attraction is crowded, the service provider can notify the user via push notification to encourage them to take action to avoid the crowd. The service provider updates this information in real time, always providing users with the latest information. The service provider can also collect user feedback and continuously improve the accuracy and usefulness of the information provided. For example, based on feedback on the congestion status of tourist attractions that users have actually visited, the service provider can adjust the analysis algorithm to provide more accurate information. In this way, the service provider can provide users with quick and accurate information and support them in planning and executing their travels. Furthermore, the service provider can integrate with other systems and services to offer users comprehensive tourism support. For example, by integrating with traffic and weather information, it can provide users with more comprehensive information, allowing them to plan their trips more efficiently.

[0073] The prediction unit performs congestion predictions based on data obtained by the analysis unit. For example, the prediction unit uses AI to predict congestion based on past data. Specifically, it analyzes past data and, if there is a tendency for congestion during certain times of the day or on certain days of the week, it uses that information to suggest the optimal time to visit the user. The AI ​​uses machine learning algorithms to learn from past congestion data and predict future congestion levels. For example, if a particular tourist destination tends to be crowded on weekends and holidays, it uses that information to suggest that the user visit on a weekday. It can also perform congestion predictions based on specific events or seasons. For example, for tourist destinations that are crowded during certain seasons, such as cherry blossom season or autumn foliage season, it performs congestion predictions based on past data and suggests the optimal time to visit the user. Furthermore, the prediction unit can continuously revise its prediction results based on data that is updated in real time, allowing it to respond to the latest situation. For example, it can update congestion predictions in real time in response to changes in weather or traffic conditions, providing users with the latest information. In addition, the prediction unit can perform more accurate risk assessments by considering the characteristics of each region and past disaster history. This allows the prediction unit to provide highly accurate congestion predictions based on the latest information at all times, and to suggest optimal visit times and routes to users. Furthermore, the prediction unit can learn the user's behavioral history and preferences, and provide individually customized prediction results. This enables the prediction unit to provide users with more personalized tourism support and improve their satisfaction with their trip.

[0074] The suggestion unit proposes less crowded spots and optimal visiting times based on the results obtained by the prediction unit. For example, the suggestion unit suggests less crowded spots and optimal visiting times to the user based on the prediction results. Specifically, by suggesting less crowded spots, the user can enjoy sightseeing efficiently. The suggestion unit proposes the optimal sightseeing route considering the user's current location and interests. For example, when a user visits a particular tourist destination, it suggests less crowded spots nearby, allowing the user to enjoy sightseeing efficiently. In addition, the suggestion unit can provide personalized suggestions based on the user's preferences and past behavior history. For example, it suggests similar tourist destinations and new spots based on tourist destinations the user has visited in the past or spots they were interested in. Furthermore, the suggestion unit can continuously revise its suggestions based on real-time updated data to respond to the latest situation. For example, if a particular tourist destination suddenly becomes crowded, it can immediately update its suggestions and provide new suggestions to the user. The suggestion unit can also collect user feedback and continuously improve the accuracy and usefulness of its suggestions. As a result, the suggestion unit can propose optimal sightseeing routes and spots to users, improving their satisfaction with their sightseeing. Furthermore, the proposal department can integrate with other systems and services to provide users with comprehensive tourism support. For example, by integrating with traffic and weather information, it can provide users with more comprehensive information and enable them to plan their trips more efficiently. In this way, the proposal department can help users enjoy their trips efficiently and comfortably.

[0075] The data collection unit can collect real-time information on the congestion status of tourist destinations and facilities using sensors, image acquisition devices, and location information acquisition means. For example, the data collection unit can monitor the flow of people using cameras installed in tourist destinations and determine the degree of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. For example, cameras installed in tourist destinations can monitor the flow of people and determine the degree of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. This allows for an accurate understanding of the congestion status of tourist destinations and facilities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data from cameras installed in tourist destinations into a generating AI and have the generating AI perform a congestion determination.

[0076] The analysis unit can analyze the collected data using AI to determine the current congestion level. For example, the analysis unit can analyze the collected data using AI to determine the current congestion level. For example, the AI ​​uses techniques such as machine learning and deep learning to analyze the collected data and calculate the congestion level. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the congestion level determination.

[0077] The service provider can provide the analysis results to the user. For example, the service provider can provide the analysis results to the user. For example, the user can check the current congestion status through a smartphone app or website. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate information to be provided to the user.

[0078] The prediction unit can use AI to predict congestion based on past data. For example, the prediction unit analyzes past data and, if there is a tendency for congestion during specific times or days of the week, it uses that information to suggest the optimal time for the user to visit. Some or all of the above-described processes in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input past data into a generating AI and have the generating AI perform congestion prediction.

[0079] The suggestion unit can suggest available spots and optimal visiting times to the user based on the prediction results. For example, by suggesting less crowded spots, the user can enjoy sightseeing more efficiently. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the prediction results into a generating AI and have the generating AI perform the task of suggesting available spots and optimal visiting times.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of congestion data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of congestion data collection and provide real-time updates. Conversely, if the user is relaxed, the data collection unit can decrease the collection frequency and update information only when necessary. Furthermore, if the user is in a hurry, the data collection unit can shorten the collection timing and provide updates quickly. This allows for the provision of more appropriate information by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can collect detailed congestion data for specific areas of a tourist destination and provide congestion information for each area. For example, the data collection unit can collect congestion data for the main area and surrounding areas of a tourist destination separately and provide detailed congestion information. The data collection unit can also collect congestion data for specific spots within a tourist destination (e.g., observation decks, restaurants) individually and provide this information to the user. Furthermore, the data collection unit can update the congestion data for each area of ​​the tourist destination in real time, providing the user with the latest information. This allows users to obtain more specific information by providing detailed congestion information for each area. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input congestion data for specific areas of a tourist destination into a generating AI and have the generating AI collect detailed congestion information.

[0082] The data collection unit can correct the data based on weather and event information when collecting congestion data. For example, the data collection unit can correct the congestion level during rainy weather based on weather information and provide it to the user. The data collection unit can also correct the congestion level during specific events based on event information and provide it to the user. Furthermore, the data collection unit can combine weather and event information to correct the congestion level and provide more accurate information to the user. This allows for the provision of more accurate congestion information by considering weather and event information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information and event information into a generating AI and have the generating AI perform the data correction.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data from crowded areas. Conversely, if the user is relaxed, the data collection unit may prioritize collecting data from less crowded areas. Furthermore, if the user is in a hurry, the data collection unit may prioritize collecting data from major tourist attractions. This allows for the provision of more relevant information by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit can analyze audio data from tourist destinations to estimate the degree of congestion when collecting congestion information. For example, the data collection unit can analyze audio data from tourist destinations in real time to estimate the degree of congestion. The data collection unit can also identify areas with high congestion levels based on the results of the audio data analysis. Furthermore, the data collection unit can combine the results of the audio data analysis with other data to estimate the degree of congestion more accurately. This allows for a more accurate estimation of congestion levels by analyzing audio data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input audio data from tourist destinations into a generating AI and have the generating AI perform the congestion level estimation.

[0085] The data collection unit can estimate congestion levels by utilizing Wi-Fi connection information from tourist destinations when collecting congestion data. For example, the data collection unit can collect Wi-Fi connection information from tourist destinations in real time and estimate congestion levels. The data collection unit can also identify areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, the data collection unit can combine the analysis results of the Wi-Fi connection information with other data to estimate congestion levels more accurately. This allows for more accurate congestion level estimation by utilizing Wi-Fi connection information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input Wi-Fi connection information from tourist destinations into a generating AI and have the generating AI perform congestion level estimation.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis results according to the user's emotions, 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can perform a detailed analysis of congestion levels in specific areas of a tourist destination. For example, the analysis unit can analyze congestion levels separately for the main area and surrounding areas of the tourist destination. Furthermore, the analysis unit can individually analyze congestion levels at specific spots within the tourist destination (e.g., observation decks, restaurants). In addition, the analysis unit can analyze congestion levels for each area of ​​the tourist destination in real time and provide this information to the user. This allows users to obtain more specific information by analyzing detailed congestion levels for each area. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input congestion data for specific areas of the tourist destination into a generating AI and have the generating AI perform a detailed analysis of the congestion levels.

[0088] The analysis unit can correct the current congestion status by referring to past congestion data of the tourist destination during the analysis. For example, the analysis unit corrects the current congestion status based on past congestion data. The analysis unit can also compare past congestion data with current data and correct outliers. Furthermore, the analysis unit can correct the congestion status for specific time periods or days of the week based on past congestion data. This allows for a more accurate correction of the current congestion status by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past congestion data of the tourist destination into a generating AI and have the generating AI perform the correction of the current congestion status.

[0089] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit may prioritize providing analysis results for crowded areas. Similarly, if the user is relaxed, the analysis unit may prioritize providing analysis results for less crowded areas. Furthermore, if the user is in a hurry, the analysis unit may prioritize providing analysis results for major tourist attractions. This allows for the provision of more appropriate information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can estimate the degree of congestion by analyzing audio data from tourist destinations during the analysis process. For example, the analysis unit can analyze audio data from tourist destinations in real time and estimate the degree of congestion. The analysis unit can also identify areas with high congestion levels based on the results of the audio data analysis. Furthermore, the analysis unit can combine the results of the audio data analysis with other data to estimate the degree of congestion more accurately. This allows for a more accurate estimation of congestion levels by analyzing audio data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data from tourist destinations into a generating AI and have the generating AI perform the congestion level estimation.

[0091] The analysis unit can estimate congestion levels using Wi-Fi connection information from tourist destinations during analysis. For example, the analysis unit can analyze Wi-Fi connection information from tourist destinations in real time and estimate congestion levels. The analysis unit can also identify areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, the analysis unit can combine the analysis results of the Wi-Fi connection information with other data to estimate congestion levels more accurately. This allows for more accurate congestion estimation by utilizing Wi-Fi connection information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input Wi-Fi connection information from tourist destinations into a generating AI and have the generating AI perform congestion level estimation.

[0092] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is stressed, the provider can provide simple and easily visible information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can provide concise information. By adjusting the way information is presented according to the user's emotions, 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 information provider may be performed using AI, or not using AI. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The service provider can suggest the optimal sightseeing route based on the user's current location at the time of service provision. For example, the service provider can suggest the nearest tourist spot from the user's current location. The service provider can also suggest a sightseeing route that can be efficiently visited from the user's current location. Furthermore, the service provider can suggest the optimal sightseeing route considering the user's current location and congestion levels. This allows users to enjoy sightseeing efficiently by suggesting the optimal sightseeing route based on their current location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location data into a generating AI and have the generating AI suggest the optimal sightseeing route.

[0094] The information provider can provide customized information by referring to the user's past travel history at the time of delivery. For example, the information provider can provide customized information based on tourist spots the user has visited in the past. The information provider can also suggest spots of interest based on the user's past travel history. Furthermore, the information provider can analyze the user's past travel history and suggest the optimal travel route. This allows for the provision of more customized information by referring to the user's past travel history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing customized information.

[0095] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is feeling stressed, the information provider may prioritize providing information about crowded areas. Similarly, if the user is relaxed, it may prioritize providing information about less crowded areas. Furthermore, if the user is in a hurry, it may prioritize providing information about major tourist attractions. This allows for the provision of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 information provider may be performed using AI, or not. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The service provider can select the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This improves visibility by providing the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0097] The service provider can analyze the user's social media activity and provide relevant tourism information at the time of delivery. For example, the service provider can analyze the user's social media posts and suggest tourist spots of interest. The service provider can also provide relevant tourism information based on the user's social media check-in information. Furthermore, the service provider can analyze the activity of the user's social media followers and provide relevant tourism information. This allows for the provision of more relevant tourism information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant tourism information.

[0098] The prediction unit can estimate the user's emotions and adjust the congestion prediction method based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit may prioritize predicting congestion in highly crowded areas. Similarly, if the user is relaxed, the prediction unit may prioritize predicting congestion in less crowded areas. Furthermore, if the user is in a hurry, the prediction unit may prioritize predicting congestion in major tourist spots. This allows for more accurate prediction results by adjusting the congestion prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 prediction unit may be performed using AI, or not. For example, the prediction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The prediction unit can perform detailed congestion predictions for specific areas of a tourist destination during the prediction process. For example, the prediction unit can perform congestion predictions separately for the main area and surrounding areas of the tourist destination. The prediction unit can also perform congestion predictions individually for specific spots within the tourist destination (e.g., observation decks, restaurants). Furthermore, the prediction unit can perform real-time congestion predictions for each area of ​​the tourist destination and provide them to the user. This allows users to obtain more specific information by providing detailed congestion predictions for each area. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input congestion data for specific areas of the tourist destination into a generating AI and have the generating AI perform detailed congestion predictions.

[0100] The prediction unit can improve prediction accuracy by referring to past congestion data of tourist destinations during the prediction process. For example, the prediction unit makes a current congestion prediction based on past congestion data. The prediction unit can also improve prediction accuracy by comparing past congestion data with current data. Furthermore, the prediction unit can make congestion predictions for specific time periods or days of the week based on past congestion data. This allows for more accurate current congestion predictions by referring to past data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past congestion data of tourist destinations into a generating AI and have the generating AI perform the task of improving prediction accuracy.

[0101] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is feeling stressed, the prediction unit may prioritize prediction results for crowded areas. Similarly, if the user is relaxed, it may prioritize prediction results for less crowded areas. Furthermore, if the user is in a hurry, it may prioritize prediction results for major tourist attractions. This allows for the provision of more appropriate information by prioritizing prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 prediction unit may be performed using AI, or not. For example, the prediction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The prediction unit can predict the degree of congestion by analyzing audio data of tourist destinations during the prediction process. For example, the prediction unit can analyze audio data of tourist destinations in real time and predict the degree of congestion. The prediction unit can also predict areas with high congestion based on the results of the audio data analysis. Furthermore, the prediction unit can combine the results of the audio data analysis with other data to predict the degree of congestion more accurately. This allows for a more accurate prediction of congestion by analyzing audio data. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input audio data of tourist destinations into a generating AI and have the generating AI perform the congestion prediction.

[0103] The prediction unit can predict congestion levels using Wi-Fi connection information of tourist destinations during the prediction process. For example, the prediction unit analyzes Wi-Fi connection information of tourist destinations in real time to predict congestion levels. The prediction unit can also predict areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, the prediction unit can combine the analysis results of the Wi-Fi connection information with other data to predict congestion levels more accurately. This allows for more accurate congestion predictions by utilizing Wi-Fi connection information. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input Wi-Fi connection information of tourist destinations into a generating AI and have the generating AI perform congestion level predictions.

[0104] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate information can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The suggestion unit can provide detailed suggestions of available spots in specific areas of a tourist destination. For example, it can suggest available spots in the main area and surrounding areas of the tourist destination separately. The suggestion unit can also suggest available times for specific spots within the tourist destination (e.g., observation decks, restaurants). Furthermore, the suggestion unit can provide real-time suggestions of available spots in each area of ​​the tourist destination. This allows users to obtain more specific information by suggesting detailed available spots in each area. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on available spots in specific areas of the tourist destination into a generating AI and have the generating AI execute detailed suggestions.

[0106] The suggestion unit can propose the optimal visit time by referring to past congestion data of the tourist destination when making a suggestion. For example, the suggestion unit can propose the optimal visit time for the current time based on past congestion data. The suggestion unit can also propose the optimal visit time by comparing past congestion data with current data. Furthermore, the suggestion unit can propose the optimal visit time for a specific time of day or day of the week based on past congestion data. In this way, the optimal visit time for the current time can be proposed by referring to past data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input past congestion data of the tourist destination into a generating AI and have the generating AI execute the proposal of the optimal visit time.

[0107] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit may prioritize suggestions for crowded areas. If the user is relaxed, the suggestion unit may prioritize suggestions for less crowded areas. Furthermore, if the user is in a hurry, the suggestion unit may prioritize suggestions for major tourist attractions. By prioritizing suggestions according to the user's emotions, more appropriate information can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The suggestion unit can propose the optimal sightseeing route based on the user's current location. For example, the suggestion unit can suggest the nearest tourist spot from the user's current location. It can also propose a sightseeing route that can be efficiently visited from the user's current location. Furthermore, the suggestion unit can propose the optimal sightseeing route considering the user's current location and congestion levels. This allows users to enjoy sightseeing efficiently by suggesting the optimal sightseeing route based on their current location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current location data into a generating AI and have the generating AI propose the optimal sightseeing route.

[0109] The suggestion unit can analyze the user's social media activity and suggest relevant tourist spots when making suggestions. For example, the suggestion unit can analyze the user's social media posts and suggest tourist spots of interest. The suggestion unit can also suggest relevant tourist spots based on the user's social media check-in information. Furthermore, the suggestion unit can analyze the activity of the user's social media followers and suggest relevant tourist spots. In this way, by analyzing the user's social media activity, it is possible to suggest more relevant tourist spots. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant tourist spots.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The tourism support system can estimate the user's emotions and provide real-time information on the congestion levels of tourist destinations based on those emotions. For example, if the user is feeling stressed, the system will prioritize displaying less crowded tourist destinations. Conversely, if the user is relaxed, the system can display even more crowded tourist destinations. Furthermore, if the user is in a hurry, the system can suggest the most efficient sightseeing route. This enables the provision of information tailored to the user's emotions, resulting in a more comfortable travel experience.

[0112] The tourism support system can estimate congestion levels at tourist destinations by analyzing audio data collected from those locations. For example, it can analyze audio data in real time to estimate congestion levels. It can also identify areas with high congestion levels based on the audio data analysis results. Furthermore, it can combine the audio data analysis results with other data to estimate congestion levels more accurately. This allows for more accurate congestion estimation by analyzing audio data.

[0113] The tourism support system can estimate the user's emotions and adjust the presentation of the analysis results based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of analysis results according to the user's emotions, it can provide more appropriate information.

[0114] The tourism support system can estimate congestion levels using Wi-Fi connection information at tourist destinations. For example, it can collect Wi-Fi connection information at tourist destinations in real time and estimate congestion levels. It can also identify areas with high congestion levels based on the analysis results of the Wi-Fi connection information. Furthermore, it can combine the analysis results of the Wi-Fi connection information with other data to estimate congestion levels more accurately. In this way, it is possible to estimate congestion levels more accurately by using Wi-Fi connection information.

[0115] The tourism support system can estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is stressed, it can provide simple, easy-to-understand information. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can provide concise information. In this way, by adjusting the way information is presented according to the user's emotions, it can provide more appropriate information.

[0116] The tourism support system can collect detailed congestion data for specific areas within a tourist destination and provide information on congestion levels for each area. For example, it can collect congestion data separately for the main area and surrounding areas of a tourist destination and provide detailed congestion information. It can also collect congestion data for specific spots within a tourist destination (e.g., observation decks, restaurants) individually and provide this information to users. Furthermore, it can update congestion data for each area of ​​a tourist destination in real time, providing users with the latest information. This allows users to obtain more specific information by providing detailed congestion information for each area.

[0117] The tourism support system can estimate the user's emotions and adjust its crowd prediction method based on those emotions. For example, if the user is feeling stressed, it can prioritize predicting crowd levels in highly crowded areas. Conversely, if the user is relaxed, it can prioritize predicting crowd levels in less crowded areas. Furthermore, if the user is in a hurry, it can prioritize predicting crowd levels at major tourist attractions. By adjusting the crowd prediction method according to the user's emotions, it can provide more accurate prediction results.

[0118] Tourism support systems can improve prediction accuracy by referencing past congestion data for tourist destinations. For example, they can make current congestion predictions based on past congestion data. They can also improve prediction accuracy by comparing past congestion data with current data. Furthermore, they can make congestion predictions for specific time periods or days of the week based on past congestion data. This allows for more accurate current congestion predictions by referencing past data.

[0119] The tourism support system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide more appropriate information.

[0120] The tourism support system can provide detailed suggestions for available spots within specific areas of a tourist destination. For example, it can suggest available spots in the main area and surrounding areas of a tourist destination separately. It can also suggest available times for specific spots within a tourist destination (e.g., observation decks, restaurants). Furthermore, it can provide real-time suggestions for available spots within each area of ​​a tourist destination. This allows users to obtain more specific information by suggesting detailed available spots in each area.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects information on the congestion levels of tourist destinations and facilities. The data collection unit uses sensors, image acquisition devices, and location information acquisition methods to collect information on the congestion levels of tourist destinations and facilities in real time. For example, cameras installed at tourist destinations monitor the flow of people and determine the level of congestion. The data collection unit can also track the movements of tourists using location information data from smartphones. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the collected data and determine the current congestion level. For example, the AI ​​uses technologies such as machine learning and deep learning to analyze the collected data and calculate the degree of congestion. Step 3: The service provider provides the results analyzed by the analysis unit. The service provider then provides the analysis results to the user. For example, the user can check the current congestion status through a smartphone app or website. Step 4: The prediction unit performs congestion prediction based on the data obtained by the analysis unit. The prediction unit uses AI to predict congestion based on past data. For example, if past data is analyzed and there is a tendency for congestion during specific times of day or days of the week, the prediction unit will use that information to suggest the optimal time for the user to visit. Step 5: The suggestion unit proposes less crowded spots and optimal visiting times based on the results obtained by the prediction unit. The suggestion unit proposes less crowded spots and optimal visiting times to the user based on the prediction results. For example, by suggesting less crowded spots, the user can enjoy sightseeing more efficiently.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, prediction unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects real-time congestion information for tourist destinations and facilities using the camera 42 and location information acquisition means of the smart device 14. The analysis unit analyzes the collected data using AI, for example, the specific processing unit 290 of the data processing unit 12, to determine the current congestion status. The provision unit provides the analysis results to the user, for example, the control unit 46A of the smart device 14. The prediction unit predicts congestion based on past data, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit suggests available spots and optimal visiting times based on the prediction results, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, prediction unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects real-time congestion information for tourist spots and facilities using the camera 42 and location information acquisition means of the smart glasses 214. The analysis unit analyzes the collected data using AI, for example, the specific processing unit 290 of the data processing unit 12, to determine the current congestion status. The provision unit provides the analysis results to the user, for example, the control unit 46A of the smart glasses 214. The prediction unit predicts congestion based on past data, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit suggests available spots and optimal visiting times based on the prediction results, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[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 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.

[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 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.

[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 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, prediction unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects real-time congestion information for tourist spots and facilities using the camera 42 and location information acquisition means of the headset terminal 314. The analysis unit analyzes the collected data using AI, for example, the specific processing unit 290 of the data processing unit 12, to determine the current congestion status. The provision unit provides the analysis results to the user, for example, the control unit 46A of the headset terminal 314. The prediction unit predicts congestion based on past data, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit suggests available spots and optimal visiting times based on the prediction results, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, prediction unit, and proposal 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 real-time congestion information for tourist spots and facilities using the camera 42 and location information acquisition means of the robot 414. The analysis unit analyzes the collected data using AI, for example, by the specific processing unit 290 of the data processing unit 12, to determine the current congestion status. The provision unit provides the analysis results to the user, for example, by the control unit 46A of the robot 414. The prediction unit makes congestion predictions based on past data, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes available spots and optimal visiting times based on the prediction results, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection department that collects information on congestion levels at tourist destinations and facilities, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides the results of the analysis performed by the aforementioned analysis unit, A prediction unit that performs congestion prediction based on the data obtained by the analysis unit, The system includes a suggestion unit that proposes available spots and optimal visiting times based on the results obtained by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Sensors, image acquisition devices, and location information acquisition methods are used to collect real-time information on congestion levels at tourist destinations and facilities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed by AI to determine the current congestion level. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the analysis results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, AI predicts congestion based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the prediction results, we suggest available spots and optimal visiting times to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of congestion data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is This system collects detailed information on congestion levels in specific areas of tourist destinations and provides congestion status data for each area. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting congestion data, the data is corrected based on weather and event information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information on congestion levels, audio data from tourist destinations is analyzed to estimate the degree of congestion. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting congestion data, the level of congestion is estimated using Wi-Fi connection information from tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Detailed analysis of congestion levels in specific areas of tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past congestion data for tourist destinations is referenced to correct for current congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, audio data from tourist destinations is analyzed to estimate the level of congestion. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, congestion levels are estimated using Wi-Fi connection information from tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When provided, the service suggests the optimal sightseeing route based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, it will be customized based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal display method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we analyze the user's social media activity to provide relevant tourism information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, We estimate user sentiment and adjust the congestion prediction method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, During the forecasting process, detailed congestion predictions are made for specific areas within tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, we improve prediction accuracy by referring to past congestion data for tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, During the prediction process, audio data from tourist destinations is analyzed to predict the level of congestion. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, we use Wi-Fi connection information from tourist destinations to predict congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, we will provide detailed information on available spots in specific areas of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, we will refer to past congestion data for tourist destinations to suggest the optimal time to visit. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a suggestion, the system will propose the optimal sightseeing route based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and propose relevant tourist spots. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on congestion levels at tourist destinations and facilities, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides the results of the analysis performed by the aforementioned analysis unit, A prediction unit that performs congestion prediction based on the data obtained by the analysis unit, The system includes a suggestion unit that proposes available spots and optimal visiting times based on the results obtained by the prediction unit. A system characterized by the following features.

2. The aforementioned collection unit is Sensors, image acquisition devices, and location information acquisition methods are used to collect real-time information on congestion levels at tourist destinations and facilities. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using AI to determine the current congestion level. The system according to feature 1.

4. The aforementioned supply unit is, Provide the analysis results to the user. The system according to feature 1.

5. The prediction unit, AI predicts congestion based on past data. The system according to feature 1.

6. The aforementioned proposal section is, Based on the prediction results, we suggest available spots and optimal visiting times to the user. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of congestion data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is This system collects detailed information on congestion levels in specific areas of tourist destinations and provides congestion status data for each area. The system according to feature 1.

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