system

Abstract

The system according to this embodiment aims to provide multi-functional information and countermeasures in a unified manner in tourist destinations. [Solution] The system according to this embodiment comprises an information provision unit, a guidance unit, a multilingual support unit, a congestion countermeasure unit, a disaster countermeasure unit, and a sales promotion unit. The information provision unit provides information that meets the needs of tourists and local residents. The guidance unit provides guidance on tourist destinations and introduces recommended spots based on the information provided by the information provision unit. The multilingual support unit provides multilingual guidance based on the information provided by the guidance unit. The congestion countermeasure unit monitors the congestion status of tourist destinations in real time based on the information provided by the multilingual support unit and provides route guidance to avoid congestion. The disaster countermeasure unit provides guidance on evacuation routes and evacuation sites in the event of a disaster based on the information provided by the congestion countermeasure unit. The sales promotion unit analyzes the user's purchase history and preferences based on the information provided by the disaster countermeasure unit and proposes the most suitable products.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a system for comprehensively providing information, taking congestion countermeasures, disaster countermeasures, etc. at tourist destinations is not fully developed, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively provide multi-functional information and take countermeasures at tourist destinations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information provision unit, a guidance unit, a multilingual support unit, a congestion management unit, a disaster response unit, and a sales promotion unit. The information provision unit provides information that meets the needs of tourists and local residents. The guidance unit provides information about tourist destinations and introduces recommended spots based on the information provided by the information provision unit. The multilingual support unit provides multilingual guidance based on the information provided by the guidance unit. The congestion management unit monitors the congestion status of tourist destinations in real time based on the information provided by the multilingual support unit and provides route guidance to avoid congestion. The disaster response unit provides information about evacuation routes and evacuation sites in the event of a disaster based on the information provided by the congestion management unit. The sales promotion unit analyzes the user's purchase history and preferences based on the information provided by the disaster response unit and proposes the most suitable products. [Effects of the Invention]

[0007] The system according to this embodiment can provide multi-functional information and countermeasures in a unified manner at tourist destinations. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 multifunctional AI vending machine system according to an embodiment of the present invention is a vending machine equipped with a generating AI that has multiple functions such as regional revitalization, inbound tourism support, overtourism countermeasures, disaster countermeasures, and sales promotion support in tourist areas. This vending machine is used as a touchpoint with tourists and local residents. The multifunctional AI vending machine system is installed in tourist areas, and when tourists or local residents access the vending machine, the generating AI provides information tailored to the user's needs. For example, it can provide information on tourist areas, introduce recommended spots, and provide information on local events. In addition, it provides multilingual guidance for foreign tourists, strengthening inbound tourism support. Furthermore, as a countermeasure against overtourism, the generating AI can monitor the congestion status of tourist areas in real time and provide route guidance to avoid congestion. This provides tourists with a comfortable sightseeing experience and alleviates congestion in tourist areas. As a disaster countermeasure, the generating AI can provide guidance on evacuation routes and evacuation sites in the event of a disaster. For example, in the event of a disaster such as an earthquake or typhoon, the generating AI provides evacuation information in real time to ensure the safety of tourists and local residents. As a sales promotion support feature, the generating AI can analyze users' purchase history and preferences to suggest the most suitable products. For example, it can suggest products exclusive to tourist areas or seasonal products to boost sales. In this way, vending machines equipped with generating AI can provide multifunctional services in tourist areas, contributing to regional revitalization and increased tourist satisfaction. Thus, a multifunctional AI vending machine system can provide multifunctional services in tourist areas, contributing to regional revitalization and increased tourist satisfaction.

[0029] The multifunctional AI vending machine system according to this embodiment comprises an information provision unit, a guidance unit, a multilingual support unit, a congestion countermeasure unit, a disaster countermeasure unit, and a sales promotion unit. The information provision unit provides information that meets the needs of tourists and local residents. For example, the information provision unit provides information on tourist destinations, recommendations for spots, and information on local events. The information provision unit uses a generation AI to analyze the needs of tourists and local residents and provide optimal information. For example, the generation AI can suggest the most suitable tourist spots based on the tourist's current location and interests. The guidance unit provides information on tourist destinations and recommendations for spots based on the information provided by the information provision unit. The guidance unit uses a generation AI to provide detailed information on tourist destinations. For example, the guidance unit can provide detailed information on the history and highlights of tourist destinations. The multilingual support unit provides multilingual guidance based on the information provided by the guidance unit. The multilingual support unit uses a generation AI to provide multilingual guidance to foreign tourists. For example, the multilingual support unit can provide guidance in multiple languages, such as English, Chinese, and Korean. The Congestion Management Department monitors congestion levels at tourist destinations in real time based on information provided by the Multilingual Support Department and provides route guidance to avoid congestion. The Congestion Management Department uses generative AI to analyze congestion levels at tourist destinations and propose the optimal route. For example, the Congestion Management Department can propose routes to avoid congestion based on the tourist's current location and destination. The Disaster Management Department provides guidance on evacuation routes and shelters in the event of a disaster based on information provided by the Congestion Management Department. The Disaster Management Department uses generative AI to guide users to the optimal evacuation routes and shelters in the event of a disaster. For example, the Disaster Management Department can provide evacuation information in real time when disasters such as earthquakes and typhoons occur. The Sales Promotion Department analyzes users' purchase history and preferences based on information provided by the Disaster Management Department and proposes the most suitable products. The Sales Promotion Department uses generative AI to analyze users' purchase history and preferences and propose the most suitable products. For example, the Sales Promotion Department can propose products exclusive to tourist destinations or seasonal products. As a result, the multi-functional AI vending machine system according to this embodiment can provide a variety of services in tourist areas, contributing to regional revitalization and improved tourist satisfaction.

[0030] The Information Department provides information tailored to the needs of tourists and local residents. For example, it provides guides to tourist destinations, recommendations for places to visit, and information on local events. Specifically, it can provide detailed information such as the history and culture of tourist destinations, access methods, opening hours, and admission fees. It also updates local event information, such as festivals, concerts, and exhibitions, in real time to provide tourists with the latest information. The Information Department uses generative AI to analyze the needs of tourists and local residents and provide optimal information. The generative AI can suggest the most suitable tourist spots based on the tourist's current location and interests. For example, if a tourist is interested in historical buildings, the generative AI will suggest nearby historical tourist spots based on that interest. Furthermore, the generative AI can analyze the tourist's past visit and search history to provide customized information tailored to individual needs. This allows the Information Department to provide more personalized information to tourists and local residents, improving their tourism experience. In addition, the Information Department can provide real-time information such as weather and traffic conditions, which tourists can use as a reference when planning their trips. This will allow the information department to provide tourists and local residents with comprehensive and up-to-date information, making their stay at tourist destinations more fulfilling.

[0031] The Information Department provides information about tourist destinations and recommends spots based on information provided by the Information Department. The Information Department uses generative AI to provide detailed information about tourist destinations. Specifically, it can provide detailed explanations of the history and highlights of tourist destinations. For example, the generative AI can explain the historical background and cultural significance of a tourist destination, promoting a deeper understanding among tourists. The generative AI can also introduce tourist attractions, recommended photo spots, and hidden gems, enabling tourists to have a more fulfilling travel experience. Furthermore, the Information Department can provide information on surrounding facilities, restaurants, and accommodations. This allows tourists to learn about the attractions of the surrounding area as well as the tourist destination itself, making it easier to plan their stay. The Information Department can provide customized guidance according to the interests and needs of tourists. For example, it can recommend child-friendly activities and facilities to families and suggest romantic spots to couples. The Information Department can also suggest efficient sightseeing routes based on the tourist's length of stay and mode of transportation. This allows the Information Department to provide tourists with more personalized guidance, enriching their stay at the tourist destination.

[0032] The multilingual support unit provides multilingual guidance based on information provided by the information unit. The multilingual support unit uses generative AI to provide multilingual guidance to foreign tourists. Specifically, it can provide guidance in multiple languages, including English, Chinese, and Korean. The generative AI translates information about tourist destinations into each language, providing accurate and easy-to-understand guidance to foreign tourists. For example, it provides information such as the history, highlights, and access methods of tourist destinations in multiple languages, allowing foreign tourists to enjoy sightseeing without feeling a language barrier. Furthermore, the multilingual support unit can respond to foreign tourists' questions in real time. The generative AI analyzes foreign tourists' questions and provides appropriate answers. For example, it can quickly provide information that foreign tourists need, such as opening hours and admission fees for tourist destinations, and information on nearby restaurants and accommodations. In addition to providing information about tourist destinations, the multilingual support unit can also provide emergency response and disaster information in multiple languages. This allows foreign tourists to feel safe and secure during their stay at tourist destinations. The multilingual support department provides foreign tourists with comprehensive and accurate information in multiple languages, making their stay at tourist destinations more comfortable.

[0033] The Congestion Management Department monitors congestion levels at tourist destinations in real time based on information provided by the Multilingual Support Department and provides route guidance to avoid congestion. The Congestion Management Department uses generative AI to analyze congestion levels at tourist destinations and propose optimal routes. Specifically, it monitors congestion levels in each area of ​​a tourist destination in real time and proposes routes to tourists to avoid congestion. The generative AI calculates the optimal route to avoid congestion based on the tourist's current location and destination, and guides them accordingly. For example, if a major attraction at a tourist destination is crowded, the generative AI can suggest alternative routes or other attractions to avoid congestion. The Congestion Management Department can also predict congestion levels at tourist destinations. Based on past data and current conditions, the generative AI predicts congestion trends for specific times of day and days of the week, and proposes the optimal time to visit to tourists to avoid congestion. This allows tourists to avoid congestion and enjoy sightseeing comfortably. Furthermore, the Congestion Management Department can also provide congestion information to tourist destination operators, supporting them in improving operational efficiency. This enables the Congestion Management Department to provide tourists with a comfortable sightseeing experience and improve the operational efficiency of tourist destinations.

[0034] The Disaster Response Department provides information on evacuation routes and shelters in the event of a disaster, based on information provided by the Congestion Management Department. The Disaster Response Department uses a generative AI to guide people to the most suitable evacuation routes and shelters during a disaster. Specifically, it can provide real-time evacuation information in the event of disasters such as earthquakes and typhoons. The generative AI analyzes the type and scale of the disaster and the current situation to propose the most suitable evacuation routes and shelters for tourists and local residents. For example, in the event of an earthquake, the generative AI considers the risk of building collapse and road conditions to guide people to safe evacuation routes. Similarly, if a typhoon is approaching, the generative AI can suggest the necessity of evacuation and suitable shelters based on wind speed and rainfall forecasts. Furthermore, the Disaster Response Department can provide not only evacuation information but also information on first aid and emergency contacts. This allows tourists and local residents to take quick and appropriate action in the event of a disaster. The Disaster Response Department can provide comprehensive and accurate disaster information to tourists and local residents, ensuring their safety during disasters.

[0035] The Sales Promotion Department analyzes users' purchase history and preferences based on information provided by the Disaster Response Department and proposes the most suitable products. Using generative AI, the Sales Promotion Department analyzes users' purchase history and preferences to suggest the most suitable products. Specifically, based on a tourist's past purchase history and preferences, it can suggest products exclusive to tourist destinations or seasonal products. The generative AI analyzes users' purchase patterns and preferences and provides customized product suggestions tailored to each individual user. For example, it can suggest new souvenirs to tourists who have previously purchased souvenirs, and suggest seasonal products to tourists who have purchased seasonal items. The Sales Promotion Department can also provide information on events and campaigns in tourist destinations. The generative AI analyzes information on events and campaigns held in tourist destinations and provides tourists with this information at the optimal time. This allows tourists to enjoy shopping more at tourist destinations. Furthermore, the Sales Promotion Department can collect user feedback and continuously improve the accuracy and effectiveness of product suggestions. This enables the Sales Promotion Department to provide tourists with optimal product suggestions and enhance their shopping experience at tourist destinations.

[0036] The Information Service can provide information on tourist destinations, recommend spots, and provide information on local events. For example, as part of tourist destination guidance, the Information Service can provide information on tourist spots and access. The Information Service can use generative AI to provide detailed information on tourist destinations. For example, the Information Service can provide detailed explanations of the history and highlights of tourist destinations. The Information Service can use generative AI to suggest the most suitable tourist spots based on the tourist's current location and interests. For example, the Information Service can prioritize recommending spots that tourists are likely to be interested in. The Information Service can also use generative AI to provide information on events in tourist destinations. For example, the Information Service can provide real-time information on events held locally. By providing information on tourist destinations, recommending spots, and local events, the Information Service can improve tourist satisfaction.

[0037] The multilingual support unit can provide multilingual guidance to foreign tourists. For example, it can provide guidance in multiple languages, such as English, Chinese, and Korean. The multilingual support unit uses generative AI to provide multilingual guidance to foreign tourists. For example, it can provide information about tourist destinations and recommendations in multiple languages. The multilingual support unit uses generative AI to provide optimal translations based on the tourist's native language. For example, if the tourist's native language is English, the multilingual support unit can provide guidance in English. The multilingual support unit uses generative AI to improve translation accuracy by considering the cultural background of tourist destinations. For example, it can provide appropriate translations considering the historical background and cultural characteristics of tourist destinations. This strengthens inbound tourism support by providing multilingual guidance to foreign tourists.

[0038] The congestion management unit can monitor congestion levels at tourist destinations in real time and provide route guidance to avoid congestion. For example, the congestion management unit can monitor congestion levels at tourist destinations in real time and propose routes to avoid congestion. The congestion management unit can use generative AI to analyze congestion levels at tourist destinations and propose the optimal route. For example, the congestion management unit can propose routes to avoid congestion based on the tourist's current location and destination. The congestion management unit can use generative AI to refer to past congestion data of tourist destinations and provide optimal route guidance. For example, the congestion management unit can propose the optimal route to avoid congestion based on past congestion data. The congestion management unit can use generative AI to improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations. For example, the congestion management unit can propose the optimal route by considering the topography of tourist destinations. As a result, by monitoring congestion levels at tourist destinations in real time and providing route guidance to avoid congestion, it is possible to provide tourists with a comfortable sightseeing experience and alleviate congestion at tourist destinations.

[0039] The disaster response department can provide guidance on evacuation routes and shelters in the event of a disaster. For example, the disaster response department can provide real-time evacuation information when disasters such as earthquakes and typhoons occur. The disaster response department can use generative AI to guide people to the most suitable evacuation routes and shelters in the event of a disaster. For example, the disaster response department can propose the most suitable evacuation route considering the geographical characteristics of tourist areas. The disaster response department can use generative AI to guide people to the most suitable evacuation routes by referring to past disaster data. For example, the disaster response department can propose the most suitable evacuation route based on past disaster data. The disaster response department can use generative AI to adjust the order of evacuation routes by referring to relevant information about tourist areas. For example, the disaster response department can adjust the order of evacuation routes based on information from when disasters occurred in tourist areas. This ensures the safety of tourists and local residents by providing guidance on evacuation routes and shelters in the event of a disaster.

[0040] The sales promotion department can analyze users' purchase history and preferences to propose the most suitable products. For example, the sales promotion department can propose products exclusive to tourist destinations or seasonal products. The sales promotion department can use generative AI to analyze users' purchase history and preferences and propose the most suitable products. For example, the sales promotion department can propose relevant products based on a user's past purchase history. The sales promotion department can use generative AI to improve the accuracy of product proposals by considering the characteristics of tourist destinations. For example, the sales promotion department can propose the most suitable products according to the season and events of the tourist destination. In this way, sales can be promoted by analyzing users' purchase history and preferences and proposing the most suitable products.

[0041] The information provision department can analyze a user's past information provision history and select the most suitable information provision method. For example, the information provision department can suggest new related spots based on information about tourist destinations the user has visited in the past. The information provision department can use generative AI to analyze a user's past information provision history and select the most suitable information provision method. For example, the information provision department can prioritize providing information methods (text, audio, etc.) that the user has used in the past. The information provision department can use generative AI to predict and suggest information to be provided at specific times based on the user's past information provision history. In this way, the most suitable information provision method can be selected by analyzing the user's past information provision history.

[0042] The information provider can filter information based on the user's current location and time of day. For example, it can prioritize providing information on tourist spots close to the user's current location. The information provider can use generative AI to analyze the user's current location and time of day and provide optimal information. For example, it can provide information on spots that users frequently visit at specific times of day. The information provider can use generative AI to suggest the optimal tourist route, taking into account the distance from the user's current location. By filtering information based on the user's current location and time of day, it can provide more appropriate information.

[0043] The information provision unit can select the optimal display method when providing information, taking into account the user's device information. For example, if the user is using a smartphone, the information provision unit can provide a display method that matches the screen size. The information provision unit can analyze the user's device information using generative AI and select the optimal display method. For example, if the user is using a tablet, the information provision unit can provide a display method optimized for a large screen. Using generative AI, the information provision unit can provide a concise and highly visible display method if the user is using a smartwatch. In this way, by selecting the optimal display method considering the user's device information, it is possible to provide information that is easier to see.

[0044] The information provision department can analyze users' social media activity and provide relevant information when providing information. For example, the information provision department can suggest new related spots based on information about tourist destinations that users have shared on social media. The information provision department can use generative AI to analyze users' social media activity and provide optimal information. For example, the information provision department can provide event information for tourist destinations that users follow on social media. The information provision department can use generative AI to suggest information about tourist destinations that users might be interested in based on their social media activity. In this way, relevant information can be provided by analyzing users' social media activity.

[0045] The information system can adjust the level of detail in the information provided based on the importance of the tourist destination. For example, it can provide detailed information for important tourist destinations. The information system can use generative AI to analyze the importance of tourist destinations and provide optimal information. For example, it can provide concise information for less important tourist destinations. The information system can use generative AI to adjust the length and content of the information according to the importance of the tourist destination. This allows for more appropriate information to be provided by adjusting the level of detail based on the importance of the tourist destination.

[0046] The guidance system can apply different guidance algorithms depending on the category of the tourist destination. For example, for historical tourist destinations, it can provide guidance that explains the historical background in detail. The guidance system can use generative AI to analyze the category of the tourist destination and provide optimal guidance. For example, for tourist destinations with natural scenery, it can provide guidance that emphasizes the scenic highlights. The guidance system can use generative AI to apply different guidance algorithms depending on the category of the tourist destination. For example, for tourist destinations centered on activities, it can provide guidance that explains the details of the activities. In this way, by applying different guidance algorithms depending on the category of the tourist destination, more appropriate guidance can be provided.

[0047] The guidance system can determine the priority of guidance based on the congestion level of tourist destinations. For example, for crowded tourist destinations, the guidance system can suggest alternative routes to avoid congestion. The guidance system can use generative AI to analyze the congestion level of tourist destinations and provide optimal guidance. For example, for tourist destinations that are not crowded, the guidance system can provide detailed guidance. The guidance system can use generative AI to adjust the order of guidance according to the congestion level of tourist destinations. This allows for more appropriate guidance by determining the priority of guidance based on the congestion level of tourist destinations.

[0048] The guidance system can adjust the order of guidance by referring to relevant event information at the tourist destination. For example, the guidance system can adjust the order of guidance based on information about events held at the tourist destination. The guidance system can use generative AI to analyze relevant event information at the tourist destination and provide optimal guidance. For example, the guidance system can suggest the optimal guidance route according to the event schedule. The guidance system can use generative AI to adjust the content of the guidance considering relevant event information at the tourist destination. As a result, by adjusting the order of guidance by referring to relevant event information at the tourist destination, it can provide more appropriate guidance.

[0049] The multilingual support unit can select the optimal translation method based on the user's native language when providing multilingual support. For example, if the user's native language is English, the multilingual support unit will provide guidance in English. The multilingual support unit can analyze the user's native language using generative AI and select the optimal translation method. For example, if the user's native language is Chinese, the multilingual support unit can provide guidance in Chinese. The multilingual support unit can select the optimal translation method based on the user's native language using generative AI. For example, if the user's native language is Spanish, the multilingual support unit can provide guidance in Spanish. By selecting the optimal translation method based on the user's native language, more appropriate guidance can be provided.

[0050] The multilingual support unit can improve translation accuracy by considering the cultural background of tourist destinations when providing multilingual support. For example, the multilingual support unit can provide appropriate translations by considering the historical background of tourist destinations. The multilingual support unit can analyze the cultural background of tourist destinations using generative AI and provide optimal translations. For example, the multilingual support unit can provide appropriate translations by considering the cultural characteristics of tourist destinations. The multilingual support unit can provide optimal translations by considering regionally specific expressions of tourist destinations using generative AI. As a result, by improving translation accuracy by considering the cultural background of tourist destinations, more appropriate guidance can be provided.

[0051] The multilingual support unit can select the optimal display method when supporting multiple languages, taking into account the user's device information. For example, if the user is using a smartphone, the multilingual support unit can provide a display method that matches the screen size. The multilingual support unit can analyze the user's device information using generative AI and select the optimal display method. For example, if the user is using a tablet, the multilingual support unit can provide a display method optimized for a large screen. If the user is using a smartwatch, the multilingual support unit can provide a concise and highly visible display method using generative AI. In this way, by selecting the optimal display method considering the user's device information, it is possible to provide information that is easier to see.

[0052] The multilingual support unit can analyze users' social media activity and provide relevant information when implementing multilingual support. For example, it can suggest new relevant spots based on information about tourist destinations shared by users on social media. The multilingual support unit can use generative AI to analyze users' social media activity and provide optimal information. For example, it can provide event information for tourist destinations that users follow on social media. The multilingual support unit can use generative AI to suggest information about tourist destinations that users might be interested in based on their social media activity. In this way, it can provide relevant information by analyzing users' social media activity.

[0053] The congestion management unit can provide optimal route guidance by referring to past congestion data of tourist destinations during congestion management. For example, the congestion management unit can propose the optimal route to avoid congestion based on past congestion data. The congestion management unit can analyze past congestion data of tourist destinations using generative AI and propose the optimal route. For example, the congestion management unit can analyze past congestion data and propose a route that is not congested during a specific time period. The congestion management unit can predict the congestion situation of tourist destinations by referring to past congestion data of tourist destinations using generative AI and propose the optimal route. As a result, by providing optimal route guidance by referring to past congestion data of tourist destinations, more appropriate guidance can be provided.

[0054] The congestion management unit can improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations when dealing with congestion. For example, the congestion management unit can propose the optimal route by considering the topography of tourist destinations. The congestion management unit can analyze the geographical characteristics of tourist destinations using generative AI and propose the optimal route. For example, the congestion management unit can propose a route to avoid congestion based on the geographical characteristics of tourist destinations. The congestion management unit can improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations using generative AI. As a result, by improving the accuracy of route guidance by considering the geographical characteristics of tourist destinations, more appropriate guidance can be provided.

[0055] The congestion management unit can provide optimal route guidance by considering the user's device information during congestion management. For example, if the user is using a smartphone, the congestion management unit can provide route guidance that is adapted to the screen size. The congestion management unit can analyze the user's device information using generative AI and provide optimal route guidance. For example, if the user is using a tablet, the congestion management unit can provide route guidance optimized for a large screen. Using generative AI, the congestion management unit can provide concise and highly visible route guidance if the user is using a smartwatch. In this way, by providing optimal route guidance that takes the user's device information into consideration, it is possible to provide more easily viewable information.

[0056] The congestion management unit can adjust the route guidance order by referring to relevant event information at tourist destinations during congestion management. For example, the congestion management unit can adjust the route guidance order based on information about events held at tourist destinations. The congestion management unit can analyze relevant event information at tourist destinations using generational AI and provide optimal route guidance. For example, the congestion management unit can provide optimal route guidance according to the event schedule. The congestion management unit can adjust the content of route guidance by considering relevant event information at tourist destinations using generational AI. As a result, by adjusting the route guidance order by referring to relevant event information at tourist destinations, more appropriate guidance can be provided.

[0057] The disaster response department can guide people to the optimal evacuation route by referring to past disaster data during a disaster. For example, the disaster response department can propose the optimal evacuation route based on past disaster data. The disaster response department can analyze past disaster data using generative AI and propose the optimal evacuation route. For example, the disaster response department can analyze past disaster data and propose the optimal evacuation route for a specific disaster. The disaster response department can improve the accuracy of evacuation routes by referring to past disaster data using generative AI. As a result, by guiding people to the optimal evacuation route by referring to past disaster data, it can provide more appropriate guidance.

[0058] The disaster response department can improve the accuracy of evacuation routes by considering the geographical characteristics of tourist areas during disaster response. For example, the disaster response department can propose the optimal evacuation route by considering the topography of the tourist area. The disaster response department can analyze the geographical characteristics of tourist areas using generative AI and propose the optimal evacuation route. For example, the disaster response department can propose an evacuation route based on the geographical characteristics of the tourist area. The disaster response department can improve the accuracy of evacuation routes by considering the geographical characteristics of tourist areas using generative AI. As a result, by improving the accuracy of evacuation routes by considering the geographical characteristics of tourist areas, more appropriate guidance can be provided.

[0059] The disaster response department can provide optimal evacuation routes during disaster response, taking into account the user's device information. For example, if the user is using a smartphone, the disaster response department can provide an evacuation route that is optimized for the screen size. The disaster response department can also analyze the user's device information using generative AI and provide the optimal evacuation route. For example, if the user is using a tablet, the disaster response department can provide an evacuation route optimized for a larger screen. Using generative AI, the disaster response department can provide a concise and highly visible evacuation route if the user is using a smartwatch. In this way, by providing the optimal evacuation route while considering the user's device information, it is possible to provide more easily understandable information.

[0060] The disaster response department can adjust the order of evacuation routes by referring to relevant information about tourist destinations during disaster response. For example, the disaster response department can adjust the order of evacuation routes based on information about disasters occurring in tourist destinations. The disaster response department can use generative AI to analyze relevant information about tourist destinations and provide the optimal evacuation route. For example, the disaster response department can provide the optimal evacuation route according to the situation at the time of a disaster. The disaster response department can use generative AI to adjust the content of evacuation routes considering relevant information about tourist destinations. This allows for more appropriate guidance to be provided by adjusting the order of evacuation routes by referring to relevant information about tourist destinations.

[0061] The sales promotion department can analyze a user's past purchase history during sales promotions to suggest the most suitable products. For example, the sales promotion department can suggest related products based on products the user has purchased in the past. The sales promotion department can use generative AI to analyze a user's past purchase history and suggest the most suitable products. For example, the sales promotion department can predict and suggest products that a user will purchase at a specific time based on their past purchase history. The sales promotion department can use generative AI to analyze a user's past purchase history and suggest the most popular products. In this way, it becomes possible to suggest the most suitable products by analyzing a user's past purchase history.

[0062] The sales promotion department can improve the accuracy of product suggestions by considering the characteristics of tourist destinations during sales promotion. For example, the sales promotion department can propose the most suitable products by considering the characteristics of tourist destinations. The sales promotion department can use generative AI to analyze the characteristics of tourist destinations and propose the most suitable products. For example, the sales promotion department can propose the most suitable products according to the season and events of the tourist destinations. The sales promotion department can use generative AI to improve the accuracy of product suggestions by considering the characteristics of tourist destinations. As a result, the accuracy of product suggestions is improved by considering the characteristics of tourist destinations.

[0063] The sales promotion department can provide optimal product suggestions during sales promotions, taking into account the user's device information. For example, if the user is using a smartphone, the sales promotion department can provide product suggestions tailored to the screen size. The sales promotion department can use generative AI to analyze the user's device information and provide optimal product suggestions. For example, if the user is using a tablet, the sales promotion department can provide product suggestions optimized for a larger screen. Using generative AI, the sales promotion department can provide concise and highly visible product suggestions if the user is using a smartwatch. In this way, by providing optimal product suggestions that take the user's device information into account, more easily viewable information can be provided.

[0064] The sales promotion department can adjust the order of product suggestions during sales promotion by referring to relevant information about tourist destinations. For example, the sales promotion department can adjust the order of product suggestions based on event information at tourist destinations. The sales promotion department can use generative AI to analyze relevant information about tourist destinations and provide optimal product suggestions. For example, the sales promotion department can provide optimal product suggestions in accordance with the event schedule. The sales promotion department can use generative AI to adjust the content of product suggestions considering relevant information about tourist destinations. This allows the order of product suggestions to be adjusted by referring to relevant information about tourist destinations.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The multi-functional AI vending machine system can also be equipped with a health management unit that monitors the user's health status and suggests health-conscious products. For example, the health management unit can measure the user's heart rate and body temperature and suggest products appropriate to their health condition. Using generative AI, the health management unit can analyze the user's health data and suggest the most suitable products. For instance, if the user's heart rate is high, it can suggest a beverage with a relaxing effect. The health management unit can also use generative AI to adjust product suggestions based on the user's health condition. For example, if the user is tired, it can suggest a product suitable for energy replenishment. This allows the system to support healthier choices by suggesting products tailored to the user's health condition.

[0067] The multi-functional AI vending machine system can also include an inventory management unit that predicts user purchasing behavior and optimizes inventory in advance. For example, the inventory management unit can predict which products will be in high demand during specific times of day or seasons, based on past purchase data. Using generative AI, the inventory management unit can analyze user purchasing behavior and perform optimal inventory management. For instance, it can replenish products in advance based on event information in tourist areas, anticipating high demand. Using generative AI, the inventory management unit can monitor inventory levels in real time and automatically issue replenishment orders as needed. This prevents stockouts and improves the user's purchasing experience.

[0068] The multi-functional AI vending machine system can also include a promotion management unit that analyzes users' purchase history and conducts customized promotions for specific user groups. For example, the promotion management unit can offer discount coupons to specific user groups based on past purchase history. Using generative AI, the promotion management unit can analyze users' purchase history and propose optimal promotions. For example, it can offer discounts on relevant products to coincide with specific seasons or events. Using generative AI, the promotion management unit can adjust the content of promotions based on users' purchase history. For example, it can suggest new products related to items previously purchased by the user. This allows for sales promotion by conducting customized promotions based on users' purchase history.

[0069] The multi-functional AI vending machine system can also include a time-of-day suggestion unit that predicts user purchasing behavior and suggests products tailored to specific time periods. For example, the time-of-day suggestion unit might suggest breakfast items in the morning. The time-of-day suggestion unit can analyze user purchasing behavior using generative AI and suggest the most suitable products. For example, it might suggest lunch items during lunchtime. The time-of-day suggestion unit can also use generative AI to suggest products tailored to specific time periods. For example, it might suggest snacks and drinks in the evening. By providing product suggestions tailored to specific time periods, the system can offer products that meet user needs.

[0070] The multi-functional AI vending machine system can also be equipped with a seasonal suggestion unit that analyzes the user's purchase history and suggests products tailored to the specific season. For example, the seasonal suggestion unit might suggest cold drinks or ice cream in the summer. The seasonal suggestion unit can use generative AI to analyze the user's purchase history and suggest the most suitable products. For example, the seasonal suggestion unit might suggest hot drinks or soup in the winter. The seasonal suggestion unit can use generative AI to make product suggestions tailored to the specific season. For example, the seasonal suggestion unit might suggest products suitable for cherry blossom viewing in the spring. In this way, by making product suggestions tailored to the specific season, the system can provide products that meet the user's needs.

[0071] The multi-functional AI vending machine system can also include an event suggestion unit that predicts user purchasing behavior and makes product suggestions tailored to specific events. For example, the event suggestion unit could suggest relevant products based on event information in tourist areas. Using generative AI, the event suggestion unit can analyze user purchasing behavior and suggest optimal products. For example, it could offer discounts on relevant products to coincide with specific events. The event suggestion unit could also use generative AI to make product suggestions tailored to specific events. For example, it could suggest optimal products based on the event's schedule. This allows the system to provide products that meet user needs by offering product suggestions tailored to specific events.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The information provision department provides information tailored to the needs of tourists and local residents. For example, it provides information on tourist destinations, recommended spots, and local events. Using generative AI, it analyzes the needs of tourists and local residents and provides optimal information. Based on the tourist's current location and interests, it can suggest the most suitable tourist spots. Step 2: The information department provides guidance on tourist destinations and recommends spots based on the information provided by the information provision department. Using generation AI, it can provide detailed guidance on tourist destinations and explain their history and highlights in detail. Step 3: The multilingual support unit provides multilingual guidance based on the information provided by the guidance unit. Using generation AI, guidance can be provided to foreign tourists in multiple languages, such as English, Chinese, and Korean. Step 4: The congestion management unit monitors the congestion status of tourist destinations in real time based on information provided by the multilingual support unit and provides route guidance to avoid congestion. Using generation AI, it can analyze the congestion status of tourist destinations and suggest the optimal route based on the tourist's current location and destination. Step 5: The disaster response department provides information on evacuation routes and shelters in the event of a disaster, based on the information provided by the congestion management department. Using generation AI, it is possible to provide real-time guidance on the optimal evacuation routes and shelters in the event of disasters such as earthquakes and typhoons. Step 6: The Sales Promotion Department analyzes users' purchase history and preferences based on information provided by the Disaster Response Department and proposes the most suitable products. Using generative AI, it can analyze users' purchase history and preferences and propose products exclusive to tourist destinations or seasonal products.

[0074] (Example of form 2) The multifunctional AI vending machine system according to an embodiment of the present invention is a vending machine equipped with a generating AI that has multiple functions such as regional revitalization, inbound tourism support, overtourism countermeasures, disaster countermeasures, and sales promotion support in tourist areas. This vending machine is used as a touchpoint with tourists and local residents. The multifunctional AI vending machine system is installed in tourist areas, and when tourists or local residents access the vending machine, the generating AI provides information tailored to the user's needs. For example, it can provide information on tourist areas, introduce recommended spots, and provide information on local events. In addition, it provides multilingual guidance for foreign tourists, strengthening inbound tourism support. Furthermore, as a countermeasure against overtourism, the generating AI can monitor the congestion status of tourist areas in real time and provide route guidance to avoid congestion. This provides tourists with a comfortable sightseeing experience and alleviates congestion in tourist areas. As a disaster countermeasure, the generating AI can provide guidance on evacuation routes and evacuation sites in the event of a disaster. For example, in the event of a disaster such as an earthquake or typhoon, the generating AI provides evacuation information in real time to ensure the safety of tourists and local residents. As a sales promotion support feature, the generating AI can analyze users' purchase history and preferences to suggest the most suitable products. For example, it can suggest products exclusive to tourist areas or seasonal products to boost sales. In this way, vending machines equipped with generating AI can provide multifunctional services in tourist areas, contributing to regional revitalization and increased tourist satisfaction. Thus, a multifunctional AI vending machine system can provide multifunctional services in tourist areas, contributing to regional revitalization and increased tourist satisfaction.

[0075] The multifunctional AI vending machine system according to this embodiment comprises an information provision unit, a guidance unit, a multilingual support unit, a congestion countermeasure unit, a disaster countermeasure unit, and a sales promotion unit. The information provision unit provides information that meets the needs of tourists and local residents. For example, the information provision unit provides information on tourist destinations, recommendations for spots, and information on local events. The information provision unit uses a generation AI to analyze the needs of tourists and local residents and provide optimal information. For example, the generation AI can suggest the most suitable tourist spots based on the tourist's current location and interests. The guidance unit provides information on tourist destinations and recommendations for spots based on the information provided by the information provision unit. The guidance unit uses a generation AI to provide detailed information on tourist destinations. For example, the guidance unit can provide detailed information on the history and highlights of tourist destinations. The multilingual support unit provides multilingual guidance based on the information provided by the guidance unit. The multilingual support unit uses a generation AI to provide multilingual guidance to foreign tourists. For example, the multilingual support unit can provide guidance in multiple languages, such as English, Chinese, and Korean. The Congestion Management Department monitors congestion levels at tourist destinations in real time based on information provided by the Multilingual Support Department and provides route guidance to avoid congestion. The Congestion Management Department uses generative AI to analyze congestion levels at tourist destinations and propose the optimal route. For example, the Congestion Management Department can propose routes to avoid congestion based on the tourist's current location and destination. The Disaster Management Department provides guidance on evacuation routes and shelters in the event of a disaster based on information provided by the Congestion Management Department. The Disaster Management Department uses generative AI to guide users to the optimal evacuation routes and shelters in the event of a disaster. For example, the Disaster Management Department can provide evacuation information in real time when disasters such as earthquakes and typhoons occur. The Sales Promotion Department analyzes users' purchase history and preferences based on information provided by the Disaster Management Department and proposes the most suitable products. The Sales Promotion Department uses generative AI to analyze users' purchase history and preferences and propose the most suitable products. For example, the Sales Promotion Department can propose products exclusive to tourist destinations or seasonal products. As a result, the multi-functional AI vending machine system according to this embodiment can provide a variety of services in tourist areas, contributing to regional revitalization and improved tourist satisfaction.

[0076] The Information Department provides information tailored to the needs of tourists and local residents. For example, it provides guides to tourist destinations, recommendations for places to visit, and information on local events. Specifically, it can provide detailed information such as the history and culture of tourist destinations, access methods, opening hours, and admission fees. It also updates local event information, such as festivals, concerts, and exhibitions, in real time to provide tourists with the latest information. The Information Department uses generative AI to analyze the needs of tourists and local residents and provide optimal information. The generative AI can suggest the most suitable tourist spots based on the tourist's current location and interests. For example, if a tourist is interested in historical buildings, the generative AI will suggest nearby historical tourist spots based on that interest. Furthermore, the generative AI can analyze the tourist's past visit and search history to provide customized information tailored to individual needs. This allows the Information Department to provide more personalized information to tourists and local residents, improving their tourism experience. In addition, the Information Department can provide real-time information such as weather and traffic conditions, which tourists can use as a reference when planning their trips. This will allow the information department to provide tourists and local residents with comprehensive and up-to-date information, making their stay at tourist destinations more fulfilling.

[0077] The Information Department provides information about tourist destinations and recommends spots based on information provided by the Information Department. The Information Department uses generative AI to provide detailed information about tourist destinations. Specifically, it can provide detailed explanations of the history and highlights of tourist destinations. For example, the generative AI can explain the historical background and cultural significance of a tourist destination, promoting a deeper understanding among tourists. The generative AI can also introduce tourist attractions, recommended photo spots, and hidden gems, enabling tourists to have a more fulfilling travel experience. Furthermore, the Information Department can provide information on surrounding facilities, restaurants, and accommodations. This allows tourists to learn about the attractions of the surrounding area as well as the tourist destination itself, making it easier to plan their stay. The Information Department can provide customized guidance according to the interests and needs of tourists. For example, it can recommend child-friendly activities and facilities to families and suggest romantic spots to couples. The Information Department can also suggest efficient sightseeing routes based on the tourist's length of stay and mode of transportation. This allows the Information Department to provide tourists with more personalized guidance, enriching their stay at the tourist destination.

[0078] The multilingual support unit provides multilingual guidance based on information provided by the information unit. The multilingual support unit uses generative AI to provide multilingual guidance to foreign tourists. Specifically, it can provide guidance in multiple languages, including English, Chinese, and Korean. The generative AI translates information about tourist destinations into each language, providing accurate and easy-to-understand guidance to foreign tourists. For example, it provides information such as the history, highlights, and access methods of tourist destinations in multiple languages, allowing foreign tourists to enjoy sightseeing without feeling a language barrier. Furthermore, the multilingual support unit can respond to foreign tourists' questions in real time. The generative AI analyzes foreign tourists' questions and provides appropriate answers. For example, it can quickly provide information that foreign tourists need, such as opening hours and admission fees for tourist destinations, and information on nearby restaurants and accommodations. In addition to providing information about tourist destinations, the multilingual support unit can also provide emergency response and disaster information in multiple languages. This allows foreign tourists to feel safe and secure during their stay at tourist destinations. The multilingual support department provides foreign tourists with comprehensive and accurate information in multiple languages, making their stay at tourist destinations more comfortable.

[0079] The Congestion Management Department monitors congestion levels at tourist destinations in real time based on information provided by the Multilingual Support Department and provides route guidance to avoid congestion. The Congestion Management Department uses generative AI to analyze congestion levels at tourist destinations and propose optimal routes. Specifically, it monitors congestion levels in each area of ​​a tourist destination in real time and proposes routes to tourists to avoid congestion. The generative AI calculates the optimal route to avoid congestion based on the tourist's current location and destination, and guides them accordingly. For example, if a major attraction at a tourist destination is crowded, the generative AI can suggest alternative routes or other attractions to avoid congestion. The Congestion Management Department can also predict congestion levels at tourist destinations. Based on past data and current conditions, the generative AI predicts congestion trends for specific times of day and days of the week, and proposes the optimal time to visit to tourists to avoid congestion. This allows tourists to avoid congestion and enjoy sightseeing comfortably. Furthermore, the Congestion Management Department can also provide congestion information to tourist destination operators, supporting them in improving operational efficiency. This enables the Congestion Management Department to provide tourists with a comfortable sightseeing experience and improve the operational efficiency of tourist destinations.

[0080] The Disaster Response Department provides information on evacuation routes and shelters in the event of a disaster, based on information provided by the Congestion Management Department. The Disaster Response Department uses a generative AI to guide people to the most suitable evacuation routes and shelters during a disaster. Specifically, it can provide real-time evacuation information in the event of disasters such as earthquakes and typhoons. The generative AI analyzes the type and scale of the disaster and the current situation to propose the most suitable evacuation routes and shelters for tourists and local residents. For example, in the event of an earthquake, the generative AI considers the risk of building collapse and road conditions to guide people to safe evacuation routes. Similarly, if a typhoon is approaching, the generative AI can suggest the necessity of evacuation and suitable shelters based on wind speed and rainfall forecasts. Furthermore, the Disaster Response Department can provide not only evacuation information but also information on first aid and emergency contacts. This allows tourists and local residents to take quick and appropriate action in the event of a disaster. The Disaster Response Department can provide comprehensive and accurate disaster information to tourists and local residents, ensuring their safety during disasters.

[0081] The Sales Promotion Department analyzes users' purchase history and preferences based on information provided by the Disaster Response Department and proposes the most suitable products. Using generative AI, the Sales Promotion Department analyzes users' purchase history and preferences to suggest the most suitable products. Specifically, based on a tourist's past purchase history and preferences, it can suggest products exclusive to tourist destinations or seasonal products. The generative AI analyzes users' purchase patterns and preferences and provides customized product suggestions tailored to each individual user. For example, it can suggest new souvenirs to tourists who have previously purchased souvenirs, and suggest seasonal products to tourists who have purchased seasonal items. The Sales Promotion Department can also provide information on events and campaigns in tourist destinations. The generative AI analyzes information on events and campaigns held in tourist destinations and provides tourists with this information at the optimal time. This allows tourists to enjoy shopping more at tourist destinations. Furthermore, the Sales Promotion Department can collect user feedback and continuously improve the accuracy and effectiveness of product suggestions. This enables the Sales Promotion Department to provide tourists with optimal product suggestions and enhance their shopping experience at tourist destinations.

[0082] The Information Service can provide information on tourist destinations, recommend spots, and provide information on local events. For example, as part of tourist destination guidance, the Information Service can provide information on tourist spots and access. The Information Service can use generative AI to provide detailed information on tourist destinations. For example, the Information Service can provide detailed explanations of the history and highlights of tourist destinations. The Information Service can use generative AI to suggest the most suitable tourist spots based on the tourist's current location and interests. For example, the Information Service can prioritize recommending spots that tourists are likely to be interested in. The Information Service can also use generative AI to provide information on events in tourist destinations. For example, the Information Service can provide real-time information on events held locally. By providing information on tourist destinations, recommending spots, and local events, the Information Service can improve tourist satisfaction.

[0083] The multilingual support unit can provide multilingual guidance to foreign tourists. For example, it can provide guidance in multiple languages, such as English, Chinese, and Korean. The multilingual support unit uses generative AI to provide multilingual guidance to foreign tourists. For example, it can provide information about tourist destinations and recommendations in multiple languages. The multilingual support unit uses generative AI to provide optimal translations based on the tourist's native language. For example, if the tourist's native language is English, the multilingual support unit can provide guidance in English. The multilingual support unit uses generative AI to improve translation accuracy by considering the cultural background of tourist destinations. For example, it can provide appropriate translations considering the historical background and cultural characteristics of tourist destinations. This strengthens inbound tourism support by providing multilingual guidance to foreign tourists.

[0084] The congestion management unit can monitor congestion levels at tourist destinations in real time and provide route guidance to avoid congestion. For example, the congestion management unit can monitor congestion levels at tourist destinations in real time and propose routes to avoid congestion. The congestion management unit can use generative AI to analyze congestion levels at tourist destinations and propose the optimal route. For example, the congestion management unit can propose routes to avoid congestion based on the tourist's current location and destination. The congestion management unit can use generative AI to refer to past congestion data of tourist destinations and provide optimal route guidance. For example, the congestion management unit can propose the optimal route to avoid congestion based on past congestion data. The congestion management unit can use generative AI to improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations. For example, the congestion management unit can propose the optimal route by considering the topography of tourist destinations. As a result, by monitoring congestion levels at tourist destinations in real time and providing route guidance to avoid congestion, it is possible to provide tourists with a comfortable sightseeing experience and alleviate congestion at tourist destinations.

[0085] The disaster response department can provide guidance on evacuation routes and shelters in the event of a disaster. For example, the disaster response department can provide real-time evacuation information when disasters such as earthquakes and typhoons occur. The disaster response department can use generative AI to guide people to the most suitable evacuation routes and shelters in the event of a disaster. For example, the disaster response department can propose the most suitable evacuation route considering the geographical characteristics of tourist areas. The disaster response department can use generative AI to guide people to the most suitable evacuation routes by referring to past disaster data. For example, the disaster response department can propose the most suitable evacuation route based on past disaster data. The disaster response department can use generative AI to adjust the order of evacuation routes by referring to relevant information about tourist areas. For example, the disaster response department can adjust the order of evacuation routes based on information from when disasters occurred in tourist areas. This ensures the safety of tourists and local residents by providing guidance on evacuation routes and shelters in the event of a disaster.

[0086] The sales promotion department can analyze users' purchase history and preferences to propose the most suitable products. For example, the sales promotion department can propose products exclusive to tourist destinations or seasonal products. The sales promotion department can use generative AI to analyze users' purchase history and preferences and propose the most suitable products. For example, the sales promotion department can propose relevant products based on a user's past purchase history. The sales promotion department can use generative AI to improve the accuracy of product proposals by considering the characteristics of tourist destinations. For example, the sales promotion department can propose the most suitable products according to the season and events of the tourist destination. In this way, sales can be promoted by analyzing users' purchase history and preferences and proposing the most suitable products.

[0087] The information provider can estimate the user's emotions and adjust the content of the information provided based on those emotions. For example, if the user is excited, the information provider can prioritize providing information about tourist activities. The information provider can use generative AI to estimate the user's emotions and provide optimal information. For example, if the user is tired, the information provider can provide information about relaxing spots or cafes. The information provider can use generative AI to determine the priority of information based on the user's emotions. For example, if the user is lost, the information provider can provide information about the nearest information center or tourist information center. By adjusting the content of the information provided 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The information provision department can analyze a user's past information provision history and select the most suitable information provision method. For example, the information provision department can suggest new related spots based on information about tourist destinations the user has visited in the past. The information provision department can use generative AI to analyze a user's past information provision history and select the most suitable information provision method. For example, the information provision department can prioritize providing information methods (text, audio, etc.) that the user has used in the past. The information provision department can use generative AI to predict and suggest information to be provided at specific times based on the user's past information provision history. In this way, the most suitable information provision method can be selected by analyzing the user's past information provision history.

[0089] The information provider can filter information based on the user's current location and time of day. For example, it can prioritize providing information on tourist spots close to the user's current location. The information provider can use generative AI to analyze the user's current location and time of day and provide optimal information. For example, it can provide information on spots that users frequently visit at specific times of day. The information provider can use generative AI to suggest the optimal tourist route, taking into account the distance from the user's current location. By filtering information based on the user's current location and time of day, it can provide more appropriate information.

[0090] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is excited, the information provider will prioritize providing activity information. The information provider can use generative AI to estimate the user's emotions and provide optimal information. For example, if the user is relaxed, the information provider can prioritize providing information about relaxing spots. The information provider can use generative AI to prioritize information based on the user's emotions. For example, if the user is lost, the information provider can prioritize providing information about the nearest information center. By prioritizing the information provided 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The information provision unit can select the optimal display method when providing information, taking into account the user's device information. For example, if the user is using a smartphone, the information provision unit can provide a display method that matches the screen size. The information provision unit can analyze the user's device information using generative AI and select the optimal display method. For example, if the user is using a tablet, the information provision unit can provide a display method optimized for a large screen. Using generative AI, the information provision unit can provide a concise and highly visible display method if the user is using a smartwatch. In this way, by selecting the optimal display method considering the user's device information, it is possible to provide information that is easier to see.

[0092] The information provision department can analyze users' social media activity and provide relevant information when providing information. For example, the information provision department can suggest new related spots based on information about tourist destinations that users have shared on social media. The information provision department can use generative AI to analyze users' social media activity and provide optimal information. For example, the information provision department can provide event information for tourist destinations that users follow on social media. The information provision department can use generative AI to suggest information about tourist destinations that users might be interested in based on their social media activity. In this way, relevant information can be provided by analyzing users' social media activity.

[0093] The guidance system can estimate the user's emotions and adjust the way it presents the guidance based on those emotions. For example, if the user is relaxed, the guidance system will provide guidance at a leisurely pace. The guidance system can use generative AI to estimate the user's emotions and provide optimal guidance. For example, if the user is in a hurry, the guidance system can provide guidance that emphasizes the shortest route. The guidance system can use generative AI to adjust the way it presents the guidance based on the user's emotions. For example, if the user is excited, the guidance system can provide guidance with visually stimulating effects. In this way, by adjusting the way the guidance is presented according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The information system can adjust the level of detail in the information provided based on the importance of the tourist destination. For example, it can provide detailed information for important tourist destinations. The information system can use generative AI to analyze the importance of tourist destinations and provide optimal information. For example, it can provide concise information for less important tourist destinations. The information system can use generative AI to adjust the length and content of the information according to the importance of the tourist destination. This allows for more appropriate information to be provided by adjusting the level of detail based on the importance of the tourist destination.

[0095] The guidance system can apply different guidance algorithms depending on the category of the tourist destination. For example, for historical tourist destinations, it can provide guidance that explains the historical background in detail. The guidance system can use generative AI to analyze the category of the tourist destination and provide optimal guidance. For example, for tourist destinations with natural scenery, it can provide guidance that emphasizes the scenic highlights. The guidance system can use generative AI to apply different guidance algorithms depending on the category of the tourist destination. For example, for tourist destinations centered on activities, it can provide guidance that explains the details of the activities. In this way, by applying different guidance algorithms depending on the category of the tourist destination, more appropriate guidance can be provided.

[0096] The guidance system can estimate the user's emotions and adjust the length of the guidance based on those emotions. For example, if the user is in a hurry, the guidance system can provide short, concise guidance. The guidance system can use generative AI to estimate the user's emotions and provide optimal guidance. For example, if the user is relaxed, the guidance system can provide longer guidance with detailed explanations. The guidance system can use generative AI to adjust the length of the guidance based on the user's emotions. For example, if the user is excited, the guidance system can provide guidance with visually stimulating effects. By adjusting the length of the guidance according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The guidance system can determine the priority of guidance based on the congestion level of tourist destinations. For example, for crowded tourist destinations, the guidance system can suggest alternative routes to avoid congestion. The guidance system can use generative AI to analyze the congestion level of tourist destinations and provide optimal guidance. For example, for tourist destinations that are not crowded, the guidance system can provide detailed guidance. The guidance system can use generative AI to adjust the order of guidance according to the congestion level of tourist destinations. This allows for more appropriate guidance by determining the priority of guidance based on the congestion level of tourist destinations.

[0098] The guidance system can adjust the order of guidance by referring to relevant event information at the tourist destination. For example, the guidance system can adjust the order of guidance based on information about events held at the tourist destination. The guidance system can use generative AI to analyze relevant event information at the tourist destination and provide optimal guidance. For example, the guidance system can suggest the optimal guidance route according to the event schedule. The guidance system can use generative AI to adjust the content of the guidance considering relevant event information at the tourist destination. As a result, by adjusting the order of guidance by referring to relevant event information at the tourist destination, it can provide more appropriate guidance.

[0099] The multilingual support unit can estimate the user's emotions and adjust the multilingual expression based on the estimated emotions. For example, if the user is nervous, the multilingual support unit can provide concise and easy-to-understand expressions. The multilingual support unit can use generative AI to estimate the user's emotions and provide optimal guidance. For example, if the user is relaxed, the multilingual support unit can provide expressions that include detailed information. The multilingual support unit can use generative AI to adjust the multilingual expression based on the user's emotions. For example, if the user is in a hurry, the multilingual support unit can provide expressions that get straight to the point. In this way, by adjusting the multilingual expression according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The multilingual support unit can select the optimal translation method based on the user's native language when providing multilingual support. For example, if the user's native language is English, the multilingual support unit will provide guidance in English. The multilingual support unit can analyze the user's native language using generative AI and select the optimal translation method. For example, if the user's native language is Chinese, the multilingual support unit can provide guidance in Chinese. The multilingual support unit can select the optimal translation method based on the user's native language using generative AI. For example, if the user's native language is Spanish, the multilingual support unit can provide guidance in Spanish. By selecting the optimal translation method based on the user's native language, more appropriate guidance can be provided.

[0101] The multilingual support unit can improve translation accuracy by considering the cultural background of tourist destinations when providing multilingual support. For example, the multilingual support unit can provide appropriate translations by considering the historical background of tourist destinations. The multilingual support unit can analyze the cultural background of tourist destinations using generative AI and provide optimal translations. For example, the multilingual support unit can provide appropriate translations by considering the cultural characteristics of tourist destinations. The multilingual support unit can provide optimal translations by considering regionally specific expressions of tourist destinations using generative AI. As a result, by improving translation accuracy by considering the cultural background of tourist destinations, more appropriate guidance can be provided.

[0102] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the user is nervous, the multilingual support unit will prioritize providing guidance in the user's native language. The multilingual support unit can use generative AI to estimate the user's emotions and provide optimal guidance. For example, if the user is relaxed, the multilingual support unit can provide multilingual support that includes detailed information. The multilingual support unit can use generative AI to determine the priority of multilingual support based on the user's emotions. For example, if the user is in a hurry, the multilingual support unit can provide concise multilingual support. In this way, by determining the priority of multilingual support according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The multilingual support unit can select the optimal display method when supporting multiple languages, taking into account the user's device information. For example, if the user is using a smartphone, the multilingual support unit can provide a display method that matches the screen size. The multilingual support unit can analyze the user's device information using generative AI and select the optimal display method. For example, if the user is using a tablet, the multilingual support unit can provide a display method optimized for a large screen. If the user is using a smartwatch, the multilingual support unit can provide a concise and highly visible display method using generative AI. In this way, by selecting the optimal display method considering the user's device information, it is possible to provide information that is easier to see.

[0104] The multilingual support unit can analyze users' social media activity and provide relevant information when implementing multilingual support. For example, it can suggest new relevant spots based on information about tourist destinations shared by users on social media. The multilingual support unit can use generative AI to analyze users' social media activity and provide optimal information. For example, it can provide event information for tourist destinations that users follow on social media. The multilingual support unit can use generative AI to suggest information about tourist destinations that users might be interested in based on their social media activity. In this way, it can provide relevant information by analyzing users' social media activity.

[0105] The congestion management unit can estimate the user's emotions and adjust congestion management methods based on the estimated emotions. For example, if the user is feeling stressed, the congestion management unit can suggest an alternative route to avoid congestion. The congestion management unit can use generative AI to estimate the user's emotions and provide optimal guidance. For example, if the user is relaxed, the congestion management unit can provide information about crowded spots. The congestion management unit can use generative AI to adjust congestion management methods based on the user's emotions. For example, if the user is in a hurry, the congestion management unit can suggest the shortest route. In this way, by adjusting congestion management methods according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The congestion management unit can provide optimal route guidance by referring to past congestion data of tourist destinations during congestion management. For example, the congestion management unit can propose the optimal route to avoid congestion based on past congestion data. The congestion management unit can analyze past congestion data of tourist destinations using generative AI and propose the optimal route. For example, the congestion management unit can analyze past congestion data and propose a route that is not congested during a specific time period. The congestion management unit can predict the congestion situation of tourist destinations by referring to past congestion data of tourist destinations using generative AI and propose the optimal route. As a result, by providing optimal route guidance by referring to past congestion data of tourist destinations, more appropriate guidance can be provided.

[0107] The congestion management unit can improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations when dealing with congestion. For example, the congestion management unit can propose the optimal route by considering the topography of tourist destinations. The congestion management unit can analyze the geographical characteristics of tourist destinations using generative AI and propose the optimal route. For example, the congestion management unit can propose a route to avoid congestion based on the geographical characteristics of tourist destinations. The congestion management unit can improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations using generative AI. As a result, by improving the accuracy of route guidance by considering the geographical characteristics of tourist destinations, more appropriate guidance can be provided.

[0108] The congestion management unit can estimate the user's emotions and determine the priority of congestion management measures based on the estimated emotions. For example, if the user is feeling stressed, the congestion management unit will prioritize suggesting routes that avoid congestion. The congestion management unit can use generative AI to estimate the user's emotions and provide optimal guidance. For example, if the user is relaxed, the congestion management unit can provide information about crowded spots. The congestion management unit can use generative AI to determine the priority of congestion management measures based on the user's emotions. For example, if the user is in a hurry, the congestion management unit can prioritize suggesting the shortest route. In this way, by determining the priority of congestion management measures according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The congestion management unit can provide optimal route guidance by considering the user's device information during congestion management. For example, if the user is using a smartphone, the congestion management unit can provide route guidance that is adapted to the screen size. The congestion management unit can analyze the user's device information using generative AI and provide optimal route guidance. For example, if the user is using a tablet, the congestion management unit can provide route guidance optimized for a large screen. Using generative AI, the congestion management unit can provide concise and highly visible route guidance if the user is using a smartwatch. In this way, by providing optimal route guidance that takes the user's device information into consideration, it is possible to provide more easily viewable information.

[0110] The congestion management unit can adjust the route guidance order by referring to relevant event information at tourist destinations during congestion management. For example, the congestion management unit can adjust the route guidance order based on information about events held at tourist destinations. The congestion management unit can analyze relevant event information at tourist destinations using generational AI and provide optimal route guidance. For example, the congestion management unit can provide optimal route guidance according to the event schedule. The congestion management unit can adjust the content of route guidance by considering relevant event information at tourist destinations using generational AI. As a result, by adjusting the route guidance order by referring to relevant event information at tourist destinations, more appropriate guidance can be provided.

[0111] The disaster response department can estimate the user's emotions and adjust disaster response methods based on those emotions. For example, if the user is in a state of panic, the disaster response department can guide them along evacuation routes in a calm voice. The disaster response department can use generative AI to estimate the user's emotions and provide optimal evacuation information. For example, if the user is calm, the disaster response department can provide detailed evacuation information. The disaster response department can use generative AI to adjust disaster response methods based on the user's emotions. For example, if the user is feeling anxious, the disaster response department can provide reassuring evacuation information. By adjusting disaster response methods according to the user's emotions, more appropriate guidance can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The disaster response department can guide people to the optimal evacuation route by referring to past disaster data during a disaster. For example, the disaster response department can propose the optimal evacuation route based on past disaster data. The disaster response department can analyze past disaster data using generative AI and propose the optimal evacuation route. For example, the disaster response department can analyze past disaster data and propose the optimal evacuation route for a specific disaster. The disaster response department can improve the accuracy of evacuation routes by referring to past disaster data using generative AI. As a result, by guiding people to the optimal evacuation route by referring to past disaster data, it can provide more appropriate guidance.

[0113] The disaster response department can improve the accuracy of evacuation routes by considering the geographical characteristics of tourist areas during disaster response. For example, the disaster response department can propose the optimal evacuation route by considering the topography of the tourist area. The disaster response department can analyze the geographical characteristics of tourist areas using generative AI and propose the optimal evacuation route. For example, the disaster response department can propose an evacuation route based on the geographical characteristics of the tourist area. The disaster response department can improve the accuracy of evacuation routes by considering the geographical characteristics of tourist areas using generative AI. As a result, by improving the accuracy of evacuation routes by considering the geographical characteristics of tourist areas, more appropriate guidance can be provided.

[0114] The disaster response department can estimate the user's emotions and determine the priority of disaster response measures based on those emotions. For example, if the user is in a state of panic, the disaster response department will prioritize suggesting the safest evacuation route. The disaster response department can use generative AI to estimate the user's emotions and provide optimal evacuation information. For example, if the user is calm, the disaster response department can provide detailed evacuation information. The disaster response department can use generative AI to determine the priority of disaster response measures based on the user's emotions. For example, if the user is feeling anxious, the disaster response department can prioritize providing reassuring evacuation information. This allows for more appropriate guidance by determining the priority of disaster response measures 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The disaster response department can provide optimal evacuation routes during disaster response, taking into account the user's device information. For example, if the user is using a smartphone, the disaster response department can provide an evacuation route that is optimized for the screen size. The disaster response department can also analyze the user's device information using generative AI and provide the optimal evacuation route. For example, if the user is using a tablet, the disaster response department can provide an evacuation route optimized for a larger screen. Using generative AI, the disaster response department can provide a concise and highly visible evacuation route if the user is using a smartwatch. In this way, by providing the optimal evacuation route while considering the user's device information, it is possible to provide more easily understandable information.

[0116] The disaster response department can adjust the order of evacuation routes by referring to relevant information about tourist destinations during disaster response. For example, the disaster response department can adjust the order of evacuation routes based on information about disasters occurring in tourist destinations. The disaster response department can use generative AI to analyze relevant information about tourist destinations and provide the optimal evacuation route. For example, the disaster response department can provide the optimal evacuation route according to the situation at the time of a disaster. The disaster response department can use generative AI to adjust the content of evacuation routes considering relevant information about tourist destinations. This allows for more appropriate guidance to be provided by adjusting the order of evacuation routes by referring to relevant information about tourist destinations.

[0117] The sales promotion department can estimate user emotions and adjust sales promotion methods based on those emotions. For example, if a user is excited, the sales promotion department can prioritize suggesting limited-edition or new products. The sales promotion department can use generative AI to estimate user emotions and suggest the most suitable products. For example, if a user is relaxed, the sales promotion department can suggest relaxing products. The sales promotion department can use generative AI to adjust sales promotion methods based on user emotions. For example, if a user is undecided, the sales promotion department can suggest popular or recommended products. By adjusting sales promotion methods according to user emotions, more appropriate product suggestions become possible. Emotion estimation is achieved using emotion estimation functions such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The sales promotion department can analyze a user's past purchase history during sales promotions to suggest the most suitable products. For example, the sales promotion department can suggest related products based on products the user has purchased in the past. The sales promotion department can use generative AI to analyze a user's past purchase history and suggest the most suitable products. For example, the sales promotion department can predict and suggest products that a user will purchase at a specific time based on their past purchase history. The sales promotion department can use generative AI to analyze a user's past purchase history and suggest the most popular products. In this way, it becomes possible to suggest the most suitable products by analyzing a user's past purchase history.

[0119] The sales promotion department can improve the accuracy of product suggestions by considering the characteristics of tourist destinations during sales promotion. For example, the sales promotion department can propose the most suitable products by considering the characteristics of tourist destinations. The sales promotion department can use generative AI to analyze the characteristics of tourist destinations and propose the most suitable products. For example, the sales promotion department can propose the most suitable products according to the season and events of the tourist destinations. The sales promotion department can use generative AI to improve the accuracy of product suggestions by considering the characteristics of tourist destinations. As a result, the accuracy of product suggestions is improved by considering the characteristics of tourist destinations.

[0120] The sales promotion department can estimate user emotions and determine sales promotion priorities based on those emotions. For example, if a user is excited, the sales promotion department can prioritize suggesting limited-edition or new products. The sales promotion department can use generative AI to estimate user emotions and suggest the most suitable products. For example, if a user is relaxed, the sales promotion department can prioritize suggesting relaxing products. The sales promotion department can use generative AI to determine sales promotion priorities based on user emotions. For example, if a user is undecided, the sales promotion department can prioritize suggesting popular or recommended products. This allows for more appropriate product suggestions by determining sales promotion priorities according to user emotions. Emotion estimation is achieved using emotion estimation functions such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The sales promotion department can provide optimal product suggestions during sales promotions, taking into account the user's device information. For example, if the user is using a smartphone, the sales promotion department can provide product suggestions tailored to the screen size. The sales promotion department can use generative AI to analyze the user's device information and provide optimal product suggestions. For example, if the user is using a tablet, the sales promotion department can provide product suggestions optimized for a larger screen. Using generative AI, the sales promotion department can provide concise and highly visible product suggestions if the user is using a smartwatch. In this way, by providing optimal product suggestions that take the user's device information into account, more easily viewable information can be provided.

[0122] The sales promotion department can adjust the order of product suggestions during sales promotion by referring to relevant information about tourist destinations. For example, the sales promotion department can adjust the order of product suggestions based on event information at tourist destinations. The sales promotion department can use generative AI to analyze relevant information about tourist destinations and provide optimal product suggestions. For example, the sales promotion department can provide optimal product suggestions in accordance with the event schedule. The sales promotion department can use generative AI to adjust the content of product suggestions considering relevant information about tourist destinations. This allows the order of product suggestions to be adjusted by referring to relevant information about tourist destinations.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The multi-functional AI vending machine system can also be equipped with a health management unit that monitors the user's health status and suggests health-conscious products. For example, the health management unit can measure the user's heart rate and body temperature and suggest products appropriate to their health condition. Using generative AI, the health management unit can analyze the user's health data and suggest the most suitable products. For instance, if the user's heart rate is high, it can suggest a beverage with a relaxing effect. The health management unit can also use generative AI to adjust product suggestions based on the user's health condition. For example, if the user is tired, it can suggest a product suitable for energy replenishment. This allows the system to support healthier choices by suggesting products tailored to the user's health condition.

[0125] The multi-functional AI vending machine system can also include an inventory management unit that predicts user purchasing behavior and optimizes inventory in advance. For example, the inventory management unit can predict which products will be in high demand during specific times of day or seasons, based on past purchase data. Using generative AI, the inventory management unit can analyze user purchasing behavior and perform optimal inventory management. For instance, it can replenish products in advance based on event information in tourist areas, anticipating high demand. Using generative AI, the inventory management unit can monitor inventory levels in real time and automatically issue replenishment orders as needed. This prevents stockouts and improves the user's purchasing experience.

[0126] The multi-functional AI vending machine system can also include an advertising management unit that estimates the user's emotions and adjusts the content of the advertisements displayed based on those emotions. For example, if the user is excited, the advertising management unit will prioritize displaying activity-related advertisements. The advertising management unit can use generative AI to estimate the user's emotions and display the most appropriate advertisements. For example, if the user is relaxed, the advertising management unit can display advertisements for products with relaxing effects. The advertising management unit can use generative AI to adjust the order in which advertisements are displayed based on the user's emotions. For example, if the user is undecided, the advertising management unit can prioritize displaying advertisements for popular or recommended products. In this way, by adjusting the content of the advertisements displayed according to the user's emotions, more effective advertisements can be provided.

[0127] The multi-functional AI vending machine system can also include a promotion management unit that analyzes users' purchase history and conducts customized promotions for specific user groups. For example, the promotion management unit can offer discount coupons to specific user groups based on past purchase history. Using generative AI, the promotion management unit can analyze users' purchase history and propose optimal promotions. For example, it can offer discounts on relevant products to coincide with specific seasons or events. Using generative AI, the promotion management unit can adjust the content of promotions based on users' purchase history. For example, it can suggest new products related to items previously purchased by the user. This allows for sales promotion by conducting customized promotions based on users' purchase history.

[0128] The multi-functional AI vending machine system can further include a voice guidance unit that estimates the user's emotions and adjusts the tone of voice guidance based on those emotions. For example, if the user is nervous, the voice guidance unit will provide guidance in a calm tone. The voice guidance unit can use generative AI to estimate the user's emotions and provide optimal voice guidance. For example, if the user is relaxed, the voice guidance unit can provide guidance in a bright tone. The voice guidance unit can use generative AI to adjust the tone of voice guidance based on the user's emotions. For example, if the user is in a hurry, the voice guidance unit can provide quick and concise guidance. In this way, by adjusting the tone of voice guidance according to the user's emotions, more appropriate guidance can be provided.

[0129] The multi-functional AI vending machine system can also include a time-of-day suggestion unit that predicts user purchasing behavior and suggests products tailored to specific time periods. For example, the time-of-day suggestion unit might suggest breakfast items in the morning. The time-of-day suggestion unit can analyze user purchasing behavior using generative AI and suggest the most suitable products. For example, it might suggest lunch items during lunchtime. The time-of-day suggestion unit can also use generative AI to suggest products tailored to specific time periods. For example, it might suggest snacks and drinks in the evening. By providing product suggestions tailored to specific time periods, the system can offer products that meet user needs.

[0130] The multi-functional AI vending machine system can also include a design management unit that estimates the user's emotions and adjusts the product packaging design based on those emotions. For example, if the user is excited, the design management unit might suggest a package with vibrant colors. The design management unit can use generative AI to estimate the user's emotions and provide the optimal package design. For example, if the user is relaxed, the design management unit might suggest a package with calming colors. The design management unit can use generative AI to adjust the content of the package design based on the user's emotions. For example, if the user is undecided, the design management unit might suggest a simple and easy-to-understand design. In this way, by adjusting the product packaging design according to the user's emotions, more attractive products can be offered.

[0131] The multi-functional AI vending machine system can also be equipped with a seasonal suggestion unit that analyzes the user's purchase history and suggests products tailored to the specific season. For example, the seasonal suggestion unit might suggest cold drinks or ice cream in the summer. The seasonal suggestion unit can use generative AI to analyze the user's purchase history and suggest the most suitable products. For example, the seasonal suggestion unit might suggest hot drinks or soup in the winter. The seasonal suggestion unit can use generative AI to make product suggestions tailored to the specific season. For example, the seasonal suggestion unit might suggest products suitable for cherry blossom viewing in the spring. In this way, by making product suggestions tailored to the specific season, the system can provide products that meet the user's needs.

[0132] The multi-functional AI vending machine system may further include a description management unit that estimates the user's emotions and adjusts the content of the product description based on the estimated emotions. For example, if the user is excited, the description management unit can provide a description that emphasizes the product's features. The description management unit can use generative AI to estimate the user's emotions and provide the most appropriate product description. For example, if the user is relaxed, the description management unit can provide a description that emphasizes the product's relaxing effect. The description management unit can use generative AI to adjust the content of the product description based on the user's emotions. For example, if the user is hesitant, the description management unit can provide a description that emphasizes the product's convenience. In this way, by adjusting the content of the product description according to the user's emotions, a more effective product description can be provided.

[0133] The multi-functional AI vending machine system can also include an event suggestion unit that predicts user purchasing behavior and makes product suggestions tailored to specific events. For example, the event suggestion unit could suggest relevant products based on event information in tourist areas. Using generative AI, the event suggestion unit can analyze user purchasing behavior and suggest optimal products. For example, it could offer discounts on relevant products to coincide with specific events. The event suggestion unit could also use generative AI to make product suggestions tailored to specific events. For example, it could suggest optimal products based on the event's schedule. This allows the system to provide products that meet user needs by offering product suggestions tailored to specific events.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The information provision department provides information tailored to the needs of tourists and local residents. For example, it provides information on tourist destinations, recommended spots, and local events. Using generative AI, it analyzes the needs of tourists and local residents and provides optimal information. Based on the tourist's current location and interests, it can suggest the most suitable tourist spots. Step 2: The information department provides guidance on tourist destinations and recommends spots based on the information provided by the information provision department. Using generation AI, it can provide detailed guidance on tourist destinations and explain their history and highlights in detail. Step 3: The multilingual support unit provides multilingual guidance based on the information provided by the guidance unit. Using generation AI, guidance can be provided to foreign tourists in multiple languages, such as English, Chinese, and Korean. Step 4: The congestion management unit monitors the congestion status of tourist destinations in real time based on information provided by the multilingual support unit and provides route guidance to avoid congestion. Using generation AI, it can analyze the congestion status of tourist destinations and suggest the optimal route based on the tourist's current location and destination. Step 5: The disaster response department provides information on evacuation routes and shelters in the event of a disaster, based on the information provided by the congestion management department. Using generation AI, it is possible to provide real-time guidance on the optimal evacuation routes and shelters in the event of disasters such as earthquakes and typhoons. Step 6: The Sales Promotion Department analyzes users' purchase history and preferences based on information provided by the Disaster Response Department and proposes the most suitable products. Using generative AI, it can analyze users' purchase history and preferences and propose products exclusive to tourist destinations or seasonal products.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements mentioned above, including the information provision unit, guidance unit, multilingual support unit, congestion countermeasure unit, disaster countermeasure unit, and sales promotion unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the information provision unit is implemented by the control unit 46A of the smart device 14 and provides information that meets the needs of tourists and local residents. The guidance unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides detailed guidance on tourist destinations. The multilingual support unit is implemented by, for example, the control unit 46A of the smart device 14 and provides multilingual guidance to foreign tourists. The congestion countermeasure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the congestion status of tourist destinations in real time and provides route guidance to avoid congestion. The disaster countermeasure unit is implemented by, for example, the control unit 46A of the smart device 14 and provides guidance on evacuation routes and evacuation sites in the event of a disaster. The sales promotion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's purchase history and preferences and proposes the most suitable products. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements mentioned above, including the information provision unit, guidance unit, multilingual support unit, congestion countermeasure unit, disaster countermeasure unit, and sales promotion unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the information provision unit is implemented by the control unit 46A of the smart glasses 214 and provides information that meets the needs of tourists and local residents. The guidance unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides detailed guidance on tourist destinations. The multilingual support unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides multilingual guidance to foreign tourists. The congestion countermeasure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the congestion status of tourist destinations in real time and provides route guidance to avoid congestion. The disaster countermeasure unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides guidance on evacuation routes and evacuation sites in the event of a disaster. The sales promotion department is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the user's purchase history and preferences and proposes the most suitable products. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements mentioned above, including the information provision unit, guidance unit, multilingual support unit, congestion countermeasure unit, disaster countermeasure unit, and sales promotion unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the information provision unit is implemented by the control unit 46A of the headset terminal 314 and provides information that meets the needs of tourists and local residents. The guidance unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides detailed guidance on tourist destinations. The multilingual support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides multilingual guidance to foreign tourists. The congestion countermeasure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the congestion status of tourist destinations in real time and provides route guidance to avoid congestion. The disaster countermeasure unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides guidance on evacuation routes and evacuation sites in the event of a disaster. The sales promotion department is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the user's purchase history and preferences and proposes the most suitable products. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the information provision unit, guidance unit, multilingual support unit, congestion countermeasure unit, disaster countermeasure unit, and sales promotion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the information provision unit is implemented by the control unit 46A of the robot 414 and provides information that meets the needs of tourists and local residents. The guidance unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides detailed guidance on tourist destinations. The multilingual support unit is implemented by, for example, the control unit 46A of the robot 414 and provides multilingual guidance to foreign tourists. The congestion countermeasure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the congestion status of tourist destinations in real time and provides route guidance to avoid congestion. The disaster countermeasure unit is implemented by, for example, the control unit 46A of the robot 414 and provides guidance on evacuation routes and evacuation sites in the event of a disaster. The sales promotion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's purchase history and preferences and proposes the most suitable products. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) A vending machine equipped with a generation AI that is installed in tourist areas, The Information Department provides information tailored to the needs of tourists and local residents, Based on the information provided by the aforementioned information provision department, the information department provides guidance on tourist destinations and introduces recommended spots. A multilingual support unit provides multilingual guidance based on the information provided by the aforementioned guidance unit, A congestion countermeasure unit monitors the congestion status of tourist destinations in real time based on the information provided by the aforementioned multilingual support unit and provides route guidance to avoid congestion. Based on the information provided by the aforementioned congestion countermeasures department, the disaster countermeasures department provides guidance on evacuation routes and evacuation sites in the event of a disaster. Based on the information provided by the aforementioned disaster response department, the sales promotion department analyzes the user's purchase history and preferences and proposes the most suitable products. Equipped with A system characterized by the following features. (Note 2) The aforementioned information provision unit, We provide information on tourist destinations, recommended spots, and local event information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned multilingual support unit is Providing multilingual guidance to foreign tourists. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned congestion countermeasures department, It monitors congestion levels at tourist destinations in real time and provides route guidance to help avoid crowds. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned disaster response department, Provide guidance on evacuation routes and shelters in the event of a disaster. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sales promotion department, We analyze users' purchase history and preferences to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information provision unit, It estimates the user's emotions and adjusts the content of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information provision unit, Analyze the user's past information provision history and select the optimal information provision method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information provision unit, When providing information, filtering is performed based on the user's current location and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information provision unit, 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 11) The aforementioned information provision unit, When providing information, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information provision unit, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned guide section is The system estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned guide section is When providing information, we adjust the level of detail in the guide based on the importance of the tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned guide section is When providing directions, different guidance algorithms are applied depending on the category of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is It estimates the user's emotions and adjusts the length of the guidance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is When giving a tour, we will determine the priority of the tour based on the level of crowding at the tourist spots. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is When giving a tour, we adjust the order of the tour by referring to information about related events at the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the multilingual expression based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned multilingual support unit is When supporting multiple languages, the optimal translation method is selected based on the user's native language. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned multilingual support unit is When providing multilingual support, we improve translation accuracy by taking into account the cultural background of tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned multilingual support unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned multilingual support unit is When supporting multiple languages, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned multilingual support unit is When providing multilingual support, the system analyzes users' social media activity and provides relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned congestion countermeasures department, The system estimates user sentiment and adjusts congestion management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned congestion countermeasures department, During congestion management, the system uses past congestion data from tourist destinations to provide optimal route guidance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned congestion countermeasures department, To address congestion, improve the accuracy of route guidance by considering the geographical characteristics of tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned congestion countermeasures department, The system estimates user sentiment and prioritizes congestion countermeasures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned congestion countermeasures department, During congestion management, the system provides optimal route guidance while considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned congestion countermeasures department, To manage congestion, the route guidance order is adjusted by referring to information on related events at tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned disaster response department, The system estimates user sentiment and adjusts disaster response methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned disaster response department, During disaster response, the system uses past disaster data to guide users to the optimal evacuation route. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned disaster response department, To improve the accuracy of evacuation routes during disaster response, take into account the geographical characteristics of tourist areas. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned disaster response department, It estimates user sentiment and determines disaster response priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned disaster response department, During disaster response, the system provides the optimal evacuation route, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned disaster response department, During disaster response, refer to relevant information about tourist destinations to adjust the order of evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned sales promotion department, We estimate user sentiment and adjust sales promotion methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned sales promotion department, During sales promotions, we analyze users' past purchase history to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned sales promotion department, When promoting sales, improve the accuracy of product suggestions by considering the characteristics of tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned sales promotion department, It estimates user sentiment and determines sales promotion priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned sales promotion department, During sales promotion, we provide optimal product recommendations while taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned sales promotion department, When promoting sales, we adjust the order of product suggestions by referring to relevant information about tourist destinations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0208] 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 vending machine equipped with a generation AI that is installed in tourist areas, The Information Department provides information tailored to the needs of tourists and local residents, Based on the information provided by the aforementioned information provision department, the information department provides guidance on tourist destinations and introduces recommended spots. A multilingual support unit provides multilingual guidance based on the information provided by the aforementioned guidance unit, A congestion countermeasure unit monitors the congestion status of tourist destinations in real time based on the information provided by the aforementioned multilingual support unit and provides route guidance to avoid congestion. Based on the information provided by the aforementioned congestion countermeasures department, the disaster countermeasures department provides guidance on evacuation routes and evacuation sites in the event of a disaster. Based on the information provided by the aforementioned disaster response department, the sales promotion department analyzes the user's purchase history and preferences and proposes the most suitable products. Equipped with A system characterized by the following features.

2. The aforementioned information provision unit, We provide information on tourist destinations, recommended spots, and local event information. The system according to feature 1.

3. The aforementioned multilingual support unit is Providing multilingual guidance to foreign tourists. The system according to feature 1.

4. The aforementioned congestion countermeasures department, It monitors congestion levels at tourist destinations in real time and provides route guidance to help avoid crowds. The system according to feature 1.

5. The aforementioned disaster response department, Provide guidance on evacuation routes and shelters in the event of a disaster. The system according to feature 1.

6. The aforementioned sales promotion department, We analyze users' purchase history and preferences to suggest the most suitable products. The system according to feature 1.

7. The aforementioned information provision unit, It estimates the user's emotions and adjusts the content of the information provided based on those estimated emotions. The system according to feature 1.

8. The aforementioned information provision unit, Analyze the user's past information provision history and select the optimal information provision method. The system according to feature 1.

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