system

Abstract

The system according to this embodiment aims to guide users to tourist spots according to their interests and preferences and to provide detailed explanations. [Solution] The system according to the embodiment comprises a guidance unit, an explanation unit, a provision unit, an analysis unit, and an acquisition unit. The guidance unit guides the user to tourist spots according to their interests and preferences. The explanation unit provides detailed explanations about the history and culture of the tourist spots guided by the guidance unit. The provision unit provides high-quality video and audio provided by the explanation unit. The analysis unit analyzes the user's interests. The acquisition unit acquires the latest local information in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, tourism guidance tailored to the interests and preferences of users has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to guide tourist spots according to the interests and preferences of users and provide detailed explanations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a guidance unit, an explanation unit, a provision unit, an analysis unit, and an acquisition unit. The guidance unit guides the user to tourist spots according to their interests and preferences. The explanation unit provides detailed explanations about the history and culture of the tourist spots guided by the guidance unit. The provision unit provides high-quality video and audio provided by the explanation unit. The analysis unit analyzes the user's interests. The acquisition unit acquires the latest local information in real time. [Effects of the Invention]

[0007] The system according to this embodiment can guide users to tourist spots according to their interests and preferences and provide detailed explanations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tourist guide system according to an embodiment of the present invention is an innovative tourist guide service that combines 5G high-speed communication, VR / AR technology, and generative AI. This tourist guide system allows users to wear a VR headset and experience tourist destinations around the world in real time. A personalized virtual guide equipped with generative AI guides users to tourist spots according to their interests and preferences, providing detailed explanations of the history and culture of each place. 5G technology provides high-quality video and audio seamlessly, creating an immersive experience as if the user were actually there. The target audience includes travel enthusiasts, especially those who cannot travel in person due to time or budget constraints, those with physical limitations, educational institutions, and travel agencies. Challenges faced by these target groups include time and budget constraints, physical limitations for the elderly and people with disabilities, language barriers, and a lack of prior information. The tourist guide system solves these challenges as follows: First, users can experience tourist destinations around the world in VR, regardless of time or location, even from the comfort of their own homes. Second, users can freely move around in the virtual space and enjoy sightseeing, regardless of physical limitations. Furthermore, the AI ​​guide supports multiple languages, providing detailed explanations in the user's native language. Generative AI analyzes vast amounts of information and proposes optimal sightseeing plans tailored to the user's interests. The personalized AI guide analyzes the user's interests, past travel history, and real-time reactions to provide individualized guidance. It also acquires and analyzes the latest local information (weather, events, crowd levels, etc.) in real time and reflects it in the VR space. Additionally, the AI ​​recreates historical scenes, allowing users to experience historical events. The AI ​​translates conversations with locals in real time, enabling communication that transcends language barriers. Based on user reactions and questions, the AI ​​generates stories and explanations on the spot, providing a deeper sense of immersion. The AI ​​analyzes memories of visited places and generates personalized virtual souvenirs (such as 3D models and paintings). By fully utilizing the power of 5G, VR / AR technology, and generative AI, the sightseeing guide system fundamentally transforms the concept of traditional travel experiences, providing an innovative platform where everyone can deeply experience the cultures and histories of the world.This allows the tourist guide system to guide users to tourist spots based on their interests and preferences, providing detailed explanations and high-quality video and audio, thereby creating an immersive tourist experience.

[0029] The tourist guide system according to this embodiment comprises a guidance unit, an explanation unit, a provision unit, an analysis unit, and an acquisition unit. The guidance unit guides the user to tourist spots according to their interests and preferences. The guidance unit, for example, analyzes the user's past travel history and real-time reactions to select the most suitable tourist spots. The guidance unit can also add tourist spots in real time based on the user's current interests. The explanation unit provides detailed explanations about the history and culture of the tourist spots guided by the guidance unit. The explanation unit, for example, refers to background information and topic models of the tourist spots to understand the context and provide explanations. The explanation unit can also estimate the user's emotions and adjust the expression method and level of detail of the explanation based on the estimated emotions. The provision unit provides high-quality video and audio provided by the explanation unit. The provision unit, for example, seamlessly delivers high-resolution video and clear audio using 5G technology. The provision unit can also estimate the user's emotions and adjust the method of providing video and audio and the level of detail based on the estimated emotions. The analysis unit analyzes the user's interests. The analysis unit identifies user interests using, for example, data mining techniques and machine learning algorithms, and proposes optimal sightseeing plans. The analysis unit can also perform real-time analysis based on the user's past and current interests. The acquisition unit acquires the latest local information in real time. For example, the acquisition unit collects news feeds and real-time data to provide the latest information on tourist spots. The acquisition unit can also estimate the user's emotions and adjust the timing and frequency of acquiring the latest information based on the estimated emotions. As a result, the sightseeing guide system according to this embodiment guides users to tourist spots based on their interests and preferences, and provides detailed explanations and high-quality video and audio, thereby realizing an immersive sightseeing experience.

[0030] The information desk guides users to tourist spots tailored to their interests and preferences. Specifically, it analyzes users' past travel history and real-time reactions to select the most suitable tourist spots. For example, it collects data on tourist destinations and activities users have previously visited and uses this to profile their preferences. Furthermore, it monitors user reactions in real time to identify spots and activities that users have shown interest in. This allows the information desk to add tourist spots in real time based on the user's current interests. For example, if a user shows interest in a particular historical building, it can immediately suggest related nearby tourist spots. The information desk can also plan efficient sightseeing routes, taking into account the user's travel path and length of stay. This allows users to enjoy sightseeing without wasting time. In addition, the information desk can collect user feedback and incorporate it into future travel plans to provide even more personalized guidance.

[0031] The commentary section provides detailed explanations about the history and culture of the tourist spots guided by the tour guide. Specifically, it refers to background information and topic models of the tourist spots to understand the context and provide explanations accordingly. For example, it provides detailed explanations of the historical background and cultural significance of the tourist spots to encourage a deeper understanding from the user. The commentary section can also estimate the user's emotions and adjust the expression and level of detail of the explanation based on those emotions. For example, if the user is excited, it will use more emotional expressions in the explanation, and conversely, if the user is relaxed, it will use a calmer tone. Furthermore, the commentary section can provide explanations in a natural voice using speech synthesis technology. This allows users to receive information both visually and aurally, and enjoy a more immersive sightseeing experience. The commentary section will continuously improve the content of the explanations based on user feedback, aiming to provide higher quality information.

[0032] The service provider delivers high-quality video and audio provided by the commentary team. Specifically, it uses 5G technology to seamlessly deliver high-resolution video and clear audio. For example, it delivers high-resolution video in real time, providing users with a sense of immersion to beautiful scenery at tourist spots and detailed views of historical buildings. The service provider can also estimate the user's emotions and adjust the delivery method and level of detail of the video and audio based on those emotions. For example, if the user is moved, the video playback speed will be slowed down and detailed commentary will be added to deepen the emotional impact. Furthermore, the service provider automatically adjusts the streaming quality according to the device's performance and network conditions to provide video and audio optimized for the user's device. This allows users to enjoy high-quality video and audio without interruption. In addition, the service provider aims to continuously improve the quality of video and audio based on user feedback to provide a better travel experience.

[0033] The analytics department analyzes user interests. Specifically, it uses data mining techniques and machine learning algorithms to identify user interests and propose optimal travel plans. For example, it collects users' past travel history and current interest data, and uses this to profile their preferences. Furthermore, it monitors user responses in real time to identify spots and activities that have shown interest. This allows the analytics department to perform real-time analysis based on users' current interests and concerns. For example, if a user shows interest in a particular historical building, it can immediately suggest nearby related tourist attractions. The analytics department can also plan efficient sightseeing routes, taking into account the user's travel routes and length of stay. This allows users to enjoy sightseeing without wasting time. In addition, the analytics department can collect user feedback and incorporate it into future travel plans to provide more personalized guidance.

[0034] The information acquisition unit obtains the latest local information in real time. Specifically, it collects news feeds and real-time data to provide the latest information on tourist spots. For example, it obtains and provides users with real-time information on local weather, events, and traffic conditions. The information acquisition unit can also estimate the user's emotions and adjust the timing and frequency of obtaining the latest information based on the estimated emotions. For example, if the user is excited, it will provide the latest information more frequently, and conversely, if the user is relaxed, it will reduce the frequency of information provision. Furthermore, the information acquisition unit can quickly obtain the latest information by collaborating with local guides and tourist facilities. This allows users to always enjoy sightseeing based on the latest information. The information acquisition unit aims to continuously improve the accuracy and timing of information acquisition based on user feedback to provide a better sightseeing experience.

[0035] The generation unit can generate stories and explanations in response to user responses and questions. For example, if a user asks a question about a specific tourist spot, the generation unit can generate a detailed explanation in response to that question. The generation unit can also generate stories based on user responses to further personalize the tourist experience. For example, if the user is excited, it can generate an energetic story to keep the user interested. Conversely, if the user is relaxed, it can generate a calm story to provide a relaxing tourist experience. In this way, by generating stories and explanations that respond to user responses, a more personalized tourist experience can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input a user's question into a generation AI, and the generation AI can generate an explanation in response to that question.

[0036] The souvenir generation unit can analyze memories of visited places and generate personalized virtual souvenirs. For example, it can analyze photos and videos of tourist spots visited by the user and generate virtual souvenirs such as 3D models or paintings based on that analysis. The souvenir generation unit can also estimate the user's emotions and adjust the design and content of the virtual souvenirs based on those emotions. For example, if the user is excited, it can generate a virtual souvenir with an energetic design. If the user is relaxed, it can generate a virtual souvenir with a calm design. In this way, by analyzing memories of visited places and generating personalized virtual souvenirs, a special experience can be provided to the user. Some or all of the above processing in the souvenir generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the souvenir generation unit can input the user's photo data into a generation AI, and the generation AI can generate a virtual souvenir based on that data.

[0037] The guidance system can analyze a user's past travel history and select the most suitable tourist spots. For example, it can suggest similar tourist spots based on data from tourist spots the user has visited in the past. It can also analyze the characteristics of tourist spots the user has liked in the past and select tourist spots with similar characteristics. Furthermore, it can prioritize suggesting tourist spots the user has not yet visited based on their past travel history. In this way, by analyzing the user's past travel history, it is possible to suggest more personalized tourist spots. Some or all of the above processing in the guidance system may be performed using or without a generative AI. For example, the guidance system can input the user's past travel data into a generative AI, which can then select the most suitable tourist spots based on that data.

[0038] The guidance system can add tourist spots in real time based on the user's current interests and preferences during the guidance process. For example, if a user shows interest in a particular theme, the guidance system can add tourist spots related to that theme in real time. It can also add tourist spots related to an event if the user shows interest in that event, or if an activity if the user shows interest in that activity. This allows for a more dynamic sightseeing experience by adding tourist spots in real time based on the user's current interests and preferences. Some or all of the above processing in the guidance system may be performed using or without a generative AI. For example, the guidance system can input real-time user behavior data into a generative AI, which can then add tourist spots based on that data.

[0039] The guidance unit can prioritize guiding users to highly relevant tourist spots by considering their geographical location. For example, it can prioritize guiding users to tourist spots close to their current location. It can also prioritize tourist spots related to a specific region if the user is in that region. Furthermore, if the user is in a specific city, it can prioritize guiding users to major tourist spots in that city. In this way, by considering the user's geographical location, it can guide users to more relevant tourist spots. Some or all of the above processing in the guidance unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the guidance unit can input the user's GPS data into a generative AI, which can then select highly relevant tourist spots based on that data.

[0040] The guidance system can analyze the user's social media activity and guide them to relevant tourist spots. For example, the guidance system can prioritize tourist spots that the user has shown interest in on social media. It can also prioritize tourist spots that the user follows on social media. Furthermore, it can prioritize tourist spots that the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to guide them to more relevant tourist spots. Some or all of the above processing in the guidance system may be performed using generative AI, or it may be performed without generative AI. For example, the guidance system can input the user's social media data into a generative AI, and the generative AI can select relevant tourist spots based on that data.

[0041] The commentary section can adjust the level of detail in its commentary based on the importance of each tourist spot. For example, it can provide detailed commentary for important tourist spots, and concise commentary for general tourist spots. Furthermore, it can provide detailed commentary tailored to specific themes for tourist spots related to those themes. By adjusting the level of detail in the commentary based on the importance of each tourist spot, more appropriate information can be provided. Some or all of the above processing in the commentary section may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary section can input tourist spot data into a generative AI, which can then adjust the level of detail in the commentary based on that data.

[0042] The commentary section can apply different commentary algorithms depending on the category of the tourist spot. For example, the commentary section can provide commentary that emphasizes the historical background for historical tourist spots. It can also provide commentary on the natural environment and ecosystem for natural tourist spots. Furthermore, it can provide commentary on culture and traditions for cultural tourist spots. By applying different commentary algorithms depending on the category of the tourist spot, more appropriate commentary can be provided. Some or all of the above processing in the commentary section may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary section can input tourist spot category data into a generative AI, and the generative AI can apply a commentary algorithm based on that data.

[0043] The commentary unit can determine the priority of commentary based on the time of year a tourist spot is visited. For example, the commentary unit can provide commentary tailored to the season for seasonal tourist spots. It can also provide commentary related to events held at tourist spots. Furthermore, it can provide commentary relevant to the peak season for tourist spots. By prioritizing commentary based on the time of year a tourist spot is visited, more appropriate information can be provided. Some or all of the above processing in the commentary unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary unit can input tourist spot visit time data into a generative AI, and the generative AI can determine the priority of commentary based on that data.

[0044] The commentary section can adjust the order of commentary based on the relevance of the tourist spots. For example, the commentary section can prioritize commenting on major tourist spots. It can also prioritize commenting on tourist spots related to the user's interests. Furthermore, it can prioritize commenting on tourist spots close to the user's current location. By adjusting the order of commentary based on the relevance of the tourist spots, more appropriate information can be provided. Some or all of the above processing in the commentary section may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary section can input relevance data of tourist spots into a generative AI, and the generative AI can adjust the order of commentary based on that data.

[0045] The service provider can adjust the level of detail in the video and audio based on the importance of the tourist spot when providing the service. For example, the service provider can provide detailed video and audio for important tourist spots, and concise video and audio for general tourist spots. Furthermore, it can provide detailed video and audio tailored to a specific theme for tourist spots related to that theme. By adjusting the level of detail in the video and audio based on the importance of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot importance data into a generative AI, and the generative AI can adjust the level of detail in the video and audio based on that data.

[0046] The information provider can apply different information provision algorithms depending on the category of the tourist spot. For example, the provider can provide videos and audio that emphasize the historical background to historical tourist spots. It can also provide videos and audio related to the natural environment and ecosystem to natural tourist spots. Furthermore, it can provide videos and audio related to culture and tradition to cultural tourist spots. By applying different information provision algorithms depending on the category of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input tourist spot category data into a generative AI, and the generative AI can apply a information provision algorithm based on that data.

[0047] The service provider can adjust the level of detail in the video and audio based on the importance of the tourist spot when providing the service. For example, the service provider can provide detailed video and audio for important tourist spots, and concise video and audio for general tourist spots. Furthermore, it can provide detailed video and audio tailored to a specific theme for tourist spots related to that theme. By adjusting the level of detail in the video and audio based on the importance of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot importance data into a generative AI, and the generative AI can adjust the level of detail in the video and audio based on that data.

[0048] The information provider can apply different information provision algorithms depending on the category of the tourist spot. For example, the provider can provide videos and audio that emphasize the historical background to historical tourist spots. It can also provide videos and audio related to the natural environment and ecosystem to natural tourist spots. Furthermore, it can provide videos and audio related to culture and tradition to cultural tourist spots. By applying different information provision algorithms depending on the category of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input tourist spot category data into a generative AI, and the generative AI can apply a information provision algorithm based on that data.

[0049] The service provider can determine the priority of video and audio content based on the time of visit to each tourist spot. For example, it can provide seasonal video and audio content to tourist spots that are only available during that time of year. It can also provide video and audio content related to events held at tourist spots. Furthermore, it can provide video and audio content relevant to peak seasons at tourist spots. By prioritizing video and audio content based on the time of visit to each tourist spot, the service provider can provide more appropriate information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot visit time data into a generative AI, which can then determine the priority of video and audio content based on that data.

[0050] The service provider can adjust the order of video and audio based on the relevance of tourist spots during delivery. For example, the service provider can prioritize providing video and audio of major tourist spots. It can also prioritize providing video and audio of tourist spots related to the user's interests. Furthermore, it can prioritize providing video and audio of tourist spots close to the user's current location. By adjusting the order of video and audio based on the relevance of tourist spots, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot relevance data into a generative AI, and the generative AI can adjust the order of video and audio based on that data.

[0051] The analysis unit can optimize its analysis algorithm by referring to the user's past interest data during analysis. For example, the analysis unit can apply the optimal analysis algorithm based on data showing the user's past interests. It can also analyze the user's past interest data to predict their current interests by analyzing changes in their interests. Furthermore, it can optimize its analysis algorithm by referring to data from other users with similar interests, based on the user's past interest data. This allows for more appropriate interest analysis by referring to the user's past interest data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's past interest data into a generative AI, which can then optimize the analysis algorithm based on that data.

[0052] The analysis unit can perform real-time analysis based on the user's current interests. For example, the analysis unit can perform real-time analysis based on themes the user is currently interested in. It can also perform real-time analysis based on events the user is currently participating in. Furthermore, it can perform real-time analysis based on places the user is currently visiting. This allows for more appropriate interest analysis by performing real-time analysis based on the user's current interests. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input the user's real-time behavioral data into a generative AI, and the generative AI can perform real-time analysis based on that data.

[0053] The analysis unit can perform interest analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can analyze interests related to that region. It can also analyze interests related to a specific city if the user is in that city. Furthermore, if the user is at a specific tourist spot, it can analyze interests related to that tourist spot. This allows for more appropriate interest analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's GPS data into a generative AI, which can then perform interest analysis based on that data.

[0054] The analysis unit can analyze users' social media activity and perform interest analysis during the analysis process. For example, the analysis unit can analyze themes that users have shown interest in on social media. It can also analyze data on accounts that users follow on social media. Furthermore, it can analyze content that users have shared on social media. This allows for more appropriate interest analysis by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input user social media data into a generative AI, which can then perform interest analysis based on that data.

[0055] The acquisition unit can optimize the method of acquiring the latest information by referring to past local information during the acquisition process. For example, the acquisition unit can acquire the latest weather information by referring to past local weather data. It can also acquire the latest event information by referring to past local event data. Furthermore, it can acquire the latest congestion information by referring to past local congestion data. This allows for the acquisition of more appropriate and up-to-date information by referring to past local information. Some or all of the above-described processes in the acquisition unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the acquisition unit can input past local information data into a generation AI, and the generation AI can optimize the method of acquiring the latest information based on that data.

[0056] The acquisition unit can acquire information in real time based on the current situation at the site. For example, the acquisition unit can acquire current weather information at the site in real time. It can also acquire current event information at the site in real time. Furthermore, it can acquire current congestion information at the site in real time. This allows for the provision of more appropriate information by acquiring information in real time based on the current situation at the site. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input real-time data from the site into a generation AI, and the generation AI can acquire information based on that data.

[0057] The acquisition unit can acquire the latest information while considering local geographical location information. For example, the acquisition unit can acquire the latest information related to a specific region in the area. It can also acquire the latest information related to a specific city in the area. Furthermore, it can acquire the latest information related to a specific tourist spot in the area. This allows for the acquisition of more appropriate and up-to-date information by considering local geographical location information. Some or all of the above processing in the acquisition unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the acquisition unit can input local geographical location data into a generating AI, and the generating AI can acquire the latest information based on that data.

[0058] The acquisition unit can obtain the latest information by analyzing local social media activity during acquisition. For example, the acquisition unit can acquire the latest information that is trending on local social media. It can also acquire the latest information that is being shared on local social media. Furthermore, it can acquire the latest information that is being followed on local social media. This allows for the acquisition of more appropriate and up-to-date information by analyzing local social media activity. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input local social media data into a generation AI, and the generation AI can acquire the latest information based on that data.

[0059] The generation unit can optimize its generation algorithm by referring to the user's past response data during generation. For example, the generation unit can apply the optimal generation algorithm based on data of stories and explanations that the user has liked in the past. It can also analyze changes in the user's interests from their past response data and predict their current interests. Furthermore, it can optimize the generation algorithm by referring to data of other users with similar interests based on the user's past response data. This allows for the provision of more appropriate information by referring to the user's past response data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past response data into a generation AI, and the generation AI can optimize its generation algorithm based on that data.

[0060] The generation unit can generate stories and explanations while considering the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate stories and explanations related to that region. It can also generate stories and explanations related to a specific city if the user is in that city. Furthermore, if the user is at a specific tourist spot, it can generate stories and explanations related to that tourist spot. This allows for the provision of more appropriate information by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location data into a generation AI, and the generation AI can generate stories and explanations based on that data.

[0061] The souvenir generation unit can optimize its generation algorithm by referring to the user's past memory data during generation. For example, the souvenir generation unit can apply the optimal generation algorithm based on data of memories the user has liked in the past. It can also analyze changes in the user's interests from their past memory data and predict their current interests. Furthermore, it can optimize the generation algorithm by referring to data of other users with similar interests based on the user's past memory data. This allows for the provision of more appropriate information by referring to the user's past memory data. Some or all of the above processing in the souvenir generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the souvenir generation unit can input the user's past memory data into a generation AI, and the generation AI can optimize the generation algorithm based on that data.

[0062] The souvenir generation unit can generate virtual souvenirs while considering the user's geographical location information. For example, if the user is in a specific region, the souvenir generation unit can generate virtual souvenirs related to that region. It can also generate virtual souvenirs related to a specific city if the user is in that city. Furthermore, if the user is at a specific tourist spot, it can generate virtual souvenirs related to that tourist spot. This allows for the provision of more appropriate information by considering the user's geographical location information. Some or all of the above processing in the souvenir generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the souvenir generation unit can input the user's geographical location data into a generation AI, and the generation AI can generate virtual souvenirs based on that data.

[0063] The souvenir generation unit can generate virtual souvenirs by analyzing the user's social media activity during the generation process. For example, the souvenir generation unit can generate virtual souvenirs based on memories shared by the user on social media. It can also generate virtual souvenirs based on data from accounts the user follows on social media. Furthermore, it can generate virtual souvenirs based on content the user has shown interest in on social media. This allows for the provision of more appropriate information by analyzing the user's social media activity. Some or all of the above-described processes in the souvenir generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the souvenir generation unit can input the user's social media data into a generation AI, which can then generate virtual souvenirs based on that data.

[0064] The souvenir generation unit can generate virtual souvenirs by referencing the user's past travel history during the generation process. For example, the souvenir generation unit can generate virtual souvenirs based on data of tourist spots the user has visited in the past. It can also generate virtual souvenirs based on particularly memorable places from the user's past travel history. Furthermore, it can analyze the user's past travel history and generate virtual souvenirs by referencing data of other users with similar interests. This allows for the provision of more appropriate information by referencing the user's past travel history. Some or all of the above-described processes in the souvenir generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the souvenir generation unit can input the user's past travel history data into a generation AI, and the generation AI can generate virtual souvenirs based on that data.

[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 tour guide system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor vital signs such as the user's heart rate, blood pressure, and oxygen saturation in real time, and adjust the tour plan according to the user's health condition. If the user is tired, it can prioritize guiding them to relaxing tourist spots. Conversely, if the user is excited, it can suggest more active tourist spots. Furthermore, the health management unit can adjust the pace of the tour based on the user's health condition, providing a comfortable and stress-free tour experience. This allows for a personalized tour experience tailored to the user's health needs.

[0067] The tourist guide system can also include a dining suggestion section that proposes restaurants tailored to the user's dietary preferences. For example, this section can collect restaurant information around the tourist spots the user visits and suggest restaurants that match the user's preferences. If the user is vegetarian, it can prioritize suggesting vegetarian restaurants. Furthermore, if the user prefers a specific type of cuisine, it can suggest restaurants that serve that cuisine. In addition, the dining suggestion section can analyze the user's past dining history and suggest restaurants that match their preferences. This allows for personalized restaurant recommendations tailored to the user's dietary needs.

[0068] The tourist guide system can also include a media suggestion section that proposes relevant books and films based on the user's interests. For example, the media suggestion section can suggest books and films related to the tourist spots the user visits, thereby deepening the tourist experience. If the user visits a historical site, it can suggest history books and documentary films related to that location. Similarly, if the user visits a natural site, it can suggest natural science books and films related to that location. Furthermore, the media suggestion section can analyze the user's past media history and suggest books and films tailored to their interests. This enables personalized media recommendations that meet the user's preferences.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The information desk guides users to tourist spots based on their interests and preferences. For example, it analyzes the user's past travel history and real-time reactions to select the most suitable tourist spots. It can also add tourist spots in real time based on the user's current interests. Step 2: The commentary section provides detailed explanations about the history and culture of the tourist spots introduced by the guide. For example, it refers to background information and topic models of the tourist spots to understand the context and provide explanations. It can also estimate the user's emotions and adjust the expression and level of detail of the explanation based on the estimated emotions. Step 3: The delivery unit provides high-quality video and audio delivered by the commentary unit. For example, it seamlessly delivers high-resolution video and clear audio using 5G technology. It can also estimate the user's emotions and adjust the delivery method and level of detail of the video and audio based on the estimated emotions. Step 4: The analytics department analyzes user interests. For example, it uses data mining techniques and machine learning algorithms to identify user interests and propose optimal travel plans. It can also perform real-time analysis based on users' past and current interest data. Step 5: The acquisition unit obtains the latest local information in real time. For example, it collects news feeds and real-time data to provide the latest information on tourist spots. It can also estimate the user's sentiment and adjust the timing and frequency of acquiring the latest information based on the estimated sentiment.

[0071] (Example of form 2) The tourist guide system according to an embodiment of the present invention is an innovative tourist guide service that combines 5G high-speed communication, VR / AR technology, and generative AI. This tourist guide system allows users to wear a VR headset and experience tourist destinations around the world in real time. A personalized virtual guide equipped with generative AI guides users to tourist spots according to their interests and preferences, providing detailed explanations of the history and culture of each place. 5G technology provides high-quality video and audio seamlessly, creating an immersive experience as if the user were actually there. The target audience includes travel enthusiasts, especially those who cannot travel in person due to time or budget constraints, those with physical limitations, educational institutions, and travel agencies. Challenges faced by these target groups include time and budget constraints, physical limitations for the elderly and people with disabilities, language barriers, and a lack of prior information. The tourist guide system solves these challenges as follows: First, users can experience tourist destinations around the world in VR, regardless of time or location, even from the comfort of their own homes. Second, users can freely move around in the virtual space and enjoy sightseeing, regardless of physical limitations. Furthermore, the AI ​​guide supports multiple languages, providing detailed explanations in the user's native language. Generative AI analyzes vast amounts of information and proposes optimal sightseeing plans tailored to the user's interests. The personalized AI guide analyzes the user's interests, past travel history, and real-time reactions to provide individualized guidance. It also acquires and analyzes the latest local information (weather, events, crowd levels, etc.) in real time and reflects it in the VR space. Additionally, the AI ​​recreates historical scenes, allowing users to experience historical events. The AI ​​translates conversations with locals in real time, enabling communication that transcends language barriers. Based on user reactions and questions, the AI ​​generates stories and explanations on the spot, providing a deeper sense of immersion. The AI ​​analyzes memories of visited places and generates personalized virtual souvenirs (such as 3D models and paintings). By fully utilizing the power of 5G, VR / AR technology, and generative AI, the sightseeing guide system fundamentally transforms the concept of traditional travel experiences, providing an innovative platform where everyone can deeply experience the cultures and histories of the world.This allows the tourist guide system to guide users to tourist spots based on their interests and preferences, providing detailed explanations and high-quality video and audio, thereby creating an immersive tourist experience.

[0072] The tourist guide system according to this embodiment comprises a guidance unit, an explanation unit, a provision unit, an analysis unit, and an acquisition unit. The guidance unit guides the user to tourist spots according to their interests and preferences. The guidance unit, for example, analyzes the user's past travel history and real-time reactions to select the most suitable tourist spots. The guidance unit can also add tourist spots in real time based on the user's current interests. The explanation unit provides detailed explanations about the history and culture of the tourist spots guided by the guidance unit. The explanation unit, for example, refers to background information and topic models of the tourist spots to understand the context and provide explanations. The explanation unit can also estimate the user's emotions and adjust the expression method and level of detail of the explanation based on the estimated emotions. The provision unit provides high-quality video and audio provided by the explanation unit. The provision unit, for example, seamlessly delivers high-resolution video and clear audio using 5G technology. The provision unit can also estimate the user's emotions and adjust the method of providing video and audio and the level of detail based on the estimated emotions. The analysis unit analyzes the user's interests. The analysis unit identifies user interests using, for example, data mining techniques and machine learning algorithms, and proposes optimal sightseeing plans. The analysis unit can also perform real-time analysis based on the user's past and current interests. The acquisition unit acquires the latest local information in real time. For example, the acquisition unit collects news feeds and real-time data to provide the latest information on tourist spots. The acquisition unit can also estimate the user's emotions and adjust the timing and frequency of acquiring the latest information based on the estimated emotions. As a result, the sightseeing guide system according to this embodiment guides users to tourist spots based on their interests and preferences, and provides detailed explanations and high-quality video and audio, thereby realizing an immersive sightseeing experience.

[0073] The information desk guides users to tourist spots tailored to their interests and preferences. Specifically, it analyzes users' past travel history and real-time reactions to select the most suitable tourist spots. For example, it collects data on tourist destinations and activities users have previously visited and uses this to profile their preferences. Furthermore, it monitors user reactions in real time to identify spots and activities that users have shown interest in. This allows the information desk to add tourist spots in real time based on the user's current interests. For example, if a user shows interest in a particular historical building, it can immediately suggest related nearby tourist spots. The information desk can also plan efficient sightseeing routes, taking into account the user's travel path and length of stay. This allows users to enjoy sightseeing without wasting time. In addition, the information desk can collect user feedback and incorporate it into future travel plans to provide even more personalized guidance.

[0074] The commentary section provides detailed explanations about the history and culture of the tourist spots guided by the tour guide. Specifically, it refers to background information and topic models of the tourist spots to understand the context and provide explanations accordingly. For example, it provides detailed explanations of the historical background and cultural significance of the tourist spots to encourage a deeper understanding from the user. The commentary section can also estimate the user's emotions and adjust the expression and level of detail of the explanation based on those emotions. For example, if the user is excited, it will use more emotional expressions in the explanation, and conversely, if the user is relaxed, it will use a calmer tone. Furthermore, the commentary section can provide explanations in a natural voice using speech synthesis technology. This allows users to receive information both visually and aurally, and enjoy a more immersive sightseeing experience. The commentary section will continuously improve the content of the explanations based on user feedback, aiming to provide higher quality information.

[0075] The service provider delivers high-quality video and audio provided by the commentary team. Specifically, it uses 5G technology to seamlessly deliver high-resolution video and clear audio. For example, it delivers high-resolution video in real time, providing users with a sense of immersion to beautiful scenery at tourist spots and detailed views of historical buildings. The service provider can also estimate the user's emotions and adjust the delivery method and level of detail of the video and audio based on those emotions. For example, if the user is moved, the video playback speed will be slowed down and detailed commentary will be added to deepen the emotional impact. Furthermore, the service provider automatically adjusts the streaming quality according to the device's performance and network conditions to provide video and audio optimized for the user's device. This allows users to enjoy high-quality video and audio without interruption. In addition, the service provider aims to continuously improve the quality of video and audio based on user feedback to provide a better travel experience.

[0076] The analytics department analyzes user interests. Specifically, it uses data mining techniques and machine learning algorithms to identify user interests and propose optimal travel plans. For example, it collects users' past travel history and current interest data, and uses this to profile their preferences. Furthermore, it monitors user responses in real time to identify spots and activities that have shown interest. This allows the analytics department to perform real-time analysis based on users' current interests and concerns. For example, if a user shows interest in a particular historical building, it can immediately suggest nearby related tourist attractions. The analytics department can also plan efficient sightseeing routes, taking into account the user's travel routes and length of stay. This allows users to enjoy sightseeing without wasting time. In addition, the analytics department can collect user feedback and incorporate it into future travel plans to provide more personalized guidance.

[0077] The information acquisition unit obtains the latest local information in real time. Specifically, it collects news feeds and real-time data to provide the latest information on tourist spots. For example, it obtains and provides users with real-time information on local weather, events, and traffic conditions. The information acquisition unit can also estimate the user's emotions and adjust the timing and frequency of obtaining the latest information based on the estimated emotions. For example, if the user is excited, it will provide the latest information more frequently, and conversely, if the user is relaxed, it will reduce the frequency of information provision. Furthermore, the information acquisition unit can quickly obtain the latest information by collaborating with local guides and tourist facilities. This allows users to always enjoy sightseeing based on the latest information. The information acquisition unit aims to continuously improve the accuracy and timing of information acquisition based on user feedback to provide a better sightseeing experience.

[0078] The generation unit can generate stories and explanations in response to user responses and questions. For example, if a user asks a question about a specific tourist spot, the generation unit can generate a detailed explanation in response to that question. The generation unit can also generate stories based on user responses to further personalize the tourist experience. For example, if the user is excited, it can generate an energetic story to keep the user interested. Conversely, if the user is relaxed, it can generate a calm story to provide a relaxing tourist experience. In this way, by generating stories and explanations that respond to user responses, a more personalized tourist experience can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input a user's question into a generation AI, and the generation AI can generate an explanation in response to that question.

[0079] The souvenir generation unit can analyze memories of visited places and generate personalized virtual souvenirs. For example, it can analyze photos and videos of tourist spots visited by the user and generate virtual souvenirs such as 3D models or paintings based on that analysis. The souvenir generation unit can also estimate the user's emotions and adjust the design and content of the virtual souvenirs based on those emotions. For example, if the user is excited, it can generate a virtual souvenir with an energetic design. If the user is relaxed, it can generate a virtual souvenir with a calm design. In this way, by analyzing memories of visited places and generating personalized virtual souvenirs, a special experience can be provided to the user. Some or all of the above processing in the souvenir generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the souvenir generation unit can input the user's photo data into a generation AI, and the generation AI can generate a virtual souvenir based on that data.

[0080] The guide unit can estimate the user's emotions and adjust the order of sightseeing spots based on the estimated emotions. For example, if the user is excited, the guide unit can prioritize active sightseeing spots. If the user is tired, it can prioritize relaxing sightseeing spots. Furthermore, if the user is curious, it can prioritize historical sightseeing spots. By adjusting the order of sightseeing spots according to the user's emotions, a more appropriate sightseeing experience 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. Some or all of the above processing in the guide unit may be performed using generative AI or not. For example, the guide unit can input user facial expression data into a generative AI, which can estimate the user's emotions based on that data and adjust the order of sightseeing spots.

[0081] The guidance system can analyze a user's past travel history and select the most suitable tourist spots. For example, it can suggest similar tourist spots based on data from tourist spots the user has visited in the past. It can also analyze the characteristics of tourist spots the user has liked in the past and select tourist spots with similar characteristics. Furthermore, it can prioritize suggesting tourist spots the user has not yet visited based on their past travel history. In this way, by analyzing the user's past travel history, it is possible to suggest more personalized tourist spots. Some or all of the above processing in the guidance system may be performed using or without a generative AI. For example, the guidance system can input the user's past travel data into a generative AI, which can then select the most suitable tourist spots based on that data.

[0082] The guidance system can add tourist spots in real time based on the user's current interests and preferences during the guidance process. For example, if a user shows interest in a particular theme, the guidance system can add tourist spots related to that theme in real time. It can also add tourist spots related to an event if the user shows interest in that event, or if an activity if the user shows interest in that activity. This allows for a more dynamic sightseeing experience by adding tourist spots in real time based on the user's current interests and preferences. Some or all of the above processing in the guidance system may be performed using or without a generative AI. For example, the guidance system can input real-time user behavior data into a generative AI, which can then add tourist spots based on that data.

[0083] The information desk can estimate the user's emotions and adjust the level of detail of the sightseeing spots it guides users to based on those emotions. For example, if the user is excited, the information desk can provide detailed information to keep them interested. If the user is tired, it can provide concise information to reduce their burden. Furthermore, if the user is curious, it can provide in-depth information to satisfy their interest. In this way, by adjusting the level of detail of sightseeing spots 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information desk may be performed using generative AI or not. For example, the information desk can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the level of detail of the sightseeing spots.

[0084] The guidance unit can prioritize guiding users to highly relevant tourist spots by considering their geographical location. For example, it can prioritize guiding users to tourist spots close to their current location. It can also prioritize tourist spots related to a specific region if the user is in that region. Furthermore, if the user is in a specific city, it can prioritize guiding users to major tourist spots in that city. In this way, by considering the user's geographical location, it can guide users to more relevant tourist spots. Some or all of the above processing in the guidance unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the guidance unit can input the user's GPS data into a generative AI, which can then select highly relevant tourist spots based on that data.

[0085] The guidance system can analyze the user's social media activity and guide them to relevant tourist spots. For example, the guidance system can prioritize tourist spots that the user has shown interest in on social media. It can also prioritize tourist spots that the user follows on social media. Furthermore, it can prioritize tourist spots that the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to guide them to more relevant tourist spots. Some or all of the above processing in the guidance system may be performed using generative AI, or it may be performed without generative AI. For example, the guidance system can input the user's social media data into a generative AI, and the generative AI can select relevant tourist spots based on that data.

[0086] The commentary unit can estimate the user's emotions and adjust the way it presents the commentary based on those emotions. For example, if the user is excited, the commentary unit can present the commentary in an energetic manner. If the user is relaxed, it can present the commentary in a calm manner. Furthermore, if the user is curious, it can present the commentary in a detailed and interesting manner. By adjusting the way the commentary is presented according to the user's emotions, a more appropriate commentary can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the commentary unit may be performed using a generative AI or not. For example, the commentary unit can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the way it presents the commentary.

[0087] The commentary section can adjust the level of detail in its commentary based on the importance of each tourist spot. For example, it can provide detailed commentary for important tourist spots, and concise commentary for general tourist spots. Furthermore, it can provide detailed commentary tailored to specific themes for tourist spots related to those themes. By adjusting the level of detail in the commentary based on the importance of each tourist spot, more appropriate information can be provided. Some or all of the above processing in the commentary section may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary section can input tourist spot data into a generative AI, which can then adjust the level of detail in the commentary based on that data.

[0088] The commentary section can apply different commentary algorithms depending on the category of the tourist spot. For example, the commentary section can provide commentary that emphasizes the historical background for historical tourist spots. It can also provide commentary on the natural environment and ecosystem for natural tourist spots. Furthermore, it can provide commentary on culture and traditions for cultural tourist spots. By applying different commentary algorithms depending on the category of the tourist spot, more appropriate commentary can be provided. Some or all of the above processing in the commentary section may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary section can input tourist spot category data into a generative AI, and the generative AI can apply a commentary algorithm based on that data.

[0089] The commentary section can estimate the user's emotions and adjust the length of the commentary based on the estimated emotions. For example, if the user is in a hurry, the commentary section can provide a short, concise commentary. If the user is relaxed, it can provide a detailed commentary. Furthermore, if the user is excited, it can provide a commentary with visually stimulating effects. By adjusting the length of the commentary 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary section may be performed using generative AI or not. For example, the commentary section can input user facial expression data into the generative AI, which can then estimate the user's emotions based on that data and adjust the length of the commentary.

[0090] The commentary unit can determine the priority of commentary based on the time of year a tourist spot is visited. For example, the commentary unit can provide commentary tailored to the season for seasonal tourist spots. It can also provide commentary related to events held at tourist spots. Furthermore, it can provide commentary relevant to the peak season for tourist spots. By prioritizing commentary based on the time of year a tourist spot is visited, more appropriate information can be provided. Some or all of the above processing in the commentary unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary unit can input tourist spot visit time data into a generative AI, and the generative AI can determine the priority of commentary based on that data.

[0091] The commentary section can adjust the order of commentary based on the relevance of the tourist spots. For example, the commentary section can prioritize commenting on major tourist spots. It can also prioritize commenting on tourist spots related to the user's interests. Furthermore, it can prioritize commenting on tourist spots close to the user's current location. By adjusting the order of commentary based on the relevance of the tourist spots, more appropriate information can be provided. Some or all of the above processing in the commentary section may be performed using a generative AI, or it may be performed without a generative AI. For example, the commentary section can input relevance data of tourist spots into a generative AI, and the generative AI can adjust the order of commentary based on that data.

[0092] The service provider can estimate the user's emotions and adjust the way video and audio are delivered based on the estimated emotions. For example, if the user is excited, the service provider can deliver energetic video and audio. If the user is relaxed, it can deliver calm video and audio. Furthermore, if the user is curious, it can deliver detailed and interesting video and audio. This allows for more appropriate information delivery by adjusting the way video and audio are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the way video and audio are delivered.

[0093] The service provider can adjust the level of detail in the video and audio based on the importance of the tourist spot when providing the service. For example, the service provider can provide detailed video and audio for important tourist spots, and concise video and audio for general tourist spots. Furthermore, it can provide detailed video and audio tailored to a specific theme for tourist spots related to that theme. By adjusting the level of detail in the video and audio based on the importance of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot importance data into a generative AI, and the generative AI can adjust the level of detail in the video and audio based on that data.

[0094] The information provider can apply different information provision algorithms depending on the category of the tourist spot. For example, the provider can provide videos and audio that emphasize the historical background to historical tourist spots. It can also provide videos and audio related to the natural environment and ecosystem to natural tourist spots. Furthermore, it can provide videos and audio related to culture and tradition to cultural tourist spots. By applying different information provision algorithms depending on the category of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input tourist spot category data into a generative AI, and the generative AI can apply a information provision algorithm based on that data.

[0095] The service provider can estimate the user's emotions and adjust the way video and audio are delivered based on the estimated emotions. For example, if the user is excited, the service provider can deliver energetic video and audio. If the user is relaxed, it can deliver calm video and audio. Furthermore, if the user is curious, it can deliver detailed and interesting video and audio. This allows for more appropriate information delivery by adjusting the way video and audio are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the way video and audio are delivered.

[0096] The service provider can adjust the level of detail in the video and audio based on the importance of the tourist spot when providing the service. For example, the service provider can provide detailed video and audio for important tourist spots, and concise video and audio for general tourist spots. Furthermore, it can provide detailed video and audio tailored to a specific theme for tourist spots related to that theme. By adjusting the level of detail in the video and audio based on the importance of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot importance data into a generative AI, and the generative AI can adjust the level of detail in the video and audio based on that data.

[0097] The information provider can apply different information provision algorithms depending on the category of the tourist spot. For example, the provider can provide videos and audio that emphasize the historical background to historical tourist spots. It can also provide videos and audio related to the natural environment and ecosystem to natural tourist spots. Furthermore, it can provide videos and audio related to culture and tradition to cultural tourist spots. By applying different information provision algorithms depending on the category of the tourist spot, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input tourist spot category data into a generative AI, and the generative AI can apply a information provision algorithm based on that data.

[0098] The service provider can estimate the user's emotions and adjust the length of the video and audio based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise video and audio. If the user is relaxed, it can provide detailed video and audio. Furthermore, if the user is excited, it can provide video and audio with visually stimulating effects. This allows for more appropriate information to be provided by adjusting the length of the video and audio according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the length of the video and audio.

[0099] The service provider can determine the priority of video and audio content based on the time of visit to each tourist spot. For example, it can provide seasonal video and audio content to tourist spots that are only available during that time of year. It can also provide video and audio content related to events held at tourist spots. Furthermore, it can provide video and audio content relevant to peak seasons at tourist spots. By prioritizing video and audio content based on the time of visit to each tourist spot, the service provider can provide more appropriate information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot visit time data into a generative AI, which can then determine the priority of video and audio content based on that data.

[0100] The service provider can adjust the order of video and audio based on the relevance of tourist spots during delivery. For example, the service provider can prioritize providing video and audio of major tourist spots. It can also prioritize providing video and audio of tourist spots related to the user's interests. Furthermore, it can prioritize providing video and audio of tourist spots close to the user's current location. By adjusting the order of video and audio based on the relevance of tourist spots, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input tourist spot relevance data into a generative AI, and the generative AI can adjust the order of video and audio based on that data.

[0101] The analysis unit can estimate the user's emotions and adjust the method of analyzing interests based on the estimated emotions. For example, if the user is excited, the analysis unit can analyze energetic interests. If the user is relaxed, it can also analyze calm interests. Furthermore, if the user is curious, it can analyze detailed interests. This allows for more appropriate interest analysis by adjusting the method of analyzing interests 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the method of analyzing interests.

[0102] The analysis unit can optimize its analysis algorithm by referring to the user's past interest data during analysis. For example, the analysis unit can apply the optimal analysis algorithm based on data showing the user's past interests. It can also analyze the user's past interest data to predict their current interests by analyzing changes in their interests. Furthermore, it can optimize its analysis algorithm by referring to data from other users with similar interests, based on the user's past interest data. This allows for more appropriate interest analysis by referring to the user's past interest data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's past interest data into a generative AI, which can then optimize the analysis algorithm based on that data.

[0103] The analysis unit can perform real-time analysis based on the user's current interests. For example, the analysis unit can perform real-time analysis based on themes the user is currently interested in. It can also perform real-time analysis based on events the user is currently participating in. Furthermore, it can perform real-time analysis based on places the user is currently visiting. This allows for more appropriate interest analysis by performing real-time analysis based on the user's current interests. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input the user's real-time behavioral data into a generative AI, and the generative AI can perform real-time analysis based on that data.

[0104] The analysis unit can estimate the user's emotions and adjust the frequency of interest analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can perform interest analysis frequently. If the user is relaxed, it can perform interest analysis at a moderate frequency. Furthermore, if the user is tired, it can reduce the analysis frequency to alleviate the burden. In this way, by adjusting the frequency of interest analysis according to the user's emotions, more appropriate interest analysis can be performed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user facial expression data into the generative AI, which can estimate the user's emotions based on that data and adjust the frequency of interest analysis.

[0105] The analysis unit can perform interest analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can analyze interests related to that region. It can also analyze interests related to a specific city if the user is in that city. Furthermore, if the user is at a specific tourist spot, it can analyze interests related to that tourist spot. This allows for more appropriate interest analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's GPS data into a generative AI, which can then perform interest analysis based on that data.

[0106] The analysis unit can analyze users' social media activity and perform interest analysis during the analysis process. For example, the analysis unit can analyze themes that users have shown interest in on social media. It can also analyze data on accounts that users follow on social media. Furthermore, it can analyze content that users have shared on social media. This allows for more appropriate interest analysis by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input user social media data into a generative AI, which can then perform interest analysis based on that data.

[0107] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the latest information based on the estimated user emotions. For example, if the user is excited, the acquisition unit will acquire the latest information frequently. If the user is relaxed, it can acquire the latest information at a moderate frequency. Furthermore, if the user is tired, it can reduce the acquisition frequency to alleviate the burden. In this way, by adjusting the timing of acquiring the latest information 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 a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the acquisition unit may be performed using the generative AI or not. For example, the acquisition unit can input the user's facial expression data into the generative AI, which can estimate the user's emotions based on that data and adjust the timing of acquiring the latest information.

[0108] The acquisition unit can optimize the method of acquiring the latest information by referring to past local information during the acquisition process. For example, the acquisition unit can acquire the latest weather information by referring to past local weather data. It can also acquire the latest event information by referring to past local event data. Furthermore, it can acquire the latest congestion information by referring to past local congestion data. This allows for the acquisition of more appropriate and up-to-date information by referring to past local information. Some or all of the above-described processes in the acquisition unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the acquisition unit can input past local information data into a generation AI, and the generation AI can optimize the method of acquiring the latest information based on that data.

[0109] The acquisition unit can acquire information in real time based on the current situation at the site. For example, the acquisition unit can acquire current weather information at the site in real time. It can also acquire current event information at the site in real time. Furthermore, it can acquire current congestion information at the site in real time. This allows for the provision of more appropriate information by acquiring information in real time based on the current situation at the site. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input real-time data from the site into a generation AI, and the generation AI can acquire information based on that data.

[0110] The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring the latest information based on the estimated user emotions. For example, if the user is excited, the acquisition unit will acquire the latest information frequently. If the user is relaxed, it can acquire the latest information at a moderate frequency. Furthermore, if the user is tired, it can reduce the frequency of acquisition to alleviate the burden. In this way, by adjusting the frequency of acquiring the latest information 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 a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using the generative AI or not. For example, the acquisition unit can input the user's facial expression data into the generative AI, which can estimate the user's emotions based on that data and adjust the frequency of acquiring the latest information.

[0111] The acquisition unit can acquire the latest information while considering local geographical location information. For example, the acquisition unit can acquire the latest information related to a specific region in the area. It can also acquire the latest information related to a specific city in the area. Furthermore, it can acquire the latest information related to a specific tourist spot in the area. This allows for the acquisition of more appropriate and up-to-date information by considering local geographical location information. Some or all of the above processing in the acquisition unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the acquisition unit can input local geographical location data into a generating AI, and the generating AI can acquire the latest information based on that data.

[0112] The acquisition unit can obtain the latest information by analyzing local social media activity during acquisition. For example, the acquisition unit can acquire the latest information that is trending on local social media. It can also acquire the latest information that is being shared on local social media. Furthermore, it can acquire the latest information that is being followed on local social media. This allows for the acquisition of more appropriate and up-to-date information by analyzing local social media activity. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input local social media data into a generation AI, and the generation AI can acquire the latest information based on that data.

[0113] The generation unit can estimate the user's emotions and adjust the method of generating stories and explanations based on the estimated emotions. For example, if the user is excited, the generation unit can generate energetic stories and explanations. If the user is relaxed, it can also generate calm stories and explanations. Furthermore, if the user is curious, it can generate detailed and interesting stories and explanations. This allows for more appropriate information to be provided by adjusting the method of generating stories and explanations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user facial expression data into a generation AI, which can then estimate the user's emotions based on that data and adjust the method of generating stories and explanations.

[0114] The generation unit can optimize its generation algorithm by referring to the user's past response data during generation. For example, the generation unit can apply the optimal generation algorithm based on data of stories and explanations that the user has liked in the past. It can also analyze changes in the user's interests from their past response data and predict their current interests. Furthermore, it can optimize the generation algorithm by referring to data of other users with similar interests based on the user's past response data. This allows for the provision of more appropriate information by referring to the user's past response data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past response data into a generation AI, and the generation AI can optimize its generation algorithm based on that data.

[0115] The generation unit can estimate the user's emotions and adjust the frequency of story and commentary generation based on the estimated emotions. For example, if the user is excited, the generation unit can generate stories and commentary frequently. If the user is relaxed, it can generate stories and commentary at a moderate frequency. Furthermore, if the user is tired, it can reduce the generation frequency to alleviate the burden. By adjusting the frequency of story and commentary generation 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 a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user facial expression data into a generation AI, which can estimate the user's emotions based on that data and adjust the frequency of story and commentary generation.

[0116] The generation unit can generate stories and explanations while considering the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate stories and explanations related to that region. It can also generate stories and explanations related to a specific city if the user is in that city. Furthermore, if the user is at a specific tourist spot, it can generate stories and explanations related to that tourist spot. This allows for the provision of more appropriate information by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location data into a generation AI, and the generation AI can generate stories and explanations based on that data.

[0117] The souvenir generation unit can estimate the user's emotions and adjust the virtual souvenir generation method based on the estimated user emotions. For example, if the user is excited, the souvenir generation unit can generate an energetic virtual souvenir. If the user is relaxed, it can also generate a calm virtual souvenir. Furthermore, if the user is curious, it can generate a detailed and interesting virtual souvenir. This allows for more appropriate information to be provided by adjusting the virtual souvenir generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the souvenir generation unit may be performed using a generative AI or not. For example, the souvenir generation unit can input user facial expression data into a generative AI, which can then estimate the user's emotions based on that data and adjust the virtual souvenir generation method.

[0118] The souvenir generation unit can optimize its generation algorithm by referring to the user's past memory data during generation. For example, the souvenir generation unit can apply the optimal generation algorithm based on data of memories the user has liked in the past. It can also analyze changes in the user's interests from their past memory data and predict their current interests. Furthermore, it can optimize the generation algorithm by referring to data of other users with similar interests based on the user's past memory data. This allows for the provision of more appropriate information by referring to the user's past memory data. Some or all of the above processing in the souvenir generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the souvenir generation unit can input the user's past memory data into a generation AI, and the generation AI can optimize the generation algorithm based on that data.

[0119] The souvenir generation unit can estimate the user's emotions and adjust the frequency of virtual souvenir generation based on the estimated emotions. For example, if the user is excited, the souvenir generation unit can generate virtual souvenirs frequently. If the user is relaxed, it can generate virtual souvenirs at a moderate frequency. Furthermore, if the user is tired, it can reduce the generation frequency to alleviate the burden. By adjusting the frequency of virtual souvenir generation 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 a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the souvenir generation unit may be performed using a generation AI or not. For example, the souvenir generation unit can input user facial expression data into a generation AI, which can estimate the user's emotions based on that data and adjust the frequency of virtual souvenir generation.

[0120] The souvenir generation unit can generate virtual souvenirs while considering the user's geographical location information. For example, if the user is in a specific region, the souvenir generation unit can generate virtual souvenirs related to that region. It can also generate virtual souvenirs related to a specific city if the user is in that city. Furthermore, if the user is at a specific tourist spot, it can generate virtual souvenirs related to that tourist spot. This allows for the provision of more appropriate information by considering the user's geographical location information. Some or all of the above processing in the souvenir generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the souvenir generation unit can input the user's geographical location data into a generation AI, and the generation AI can generate virtual souvenirs based on that data.

[0121] The souvenir generation unit can generate virtual souvenirs by analyzing the user's social media activity during the generation process. For example, the souvenir generation unit can generate virtual souvenirs based on memories shared by the user on social media. It can also generate virtual souvenirs based on data from accounts the user follows on social media. Furthermore, it can generate virtual souvenirs based on content the user has shown interest in on social media. This allows for the provision of more appropriate information by analyzing the user's social media activity. Some or all of the above-described processes in the souvenir generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the souvenir generation unit can input the user's social media data into a generation AI, which can then generate virtual souvenirs based on that data.

[0122] The souvenir generation unit can generate virtual souvenirs by referencing the user's past travel history during the generation process. For example, the souvenir generation unit can generate virtual souvenirs based on data of tourist spots the user has visited in the past. It can also generate virtual souvenirs based on particularly memorable places from the user's past travel history. Furthermore, it can analyze the user's past travel history and generate virtual souvenirs by referencing data of other users with similar interests. This allows for the provision of more appropriate information by referencing the user's past travel history. Some or all of the above-described processes in the souvenir generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the souvenir generation unit can input the user's past travel history data into a generation AI, and the generation AI can generate virtual souvenirs based on that data.

[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 tour guide system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor vital signs such as the user's heart rate, blood pressure, and oxygen saturation in real time, and adjust the tour plan according to the user's health condition. If the user is tired, it can prioritize guiding them to relaxing tourist spots. Conversely, if the user is excited, it can suggest more active tourist spots. Furthermore, the health management unit can adjust the pace of the tour based on the user's health condition, providing a comfortable and stress-free tour experience. This allows for a personalized tour experience tailored to the user's health needs.

[0125] The tourist guide system can also include a music provider that offers music tailored to the user's preferences. For example, the music provider can select music appropriate to the tourist spots the user visits, enriching the travel experience. If the user wants to relax, it can provide calming music. If the user is excited, it can provide energetic music. Furthermore, the music provider can analyze the user's past music history and suggest music that matches their preferences. This allows for a more immersive travel experience by providing music that aligns with the user's emotions and preferences.

[0126] The tourist guide system can also include a dining suggestion section that proposes restaurants tailored to the user's dietary preferences. For example, this section can collect restaurant information around the tourist spots the user visits and suggest restaurants that match the user's preferences. If the user is vegetarian, it can prioritize suggesting vegetarian restaurants. Furthermore, if the user prefers a specific type of cuisine, it can suggest restaurants that serve that cuisine. In addition, the dining suggestion section can analyze the user's past dining history and suggest restaurants that match their preferences. This allows for personalized restaurant recommendations tailored to the user's dietary needs.

[0127] The tourist guide system can also include a media suggestion section that proposes relevant books and films based on the user's interests. For example, the media suggestion section can suggest books and films related to the tourist spots the user visits, thereby deepening the tourist experience. If the user visits a historical site, it can suggest history books and documentary films related to that location. Similarly, if the user visits a natural site, it can suggest natural science books and films related to that location. Furthermore, the media suggestion section can analyze the user's past media history and suggest books and films tailored to their interests. This enables personalized media recommendations that meet the user's preferences.

[0128] The tourist guide system can also include a crowd avoidance unit that estimates the user's emotions and makes suggestions to avoid crowded tourist spots based on those emotions. For example, if the user is feeling stressed, the crowd avoidance unit can prioritize guiding them to less crowded tourist spots. Conversely, if the user is relaxed, it can determine that crowded tourist spots are acceptable and guide them there as well. Furthermore, the crowd avoidance unit can acquire real-time information on the crowd situation at tourist spots and suggest the optimal tourist route according to the user's emotions. This allows for a more comfortable tourist experience by providing crowd avoidance suggestions tailored to the user's emotions.

[0129] The tourist guide system can also include a safety information provision unit that estimates the user's emotions and provides safety information about tourist spots based on those emotions. For example, if the user is feeling anxious, the safety information provision unit can provide detailed safety information about the tourist spot. Conversely, if the user feels at ease, it can provide concise safety information. Furthermore, the safety information provision unit can acquire the safety status of tourist spots in real time and provide the most appropriate safety information according to the user's emotions. This allows users to enjoy sightseeing with greater peace of mind by providing safety information tailored to their emotions.

[0130] The tourist guide system can also include a photo suggestion unit that estimates the user's emotions and suggests photo spots at tourist attractions based on those emotions. For example, if the user is excited, the photo suggestion unit can suggest spots where energetic photos can be taken. If the user is relaxed, it can suggest spots where calm scenery can be photographed. Furthermore, the photo suggestion unit can analyze the user's past photo history and suggest photo spots tailored to the user's preferences. This allows for personalized suggestions of photo spots that match the user's emotions and preferences.

[0131] The tourist guide system can also include an activity suggestion unit that estimates the user's emotions and proposes activities at tourist spots based on those emotions. For example, if the user is excited, the activity suggestion unit can suggest active activities. If the user is relaxed, it can suggest calming activities. Furthermore, the activity suggestion unit can analyze the user's past activity history and suggest activities tailored to the user's preferences. This allows for personalized activity suggestions that match the user's emotions and preferences.

[0132] The tourist guide system can also include a shopping information section that estimates the user's emotions and provides shopping information for tourist spots based on those emotions. For example, if the user is excited, the shopping information section can suggest energetic shopping areas. If the user is relaxed, it can suggest calmer shopping areas. Furthermore, the shopping information section can analyze the user's past shopping history and suggest shopping spots tailored to the user's preferences. This allows for the provision of personalized shopping information that matches the user's emotions and preferences.

[0133] The tourist guide system may also include an event information provision unit that estimates the user's emotions and provides event information for tourist spots based on those emotions. For example, if the user is excited, the event information provision unit can suggest energetic events. If the user is relaxed, it can suggest calm events. Furthermore, the event information provision unit can analyze the user's past event participation history and suggest events tailored to the user's preferences. This enables the provision of personalized event information that matches the user's emotions and preferences.

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

[0135] Step 1: The information desk guides users to tourist spots based on their interests and preferences. For example, it analyzes the user's past travel history and real-time reactions to select the most suitable tourist spots. It can also add tourist spots in real time based on the user's current interests. Step 2: The commentary section provides detailed explanations about the history and culture of the tourist spots introduced by the guide. For example, it refers to background information and topic models of the tourist spots to understand the context and provide explanations. It can also estimate the user's emotions and adjust the expression and level of detail of the explanation based on the estimated emotions. Step 3: The delivery unit provides high-quality video and audio delivered by the commentary unit. For example, it seamlessly delivers high-resolution video and clear audio using 5G technology. It can also estimate the user's emotions and adjust the delivery method and level of detail of the video and audio based on the estimated emotions. Step 4: The analytics department analyzes user interests. For example, it uses data mining techniques and machine learning algorithms to identify user interests and propose optimal travel plans. It can also perform real-time analysis based on users' past and current interest data. Step 5: The acquisition unit obtains the latest local information in real time. For example, it collects news feeds and real-time data to provide the latest information on tourist spots. It can also estimate the user's sentiment and adjust the timing and frequency of acquiring the latest information based on the estimated sentiment.

[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 described above, including the guidance unit, explanation unit, provision unit, analysis unit, and acquisition unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the smart device 14, which analyzes the user's past travel history and real-time reactions to select the optimal tourist spot. The explanation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides detailed explanations about the history and culture of the tourist spot. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, which provides high-quality video and audio. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's interests. The acquisition unit is implemented by, for example, the control unit 46A of the smart device 14, which acquires the latest local information in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes 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 commands and other instructions from the user by receiving voice signals. 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 described above, including the guidance unit, explanation unit, provision unit, analysis unit, and acquisition unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the user's past travel history and real-time reactions to select the optimal tourist spot. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides detailed explanations about the history and culture of the tourist spot. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides high-quality video and audio. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's interests. The acquisition unit is implemented by the control unit 46A of the smart glasses 214, which acquires the latest local information in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 commands and other instructions from the user by receiving voice signals. 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 described above, including the guidance unit, explanation unit, provision unit, analysis unit, and acquisition unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the user's past travel history and real-time reactions to select the optimal tourist spot. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides detailed explanations about the history and culture of the tourist spot. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides high-quality video and audio. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's interests. The acquisition unit is implemented by the control unit 46A of the headset terminal 314, which acquires the latest local information in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 commands and other instructions from the user by receiving voice signals. 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 guidance unit, explanation unit, provision unit, analysis unit, and acquisition unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the guidance unit is implemented by the control unit 46A of the robot 414, which analyzes the user's past travel history and real-time reactions to select the optimal tourist spot. The explanation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides detailed explanations about the history and culture of the tourist spot. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides high-quality video and audio. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's interests. The acquisition unit is implemented by, for example, the control unit 46A of the robot 414, which acquires the latest local information in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes 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) The information department guides users to tourist spots according to their interests and preferences, The aforementioned information desk provides a detailed explanation of the history and culture of the tourist spots it guided visitors to, The above-mentioned commentary unit provides high-quality video and audio, and the providing unit provides high-quality video and audio. The analytics department analyzes user interests, It includes an acquisition unit that obtains the latest local information in real time. A system characterized by the following features. (Note 2) It features a generation unit that generates stories and explanations in response to user reactions and questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a souvenir generation unit that analyzes memories of places visited and generates personalized virtual souvenirs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is It estimates the user's emotions and adjusts the order of recommended tourist spots based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is Analyze the user's past travel history to select the most suitable tourist spots. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guide section is During navigation, tourist spots are added in real time based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned guide section is The system estimates the user's emotions and adjusts the level of detail in the recommended tourist spots based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned guide section is When providing directions, the system prioritizes recommending highly relevant tourist spots, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned guide section is During the guidance process, the system analyzes the user's social media activity and recommends relevant tourist spots. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned explanatory section is, The system estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned explanatory section is, When providing explanations, adjust the level of detail based on the importance of each tourist spot. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned explanatory section is, When providing explanations, different explanation algorithms are applied depending on the category of the tourist spot. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned explanatory section is, When providing commentary, we will prioritize the explanations based on when you visit the tourist spots. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned explanatory section is, During the explanation, the order of explanations will be adjusted based on the relevance of the tourist spots. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way video and audio are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the content, the level of detail in the video and audio will be adjusted based on the importance of the tourist attraction. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, different provision algorithms are applied depending on the category of the tourist spot. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way video and audio are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the content, the level of detail in the video and audio will be adjusted based on the importance of the tourist attraction. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, different provision algorithms are applied depending on the category of the tourist spot. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the video and audio based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the content, we will prioritize video and audio based on when you visit the tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the content, the order of the video and audio will be adjusted based on the relevance of the tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis of their interests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referencing the user's past interest data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During analysis, the analysis is performed in real time based on the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is It estimates the user's emotions and adjusts the frequency of interest analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, we take into account the user's geographical location to analyze their interests. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, we analyze users' social media activity to understand their interests. The system described in Appendix 1, characterized by the features described herein. (Note 31) The acquisition unit is, It estimates the user's emotions and adjusts the timing of obtaining the latest information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The acquisition unit is, When acquiring data, the method for obtaining the latest information is optimized by referring to historical local data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The acquisition unit is, When acquiring data, information is obtained in real time based on the current situation on the ground. The system described in Appendix 1, characterized by the features described herein. (Note 34) The acquisition unit is, It estimates the user's sentiment and adjusts the frequency of updates based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The acquisition unit is, When acquiring data, the latest information is obtained by taking into account the local geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The acquisition unit is, When acquiring data, we analyze local social media activity to obtain the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is It estimates the user's emotions and adjusts how stories and explanations are generated based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The generating unit is During generation, the generation algorithm is optimized by referring to the user's past response data. The system described in Appendix 2, characterized by the features described herein. (Note 39) The generating unit is It estimates the user's emotions and adjusts the frequency of story and explanation generation based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The generating unit is During generation, the story and explanation are generated while taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned souvenir generating unit is The system estimates the user's emotions and adjusts the virtual souvenir generation method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned souvenir generating unit is During generation, the generation algorithm is optimized by referencing the user's past memories data. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned souvenir generating unit is The system estimates the user's emotions and adjusts the frequency of generating virtual souvenirs based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned souvenir generating unit is When generating virtual souvenirs, the user's geographical location information is taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned souvenir generating unit is During creation, the system analyzes the user's social media activity to generate virtual souvenirs. The system described in Appendix 3, characterized by the features described herein. (Note 46) The aforementioned souvenir generating unit is During creation, the system references the user's past travel history to generate virtual souvenirs. The system described in Appendix 3, 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. The information department guides users to tourist spots according to their interests and preferences, The aforementioned information desk provides a detailed explanation of the history and culture of the tourist spots it guided visitors to, The above-mentioned commentary unit provides high-quality video and audio, and the providing unit provides high-quality video and audio. The analytics department analyzes user interests, It includes an acquisition unit that obtains the latest local information in real time. A system characterized by the following features.

2. It features a generation unit that generates stories and explanations in response to user reactions and questions. The system according to feature 1.

3. It features a souvenir generation unit that analyzes memories of places visited and generates personalized virtual souvenirs. The system according to feature 1.

4. The aforementioned guide section is It estimates the user's emotions and adjusts the order of recommended tourist spots based on those emotions. The system according to feature 1.

5. The aforementioned guide section is Analyze the user's past travel history to select the most suitable tourist spots. The system according to feature 1.

6. The aforementioned guide section is During navigation, tourist spots are added in real time based on the user's current interests and preferences. The system according to feature 1.

7. The aforementioned guide section is The system estimates the user's emotions and adjusts the level of detail in the recommended tourist spots based on those emotions. The system according to feature 1.

8. The aforementioned guide section is When providing directions, the system prioritizes recommending highly relevant tourist spots, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned guide section is During the guidance process, the system analyzes the user's social media activity and recommends relevant tourist spots. The system according to feature 1.

10. The aforementioned explanatory section is, The system estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system according to feature 1.

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