system

Abstract

The system according to the embodiment aims to provide entertainment content in multiple languages ​​to tourists. [Solution] A system according to an embodiment includes a location information acquisition unit, an information display unit, a content generation unit, and a multilingual display unit. The location information acquisition unit acquires location information of tourists. The information display unit displays information about tourist spots based on the location information acquired by the location information acquisition unit. The content generation unit generates entertainment content using a generation AI based on the information displayed by the information display unit. The multilingual display unit displays the entertainment content generated by the content generation unit in multiple languages.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided entertainment content to tourists in multiple languages, and there is room for improvement.

[0005] The system according to the embodiment aims to provide entertainment content in multiple languages ​​to tourists. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, an information display unit, a content generation unit, and a multilingual display unit. The location information acquisition unit acquires location information of tourists. The information display unit displays information about tourist spots based on the location information acquired by the location information acquisition unit. The content generation unit generates entertainment content using a generation AI based on the information displayed by the information display unit. The multilingual display unit displays the entertainment content generated by the content generation unit in multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment can provide entertainment content to tourists in multiple languages. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An AR tourist guide system according to an embodiment of the present invention allows tourists to wear a head-mounted display (HMD) and experience cultural, historical, natural, anime, and manga pilgrimage tours. This AR tourist guide system provides tourists with high-quality information in collaboration with local guides and experts, as well as entertainment created by generative AI. It also offers a multilingual AR application. It partners with hotels, tourism associations, airlines, and other organizations to promote the AR tourist guide and develop effective marketing strategies. It also collects usage data, gains insights into user preferences and behavior, and improves tour content and functionality, enhancing the user experience. For example, a tourist puts on an HMD and launches an AR application. The application acquires the tourist's location information and displays information about surrounding tourist attractions. For example, if the tourist is standing in front of a historical building, information about the building's history and cultural background is displayed. This information is provided with the cooperation of local guides and experts, ensuring high quality and reliability. Entertainment content created by generative AI is then displayed. For example, if a tourist is participating in an anime or manga pilgrimage tour, characters and scenes created by the generative AI will be displayed in AR, allowing the tourist to feel as if they are immersed in that world. This entertainment content is available in multiple languages, so foreign tourists can also enjoy it. Furthermore, we will partner with hotels, tourism associations, airlines, and others to promote the AR tour guide. This will enable us to develop effective marketing strategies for tourists and increase awareness of our services. For example, we could set up an AR tour guide introduction booth in a hotel lobby to allow tourists to experience the service. We will also collect usage data to gain insights into user preferences and behavior. For example, we will collect data on which tourist spots are popular and which content is frequently used. This will enable us to improve tour content and features and enhance the user experience.For example, it is possible to enrich information about popular tourist spots and add new entertainment content. In this way, the present invention is an AR tourist guide system that provides tourists with high-quality information and entertainment, improving the user experience. As a result, the AR tourist guide system can provide tourists with high-quality information and entertainment, improving the user experience.

[0029] The AR tourist guide system according to the embodiment includes a location information acquisition unit, an information display unit, a content generation unit, and a multilingual display unit. The location information acquisition unit acquires location information of tourists. Examples of location information of tourists include, but are not limited to, GPS, beacons, Wi-Fi, etc. The location information acquisition unit acquires location information of tourists using, for example, GPS. The location information acquisition unit can also acquire location information of tourists using beacons. The location information acquisition unit can also acquire location information of tourists using Wi-Fi. For example, the location information acquisition unit acquires location information of tourists in real time using a GPS device. A beacon transmits a signal within a specific range and acquires location information of tourists by receiving the signal. Wi-Fi estimates location information of tourists based on location information of access points. The information display unit displays information about tourist spots based on the location information acquired by the location information acquisition unit. Information about tourist spots includes, for example, history, culture, business hours, admission fees, etc., but is not limited to these examples. The information display unit displays information about the history of the tourist spot, for example. The information display unit can also display information about the cultural background of the tourist spot. The information display unit can also display information about the tourist spot's business hours and admission fees. For example, the information display unit displays the historical background of the tourist spot in text format. The information about the cultural background can be visually displayed using images or videos. The information about the business hours and admission fees is updated in real time. The content generation unit generates entertainment content using a generation AI based on the information displayed by the information display unit. Entertainment content includes, but is not limited to, anime, manga, games, etc. For example, the content generation unit can generate anime characters using the generation AI. The content generation unit can also generate manga scenes using the generation AI. The content generation unit can also generate game scenarios using the generation AI. For example, the generation AI generates anime characters using deep learning technology.The generation AI generates manga scenes using a generative artificial network (GAN). The generation AI generates game scenarios using natural language processing technology. The multilingual display unit displays the entertainment content generated by the content generation unit in multiple languages. Examples of the multilingual display include, but are not limited to, Japanese, English, and Chinese. For example, the multilingual display unit displays anime characters generated by the generation AI in Japanese. The multilingual display unit can also display manga scenes generated by the generation AI in English. The multilingual display unit can also display game scenarios generated by the generation AI in Chinese. For example, the multilingual display unit displays entertainment content in multiple languages ​​using real-time translation technology. The entertainment content can also be displayed in multiple languages ​​using pre-translation technology. As a result, the AR tour guide system according to the embodiment can provide high-quality information and entertainment based on tourists' location information and realize multilingual AR applications.

[0030] The location information acquisition unit can acquire location information of tourists using GPS or beacons. The location information acquisition unit can acquire location information of tourists using, for example, GPS. For example, the location information acquisition unit can acquire location information of tourists in real time using a GPS device. The location information acquisition unit can also acquire location information of tourists using beacons. For example, a beacon emits a signal within a specific range and acquires location information of tourists by receiving the signal. Thus, accurate location information can be acquired using GPS or beacons. Some or all of the above-described processing in the location information acquisition unit can be performed using, for example, AI, or without AI. For example, the location information acquisition unit can input location information acquired from a GPS device into AI and have the AI ​​perform corrections to improve the accuracy of the location information.

[0031] The information display unit can display information about the history and cultural background of the tourist spot. The information display unit, for example, displays information about the history of the tourist spot. For example, the information display unit displays the historical background of the tourist spot in text format. The information display unit can also display information about the cultural background of the tourist spot. For example, the information about the cultural background can be displayed visually using images or videos. This makes it possible to provide high-quality information about the history and cultural background of the tourist spot. Some or all of the above-described processing in the information display unit may be performed using, for example, AI, or may be performed without using AI. For example, the information display unit can input information about the historical background of the tourist spot into AI, which then automatically organizes and displays the information.

[0032] The content generation unit can generate anime or manga characters and scenes using a generation AI. The content generation unit, for example, generates anime characters using a generation AI. For example, the generation AI generates anime characters using deep learning technology. The content generation unit can also generate manga scenes using the generation AI. For example, the generation AI generates manga scenes using a GAN (generative artificial network). This makes it possible to provide tourists with highly entertaining content by using the generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the content generation unit may be performed using AI, or may be performed without using AI. For example, the content generation unit inputs an anime character prompt to the generation AI, and the generation AI generates the character.

[0033] The multilingual display unit can display entertainment content generated by the generation AI in multiple languages. For example, the multilingual display unit displays anime characters generated by the generation AI in Japanese. For example, the multilingual display unit displays entertainment content in multiple languages ​​using real-time translation technology. The multilingual display unit can also display manga scenes generated by the generation AI in English. For example, the multilingual display unit can also display entertainment content in multiple languages ​​using pre-translation technology. This allows for multilingual support, making it enjoyable for foreign tourists as well. Some or all of the above-mentioned processing in the multilingual display unit may be performed using AI, or may be performed without using AI. For example, the multilingual display unit inputs entertainment content generated by the generation AI into AI, which automatically translates and displays the content in multiple languages.

[0034] Furthermore, the AR tourist guide system includes a partnering unit that partners with hotels, tourist associations, and airlines to cooperate in introducing the AR tourist guide. The partnering unit partners with hotels, tourist associations, and airlines to cooperate in introducing the AR tourist guide. For example, the partnering unit can set up an AR tourist guide introduction booth in a hotel lobby to allow tourists to experience the service. The partnering unit can also work with tourist associations to promote the use of the AR tourist guide at tourist spots. Furthermore, the partnering unit can partner with airlines to introduce the AR tourist guide in-flight. This can increase awareness of the service through partnerships. Some or all of the above-mentioned processing in the partnering unit may be performed using, for example, AI, or may be performed without using AI. For example, the partnering unit can have AI select partners and adjust the content of the partnership.

[0035] The AR tourist guide system further includes a data collection unit that collects usage data and gains insights into user preferences and behavior. The data collection unit collects usage data and gains insights into user preferences and behavior. For example, the data collection unit collects access logs and analyzes which tourist spots are popular. The data collection unit can also collect user behavior data and analyze which content is frequently used. By collecting usage data, tour content and functions can be improved and the user experience can be enhanced. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs the collected data into AI, which then automatically analyzes the data and gains insights.

[0036] The AR tourist guide system further includes a data analysis unit that analyzes the data collected by the data collection unit and improves the tour content and functions. The data analysis unit analyzes the data collected by the data collection unit and improves the tour content and functions. The data analysis unit analyzes the collected data using, for example, statistical analysis. The data analysis unit can also analyze the data using a machine learning algorithm to identify user preferences and behavioral patterns. This allows the tour content and functions to be effectively improved through data analysis. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit inputs the collected data into AI, which then automatically analyzes the data and identifies areas for improvement.

[0037] The location information acquisition unit can analyze the tourist's past movement history and select the optimal location information acquisition method. The location information acquisition unit analyzes the tourist's past movement history and selects the optimal location information acquisition method. For example, the location information acquisition unit selects the location information acquisition method based on places the tourist has visited in the past. The location information acquisition unit can also analyze the tourist's past movement patterns and select the most efficient location information acquisition method. The location information acquisition method can also be selected taking into account the means of transportation the tourist has used in the past. In this way, efficient location information acquisition is possible by analyzing the past movement history. Some or all of the above-mentioned processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit inputs the tourist's past movement history data into a generation AI, and the generation AI selects the optimal location information acquisition method.

[0038] The location information acquisition unit can filter the location information based on the tourist's current interests and concerns when acquiring the location information. The location information acquisition unit filters the location information based on the tourist's current interests and concerns when acquiring the location information. For example, if a tourist is interested in historical buildings, location information of the surrounding area can be preferentially acquired. Also, if a tourist is interested in natural landscapes, location information of the surrounding area can be preferentially acquired. Also, if a tourist is interested in anime or manga, location information of related spots can be preferentially acquired. In this way, by filtering the location information based on the tourist's interests and concerns, more relevant information can be provided. Interests and concerns can be identified using, for example, survey results or browsing history. Some or all of the above-mentioned processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit inputs the tourist's interest and concern data into a generation AI, and the generation AI performs filtering.

[0039] The information display unit can adjust the level of detail of the display based on the importance of the tourist spot when displaying information. The information display unit adjusts the level of detail of the display based on the importance of the tourist spot when displaying information. For example, for important tourist spots, detailed information can be displayed. For general tourist spots, basic information can be displayed. For lesser-known tourist spots, brief information can be displayed. This makes it possible to provide appropriate information by adjusting the level of detail of the display based on the importance of the tourist spot. The importance of a tourist spot is evaluated based on, for example, the number of visitors or historical value. Some or all of the above-mentioned processing in the information display unit may be performed using, or without, AI. For example, the information display unit inputs tourist spot importance data into a generation AI, and the generation AI adjusts the level of detail of the display.

[0040] When displaying information, the information display unit can apply different display algorithms depending on the category of the tourist spot. When displaying information, the information display unit applies different display algorithms depending on the category of the tourist spot. For example, in the case of a historical tourist spot, information can be displayed in chronological order. In addition, in the case of a tourist spot with a natural landscape, information can be displayed by season. In addition, in the case of a tourist spot from an anime or manga, information can be displayed by character or scene. By applying a display algorithm depending on the category of the tourist spot, more relevant information can be provided. The display algorithm is applied using, for example, a recommendation algorithm or a filtering algorithm. Some or all of the above-mentioned processing in the information display unit may be performed using, for example, AI, or may be performed without using AI. For example, the information display unit inputs tourist spot category data into a generation AI, and the generation AI applies the display algorithm.

[0041] The content generation unit can customize the characters and scenes to be generated based on the characteristics of the tourist spot when generating content. The content generation unit customizes the characters and scenes to be generated based on the characteristics of the tourist spot when generating content. For example, in the case of a historical tourist spot, characters and scenes can be generated based on historical figures and events. In addition, in the case of a tourist spot with a natural landscape, characters and scenes incorporating natural elements can be generated. In addition, in the case of a tourist spot from an anime or manga, related characters and scenes can be generated. In this way, by customizing characters and scenes based on the characteristics of the tourist spot, it is possible to provide more relevant content. The characteristics of the tourist spot are identified based on, for example, architectural style or natural landscape. Some or all of the above-mentioned processing in the content generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the content generation unit inputs characteristic data of the tourist spot into a generation AI, and the generation AI customizes the characters and scenes.

[0042] The content generation unit can analyze the tourist's past preferences to generate optimal content when generating content. The content generation unit analyzes the tourist's past preferences to generate optimal content when generating content. For example, content is generated based on themes that the tourist has been interested in in the past. Content related to tourist spots that the tourist has visited in the past can also be generated. New content can also be generated based on content that the tourist has previously rated. This makes it possible to provide more appropriate content by analyzing the tourist's past preferences. Past preferences are identified based on, for example, survey results or browsing history. Some or all of the above-mentioned processing in the content generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the content generation unit inputs the tourist's past preference data into a generation AI, which then generates optimal content.

[0043] The multilingual display unit can adjust the level of detail of the display based on the tourist's native language when displaying in multiple languages. The multilingual display unit adjusts the level of detail of the display based on the tourist's native language when displaying in multiple languages. For example, if the tourist's native language is English, detailed information can be displayed. Also, if the tourist's native language is Japanese, detailed information can be displayed. Also, if the tourist's native language is another language, basic information can be displayed. By adjusting the level of detail of the display based on the tourist's native language, more appropriate information can be provided. The native language is identified based on, for example, a user profile or device settings. Some or all of the above-mentioned processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs the tourist's native language data into a generation AI, and the generation AI adjusts the level of detail of the display.

[0044] The multilingual display unit can apply different display algorithms depending on the category of the tourist spot when displaying in multiple languages. The multilingual display unit applies different display algorithms depending on the category of the tourist spot when displaying in multiple languages. For example, in the case of a historical tourist spot, information can be displayed in chronological order. In addition, in the case of a tourist spot with a natural landscape, information by season can be displayed. In addition, in the case of a tourist spot from an anime or manga, information by character or scene can be displayed. In this way, by applying a display algorithm depending on the category of the tourist spot, it is possible to provide more relevant information. The display algorithm is applied using, for example, a recommendation algorithm or a filtering algorithm. Some or all of the above-mentioned processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs tourist spot category data into a generation AI, and the generation AI applies the display algorithm.

[0045] When displaying in multiple languages, the multilingual display unit can customize the display content by referring to the tourist's past visit history. When displaying in multiple languages, the multilingual display unit customizes the display content by referring to the tourist's past visit history. For example, the multilingual display unit prioritizes displaying information about tourist spots that the tourist has previously visited. It can also display information related to tourist spots that the tourist has previously visited. It can also display information based on the tourist's evaluation of tourist spots that the tourist has previously visited. By referring to the tourist's past visit history, it is possible to provide more relevant information. The past visit history is identified based on, for example, location information logs or visit pattern analysis. Some or all of the above-mentioned processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs the tourist's past visit history data into a generation AI, which then customizes the display content.

[0046] The multilingual display unit can select the optimal display method by taking into consideration the tourist's device information when displaying in multiple languages. The multilingual display unit selects the optimal display method by taking into consideration the tourist's device information when displaying in multiple languages. For example, if the tourist is using a smartphone, a display method tailored to the screen size can be provided. Also, if the tourist is using a tablet, a display method optimized for a large screen can be provided. Also, if the tourist is using a smartwatch, a display method that is simple and highly visible can be provided. This allows the optimal display method to be provided by taking into consideration the tourist's device information. The device information is identified based on, for example, the device type and OS version. Some or all of the above-described processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs the tourist's device information into a generation AI, which selects the optimal display method.

[0047] The alliance unit can select the most suitable partner by analyzing the tourist's past usage history when forming an alliance. The alliance unit analyzes the tourist's past usage history when forming an alliance and selects the most suitable partner. For example, the alliance unit selects the most suitable partner based on partner partners that the tourist has used in the past. It can also select a popular partner from the tourist's past usage history. It can also analyze the tourist's past usage history and select the partner that provides the highest satisfaction. In this way, the most suitable partner can be selected by analyzing the tourist's past usage history. The past usage history is identified based on, for example, service usage logs and purchase history. Some or all of the above-mentioned processing in the alliance unit may be performed using, for example, AI, or may be performed without using AI. For example, the alliance unit inputs the tourist's past usage history data into a generation AI, and the generation AI selects the most suitable partner.

[0048] The alliance unit can select the optimal alliance partner by taking into consideration the geographical characteristics of the tourist when forming an alliance. The alliance unit selects the optimal alliance partner by taking into consideration the geographical characteristics of the tourist when forming an alliance. For example, if the tourist is in an urban area, an alliance partner in the urban area is selected. Also, if the tourist is in a suburban area, an alliance partner in the suburban area can be selected. Also, if the tourist is in a tourist destination, an alliance partner in the tourist destination can be selected. In this way, the optimal alliance partner can be selected by taking into consideration the geographical characteristics of the tourist. The geographical characteristics are identified based on, for example, location information and regional characteristics. Some or all of the above-mentioned processing in the alliance unit may be performed using, or without, AI. For example, the alliance unit inputs the tourist's geographical characteristic data into a generation AI, and the generation AI selects the optimal alliance partner.

[0049] When collecting data, the data collection unit can select the optimal data collection method by referring to the tourist's past behavioral history. When collecting data, the data collection unit selects the optimal data collection method by referring to the tourist's past behavioral history. For example, the data collection unit prioritizes collecting data on places the tourist has visited in the past. The data collection unit can also analyze the tourist's past behavioral patterns and select the most efficient data collection method. The data collection unit can also prioritize collecting data on services the tourist has used in the past. This allows the optimal data collection method to be selected by referring to the tourist's past behavioral history. The past behavioral history is identified based on, for example, location logs or behavioral pattern analysis. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs the tourist's past behavioral history data into a generation AI, which selects the optimal data collection method.

[0050] The data collection unit can select the optimal data collection method by taking into account the tourist's device information when collecting data. The data collection unit selects the optimal data collection method by taking into account the tourist's device information when collecting data. For example, if the tourist is using a smartphone, GPS data can be collected preferentially. Also, if the tourist is using a tablet, Wi-Fi data can be collected preferentially. Also, if the tourist is using a smartwatch, Bluetooth (registered trademark) data can be collected preferentially. This allows the optimal data collection method to be provided by taking into account the tourist's device information. The device information is identified based on, for example, the device type and OS version. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs the tourist's device information into a generation AI, which then selects the optimal data collection method.

[0051] When analyzing data, the data analysis unit can select the optimal analysis method by referring to the tourist's past behavioral history. When analyzing data, the data analysis unit selects the optimal analysis method by referring to the tourist's past behavioral history. For example, the analysis is performed based on data on places the tourist has visited in the past. The most efficient analysis method can also be selected by analyzing the tourist's past behavioral patterns. The analysis can also be performed based on data on services the tourist has used in the past. In this way, the optimal analysis method can be selected by referring to the tourist's past behavioral history. The past behavioral history is identified based on, for example, location logs or behavioral pattern analysis. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit inputs the tourist's past behavioral history data into a generation AI, which selects the optimal analysis method.

[0052] The data analysis unit can select the optimal analysis method by taking into account the geographical characteristics of tourists when analyzing data. The data analysis unit selects the optimal analysis method by taking into account the geographical characteristics of tourists when analyzing data. For example, if tourists are in urban areas, the analysis can be performed based on data from the urban area. Also, if tourists are in suburban areas, the analysis can be performed based on data from the suburban area. Also, if tourists are in tourist destinations, the analysis can be performed based on data from the tourist destinations. This makes it possible to provide an optimal analysis method by taking into account the geographical characteristics of tourists. Geographical characteristics are identified based on, for example, location information and regional characteristics. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit inputs geographical characteristic data of tourists into a generation AI, which then selects the optimal analysis method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The AR tourist guide system can also be equipped with a health monitoring unit that monitors tourists' health status. The health monitoring unit collects tourists' biometric information, such as heart rate, blood pressure, and body temperature, in real time to monitor their health. For example, if a tourist has been walking for a long time, the unit can detect an increase in heart rate or body temperature and display a notification urging them to take a break. If a tourist has high blood pressure, the unit can advise them to avoid excessive exercise. Furthermore, if a tourist complains of feeling unwell, the unit can provide information about the nearest medical facility and support a rapid response. This allows for real-time monitoring of tourists' health status and the provision of appropriate advice and support, ensuring a safe and comfortable tourist experience.

[0055] The AR tourist guide system can further include a tour suggestion unit that proposes customized tours based on the tourist's interests. The tour suggestion unit analyzes the tourist's past visit history and preference data to propose optimal sightseeing routes and spots. For example, if a tourist is interested in historical buildings, a tour centered on historical tourist spots can be proposed. For tourists who want to enjoy natural landscapes, a tour of natural parks and scenic spots can be proposed. Furthermore, for anime and manga fans, a pilgrimage tour visiting related spots can be proposed. This makes it possible to provide customized tours based on the tourist's interests and achieve a more satisfying tourist experience.

[0056] The AR tourist guide system can further include a behavior prediction unit that predicts tourist behavior and suggests the next tourist spot based on the predicted behavior. The behavior prediction unit analyzes the tourist's current location information and past behavior history to predict the tourist's likely next visit. For example, after a tourist visits a historical building, it can suggest related tourist spots that the tourist is likely to visit next. Also, if a tourist is enjoying natural scenery, it can suggest natural parks and scenic spots that the tourist is likely to visit next. Furthermore, if a tourist is visiting anime or manga spots, it can suggest related spots that the tourist is likely to visit next. In this way, by predicting tourist behavior and suggesting the optimal sightseeing route, it is possible to achieve a more efficient and satisfying tourist experience.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The location information acquisition unit acquires tourist location information. Location information for tourists includes, for example, GPS, beacons, Wi-Fi, etc. The location information acquisition unit acquires tourist location information in real time using a GPS device. Beacons transmit signals within a specific range and acquire tourist location information by receiving the signals. Wi-Fi estimates tourist location information based on the location information of access points. Step 2: The information display unit displays information about the tourist spot based on the location information acquired by the location information acquisition unit. The information about the tourist spot includes, for example, history, culture, opening hours, and admission fees. The information display unit displays the historical background of the tourist spot in text format and visually displays information about the cultural background using images and videos. Information about opening hours and admission fees is updated in real time. Step 3: The content generation unit uses the generation AI to generate entertainment content based on the information displayed by the information display unit. Entertainment content includes, for example, anime, manga, and games. The generation AI uses deep learning technology to generate anime characters, GAN (generative artificial network) to generate manga scenes, and natural language processing technology to generate game scenarios. Step 4: The multilingual display unit displays the entertainment content generated by the content generation unit in multiple languages. The multiple languages ​​include, for example, Japanese, English, Chinese, etc. The multilingual display unit displays the entertainment content in multiple languages ​​using real-time translation technology, and can also display in multiple languages ​​using pre-translation technology.

[0059] (Example 2) An AR tourist guide system according to an embodiment of the present invention allows tourists to wear a head-mounted display (HMD) and experience cultural, historical, natural, anime, and manga pilgrimage tours. This AR tourist guide system provides tourists with high-quality information in collaboration with local guides and experts, as well as entertainment created by generative AI. It also offers a multilingual AR application. It partners with hotels, tourism associations, airlines, and other organizations to promote the AR tourist guide and develop effective marketing strategies. It also collects usage data, gains insights into user preferences and behavior, and improves tour content and functionality, enhancing the user experience. For example, a tourist puts on an HMD and launches an AR application. The application acquires the tourist's location information and displays information about surrounding tourist attractions. For example, if the tourist is standing in front of a historical building, information about the building's history and cultural background is displayed. This information is provided with the cooperation of local guides and experts, ensuring high quality and reliability. Entertainment content created by generative AI is then displayed. For example, if a tourist is participating in an anime or manga pilgrimage tour, characters and scenes created by the generative AI will be displayed in AR, allowing the tourist to feel as if they are immersed in that world. This entertainment content is available in multiple languages, so foreign tourists can also enjoy it. Furthermore, we will partner with hotels, tourism associations, airlines, and others to promote the AR tour guide. This will enable us to develop effective marketing strategies for tourists and increase awareness of our services. For example, we could set up an AR tour guide introduction booth in a hotel lobby to allow tourists to experience the service. We will also collect usage data to gain insights into user preferences and behavior. For example, we will collect data on which tourist spots are popular and which content is frequently used. This will enable us to improve tour content and features and enhance the user experience.For example, it is possible to enrich information about popular tourist spots and add new entertainment content. In this way, the present invention is an AR tourist guide system that provides tourists with high-quality information and entertainment, improving the user experience. As a result, the AR tourist guide system can provide tourists with high-quality information and entertainment, improving the user experience.

[0060] The AR tourist guide system according to the embodiment includes a location information acquisition unit, an information display unit, a content generation unit, and a multilingual display unit. The location information acquisition unit acquires location information of tourists. Examples of location information of tourists include, but are not limited to, GPS, beacons, Wi-Fi, etc. The location information acquisition unit acquires location information of tourists using, for example, GPS. The location information acquisition unit can also acquire location information of tourists using beacons. The location information acquisition unit can also acquire location information of tourists using Wi-Fi. For example, the location information acquisition unit acquires location information of tourists in real time using a GPS device. A beacon transmits a signal within a specific range and acquires location information of tourists by receiving the signal. Wi-Fi estimates location information of tourists based on location information of access points. The information display unit displays information about tourist spots based on the location information acquired by the location information acquisition unit. Information about tourist spots includes, for example, history, culture, business hours, admission fees, etc., but is not limited to these examples. The information display unit displays information about the history of the tourist spot, for example. The information display unit can also display information about the cultural background of the tourist spot. The information display unit can also display information about the tourist spot's business hours and admission fees. For example, the information display unit displays the historical background of the tourist spot in text format. The information about the cultural background can be visually displayed using images or videos. The information about the business hours and admission fees is updated in real time. The content generation unit generates entertainment content using a generation AI based on the information displayed by the information display unit. Entertainment content includes, but is not limited to, anime, manga, games, etc. For example, the content generation unit can generate anime characters using the generation AI. The content generation unit can also generate manga scenes using the generation AI. The content generation unit can also generate game scenarios using the generation AI. For example, the generation AI generates anime characters using deep learning technology.The generation AI generates manga scenes using a generative artificial network (GAN). The generation AI generates game scenarios using natural language processing technology. The multilingual display unit displays the entertainment content generated by the content generation unit in multiple languages. Examples of the multilingual display include, but are not limited to, Japanese, English, and Chinese. For example, the multilingual display unit displays anime characters generated by the generation AI in Japanese. The multilingual display unit can also display manga scenes generated by the generation AI in English. The multilingual display unit can also display game scenarios generated by the generation AI in Chinese. For example, the multilingual display unit displays entertainment content in multiple languages ​​using real-time translation technology. The entertainment content can also be displayed in multiple languages ​​using pre-translation technology. As a result, the AR tour guide system according to the embodiment can provide high-quality information and entertainment based on tourists' location information and realize multilingual AR applications.

[0061] The location information acquisition unit can acquire location information of tourists using GPS or beacons. The location information acquisition unit can acquire location information of tourists using, for example, GPS. For example, the location information acquisition unit can acquire location information of tourists in real time using a GPS device. The location information acquisition unit can also acquire location information of tourists using beacons. For example, a beacon emits a signal within a specific range and acquires location information of tourists by receiving the signal. Thus, accurate location information can be acquired using GPS or beacons. Some or all of the above-described processing in the location information acquisition unit can be performed using, for example, AI, or without AI. For example, the location information acquisition unit can input location information acquired from a GPS device into AI and have the AI ​​perform corrections to improve the accuracy of the location information.

[0062] The information display unit can display information about the history and cultural background of the tourist spot. The information display unit, for example, displays information about the history of the tourist spot. For example, the information display unit displays the historical background of the tourist spot in text format. The information display unit can also display information about the cultural background of the tourist spot. For example, the information about the cultural background can be displayed visually using images or videos. This makes it possible to provide high-quality information about the history and cultural background of the tourist spot. Some or all of the above-described processing in the information display unit may be performed using, for example, AI, or may be performed without using AI. For example, the information display unit can input information about the historical background of the tourist spot into AI, which then automatically organizes and displays the information.

[0063] The content generation unit can generate anime or manga characters and scenes using a generation AI. The content generation unit, for example, generates anime characters using a generation AI. For example, the generation AI generates anime characters using deep learning technology. The content generation unit can also generate manga scenes using the generation AI. For example, the generation AI generates manga scenes using a GAN (generative artificial network). This makes it possible to provide tourists with highly entertaining content by using the generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the content generation unit may be performed using AI, or may be performed without using AI. For example, the content generation unit inputs an anime character prompt to the generation AI, and the generation AI generates the character.

[0064] The multilingual display unit can display entertainment content generated by the generation AI in multiple languages. For example, the multilingual display unit displays anime characters generated by the generation AI in Japanese. For example, the multilingual display unit displays entertainment content in multiple languages ​​using real-time translation technology. The multilingual display unit can also display manga scenes generated by the generation AI in English. For example, the multilingual display unit can also display entertainment content in multiple languages ​​using pre-translation technology. This allows for multilingual support, making it enjoyable for foreign tourists as well. Some or all of the above-mentioned processing in the multilingual display unit may be performed using AI, or may be performed without using AI. For example, the multilingual display unit inputs entertainment content generated by the generation AI into AI, which automatically translates and displays the content in multiple languages.

[0065] Furthermore, the AR tourist guide system includes a partnering unit that partners with hotels, tourist associations, and airlines to cooperate in introducing the AR tourist guide. The partnering unit partners with hotels, tourist associations, and airlines to cooperate in introducing the AR tourist guide. For example, the partnering unit can set up an AR tourist guide introduction booth in a hotel lobby to allow tourists to experience the service. The partnering unit can also work with tourist associations to promote the use of the AR tourist guide at tourist spots. Furthermore, the partnering unit can partner with airlines to introduce the AR tourist guide in-flight. This can increase awareness of the service through partnerships. Some or all of the above-mentioned processing in the partnering unit may be performed using, for example, AI, or may be performed without using AI. For example, the partnering unit can have AI select partners and adjust the content of the partnership.

[0066] The AR tourist guide system further includes a data collection unit that collects usage data and gains insights into user preferences and behavior. The data collection unit collects usage data and gains insights into user preferences and behavior. For example, the data collection unit collects access logs and analyzes which tourist spots are popular. The data collection unit can also collect user behavior data and analyze which content is frequently used. By collecting usage data, tour content and functions can be improved and the user experience can be enhanced. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs the collected data into AI, which then automatically analyzes the data and gains insights.

[0067] The AR tourist guide system further includes a data analysis unit that analyzes the data collected by the data collection unit and improves the tour content and functions. The data analysis unit analyzes the data collected by the data collection unit and improves the tour content and functions. The data analysis unit analyzes the collected data using, for example, statistical analysis. The data analysis unit can also analyze the data using a machine learning algorithm to identify user preferences and behavioral patterns. This allows the tour content and functions to be effectively improved through data analysis. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit inputs the collected data into AI, which then automatically analyzes the data and identifies areas for improvement.

[0068] Furthermore, in the AR tourist guide system, the location information acquisition unit estimates the tourist's emotions and adjusts the frequency of location information acquisition based on the estimated tourist's emotions. The location information acquisition unit estimates the tourist's emotions and adjusts the frequency of location information acquisition based on the estimated tourist's emotions. For example, if the tourist is excited, the frequency of location information acquisition can be increased to provide detailed information in real time. Alternatively, if the tourist is relaxed, the frequency of location information acquisition can be reduced to reduce battery consumption. Alternatively, if the tourist is tired, the frequency of location information acquisition can be set to a medium level to provide only necessary information. This allows for more appropriate information provision by adjusting the frequency of location information acquisition according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 location information acquisition unit can be performed using, for example, AI, or without AI. For example, the location information acquisition unit inputs tourist emotion data into the generation AI, which then estimates the emotion and adjusts the frequency of location information acquisition based on the results.

[0069] The location information acquisition unit can analyze the tourist's past movement history and select the optimal location information acquisition method. The location information acquisition unit analyzes the tourist's past movement history and selects the optimal location information acquisition method. For example, the location information acquisition unit selects the location information acquisition method based on places the tourist has visited in the past. The location information acquisition unit can also analyze the tourist's past movement patterns and select the most efficient location information acquisition method. The location information acquisition method can also be selected taking into account the means of transportation the tourist has used in the past. In this way, efficient location information acquisition is possible by analyzing the past movement history. Some or all of the above-mentioned processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit inputs the tourist's past movement history data into a generation AI, and the generation AI selects the optimal location information acquisition method.

[0070] The location information acquisition unit can filter the location information based on the tourist's current interests and concerns when acquiring the location information. The location information acquisition unit filters the location information based on the tourist's current interests and concerns when acquiring the location information. For example, if a tourist is interested in historical buildings, location information of the surrounding area can be preferentially acquired. Also, if a tourist is interested in natural landscapes, location information of the surrounding area can be preferentially acquired. Also, if a tourist is interested in anime or manga, location information of related spots can be preferentially acquired. In this way, by filtering the location information based on the tourist's interests and concerns, more relevant information can be provided. Interests and concerns can be identified using, for example, survey results or browsing history. Some or all of the above-mentioned processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit inputs the tourist's interest and concern data into a generation AI, and the generation AI performs filtering.

[0071] The information display unit can estimate the tourist's emotions and adjust the information display method based on the estimated tourist's emotions. The information display unit can estimate the tourist's emotions and adjust the information display method based on the estimated tourist's emotions. For example, if the tourist is excited, a visually stimulating display method can be provided. If the tourist is relaxed, a calm display method can be provided. If the tourist is tired, a simple, highly visible display method can be provided. This enables more appropriate information to be provided by adjusting the information display method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the information display unit can be performed using AI, for example, or without AI. For example, the information display unit inputs tourist's emotion data into the generation AI, which then estimates the emotion and adjusts the information display method based on the result.

[0072] The information display unit can adjust the level of detail of the display based on the importance of the tourist spot when displaying information. The information display unit adjusts the level of detail of the display based on the importance of the tourist spot when displaying information. For example, for important tourist spots, detailed information can be displayed. For general tourist spots, basic information can be displayed. For lesser-known tourist spots, brief information can be displayed. This makes it possible to provide appropriate information by adjusting the level of detail of the display based on the importance of the tourist spot. The importance of a tourist spot is evaluated based on, for example, the number of visitors or historical value. Some or all of the above-mentioned processing in the information display unit may be performed using, or without, AI. For example, the information display unit inputs tourist spot importance data into a generation AI, and the generation AI adjusts the level of detail of the display.

[0073] When displaying information, the information display unit can apply different display algorithms depending on the category of the tourist spot. When displaying information, the information display unit applies different display algorithms depending on the category of the tourist spot. For example, in the case of a historical tourist spot, information can be displayed in chronological order. In addition, in the case of a tourist spot with a natural landscape, information can be displayed by season. In addition, in the case of a tourist spot from an anime or manga, information can be displayed by character or scene. By applying a display algorithm depending on the category of the tourist spot, more relevant information can be provided. The display algorithm is applied using, for example, a recommendation algorithm or a filtering algorithm. Some or all of the above-mentioned processing in the information display unit may be performed using, for example, AI, or may be performed without using AI. For example, the information display unit inputs tourist spot category data into a generation AI, and the generation AI applies the display algorithm.

[0074] The content generation unit can estimate the tourist's emotions and adjust the entertainment content generation method based on the estimated tourist's emotions. The content generation unit can estimate the tourist's emotions and adjust the entertainment content generation method based on the estimated tourist's emotions. For example, if the tourist is excited, visually stimulating entertainment content can be generated. If the tourist is relaxed, calming entertainment content can be generated. If the tourist is tired, simple, highly visible entertainment content can be generated. This enables more appropriate content to be provided by adjusting the entertainment content generation method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the content generation unit can be performed using AI, for example, or without AI. For example, the content generation unit inputs tourist's emotion data into the generation AI, which then estimates the emotion and adjusts the entertainment content generation method based on the result.

[0075] The content generation unit can customize the characters and scenes to be generated based on the characteristics of the tourist spot when generating content. The content generation unit customizes the characters and scenes to be generated based on the characteristics of the tourist spot when generating content. For example, in the case of a historical tourist spot, characters and scenes can be generated based on historical figures and events. In addition, in the case of a tourist spot with a natural landscape, characters and scenes incorporating natural elements can be generated. In addition, in the case of a tourist spot from an anime or manga, related characters and scenes can be generated. In this way, by customizing characters and scenes based on the characteristics of the tourist spot, it is possible to provide more relevant content. The characteristics of the tourist spot are identified based on, for example, architectural style or natural landscape. Some or all of the above-mentioned processing in the content generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the content generation unit inputs characteristic data of the tourist spot into a generation AI, and the generation AI customizes the characters and scenes.

[0076] The content generation unit can analyze the tourist's past preferences to generate optimal content when generating content. The content generation unit analyzes the tourist's past preferences to generate optimal content when generating content. For example, content is generated based on themes that the tourist has been interested in in the past. Content related to tourist spots that the tourist has visited in the past can also be generated. New content can also be generated based on content that the tourist has previously rated. This makes it possible to provide more appropriate content by analyzing the tourist's past preferences. Past preferences are identified based on, for example, survey results or browsing history. Some or all of the above-mentioned processing in the content generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the content generation unit inputs the tourist's past preference data into a generation AI, which then generates optimal content.

[0077] The multilingual display unit can estimate the tourist's emotions and adjust the multilingual display method based on the estimated tourist's emotions. The multilingual display unit can estimate the tourist's emotions and adjust the multilingual display method based on the estimated tourist's emotions. For example, if the tourist is excited, a visually stimulating multilingual display can be provided. If the tourist is relaxed, a calm multilingual display can be provided. If the tourist is tired, a simple, highly visible multilingual display can be provided. This enables more appropriate information to be provided by adjusting the multilingual display method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the multilingual display unit can be performed using AI, for example, or without AI. For example, the multilingual display unit inputs tourist's emotion data into the generation AI, which then estimates the emotion and adjusts the multilingual display method based on the result.

[0078] The multilingual display unit can adjust the level of detail of the display based on the tourist's native language when displaying in multiple languages. The multilingual display unit adjusts the level of detail of the display based on the tourist's native language when displaying in multiple languages. For example, if the tourist's native language is English, detailed information can be displayed. Also, if the tourist's native language is Japanese, detailed information can be displayed. Also, if the tourist's native language is another language, basic information can be displayed. By adjusting the level of detail of the display based on the tourist's native language, more appropriate information can be provided. The native language is identified based on, for example, a user profile or device settings. Some or all of the above-mentioned processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs the tourist's native language data into a generation AI, and the generation AI adjusts the level of detail of the display.

[0079] The multilingual display unit can apply different display algorithms depending on the category of the tourist spot when displaying in multiple languages. The multilingual display unit applies different display algorithms depending on the category of the tourist spot when displaying in multiple languages. For example, in the case of a historical tourist spot, information can be displayed in chronological order. In addition, in the case of a tourist spot with a natural landscape, information by season can be displayed. In addition, in the case of a tourist spot from an anime or manga, information by character or scene can be displayed. In this way, by applying a display algorithm depending on the category of the tourist spot, it is possible to provide more relevant information. The display algorithm is applied using, for example, a recommendation algorithm or a filtering algorithm. Some or all of the above-mentioned processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs tourist spot category data into a generation AI, and the generation AI applies the display algorithm.

[0080] The multilingual display unit can estimate the tourist's emotions and determine the priority of multilingual display based on the estimated tourist's emotions. The multilingual display unit can estimate the tourist's emotions and determine the priority of multilingual display based on the estimated tourist's emotions. For example, if the tourist is excited, visually stimulating information can be displayed preferentially. Also, if the tourist is relaxed, calming information can be displayed preferentially. Also, if the tourist is tired, simple, highly visible information can be displayed preferentially. This enables more appropriate information to be provided by determining the priority of multilingual display based on the tourist's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the multilingual display unit can be performed using, for example, AI, or without AI. For example, the multilingual display unit inputs tourist's emotion data into the generation AI, which estimates the emotion, and determines the priority of multilingual display based on the result.

[0081] When displaying in multiple languages, the multilingual display unit can customize the display content by referring to the tourist's past visit history. When displaying in multiple languages, the multilingual display unit customizes the display content by referring to the tourist's past visit history. For example, the multilingual display unit prioritizes displaying information about tourist spots that the tourist has previously visited. It can also display information related to tourist spots that the tourist has previously visited. It can also display information based on the tourist's evaluation of tourist spots that the tourist has previously visited. By referring to the tourist's past visit history, it is possible to provide more relevant information. The past visit history is identified based on, for example, location information logs or visit pattern analysis. Some or all of the above-mentioned processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs the tourist's past visit history data into a generation AI, which then customizes the display content.

[0082] The multilingual display unit can select the optimal display method by taking into consideration the tourist's device information when displaying in multiple languages. The multilingual display unit selects the optimal display method by taking into consideration the tourist's device information when displaying in multiple languages. For example, if the tourist is using a smartphone, a display method tailored to the screen size can be provided. Also, if the tourist is using a tablet, a display method optimized for a large screen can be provided. Also, if the tourist is using a smartwatch, a display method that is simple and highly visible can be provided. This allows the optimal display method to be provided by taking into consideration the tourist's device information. The device information is identified based on, for example, the device type and OS version. Some or all of the above-described processing in the multilingual display unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual display unit inputs the tourist's device information into a generation AI, which selects the optimal display method.

[0083] The alliance unit can estimate the tourist's emotions and select an affiliate based on the estimated tourist's emotions. The alliance unit estimates the tourist's emotions and selects an affiliate based on the estimated tourist's emotions. For example, if the tourist is excited, an affiliate that offers high entertainment value can be selected. Also, if the tourist is relaxed, an affiliate that allows the tourist to relax can be selected. Also, if the tourist is tired, an affiliate that allows the tourist to rest can be selected. This allows for selecting an affiliate based on the tourist's emotions, thereby providing a more appropriate affiliate. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-described processing in the alliance unit may be performed using, for example, AI, or may be performed without using AI. For example, the alliance unit inputs tourist's emotion data into the generation AI, which then estimates the emotion and selects an affiliate based on the result.

[0084] The alliance unit can select the most suitable partner by analyzing the tourist's past usage history when forming an alliance. The alliance unit analyzes the tourist's past usage history when forming an alliance and selects the most suitable partner. For example, the alliance unit selects the most suitable partner based on partner partners that the tourist has used in the past. It can also select a popular partner from the tourist's past usage history. It can also analyze the tourist's past usage history and select the partner that provides the highest satisfaction. In this way, the most suitable partner can be selected by analyzing the tourist's past usage history. The past usage history is identified based on, for example, service usage logs and purchase history. Some or all of the above-mentioned processing in the alliance unit may be performed using, for example, AI, or may be performed without using AI. For example, the alliance unit inputs the tourist's past usage history data into a generation AI, and the generation AI selects the most suitable partner.

[0085] The alliance unit can estimate the tourist's emotions and determine the priority of alliances based on the estimated tourist's emotions. The alliance unit estimates the tourist's emotions and determines the priority of alliances based on the estimated tourist's emotions. For example, if the tourist is excited, it can prioritize entertainment-rich alliances. Also, if the tourist is relaxed, it can prioritize relaxation-friendly alliances. Also, if the tourist is tired, it can prioritize rest-friendly alliances. By determining the priority of alliances based on the tourist's emotions, it is possible to provide more appropriate alliances. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the alliance unit may be performed using AI, for example, or without AI. For example, the alliance unit inputs tourist's emotion data into the generation AI, which estimates the emotion and determines the priority of alliances based on the result.

[0086] The alliance unit can select the optimal alliance partner by taking into consideration the geographical characteristics of the tourist when forming an alliance. The alliance unit selects the optimal alliance partner by taking into consideration the geographical characteristics of the tourist when forming an alliance. For example, if the tourist is in an urban area, an alliance partner in the urban area is selected. Also, if the tourist is in a suburban area, an alliance partner in the suburban area can be selected. Also, if the tourist is in a tourist destination, an alliance partner in the tourist destination can be selected. In this way, the optimal alliance partner can be selected by taking into consideration the geographical characteristics of the tourist. The geographical characteristics are identified based on, for example, location information and regional characteristics. Some or all of the above-mentioned processing in the alliance unit may be performed using, or without, AI. For example, the alliance unit inputs the tourist's geographical characteristic data into a generation AI, and the generation AI selects the optimal alliance partner.

[0087] The data collection unit can estimate the tourist's emotions and adjust the data collection method based on the estimated tourist's emotions. The data collection unit estimates the tourist's emotions and adjusts the data collection method based on the estimated tourist's emotions. For example, if the tourist is excited, detailed data can be collected. If the tourist is relaxed, basic data can be collected. If the tourist is tired, the minimum necessary data can be collected. This allows for more appropriate data collection by adjusting the data collection method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the data collection unit can be performed using AI, for example, or without AI. For example, the data collection unit inputs tourist's emotion data into a generation AI, which then estimates the emotion and adjusts the data collection method based on the result.

[0088] When collecting data, the data collection unit can select the optimal data collection method by referring to the tourist's past behavioral history. When collecting data, the data collection unit selects the optimal data collection method by referring to the tourist's past behavioral history. For example, the data collection unit prioritizes collecting data on places the tourist has visited in the past. The data collection unit can also analyze the tourist's past behavioral patterns and select the most efficient data collection method. The data collection unit can also prioritize collecting data on services the tourist has used in the past. This allows the optimal data collection method to be selected by referring to the tourist's past behavioral history. The past behavioral history is identified based on, for example, location logs or behavioral pattern analysis. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs the tourist's past behavioral history data into a generation AI, which selects the optimal data collection method.

[0089] The data collection unit can estimate the emotions of tourists and determine the priorities of data collection based on the estimated emotions of tourists. The data collection unit estimates the emotions of tourists and determines the priorities of data collection based on the estimated emotions of tourists. For example, if a tourist is excited, data related to entertainment can be collected preferentially. If a tourist is relaxed, data related to places where tourists can relax can be collected preferentially. If a tourist is tired, data related to places where tourists can rest can be collected preferentially. This enables more appropriate data collection by determining the priorities of data collection based on the emotions of tourists. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs tourist emotion data into a generation AI, which estimates the emotions and determines the priorities of data collection based on the results.

[0090] The data collection unit can select the optimal data collection method by taking into account the tourist's device information when collecting data. The data collection unit selects the optimal data collection method by taking into account the tourist's device information when collecting data. For example, if the tourist is using a smartphone, GPS data can be collected preferentially. Also, if the tourist is using a tablet, Wi-Fi data can be collected preferentially. Also, if the tourist is using a smartwatch, Bluetooth data can be collected preferentially. This makes it possible to provide the optimal data collection method by taking into account the tourist's device information. The device information is identified based on, for example, the device type and OS version. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs the tourist's device information into a generation AI, which selects the optimal data collection method.

[0091] The data analysis unit can estimate the tourist's emotions and adjust the data analysis method based on the estimated tourist's emotions. The data analysis unit can estimate the tourist's emotions and adjust the data analysis method based on the estimated tourist's emotions. For example, if the tourist is excited, detailed data analysis can be performed. If the tourist is relaxed, basic data analysis can be performed. If the tourist is tired, minimal data analysis can be performed. This allows for more appropriate data analysis by adjusting the data analysis method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the data analysis unit may be performed using AI, or without AI. For example, the data analysis unit inputs tourist's emotion data into a generative AI, which then estimates the emotion and adjusts the data analysis method based on the result.

[0092] When analyzing data, the data analysis unit can select the optimal analysis method by referring to the tourist's past behavioral history. When analyzing data, the data analysis unit selects the optimal analysis method by referring to the tourist's past behavioral history. For example, the analysis is performed based on data on places the tourist has visited in the past. The most efficient analysis method can also be selected by analyzing the tourist's past behavioral patterns. The analysis can also be performed based on data on services the tourist has used in the past. In this way, the optimal analysis method can be selected by referring to the tourist's past behavioral history. The past behavioral history is identified based on, for example, location logs or behavioral pattern analysis. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit inputs the tourist's past behavioral history data into a generation AI, which selects the optimal analysis method.

[0093] The data analysis unit can estimate the tourist's emotions and determine the priorities of data analysis based on the estimated tourist's emotions. The data analysis unit estimates the tourist's emotions and determines the priorities of data analysis based on the estimated tourist's emotions. For example, if the tourist is excited, data related to entertainment can be prioritized for analysis. Similarly, if the tourist is relaxed, data related to places where the tourist can relax can be prioritized for analysis. Similarly, if the tourist is tired, data related to places where the tourist can rest can be prioritized for analysis. This enables more appropriate data analysis by determining the priorities of data analysis based on the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, 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 data analysis unit can be performed using, for example, AI, or without AI. For example, the data analysis unit inputs tourist's emotion data into a generative AI, which then estimates the emotion and determines the priorities of data analysis based on the results.

[0094] The data analysis unit can select the optimal analysis method by taking into account the geographical characteristics of tourists when analyzing data. The data analysis unit selects the optimal analysis method by taking into account the geographical characteristics of tourists when analyzing data. For example, if tourists are in urban areas, the analysis can be performed based on data from the urban area. Also, if tourists are in suburban areas, the analysis can be performed based on data from the suburban area. Also, if tourists are in tourist destinations, the analysis can be performed based on data from the tourist destinations. This makes it possible to provide an optimal analysis method by taking into account the geographical characteristics of tourists. Geographical characteristics are identified based on, for example, location information and regional characteristics. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit inputs geographical characteristic data of tourists into a generation AI, which then selects the optimal analysis method. === Hard Collateral 1-1 === Each of the multiple elements, including the location information acquisition unit, information display unit, content generation unit, multilingual display unit, alliance unit, data collection unit, data analysis unit, and emotion estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the location information acquisition unit acquires tourist location information using a GPS device or beacon of the smart device 14. The information display unit displays information about tourist spots using the display 40A of the smart device 14. The content generation unit generates entertainment content using a generation AI by the specific processing unit 290 of the data processing device 12. The multilingual display unit displays content in multiple languages ​​using the display 40A of the smart device 14. The alliance unit selects alliance partners and adjusts the details of the alliance by the specific processing unit 290 of the data processing device 12. The data collection unit collects usage data using the communication I / F 44 of the smart device 14. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12. The emotion estimation unit estimates the emotion of the tourist using the specific processing unit 290 of the data processing device 12, and adjusts the frequency of acquiring location information. === Hard Collateral 1-2 === Each of the multiple elements, including the location information acquisition unit, information display unit, content generation unit, multilingual display unit, alliance unit, data collection unit, data analysis unit, and emotion estimation unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the location information acquisition unit acquires tourist location information using a GPS device or beacon of the smart glasses 214. The information display unit displays information about tourist spots using the display of the smart glasses 214. The content generation unit generates entertainment content using a generation AI by the specific processing unit 290 of the data processing device 12. The multilingual display unit displays content in multiple languages ​​using the display of the smart glasses 214. The alliance unit selects alliance partners and adjusts the details of the alliance by the specific processing unit 290 of the data processing device 12. The data collection unit collects usage data using the communication I / F 44 of the smart glasses 214. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12. The emotion estimation unit estimates the emotion of the tourist using the specific processing unit 290 of the data processing device 12, and adjusts the frequency of acquiring location information. === Hard Collateral 1-3 === Each of the multiple elements including the location information acquisition unit, information display unit, content generation unit, multilingual display unit, alliance unit, data collection unit, data analysis unit, and emotion estimation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the location information acquisition unit acquires location information of tourists using a GPS device or beacon of the headset-type terminal 314. The information display unit displays information about tourist spots using the display 343 of the headset-type terminal 314. The content generation unit generates entertainment content using a generation AI by the specific processing unit 290 of the data processing device 12. The multilingual display unit displays content in multiple languages ​​using the display 343 of the headset-type terminal 314. The alliance unit selects alliance partners and adjusts the details of the alliance by the specific processing unit 290 of the data processing device 12. The data collection unit collects usage data using the communication I / F 44 of the headset-type terminal 314. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12. The emotion estimation unit estimates the emotion of the tourist using the specific processing unit 290 of the data processing device 12, and adjusts the frequency of acquiring location information. === Hard Collateral 1-4 === Each of the multiple elements, including the location information acquisition unit, information display unit, content generation unit, multilingual display unit, alliance unit, data collection unit, data analysis unit, and emotion estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the location information acquisition unit acquires tourist location information using a GPS device or beacon of the robot 414. The information display unit displays information about tourist spots using the display of the robot 414. The content generation unit generates entertainment content using a generation AI by the specific processing unit 290 of the data processing device 12. The multilingual display unit displays content in multiple languages ​​using the display of the robot 414. The alliance unit selects alliance partners and adjusts the details of the alliance by the specific processing unit 290 of the data processing device 12. The data collection unit collects usage data using the communication I / F 44 of the robot 414. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12. The emotion estimation unit estimates tourist emotions by the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring location information.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The AR tourist guide system can also be equipped with a health monitoring unit that monitors tourists' health status. The health monitoring unit collects tourists' biometric information, such as heart rate, blood pressure, and body temperature, in real time to monitor their health. For example, if a tourist has been walking for a long time, the unit can detect an increase in heart rate or body temperature and display a notification urging them to take a break. If a tourist has high blood pressure, the unit can advise them to avoid excessive exercise. Furthermore, if a tourist complains of feeling unwell, the unit can provide information about the nearest medical facility and support a rapid response. This allows for real-time monitoring of tourists' health status and the provision of appropriate advice and support, ensuring a safe and comfortable tourist experience.

[0097] The AR tourist guide system can further include a tour suggestion unit that proposes customized tours based on the tourist's interests. The tour suggestion unit analyzes the tourist's past visit history and preference data to propose optimal sightseeing routes and spots. For example, if a tourist is interested in historical buildings, a tour centered on historical tourist spots can be proposed. For tourists who want to enjoy natural landscapes, a tour of natural parks and scenic spots can be proposed. Furthermore, for anime and manga fans, a pilgrimage tour visiting related spots can be proposed. This makes it possible to provide customized tours based on the tourist's interests and achieve a more satisfying tourist experience.

[0098] The AR tourist guide system can further include an emotion adjustment unit that estimates the tourist's emotion and adjusts the way tourist spots are introduced based on the estimated emotion. The emotion adjustment unit estimates the tourist's emotion from the tourist's facial expression and voice and changes the way tourist spots are introduced. For example, if the tourist is excited, more detailed and entertaining information can be provided. If the tourist is relaxed, information can be provided in a calm tone. Furthermore, if the tourist is tired, concise and highly visible information can be provided. This makes it possible to provide optimal information according to the tourist's emotion, resulting in a more comfortable tourist experience.

[0099] The AR tourist guide system can further include a behavior prediction unit that predicts tourist behavior and suggests the next tourist spot based on the predicted behavior. The behavior prediction unit analyzes the tourist's current location information and past behavior history to predict the tourist's likely next visit. For example, after a tourist visits a historical building, it can suggest related tourist spots that the tourist is likely to visit next. Also, if a tourist is enjoying natural scenery, it can suggest natural parks and scenic spots that the tourist is likely to visit next. Furthermore, if a tourist is visiting anime or manga spots, it can suggest related spots that the tourist is likely to visit next. In this way, by predicting tourist behavior and suggesting the optimal sightseeing route, it is possible to achieve a more efficient and satisfying tourist experience.

[0100] The AR tourist guide system can further include a congestion prediction unit that estimates tourists' emotions and predicts the congestion status of tourist spots based on the estimated emotions. The congestion prediction unit analyzes tourists' emotional data and location information to predict the congestion status of tourist spots in real time. For example, if a tourist is excited, it can suggest that the tourist avoid crowded spots. Also, if a tourist is relaxed, it can suggest uncrowded spots. Furthermore, if a tourist is tired, it can preferentially suggest less crowded spots. This makes it possible to suggest optimal tourist spots according to the tourist's emotions, resulting in a more comfortable tourist experience.

[0101] The AR tourist guide system can further include a safety information provision unit that estimates the tourist's emotions and provides safety information about tourist spots based on the estimated emotions. The safety information provision unit analyzes the tourist's emotion data and location information and provides safety information about tourist spots in real time. For example, if the tourist is excited, it can provide information about places requiring caution or dangerous areas. If the tourist is relaxed, it can provide information about safe routes and evacuation sites. Furthermore, if the tourist is tired, it can provide information about safe places where they can rest. This makes it possible to provide optimal safety information according to the tourist's emotions, allowing them to enjoy sightseeing with greater peace of mind.

[0102] The AR tourist guide system can further include an evaluation unit that estimates the tourists' emotions and evaluates tourist spots based on the estimated emotions. The evaluation unit analyzes the tourists' emotional data and feedback and evaluates tourist spots in real time. For example, if the tourists are excited, the spot can be evaluated as having a high entertainment value. If the tourists are relaxed, the spot can be evaluated as having a high relaxation effect. Furthermore, if the tourists are tired, the spot can be evaluated as having a high resting effect. This makes it possible to perform real-time evaluations based on the tourists' emotions, and to provide information that can be useful to other tourists.

[0103] The AR tourist guide system can further include a photo suggestion unit that estimates the tourist's emotions and suggests photo spots at tourist spots based on the estimated emotions. The photo suggestion unit analyzes the tourist's emotion data and location information to suggest optimal photo spots. For example, if the tourist is excited, it can suggest visually stimulating photo spots. Also, if the tourist is relaxed, it can suggest photo spots with a calm atmosphere. Furthermore, if the tourist is tired, it can suggest photo spots that are easily accessible. This makes it possible to suggest optimal photo spots according to the tourist's emotions, resulting in a more satisfying tourist experience.

[0104] The AR tourist guide system can further include an audio guide unit that estimates the tourist's emotions and provides an audio guide of tourist spots based on the estimated emotions. The audio guide unit analyzes the tourist's emotional data and location information to provide the optimal audio guide. For example, if the tourist is excited, it can provide information in an energetic tone. If the tourist is relaxed, it can provide information in a calm tone. Furthermore, if the tourist is tired, it can provide concise, highly visible information. This makes it possible to provide the optimal audio guide according to the tourist's emotions, resulting in a more comfortable tourist experience.

[0105] The AR tourist guide system can further include a review generation unit that estimates the tourist's emotions and generates reviews of tourist spots based on the estimated emotions. The review generation unit analyzes the tourist's emotional data and feedback and automatically generates reviews of tourist spots. For example, if the tourist is excited, a review can be generated that rates the spot as highly entertaining. If the tourist is relaxed, a review can be generated that rates the spot as highly relaxing. Furthermore, if the tourist is tired, a review can be generated that rates the spot as highly effective for resting. This makes it possible to generate reviews in real time based on the tourist's emotions and provide information that can be useful to other tourists.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The location information acquisition unit acquires tourist location information. Location information for tourists includes, for example, GPS, beacons, Wi-Fi, etc. The location information acquisition unit acquires tourist location information in real time using a GPS device. Beacons transmit signals within a specific range and acquire tourist location information by receiving the signals. Wi-Fi estimates tourist location information based on the location information of access points. Step 2: The information display unit displays information about the tourist spot based on the location information acquired by the location information acquisition unit. The information about the tourist spot includes, for example, history, culture, opening hours, and admission fees. The information display unit displays the historical background of the tourist spot in text format and visually displays information about the cultural background using images and videos. Information about opening hours and admission fees is updated in real time. Step 3: The content generation unit uses the generation AI to generate entertainment content based on the information displayed by the information display unit. Entertainment content includes, for example, anime, manga, and games. The generation AI uses deep learning technology to generate anime characters, GAN (generative artificial network) to generate manga scenes, and natural language processing technology to generate game scenarios. Step 4: The multilingual display unit displays the entertainment content generated by the content generation unit in multiple languages. The multiple languages ​​include, for example, Japanese, English, Chinese, etc. The multilingual display unit displays the entertainment content in multiple languages ​​using real-time translation technology, and can also display in multiple languages ​​using pre-translation technology.

[0108] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a 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.

[0146] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0164] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0169] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0170] 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.

[0171] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0172] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0173] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0174] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0177] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a location information acquisition unit that acquires location information of tourists; an information display unit that displays information about tourist spots based on the location information acquired by the location information acquisition unit; a content generation unit that generates entertainment content based on the information displayed by the information display unit; a multilingual display unit that displays the entertainment content generated by the content generation unit in multiple languages. A system characterized by:

2. The location information acquisition unit Obtaining tourist location information using GPS or beacons The system of claim 1 .

3. The information display unit Display information about the history and cultural background of attractions The system of claim 1 .

4. The content generation unit Generative AI generates anime and manga characters and scenes The system of claim 1 .

5. The multilingual display unit Displaying entertainment content generated by generative AI in multiple languages The system of claim 1 .

6. The company has a partnership department that cooperates with hotels, tourism associations, and airlines to introduce AR tourist guides. The system of claim 1 .

7. It has a data collection unit that collects usage data and gains insights into user preferences and behavior. The system of claim 1 .

8. The location information acquisition unit Estimate the tourist's emotions and adjust the frequency of location information acquisition based on the estimated tourist's emotions. The system of claim 1 .

Images