system

Abstract

The system according to this embodiment aims to provide personalized information based on the traveler's location information and interests. [Solution] The system according to the embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the traveler's location information. The collection unit collects the traveler's interests. The analysis unit analyzes the information obtained by the acquisition unit and the collection unit. The generation unit generates personalized information based on the analysis results obtained by the analysis unit. The display unit displays the information generated by the generation unit using AR technology.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, personalized tourism information based on the location information and interests of travelers has not been sufficiently provided in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized information based on the location information and interests of travelers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a data collection unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the traveler's location information. The data collection unit collects the traveler's interests. The analysis unit analyzes the information obtained by the acquisition unit and the data collection unit. The generation unit generates personalized information based on the analysis results obtained by the analysis unit. The display unit displays the information generated by the generation unit using augmented reality (AR) technology. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized information based on the traveler's location and interests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a personalized AR tour guide system that utilizes multimodal generating AI and AR technology to provide a personalized AR tour guide based on the traveler's location information and interests. When a traveler arrives at a specific tourist destination, the generating AI collects information related to that location based on the device's location information and displays it in real time using AR technology. For example, when a traveler stands in front of a historical building, the generating AI analyzes the building's history and cultural background and overlays the information onto the building using AR technology. The system can also provide information on related tourist spots and events based on the traveler's interests. This allows travelers to enjoy sightseeing at their own pace and gain a high-quality sightseeing experience that transcends language and cultural barriers. Furthermore, the system is available 24 hours a day and can provide more detailed and comprehensive information in real time than traditional human guides. For example, even if a traveler visits a tourist destination at night, the generating AI can provide information related to that location and display it visually using AR technology. This allows travelers to enjoy sightseeing without time constraints. This system brings new value to travelers and the tourism industry, significantly improving the quality of the sightseeing experience. Travelers can obtain information based on their interests and maximize the appeal of tourist destinations. Furthermore, for the tourism industry, utilizing generative AI and AR technology can contribute to promoting tourist destinations and improving tourist satisfaction. This allows personalized AR tour guide systems to provide high-quality tourism experiences based on travelers' location information and interests.

[0029] The personalized AR tour guide system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the traveler's location information. The traveler's location information includes, but is not limited to, GPS data, Wi-Fi location information, and beacons. The acquisition unit can, for example, use GPS data to determine the traveler's current location. The acquisition unit can also determine the traveler's location using Wi-Fi location information. The acquisition unit can also determine the traveler's location using beacons. For example, the acquisition unit acquires GPS data in real time to determine the traveler's location. Wi-Fi location information determines the location based on the signal strength of surrounding Wi-Fi access points. Beacons can determine the location within a specific area with high accuracy. The collection unit collects the traveler's interests. The traveler's interests include, for example, questionnaires, social media data, and past behavioral history. The collection unit can, for example, collect the traveler's interests using questionnaires. The collection unit can also collect the traveler's interests by analyzing social media data. The data collection unit can also collect travelers' interests based on their past behavioral history. For example, the data collection unit can conduct surveys with travelers to gather their interests. Social media data can be used to identify interests by analyzing travelers' posts and the number of likes they receive. Past behavioral history can be used to identify interests based on places travelers have visited and events they have attended. The analysis unit analyzes the information obtained by the acquisition and collection units. Analysis includes, but is not limited to, data mining and machine learning algorithms. For example, the analysis unit can analyze data acquired using data mining techniques. The analysis unit can also analyze data using machine learning algorithms. Furthermore, the analysis unit can analyze data using statistical analysis. For example, the analysis unit can extract useful information from large amounts of data using data mining techniques. Machine learning algorithms learn data patterns and perform predictions and classifications. Statistical analysis analyzes the distribution and correlations of data. The generation unit generates personalized information based on the analysis results obtained by the analysis unit.The generation process may use, but is not limited to, generation AI (e.g., text generation AI or multimodal generation AI). The generation unit may, for example, use text generation AI to generate information suitable for travelers. The generation unit may also use multimodal generation AI to generate information such as text, images, and audio. Furthermore, the generation unit may use generation AI to generate information based on the traveler's interests. For example, the generation unit may use text generation AI to generate information on tourist spots suitable for travelers. Multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI generates relevant information based on the traveler's interests. The display unit displays the information generated by the generation unit using AR technology. Display includes, but is not limited to, AR glasses and smartphone apps. For example, the display unit may display information generated using AR glasses. The display unit may also display information generated using a smartphone app. Furthermore, the display unit may display information generated using a tablet device. For example, the display unit may overlay information onto the traveler's field of view using AR glasses. A smartphone app displays information on the traveler's smartphone screen. Tablet devices can display information on a large screen. This allows the personalized AR tour guide system according to this embodiment to provide a high-quality sightseeing experience based on the traveler's location and interests.

[0030] The acquisition unit acquires the traveler's location information. This location information includes, but is not limited to, GPS data, Wi-Fi location information, and beacons. For example, the acquisition unit can use GPS data to determine the traveler's current location. It can also use Wi-Fi location information to determine the traveler's location. Furthermore, it can use beacons to determine the traveler's location. For example, the acquisition unit can acquire GPS data in real time to determine the traveler's location. Wi-Fi location information determines the location based on the signal strength of surrounding Wi-Fi access points. Beacons can pinpoint locations within a specific area with high accuracy. This allows the acquisition unit to accurately understand where the traveler is and provide basic data to appropriately guide their next actions. In addition, the acquisition unit can centrally manage and update this location information in real time, constantly tracking the traveler's movements. For example, when a traveler moves between tourist attractions, the acquisition unit records their route and suggests the optimal route to the next tourist spot. Furthermore, the acquisition unit can combine multiple location information acquisition methods to improve the accuracy of location information. For example, by combining GPS data and Wi-Fi location information, highly accurate location information can be provided both indoors and outdoors. This allows the acquisition unit to obtain travelers' location information with high accuracy and in real time, improving the accuracy and reliability of the entire system.

[0031] The data collection unit collects information about travelers' interests. This includes, but is not limited to, surveys, social media data, and past travel history. For example, the unit can collect traveler interests using surveys. It can also collect traveler interests by analyzing social media data. Furthermore, it can collect traveler interests based on past travel history. For instance, the unit can conduct surveys with travelers to collect their interests. Social media data can be used to identify interests by analyzing the content of travelers' posts and the number of likes they receive. Past travel history can be used to identify interests based on places travelers have visited and events they have attended. This allows the data collection unit to gain a detailed understanding of individual travelers' interests and collect foundational data to provide personalized travel experiences. Moreover, by centrally managing and updating this data in real time, the unit can flexibly respond to changes in travelers' interests. For example, if a traveler develops a new interest, this information can be immediately reflected and used to suggest future tourist spots. Furthermore, it is crucial for the data collection unit to implement strict security measures in data collection and management to protect travelers' privacy. This will enable the data collection unit to collect travelers' interests with high accuracy and security, improving the overall reliability of the system and user satisfaction.

[0032] The analysis unit analyzes the information obtained by the acquisition and collection units. Analysis includes, but is not limited to, data mining and machine learning algorithms. For example, the analysis unit analyzes data acquired using data mining techniques. It can also analyze data using machine learning algorithms. Furthermore, it can analyze data using statistical analysis. For instance, the analysis unit extracts useful information from large amounts of data using data mining techniques. Machine learning algorithms learn data patterns and perform predictions and classifications. Statistical analysis analyzes data distribution and correlations. This allows the analysis unit to analyze travelers' location information and interests in detail, generating foundational data for providing personalized travel experiences. Moreover, the analysis unit can analyze this data in real time, responding immediately to changes in travelers' movements and interests. For example, if a traveler becomes interested in a new tourist spot, the information can be immediately analyzed and reflected in suggestions for the next tourist spot. The analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns, which can be used to suggest future tourist spots and plan events. This allows the analysis unit to analyze travelers' interests and preferences with high accuracy and in real time, improving the overall accuracy and reliability of the system.

[0033] The generation unit generates personalized information based on the analysis results obtained by the analysis unit. Generation may, but is not limited to, the use of generation AI (e.g., text generation AI or multimodal generation AI). For example, the generation unit can use text generation AI to generate information suitable for travelers. It can also use multimodal generation AI to generate information such as text, images, and audio. Furthermore, the generation unit can use generation AI to generate information based on the traveler's interests. For example, the generation unit can use text generation AI to generate information on tourist spots suitable for travelers. Multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI generates relevant information based on the traveler's interests. This allows the generation unit to generate personalized information tailored to the individual interests of travelers, providing a high-quality travel experience. Moreover, the generation unit can generate this information in real time and respond immediately to changes in travelers' movements and interests. For example, if a traveler becomes interested in a new tourist spot, the generation unit can immediately generate that information and reflect it in the suggestion of the next tourist spot. Furthermore, the generation unit can provide travelers with richer information by combining multiple modals. This allows the generation unit to generate information that accurately and in real time reflects travelers' interests, thereby improving the overall accuracy and reliability of the system.

[0034] The display unit displays information generated by the generation unit using AR technology. The display includes, but is not limited to, AR glasses and smartphone apps. For example, the display unit can display information generated using AR glasses. It can also display information generated using a smartphone app. Furthermore, it can display information generated using a tablet device. For example, the display unit can overlay information onto the traveler's field of view using AR glasses. A smartphone app displays information on the traveler's smartphone screen. A tablet device can display information on a large screen. This allows the display unit to provide travelers with intuitive and visual information, offering a high-quality travel experience. Moreover, the display unit can update this information in real time, responding immediately to changes in the traveler's movements and interests. For example, if a traveler becomes interested in a new tourist spot, that information can be immediately displayed and reflected in suggestions for the next tourist spot. Additionally, by using AR technology, the display unit can provide travelers with a rich experience that fuses the real world with digital information. This allows the display unit to display information that accurately and in real time reflects the traveler's interests, improving the overall accuracy and reliability of the system.

[0035] The generation unit can generate information on relevant tourist attractions and events based on the traveler's interests. For example, it can generate information on local tourist attractions and seasonal events based on the traveler's interests. For example, if the traveler is interested in history, it can generate information on historical tourist attractions. It can also generate information on natural scenic spots if the traveler is interested in nature. It can also generate information on art galleries and museums if the traveler is interested in art. For example, the generation unit generates information on local tourist attractions based on the traveler's interests. Information on seasonal events is generated to match the time of year the traveler visits. Information on historical tourist attractions includes the history and cultural background of the place. Information on natural scenic spots showcases the beauty of the scenery and the charm of nature. Information on art galleries and museums includes the contents of exhibitions and ongoing events. In this way, the generation unit can provide information on relevant tourist attractions and events based on the traveler's interests.

[0036] The display unit can show information that is available 24 hours a day. For example, it can show information such as opening hours of tourist attractions and emergency contact information. The display unit can also show emergency contact information. Furthermore, it can display information about tourist attractions that are available 24 hours a day. For example, the display unit can show the opening hours of tourist attractions in real time. Emergency contact information provides travelers with contact information in case of an emergency. Information on tourist attractions available 24 hours a day introduces places that travelers can visit at any time. Thus, the display unit can provide information that is available 24 hours a day.

[0037] The display unit can provide navigation that allows travelers to enjoy sightseeing at their own pace. For example, the display unit can provide real-time route guidance and guidance that adjusts to the traveler's walking speed. The display unit can, for example, provide real-time route guidance. It can also provide guidance that adjusts to the traveler's walking speed. Furthermore, the display unit can guide travelers along the optimal route based on their current location. For example, the display unit can identify the traveler's current location in real time and guide them along the optimal route. Guidance that adjusts to walking speed adjusts the route guidance to match the traveler's walking speed. Guidance based on the traveler's current location ensures that travelers reach their destination without getting lost. In this way, the display unit can provide navigation that allows travelers to enjoy sightseeing at their own pace.

[0038] The generation unit can generate information that contributes to the promotion of tourist destinations and the improvement of tourist satisfaction. For example, the generation unit generates information such as special offers and customer reviews. For instance, it generates special offers for tourist destinations. It can also generate customer reviews from tourists. Furthermore, it can generate promotional videos for tourist destinations. For example, the generation unit provides special offers for tourist destinations to travelers. Customer reviews are generated based on the ratings and impressions of other travelers. Promotional videos generate visuals that convey the appeal of tourist destinations. In this way, the generation unit can provide information that contributes to the promotion of tourist destinations and the improvement of tourist satisfaction.

[0039] The acquisition unit can analyze the traveler's past travel history and select the optimal method for acquiring location information. The acquisition unit can adjust the frequency of acquiring location information based on places the traveler has visited in the past. The acquisition unit can also analyze the traveler's past travel patterns and select an efficient method for acquiring location information. Furthermore, the acquisition unit can optimize the method for acquiring location information by considering the modes of transportation the traveler has used in the past. For example, the acquisition unit adjusts the frequency of acquiring location information based on places the traveler has visited in the past. It analyzes the traveler's past travel patterns and selects an efficient method for acquiring location information. It optimizes the method for acquiring location information by considering the modes of transportation the traveler has used in the past. As a result, the acquisition unit can provide the optimal method for acquiring location information based on the traveler's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the traveler's past travel history data into a generating AI and have the generating AI select the optimal method for acquiring location information.

[0040] The acquisition unit can filter location information based on the traveler's current activities and areas of interest. For example, if the traveler is sightseeing, the acquisition unit will prioritize acquiring location information around tourist spots. The acquisition unit can also prioritize acquiring location information in shopping areas if the traveler is shopping, or in restaurants if the traveler is dining. This allows the acquisition unit to provide relevant location information based on the traveler's current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The acquisition unit can prioritize acquiring highly relevant information by considering the traveler's geographical location when acquiring location information. For example, if the traveler is in a tourist destination, the acquisition unit will prioritize acquiring location information related to that tourist destination. The acquisition unit can also prioritize acquiring store information in a shopping area if the traveler is in a shopping area. The acquisition unit can also prioritize acquiring location information related to an event if the traveler is at an event venue. For example, if the acquisition unit is in a tourist destination, it will prioritize acquiring location information related to that tourist destination. If the traveler is in a shopping area, it will prioritize acquiring store information in that area. If the traveler is at an event venue, it will prioritize acquiring location information related to that event. In this way, the acquisition unit can provide highly relevant information by considering the traveler's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the traveler's geographic location data into the generating AI, causing the generating AI to acquire highly relevant information.

[0042] The acquisition unit can analyze the traveler's social media activity when acquiring location information and acquire relevant location information. For example, the acquisition unit can prioritize acquiring location information of places where the traveler has checked in on social media. The acquisition unit can also prioritize acquiring location information of places where the traveler has checked in on social media. Furthermore, the acquisition unit can acquire information about relevant places based on the location information of photos shared by the traveler on social media. Furthermore, the acquisition unit can acquire information about relevant places based on the location information of accounts that the traveler follows on social media. For example, the acquisition unit can prioritize acquiring location information of places where the traveler has checked in on social media. For example, it can acquire information about relevant places based on the location information of photos shared by the traveler on social media. For example, it can acquire information about relevant places based on the location information of accounts that the traveler follows on social media. In this way, the acquisition unit can provide relevant location information based on the traveler's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant location information.

[0043] The data collection unit can analyze travelers' past interests and select the optimal data collection method. For example, the data collection unit can collect relevant information based on places travelers have visited and events they have attended in the past. The data collection unit can also analyze travelers' past search history and collect information based on their interests. Furthermore, the data collection unit can collect relevant information based on tickets and reservations travelers have purchased in the past. For example, the data collection unit can collect relevant information based on places travelers have visited and events they have attended in the past. It can also analyze travelers' past search history and collect information based on their interests. It can also collect relevant information based on tickets and reservations travelers have purchased in the past. This allows the data collection unit to provide the optimal data collection method based on travelers' past interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input travelers' past interests into a generating AI and have the generating AI select the optimal data collection method.

[0044] The data collection unit can filter the data based on the traveler's current activities and areas of interest when collecting information on interests. For example, if the traveler is sightseeing, the data collection unit can prioritize collecting information on tourist attractions. The data collection unit can also prioritize collecting information on shopping areas if the traveler is shopping. The data collection unit can also prioritize collecting information on restaurants and cafes if the traveler is dining. For example, if the traveler is sightseeing, the data collection unit can prioritize collecting information on tourist attractions. If the traveler is shopping, the data collection unit can prioritize collecting information on shopping areas. If the traveler is dining, the data collection unit can prioritize collecting information on restaurants and cafes. This allows the data collection unit to provide relevant information based on the traveler's current activities and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the filtering.

[0045] The data collection unit can prioritize collecting highly relevant information by considering the traveler's geographical location when collecting information on their interests. For example, if the traveler is in a tourist destination, the data collection unit will prioritize collecting information related to that tourist destination. The data collection unit can also prioritize collecting information about shops in a shopping area if the traveler is in a shopping area. The data collection unit can also prioritize collecting information related to an event if the traveler is at an event venue. For example, if the traveler is in a tourist destination, the data collection unit will prioritize collecting information related to that tourist destination. If the traveler is in a shopping area, the data collection unit will prioritize collecting information about shops in that area if the traveler is in a shopping area. If the traveler is at an event venue, the data collection unit will prioritize collecting information related to the event if the traveler is in an event venue. In this way, the data collection unit can provide highly relevant information by considering the traveler's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the traveler's geographic location data into a generating AI, which can then perform the task of collecting highly relevant information.

[0046] The data collection unit can analyze a traveler's social media activity and collect relevant interests when gathering information on their social media activities. For example, the data collection unit can prioritize collecting information related to places the traveler has checked in on social media. The data collection unit can also prioritize collecting information related to photos the traveler has shared on social media. The data collection unit can also prioritize collecting information related to accounts the traveler follows on social media. For example, the data collection unit can prioritize collecting information related to places the traveler has checked in on social media. For example, the data collection unit can prioritize collecting information related to photos the traveler has shared on social media. For example, the data collection unit can prioritize collecting information related to accounts the traveler follows on social media. This allows the data collection unit to provide relevant interests based on the traveler's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the collection of relevant interests.

[0047] The analysis unit can optimize the analysis algorithm by referring to the traveler's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the traveler's past travel history. The analysis unit can also optimize the analysis algorithm by referring to the traveler's past interest data. The analysis unit can also adjust the analysis algorithm based on the traveler's past search history. For example, the analysis unit selects the optimal analysis algorithm based on the traveler's past travel history. It optimizes the analysis algorithm by referring to the traveler's past interest data. It adjusts the analysis algorithm based on the traveler's past search history. In this way, the analysis unit can provide the optimal analysis algorithm based on the traveler's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the traveler's past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0048] The analysis unit can improve the accuracy of its analysis based on the traveler's current activities and areas of interest. For example, if the traveler is sightseeing, the analysis unit will prioritize analyzing information about tourist spots. The analysis unit can also prioritize analyzing information about shopping areas if the traveler is shopping, or information about restaurants and cafes if the traveler is dining. This allows the analysis unit to improve the accuracy of its analysis based on the traveler's current activities and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0049] The analysis unit can improve the accuracy of its analysis by considering the traveler's geographical location information during the analysis. For example, if the traveler is in a tourist destination, the analysis unit will prioritize analyzing information related to that tourist destination. The analysis unit can also prioritize analyzing information related to a shopping area if the traveler is in a shopping area. The analysis unit can also prioritize analyzing information related to an event if the traveler is in an event venue. For example, if the analysis unit is in a tourist destination, it will prioritize analyzing information related to that tourist destination. If the traveler is in a shopping area, it will prioritize analyzing information related to that area. If the traveler is in an event venue, it will prioritize analyzing information related to that event. This allows the analysis unit to improve the accuracy of its analysis by considering the traveler's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The analysis unit can analyze the traveler's social media activity during analysis and utilize the relevant data for analysis. For example, the analysis unit can analyze information related to places the traveler has checked in on social media. The analysis unit can also analyze information related to photos the traveler has shared on social media. The analysis unit can also analyze information related to accounts the traveler follows on social media. For example, the analysis unit can analyze information related to places the traveler has checked in on social media. It can analyze information related to photos the traveler has shared on social media. It can analyze information related to accounts the traveler follows on social media. This allows the analysis unit to utilize relevant data for analysis based on the traveler's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the analysis of the relevant data.

[0051] The generation unit can adjust the level of detail of the information it generates based on the traveler's interests. For example, if the traveler is interested in something, the generation unit generates detailed information. The generation unit can also generate concise information if the traveler is not very interested. Furthermore, if the traveler is interested in a particular field, the generation unit can generate information specific to that field. For example, if the traveler is interested in something, the generation unit generates detailed information. If the traveler is not very interested, it generates concise information. If the traveler is interested in a particular field, it generates information specific to that field. This allows the generation unit to adjust the level of detail of the information it generates based on the traveler's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input traveler interest data into a generation AI and have the generation AI adjust the level of detail of the information.

[0052] The generation unit can apply different generation algorithms depending on the traveler's category during generation. For example, if the traveler is interested in history, the generation unit can apply a generation algorithm specialized in historical background. The generation unit can also apply a generation algorithm specialized in natural landscapes if the traveler is interested in nature. The generation unit can also apply an art-specific generation algorithm if the traveler is interested in art. For example, if the traveler is interested in history, the generation unit can apply a generation algorithm specialized in historical background. If the traveler is interested in nature, the generation unit can apply a generation algorithm specialized in natural landscapes. If the traveler is interested in art, the generation unit can apply an art-specific generation algorithm. This allows the generation unit to provide the optimal generation algorithm according to the traveler's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input traveler category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0053] The generation unit can prioritize generating highly relevant information by considering the traveler's geographical location during the generation process. For example, if the traveler is in a tourist destination, the generation unit will prioritize generating information related to that tourist destination. The generation unit can also prioritize generating store information in a shopping area if the traveler is in a shopping area. The generation unit can also prioritize generating information related to an event if the traveler is at an event venue. For example, if the traveler is in a tourist destination, the generation unit will prioritize generating information related to that tourist destination. If the traveler is in a shopping area, it will prioritize generating store information in that area. If the traveler is at an event venue, it will prioritize generating information related to that event. In this way, the generation unit can provide highly relevant information by considering the traveler's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the traveler's geographic location data into the generation AI, and have the generation AI generate highly relevant information.

[0054] The generation unit can analyze the traveler's social media activity and generate relevant information during the generation process. For example, the generation unit can generate information related to places the traveler has checked in to on social media. The generation unit can also generate information related to photos the traveler has shared on social media. The generation unit can also generate information related to accounts the traveler follows on social media. For example, the generation unit can generate information related to places the traveler has checked in to on social media. It can generate information related to photos the traveler has shared on social media. It can generate information related to accounts the traveler follows on social media. This allows the generation unit to provide relevant information based on the traveler's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the traveler's social media activity data into a generation AI and have the generation AI perform the generation of relevant information.

[0055] The display unit can select the optimal display method by referring to the traveler's past operation history when displaying information. For example, the display unit can suggest the optimal display method based on the display methods the traveler has used in the past. The display unit can also suggest the optimal display method based on the display methods the traveler has used in the past. The display unit can also analyze the traveler's past operation history and select a display method with high visibility. The display unit can also prioritize providing the display method the traveler has preferred to use in the past. For example, the display unit suggests the optimal display method based on the display methods the traveler has used in the past. It analyzes the traveler's past operation history and selects a display method with high visibility. It prioritizes providing the display method the traveler has preferred to use in the past. In this way, the display unit can provide the optimal display method based on the traveler's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the traveler's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0056] The display unit can customize the displayed content based on the traveler's current activities and areas of interest when displaying information. For example, if the traveler is sightseeing, the display unit will prioritize displaying information about tourist attractions. The display unit can also prioritize displaying information about shopping areas if the traveler is shopping. The display unit can also prioritize displaying information about restaurants and cafes if the traveler is dining. For example, if the traveler is sightseeing, the display unit will prioritize displaying information about tourist attractions. If the traveler is shopping, the display unit will prioritize displaying information about shopping areas. If the traveler is dining, the display unit will prioritize displaying information about restaurants and cafes. In this way, the display unit can customize the displayed content based on the traveler's current activities and areas of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the customization of the displayed content.

[0057] The display unit can select the optimal display method when displaying information, taking into account the traveler's geographical location. For example, if the traveler is in a tourist destination, the display unit will prioritize displaying information related to that tourist destination. The display unit can also prioritize displaying information about shops in a shopping area if the traveler is in a shopping area. The display unit can also prioritize displaying information about an event if the traveler is at an event venue. For example, if the traveler is in a tourist destination, the display unit will prioritize displaying information related to that tourist destination. If the traveler is in a shopping area, the display unit will prioritize displaying information about shops in that area if the traveler is in a shopping area. If the traveler is at an event venue, the display unit will prioritize displaying information about an event if the traveler is in an event venue. In this way, the display unit can provide the optimal display method, taking into account the traveler's geographical location. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the traveler's geographical location data into a generating AI and have the generating AI select the optimal display method.

[0058] The display unit can analyze the traveler's social media activity and display relevant information when it is displayed. For example, the display unit can display information related to places the traveler has checked in to on social media. The display unit can also display information related to photos the traveler has shared on social media. The display unit can also display information related to accounts the traveler follows on social media. For example, the display unit can display information related to places the traveler has checked in to on social media. For example, the display unit can display information related to photos the traveler has shared on social media. For example, the display unit can display information related to accounts the traveler follows on social media. In this way, the display unit can provide relevant information based on the traveler's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the display of relevant information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The location acquisition unit can adjust the frequency of location acquisition considering the traveler's health condition when acquiring the traveler's location information. For example, if the traveler is in good health, location information will be acquired at the normal frequency. If the traveler's health deteriorates, the frequency of location acquisition can be increased to enable a quick response in emergencies. Furthermore, if the traveler has a specific health risk, the unit can select a location acquisition method appropriate to that risk. As a result, the acquisition unit can acquire location information more safely and appropriately based on the traveler's health condition.

[0061] The analysis unit can optimize its analysis algorithm by referring to the traveler's past data. For example, it can select the optimal analysis algorithm based on the traveler's past travel history. It can also optimize the analysis algorithm by referring to the traveler's past interests and preferences. Furthermore, it can adjust the analysis algorithm based on the traveler's past search history. As a result, the analysis unit can provide the optimal analysis algorithm based on the traveler's past data.

[0062] The display unit can select the optimal display method by referring to the traveler's past operation history. For example, it can suggest the optimal display method based on the display methods the traveler has used in the past. It can also analyze the traveler's past operation history and select a display method with high visibility. Furthermore, it can prioritize providing the display method that the traveler has preferred to use in the past. In this way, the display unit can provide the optimal display method based on the traveler's past operation history.

[0063] The data collection unit can analyze travelers' past interests and select the most suitable collection method. For example, it can collect relevant information based on places travelers have visited and events they have attended in the past. It can also analyze travelers' past search history to collect interest-based information. Furthermore, it can collect relevant information based on tickets and booking information travelers have purchased in the past. In this way, the data collection unit can provide the most suitable collection method based on travelers' past interests.

[0064] The generation unit can adjust the level of detail of the information it generates based on the traveler's interests. For example, if the traveler is interested in something, it can generate detailed information. Conversely, if the traveler is not very interested, it can generate concise information. Furthermore, if the traveler is interested in a particular field, it can generate information specific to that field. In this way, the generation unit can adjust the level of detail of the information it generates based on the traveler's interests.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition unit acquires the traveler's location information. This information includes, for example, GPS data, Wi-Fi location information, and beacons. The acquisition unit uses this data to determine the traveler's current location. For example, it acquires GPS data in real time to determine the traveler's location. Wi-Fi location information determines the location based on the signal strength of surrounding Wi-Fi access points. Beacons can determine the location within a specific area with high accuracy. Step 2: The data collection unit gathers information about travelers' interests. This includes, for example, surveys, social media data, and past activity history. The data collection unit uses this data to identify travelers' interests. For example, surveys are used to gather information about travelers' interests. Social media data is used to identify interests by analyzing travelers' posts and the number of likes they receive. Past activity history is used to identify interests based on places travelers have visited and events they have attended in the past. Step 3: The analysis unit analyzes the information obtained by the acquisition and collection units. Analysis includes, for example, data mining, machine learning algorithms, and statistical analysis. The analysis unit uses these techniques to analyze the data and extract useful information. For example, it uses data mining techniques to extract useful information from large amounts of data. Machine learning algorithms learn data patterns and perform predictions and classifications. Statistical analysis analyzes the distribution and correlations of the data. Step 4: The generation unit generates personalized information based on the analysis results obtained by the analysis unit. For generation, for example, a generation AI (e.g., a text generation AI or a multimodal generation AI) is used. The generation unit uses these technologies to generate information suitable for travelers. For example, a text generation AI is used to generate information on tourist spots suitable for travelers. A multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI generates relevant information based on the traveler's interests. Step 5: The display unit displays the information generated by the generation unit using AR technology. The display includes, for example, AR glasses, smartphone apps, and tablet devices. The display unit displays the information generated using these devices. For example, AR glasses overlay the information onto the traveler's field of view. A smartphone app displays the information on the traveler's smartphone screen. A tablet device can display the information on a larger screen.

[0067] (Example of form 2) An embodiment of the present invention provides a personalized AR tour guide system that utilizes multimodal generating AI and AR technology to provide a personalized AR tour guide based on the traveler's location information and interests. When a traveler arrives at a specific tourist destination, the generating AI collects information related to that location based on the device's location information and displays it in real time using AR technology. For example, when a traveler stands in front of a historical building, the generating AI analyzes the building's history and cultural background and overlays the information onto the building using AR technology. The system can also provide information on related tourist spots and events based on the traveler's interests. This allows travelers to enjoy sightseeing at their own pace and gain a high-quality sightseeing experience that transcends language and cultural barriers. Furthermore, the system is available 24 hours a day and can provide more detailed and comprehensive information in real time than traditional human guides. For example, even if a traveler visits a tourist destination at night, the generating AI can provide information related to that location and display it visually using AR technology. This allows travelers to enjoy sightseeing without time constraints. This system brings new value to travelers and the tourism industry, significantly improving the quality of the sightseeing experience. Travelers can obtain information based on their interests and maximize the appeal of tourist destinations. Furthermore, for the tourism industry, utilizing generative AI and AR technology can contribute to promoting tourist destinations and improving tourist satisfaction. This allows personalized AR tour guide systems to provide high-quality tourism experiences based on travelers' location information and interests.

[0068] The personalized AR tour guide system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the traveler's location information. The traveler's location information includes, but is not limited to, GPS data, Wi-Fi location information, and beacons. The acquisition unit can, for example, use GPS data to determine the traveler's current location. The acquisition unit can also determine the traveler's location using Wi-Fi location information. The acquisition unit can also determine the traveler's location using beacons. For example, the acquisition unit acquires GPS data in real time to determine the traveler's location. Wi-Fi location information determines the location based on the signal strength of surrounding Wi-Fi access points. Beacons can determine the location within a specific area with high accuracy. The collection unit collects the traveler's interests. The traveler's interests include, for example, questionnaires, social media data, and past behavioral history. The collection unit can, for example, collect the traveler's interests using questionnaires. The collection unit can also collect the traveler's interests by analyzing social media data. The data collection unit can also collect travelers' interests based on their past behavioral history. For example, the data collection unit can conduct surveys with travelers to gather their interests. Social media data can be used to identify interests by analyzing travelers' posts and the number of likes they receive. Past behavioral history can be used to identify interests based on places travelers have visited and events they have attended. The analysis unit analyzes the information obtained by the acquisition and collection units. Analysis includes, but is not limited to, data mining and machine learning algorithms. For example, the analysis unit can analyze data acquired using data mining techniques. The analysis unit can also analyze data using machine learning algorithms. Furthermore, the analysis unit can analyze data using statistical analysis. For example, the analysis unit can extract useful information from large amounts of data using data mining techniques. Machine learning algorithms learn data patterns and perform predictions and classifications. Statistical analysis analyzes the distribution and correlations of data. The generation unit generates personalized information based on the analysis results obtained by the analysis unit.The generation process may use, but is not limited to, generation AI (e.g., text generation AI or multimodal generation AI). The generation unit may, for example, use text generation AI to generate information suitable for travelers. The generation unit may also use multimodal generation AI to generate information such as text, images, and audio. Furthermore, the generation unit may use generation AI to generate information based on the traveler's interests. For example, the generation unit may use text generation AI to generate information on tourist spots suitable for travelers. Multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI generates relevant information based on the traveler's interests. The display unit displays the information generated by the generation unit using AR technology. Display includes, but is not limited to, AR glasses and smartphone apps. For example, the display unit may display information generated using AR glasses. The display unit may also display information generated using a smartphone app. Furthermore, the display unit may display information generated using a tablet device. For example, the display unit may overlay information onto the traveler's field of view using AR glasses. A smartphone app displays information on the traveler's smartphone screen. Tablet devices can display information on a large screen. This allows the personalized AR tour guide system according to this embodiment to provide a high-quality sightseeing experience based on the traveler's location and interests.

[0069] The acquisition unit acquires the traveler's location information. This location information includes, but is not limited to, GPS data, Wi-Fi location information, and beacons. For example, the acquisition unit can use GPS data to determine the traveler's current location. It can also use Wi-Fi location information to determine the traveler's location. Furthermore, it can use beacons to determine the traveler's location. For example, the acquisition unit can acquire GPS data in real time to determine the traveler's location. Wi-Fi location information determines the location based on the signal strength of surrounding Wi-Fi access points. Beacons can pinpoint locations within a specific area with high accuracy. This allows the acquisition unit to accurately understand where the traveler is and provide basic data to appropriately guide their next actions. In addition, the acquisition unit can centrally manage and update this location information in real time, constantly tracking the traveler's movements. For example, when a traveler moves between tourist attractions, the acquisition unit records their route and suggests the optimal route to the next tourist spot. Furthermore, the acquisition unit can combine multiple location information acquisition methods to improve the accuracy of location information. For example, by combining GPS data and Wi-Fi location information, highly accurate location information can be provided both indoors and outdoors. This allows the acquisition unit to obtain travelers' location information with high accuracy and in real time, improving the accuracy and reliability of the entire system.

[0070] The data collection unit collects information about travelers' interests. This includes, but is not limited to, surveys, social media data, and past travel history. For example, the unit can collect traveler interests using surveys. It can also collect traveler interests by analyzing social media data. Furthermore, it can collect traveler interests based on past travel history. For instance, the unit can conduct surveys with travelers to collect their interests. Social media data can be used to identify interests by analyzing the content of travelers' posts and the number of likes they receive. Past travel history can be used to identify interests based on places travelers have visited and events they have attended. This allows the data collection unit to gain a detailed understanding of individual travelers' interests and collect foundational data to provide personalized travel experiences. Moreover, by centrally managing and updating this data in real time, the unit can flexibly respond to changes in travelers' interests. For example, if a traveler develops a new interest, this information can be immediately reflected and used to suggest future tourist spots. Furthermore, it is crucial for the data collection unit to implement strict security measures in data collection and management to protect travelers' privacy. This will enable the data collection unit to collect travelers' interests with high accuracy and security, improving the overall reliability of the system and user satisfaction.

[0071] The analysis unit analyzes the information obtained by the acquisition and collection units. Analysis includes, but is not limited to, data mining and machine learning algorithms. For example, the analysis unit analyzes data acquired using data mining techniques. It can also analyze data using machine learning algorithms. Furthermore, it can analyze data using statistical analysis. For instance, the analysis unit extracts useful information from large amounts of data using data mining techniques. Machine learning algorithms learn data patterns and perform predictions and classifications. Statistical analysis analyzes data distribution and correlations. This allows the analysis unit to analyze travelers' location information and interests in detail, generating foundational data for providing personalized travel experiences. Moreover, the analysis unit can analyze this data in real time, responding immediately to changes in travelers' movements and interests. For example, if a traveler becomes interested in a new tourist spot, the information can be immediately analyzed and reflected in suggestions for the next tourist spot. The analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns, which can be used to suggest future tourist spots and plan events. This allows the analysis unit to analyze travelers' interests and preferences with high accuracy and in real time, improving the overall accuracy and reliability of the system.

[0072] The generation unit generates personalized information based on the analysis results obtained by the analysis unit. Generation may, but is not limited to, the use of generation AI (e.g., text generation AI or multimodal generation AI). For example, the generation unit can use text generation AI to generate information suitable for travelers. It can also use multimodal generation AI to generate information such as text, images, and audio. Furthermore, the generation unit can use generation AI to generate information based on the traveler's interests. For example, the generation unit can use text generation AI to generate information on tourist spots suitable for travelers. Multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI generates relevant information based on the traveler's interests. This allows the generation unit to generate personalized information tailored to the individual interests of travelers, providing a high-quality travel experience. Moreover, the generation unit can generate this information in real time and respond immediately to changes in travelers' movements and interests. For example, if a traveler becomes interested in a new tourist spot, the generation unit can immediately generate that information and reflect it in the suggestion of the next tourist spot. Furthermore, the generation unit can provide travelers with richer information by combining multiple modals. This allows the generation unit to generate information that accurately and in real time reflects travelers' interests, thereby improving the overall accuracy and reliability of the system.

[0073] The display unit displays information generated by the generation unit using AR technology. The display includes, but is not limited to, AR glasses and smartphone apps. For example, the display unit can display information generated using AR glasses. It can also display information generated using a smartphone app. Furthermore, it can display information generated using a tablet device. For example, the display unit can overlay information onto the traveler's field of view using AR glasses. A smartphone app displays information on the traveler's smartphone screen. A tablet device can display information on a large screen. This allows the display unit to provide travelers with intuitive and visual information, offering a high-quality travel experience. Moreover, the display unit can update this information in real time, responding immediately to changes in the traveler's movements and interests. For example, if a traveler becomes interested in a new tourist spot, that information can be immediately displayed and reflected in suggestions for the next tourist spot. Additionally, by using AR technology, the display unit can provide travelers with a rich experience that fuses the real world with digital information. This allows the display unit to display information that accurately and in real time reflects the traveler's interests, improving the overall accuracy and reliability of the system.

[0074] The generation unit can generate information on relevant tourist attractions and events based on the traveler's interests. For example, it can generate information on local tourist attractions and seasonal events based on the traveler's interests. For example, if the traveler is interested in history, it can generate information on historical tourist attractions. It can also generate information on natural scenic spots if the traveler is interested in nature. It can also generate information on art galleries and museums if the traveler is interested in art. For example, the generation unit generates information on local tourist attractions based on the traveler's interests. Information on seasonal events is generated to match the time of year the traveler visits. Information on historical tourist attractions includes the history and cultural background of the place. Information on natural scenic spots showcases the beauty of the scenery and the charm of nature. Information on art galleries and museums includes the contents of exhibitions and ongoing events. In this way, the generation unit can provide information on relevant tourist attractions and events based on the traveler's interests.

[0075] The display unit can show information that is available 24 hours a day. For example, it can show information such as opening hours of tourist attractions and emergency contact information. The display unit can also show emergency contact information. Furthermore, it can display information about tourist attractions that are available 24 hours a day. For example, the display unit can show the opening hours of tourist attractions in real time. Emergency contact information provides travelers with contact information in case of an emergency. Information on tourist attractions available 24 hours a day introduces places that travelers can visit at any time. Thus, the display unit can provide information that is available 24 hours a day.

[0076] The display unit can provide navigation that allows travelers to enjoy sightseeing at their own pace. For example, the display unit can provide real-time route guidance and guidance that adjusts to the traveler's walking speed. The display unit can, for example, provide real-time route guidance. It can also provide guidance that adjusts to the traveler's walking speed. Furthermore, the display unit can guide travelers along the optimal route based on their current location. For example, the display unit can identify the traveler's current location in real time and guide them along the optimal route. Guidance that adjusts to walking speed adjusts the route guidance to match the traveler's walking speed. Guidance based on the traveler's current location ensures that travelers reach their destination without getting lost. In this way, the display unit can provide navigation that allows travelers to enjoy sightseeing at their own pace.

[0077] The generation unit can generate information that contributes to the promotion of tourist destinations and the improvement of tourist satisfaction. For example, the generation unit generates information such as special offers and customer reviews. For instance, it generates special offers for tourist destinations. It can also generate customer reviews from tourists. Furthermore, it can generate promotional videos for tourist destinations. For example, the generation unit provides special offers for tourist destinations to travelers. Customer reviews are generated based on the ratings and impressions of other travelers. Promotional videos generate visuals that convey the appeal of tourist destinations. In this way, the generation unit can provide information that contributes to the promotion of tourist destinations and the improvement of tourist satisfaction.

[0078] The acquisition unit can estimate the traveler's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the traveler is excited, the acquisition unit will acquire location information frequently and update it in real time. The acquisition unit can also reduce the frequency of location information acquisition when the traveler is relaxed, thereby reducing battery consumption. Furthermore, if the traveler is tired, the acquisition unit can temporarily stop acquiring location information and refrain from providing information during breaks. For example, if the traveler is excited, the acquisition unit will acquire location information frequently and update it in real time. If the traveler is relaxed, the acquisition unit will reduce the frequency of location information acquisition to reduce battery consumption. If the traveler is tired, the acquisition unit will temporarily stop acquiring location information and refrain from providing information during breaks. In this way, the acquisition unit can provide more appropriate information by adjusting the timing of location information acquisition based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit may input traveler sentiment data into the generating AI and have the generating AI perform sentiment estimation.

[0079] The acquisition unit can analyze the traveler's past travel history and select the optimal method for acquiring location information. The acquisition unit can adjust the frequency of acquiring location information based on places the traveler has visited in the past. The acquisition unit can also analyze the traveler's past travel patterns and select an efficient method for acquiring location information. Furthermore, the acquisition unit can optimize the method for acquiring location information by considering the modes of transportation the traveler has used in the past. For example, the acquisition unit adjusts the frequency of acquiring location information based on places the traveler has visited in the past. It analyzes the traveler's past travel patterns and selects an efficient method for acquiring location information. It optimizes the method for acquiring location information by considering the modes of transportation the traveler has used in the past. As a result, the acquisition unit can provide the optimal method for acquiring location information based on the traveler's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the traveler's past travel history data into a generating AI and have the generating AI select the optimal method for acquiring location information.

[0080] The acquisition unit can filter location information based on the traveler's current activities and areas of interest. For example, if the traveler is sightseeing, the acquisition unit will prioritize acquiring location information around tourist spots. The acquisition unit can also prioritize acquiring location information in shopping areas if the traveler is shopping, or in restaurants if the traveler is dining. This allows the acquisition unit to provide relevant location information based on the traveler's current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the filtering.

[0081] The acquisition unit can estimate the traveler's emotions and determine the priority of location information to acquire based on the estimated emotions. For example, if the traveler is excited, the acquisition unit will prioritize acquiring location information of tourist attractions. The acquisition unit can also prioritize acquiring location information of rest spots if the traveler is relaxed. The acquisition unit can also prioritize acquiring location information of accommodations if the traveler is tired. For example, if the traveler is excited, the acquisition unit will prioritize acquiring location information of tourist attractions. If the traveler is relaxed, it will prioritize acquiring location information of rest spots. If the traveler is tired, it will prioritize acquiring location information of accommodations. This allows the acquisition unit to determine the priority of location information to acquire based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input traveler's emotional data into a generating AI and have the generating AI perform emotion estimation.

[0082] The acquisition unit can prioritize acquiring highly relevant information by considering the traveler's geographical location when acquiring location information. For example, if the traveler is in a tourist destination, the acquisition unit will prioritize acquiring location information related to that tourist destination. The acquisition unit can also prioritize acquiring store information in a shopping area if the traveler is in a shopping area. The acquisition unit can also prioritize acquiring location information related to an event if the traveler is at an event venue. For example, if the acquisition unit is in a tourist destination, it will prioritize acquiring location information related to that tourist destination. If the traveler is in a shopping area, it will prioritize acquiring store information in that area. If the traveler is at an event venue, it will prioritize acquiring location information related to that event. In this way, the acquisition unit can provide highly relevant information by considering the traveler's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the traveler's geographic location data into the generating AI, causing the generating AI to acquire highly relevant information.

[0083] The acquisition unit can analyze the traveler's social media activity when acquiring location information and acquire relevant location information. For example, the acquisition unit can prioritize acquiring location information of places where the traveler has checked in on social media. The acquisition unit can also prioritize acquiring location information of places where the traveler has checked in on social media. Furthermore, the acquisition unit can acquire information about relevant places based on the location information of photos shared by the traveler on social media. Furthermore, the acquisition unit can acquire information about relevant places based on the location information of accounts that the traveler follows on social media. For example, the acquisition unit can prioritize acquiring location information of places where the traveler has checked in on social media. For example, it can acquire information about relevant places based on the location information of photos shared by the traveler on social media. For example, it can acquire information about relevant places based on the location information of accounts that the traveler follows on social media. In this way, the acquisition unit can provide relevant location information based on the traveler's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant location information.

[0084] The data collection unit can estimate the traveler's emotions and adjust the method of collecting information on their interests based on the estimated emotions. For example, if the traveler is excited, the data collection unit will prioritize collecting information on active activities. The data collection unit can also prioritize collecting information on places and activities where the traveler can relax if they are relaxed. The data collection unit can also prioritize collecting information on rest stops and places to refresh if the traveler is tired. For example, if the traveler is excited, the data collection unit will prioritize collecting information on active activities. If the traveler is relaxed, the data collection unit will prioritize collecting information on places and activities where the traveler can relax. If the traveler is tired, the data collection unit will prioritize collecting information on rest stops and places to refresh. This allows the data collection unit to adjust the method of collecting information on their interests based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input traveler sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0085] The data collection unit can analyze travelers' past interests and select the optimal data collection method. For example, the data collection unit can collect relevant information based on places travelers have visited and events they have attended in the past. The data collection unit can also analyze travelers' past search history and collect information based on their interests. Furthermore, the data collection unit can collect relevant information based on tickets and reservations travelers have purchased in the past. For example, the data collection unit can collect relevant information based on places travelers have visited and events they have attended in the past. It can also analyze travelers' past search history and collect information based on their interests. It can also collect relevant information based on tickets and reservations travelers have purchased in the past. This allows the data collection unit to provide the optimal data collection method based on travelers' past interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input travelers' past interests into a generating AI and have the generating AI select the optimal data collection method.

[0086] The data collection unit can filter the data based on the traveler's current activities and areas of interest when collecting information on interests. For example, if the traveler is sightseeing, the data collection unit can prioritize collecting information on tourist attractions. The data collection unit can also prioritize collecting information on shopping areas if the traveler is shopping. The data collection unit can also prioritize collecting information on restaurants and cafes if the traveler is dining. For example, if the traveler is sightseeing, the data collection unit can prioritize collecting information on tourist attractions. If the traveler is shopping, the data collection unit can prioritize collecting information on shopping areas. If the traveler is dining, the data collection unit can prioritize collecting information on restaurants and cafes. This allows the data collection unit to provide relevant information based on the traveler's current activities and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the filtering.

[0087] The data collection unit can estimate the traveler's emotions and determine the priority of interests to collect based on the estimated emotions. For example, if the traveler is excited, the data collection unit will prioritize collecting information about active activities. If the traveler is relaxed, the data collection unit can also prioritize collecting information about places and activities where they can relax. If the traveler is tired, the data collection unit can also prioritize collecting information about rest stops and places where they can refresh themselves. For example, if the traveler is excited, the data collection unit will prioritize collecting information about active activities. If the traveler is relaxed, the data collection unit will prioritize collecting information about places and activities where they can relax. If the traveler is tired, the data collection unit will prioritize collecting information about rest stops and places where they can refresh themselves. This allows the data collection unit to determine the priority of interests to collect based on the traveler's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input traveler sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0088] The data collection unit can prioritize collecting highly relevant information by considering the traveler's geographical location when collecting information on their interests. For example, if the traveler is in a tourist destination, the data collection unit will prioritize collecting information related to that tourist destination. The data collection unit can also prioritize collecting information about shops in a shopping area if the traveler is in a shopping area. The data collection unit can also prioritize collecting information related to an event if the traveler is at an event venue. For example, if the traveler is in a tourist destination, the data collection unit will prioritize collecting information related to that tourist destination. If the traveler is in a shopping area, the data collection unit will prioritize collecting information about shops in that area if the traveler is in a shopping area. If the traveler is at an event venue, the data collection unit will prioritize collecting information related to the event if the traveler is in an event venue. In this way, the data collection unit can provide highly relevant information by considering the traveler's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the traveler's geographic location data into a generating AI, which can then perform the task of collecting highly relevant information.

[0089] The data collection unit can analyze a traveler's social media activity and collect relevant interests when gathering information on their social media activities. For example, the data collection unit can prioritize collecting information related to places the traveler has checked in on social media. The data collection unit can also prioritize collecting information related to photos the traveler has shared on social media. The data collection unit can also prioritize collecting information related to accounts the traveler follows on social media. For example, the data collection unit can prioritize collecting information related to places the traveler has checked in on social media. For example, the data collection unit can prioritize collecting information related to photos the traveler has shared on social media. For example, the data collection unit can prioritize collecting information related to accounts the traveler follows on social media. This allows the data collection unit to provide relevant interests based on the traveler's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the collection of relevant interests.

[0090] The analysis unit can estimate the traveler's emotions and adjust the analysis method based on the estimated emotions. For example, if the traveler is excited, the analysis unit can perform a detailed analysis and provide relevant information. The analysis unit can also perform a concise analysis and provide only the necessary information if the traveler is relaxed. Furthermore, if the traveler is tired, the analysis unit can temporarily suspend the analysis and refrain from providing information during the rest period. For example, if the traveler is excited, the analysis unit can perform a detailed analysis and provide relevant information. If the traveler is relaxed, it can perform a concise analysis and provide only the necessary information. If the traveler is tired, it can temporarily suspend the analysis and refrain from providing information during the rest period. This allows the analysis unit to adjust the analysis method based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input traveler emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The analysis unit can optimize the analysis algorithm by referring to the traveler's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the traveler's past travel history. The analysis unit can also optimize the analysis algorithm by referring to the traveler's past interest data. The analysis unit can also adjust the analysis algorithm based on the traveler's past search history. For example, the analysis unit selects the optimal analysis algorithm based on the traveler's past travel history. It optimizes the analysis algorithm by referring to the traveler's past interest data. It adjusts the analysis algorithm based on the traveler's past search history. In this way, the analysis unit can provide the optimal analysis algorithm based on the traveler's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the traveler's past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0092] The analysis unit can improve the accuracy of its analysis based on the traveler's current activities and areas of interest. For example, if the traveler is sightseeing, the analysis unit will prioritize analyzing information about tourist spots. The analysis unit can also prioritize analyzing information about shopping areas if the traveler is shopping, or information about restaurants and cafes if the traveler is dining. This allows the analysis unit to improve the accuracy of its analysis based on the traveler's current activities and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0093] The analysis unit can estimate the traveler's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the traveler is excited, the analysis unit can display detailed analysis results. The analysis unit can also display concise analysis results if the traveler is relaxed. The analysis unit can also temporarily withhold the display of analysis results if the traveler is tired. For example, if the traveler is excited, the analysis unit can display detailed analysis results. If the traveler is relaxed, it can display concise analysis results. If the traveler is tired, it can temporarily withhold the display of analysis results. This allows the analysis unit to adjust how the analysis results are displayed based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input traveler emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The analysis unit can improve the accuracy of its analysis by considering the traveler's geographical location information during the analysis. For example, if the traveler is in a tourist destination, the analysis unit will prioritize analyzing information related to that tourist destination. The analysis unit can also prioritize analyzing information related to a shopping area if the traveler is in a shopping area. The analysis unit can also prioritize analyzing information related to an event if the traveler is in an event venue. For example, if the analysis unit is in a tourist destination, it will prioritize analyzing information related to that tourist destination. If the traveler is in a shopping area, it will prioritize analyzing information related to that area. If the traveler is in an event venue, it will prioritize analyzing information related to that event. This allows the analysis unit to improve the accuracy of its analysis by considering the traveler's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0095] The analysis unit can analyze the traveler's social media activity during analysis and utilize the relevant data for analysis. For example, the analysis unit can analyze information related to places the traveler has checked in on social media. The analysis unit can also analyze information related to photos the traveler has shared on social media. The analysis unit can also analyze information related to accounts the traveler follows on social media. For example, the analysis unit can analyze information related to places the traveler has checked in on social media. It can analyze information related to photos the traveler has shared on social media. It can analyze information related to accounts the traveler follows on social media. This allows the analysis unit to utilize relevant data for analysis based on the traveler's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the analysis of the relevant data.

[0096] The generation unit can estimate the traveler's emotions and adjust the way it presents the information it generates based on the estimated emotions. For example, if the traveler is excited, the generation unit can generate information with visually stimulating effects. The generation unit can also generate information in a calm tone if the traveler is relaxed. The generation unit can also generate information in a simple and easily visible tone if the traveler is tired. For example, if the traveler is excited, the generation unit can generate information with visually stimulating effects. If the traveler is relaxed, the information can be presented in a calm tone. If the traveler is tired, the information can be presented in a simple and easily visible tone. This allows the generation unit to adjust the way it presents the information it generates based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input traveler's emotional data into a generation AI and have the generation AI perform emotion estimation.

[0097] The generation unit can adjust the level of detail of the information it generates based on the traveler's interests. For example, if the traveler is interested in something, the generation unit generates detailed information. The generation unit can also generate concise information if the traveler is not very interested. Furthermore, if the traveler is interested in a particular field, the generation unit can generate information specific to that field. For example, if the traveler is interested in something, the generation unit generates detailed information. If the traveler is not very interested, it generates concise information. If the traveler is interested in a particular field, it generates information specific to that field. This allows the generation unit to adjust the level of detail of the information it generates based on the traveler's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input traveler interest data into a generation AI and have the generation AI adjust the level of detail of the information.

[0098] The generation unit can apply different generation algorithms depending on the traveler's category during generation. For example, if the traveler is interested in history, the generation unit can apply a generation algorithm specialized in historical background. The generation unit can also apply a generation algorithm specialized in natural landscapes if the traveler is interested in nature. The generation unit can also apply an art-specific generation algorithm if the traveler is interested in art. For example, if the traveler is interested in history, the generation unit can apply a generation algorithm specialized in historical background. If the traveler is interested in nature, the generation unit can apply a generation algorithm specialized in natural landscapes. If the traveler is interested in art, the generation unit can apply an art-specific generation algorithm. This allows the generation unit to provide the optimal generation algorithm according to the traveler's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input traveler category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0099] The generation unit can estimate the traveler's emotions and determine the priority of the information to generate based on the estimated emotions. For example, if the traveler is excited, the generation unit will prioritize generating information about active activities. The generation unit can also prioritize generating information about places and activities where the traveler can relax if they are relaxed. The generation unit can also prioritize generating information about rest stops and places to refresh if the traveler is tired. For example, if the traveler is excited, the generation unit will prioritize generating information about active activities. If the traveler is relaxed, the generation unit will prioritize generating information about places and activities where the traveler can relax. If the traveler is tired, the generation unit will prioritize generating information about rest stops and places to refresh. This allows the generation unit to determine the priority of the information to generate based on the traveler's emotions. Emotion estimation is achieved using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input traveler's emotional data into a generation AI and have the generation AI perform emotional estimation.

[0100] The generation unit can prioritize generating highly relevant information by considering the traveler's geographical location during the generation process. For example, if the traveler is in a tourist destination, the generation unit will prioritize generating information related to that tourist destination. The generation unit can also prioritize generating store information in a shopping area if the traveler is in a shopping area. The generation unit can also prioritize generating information related to an event if the traveler is at an event venue. For example, if the traveler is in a tourist destination, the generation unit will prioritize generating information related to that tourist destination. If the traveler is in a shopping area, it will prioritize generating store information in that area. If the traveler is at an event venue, it will prioritize generating information related to that event. In this way, the generation unit can provide highly relevant information by considering the traveler's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the traveler's geographic location data into the generation AI, and have the generation AI generate highly relevant information.

[0101] The generation unit can analyze the traveler's social media activity and generate relevant information during the generation process. For example, the generation unit can generate information related to places the traveler has checked in to on social media. The generation unit can also generate information related to photos the traveler has shared on social media. The generation unit can also generate information related to accounts the traveler follows on social media. For example, the generation unit can generate information related to places the traveler has checked in to on social media. It can generate information related to photos the traveler has shared on social media. It can generate information related to accounts the traveler follows on social media. This allows the generation unit to provide relevant information based on the traveler's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the traveler's social media activity data into a generation AI and have the generation AI perform the generation of relevant information.

[0102] The display unit can estimate the traveler's emotions and adjust the display method based on the estimated emotions. For example, if the traveler is excited, the display unit provides a visually stimulating display method. The display unit can also display in a calm tone if the traveler is relaxed. The display unit can also provide a simple and easy-to-read display method if the traveler is tired. For example, if the traveler is excited, the display unit provides a visually stimulating display method. If the traveler is relaxed, it displays in a calm tone. If the traveler is tired, it provides a simple and easy-to-read display method. This allows the display unit to adjust the display method based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input traveler emotion data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0103] The display unit can select the optimal display method by referring to the traveler's past operation history when displaying information. For example, the display unit can suggest the optimal display method based on the display methods the traveler has used in the past. The display unit can also suggest the optimal display method based on the display methods the traveler has used in the past. The display unit can also analyze the traveler's past operation history and select a display method with high visibility. The display unit can also prioritize providing the display method the traveler has preferred to use in the past. For example, the display unit suggests the optimal display method based on the display methods the traveler has used in the past. It analyzes the traveler's past operation history and selects a display method with high visibility. It prioritizes providing the display method the traveler has preferred to use in the past. In this way, the display unit can provide the optimal display method based on the traveler's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the traveler's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0104] The display unit can customize the displayed content based on the traveler's current activities and areas of interest when displaying information. For example, if the traveler is sightseeing, the display unit will prioritize displaying information about tourist attractions. The display unit can also prioritize displaying information about shopping areas if the traveler is shopping. The display unit can also prioritize displaying information about restaurants and cafes if the traveler is dining. For example, if the traveler is sightseeing, the display unit will prioritize displaying information about tourist attractions. If the traveler is shopping, the display unit will prioritize displaying information about shopping areas. If the traveler is dining, the display unit will prioritize displaying information about restaurants and cafes. In this way, the display unit can customize the displayed content based on the traveler's current activities and areas of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input data on the traveler's current activities and areas of interest into a generating AI and have the generating AI perform the customization of the displayed content.

[0105] The display unit can estimate the traveler's emotions and determine the priority of information to display based on the estimated emotions. For example, if the traveler is excited, the display unit will prioritize displaying information about active activities. The display unit can also prioritize displaying information about places and activities where the traveler can relax if they are relaxed. The display unit can also prioritize displaying information about rest stops and places to refresh if the traveler is tired. For example, if the traveler is excited, the display unit will prioritize displaying information about active activities. If the traveler is relaxed, the display unit will prioritize displaying information about places and activities where the traveler can relax. If the traveler is tired, the display unit will prioritize displaying information about rest stops and places to refresh. In this way, the display unit can determine the priority of information to display based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input traveler's emotion data into a generating AI and have the generating AI perform emotion estimation.

[0106] The display unit can select the optimal display method when displaying information, taking into account the traveler's geographical location. For example, if the traveler is in a tourist destination, the display unit will prioritize displaying information related to that tourist destination. The display unit can also prioritize displaying information about shops in a shopping area if the traveler is in a shopping area. The display unit can also prioritize displaying information about an event if the traveler is at an event venue. For example, if the traveler is in a tourist destination, the display unit will prioritize displaying information related to that tourist destination. If the traveler is in a shopping area, the display unit will prioritize displaying information about shops in that area if the traveler is in a shopping area. If the traveler is at an event venue, the display unit will prioritize displaying information about an event if the traveler is in an event venue. In this way, the display unit can provide the optimal display method, taking into account the traveler's geographical location. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the traveler's geographical location data into a generating AI and have the generating AI select the optimal display method.

[0107] The display unit can analyze the traveler's social media activity and display relevant information when it is displayed. For example, the display unit can display information related to places the traveler has checked in to on social media. The display unit can also display information related to photos the traveler has shared on social media. The display unit can also display information related to accounts the traveler follows on social media. For example, the display unit can display information related to places the traveler has checked in to on social media. For example, the display unit can display information related to photos the traveler has shared on social media. For example, the display unit can display information related to accounts the traveler follows on social media. In this way, the display unit can provide relevant information based on the traveler's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the display of relevant information.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The location acquisition unit can adjust the frequency of location acquisition considering the traveler's health condition when acquiring the traveler's location information. For example, if the traveler is in good health, location information will be acquired at the normal frequency. If the traveler's health deteriorates, the frequency of location acquisition can be increased to enable a quick response in emergencies. Furthermore, if the traveler has a specific health risk, the unit can select a location acquisition method appropriate to that risk. As a result, the acquisition unit can acquire location information more safely and appropriately based on the traveler's health condition.

[0110] The data collection unit can estimate the traveler's emotions and adjust the method of collecting information on their interests based on those emotions. For example, if the traveler is excited, it can prioritize collecting information on active activities. If the traveler is relaxed, it can prioritize collecting information on places and activities where they can relax. Furthermore, if the traveler is tired, it can prioritize collecting information on rest stops and places where they can refresh themselves. In this way, the data collection unit can adjust the method of collecting information on the traveler's interests based on their emotions.

[0111] The analysis unit can optimize its analysis algorithm by referring to the traveler's past data. For example, it can select the optimal analysis algorithm based on the traveler's past travel history. It can also optimize the analysis algorithm by referring to the traveler's past interests and preferences. Furthermore, it can adjust the analysis algorithm based on the traveler's past search history. As a result, the analysis unit can provide the optimal analysis algorithm based on the traveler's past data.

[0112] The generation unit can estimate the traveler's emotions and adjust the way the generated information is presented based on those emotions. For example, if the traveler is excited, it can generate information with visually stimulating effects. If the traveler is relaxed, it can generate information in a calm tone. Furthermore, if the traveler is tired, it can generate simple and highly visible information. In this way, the generation unit can adjust the way the generated information is presented based on the traveler's emotions.

[0113] The display unit can select the optimal display method by referring to the traveler's past operation history. For example, it can suggest the optimal display method based on the display methods the traveler has used in the past. It can also analyze the traveler's past operation history and select a display method with high visibility. Furthermore, it can prioritize providing the display method that the traveler has preferred to use in the past. In this way, the display unit can provide the optimal display method based on the traveler's past operation history.

[0114] The location acquisition unit can estimate the traveler's emotions and adjust the timing of location data acquisition based on those emotions. For example, if the traveler is excited, it can acquire location data frequently and update it in real time. If the traveler is relaxed, it can reduce the frequency of location data acquisition to conserve battery power. Furthermore, if the traveler is tired, it can temporarily stop acquiring location data and refrain from providing information during breaks. In this way, the location acquisition unit can provide more appropriate information by adjusting the timing of location data acquisition based on the traveler's emotions.

[0115] The data collection unit can analyze travelers' past interests and select the most suitable collection method. For example, it can collect relevant information based on places travelers have visited and events they have attended in the past. It can also analyze travelers' past search history to collect interest-based information. Furthermore, it can collect relevant information based on tickets and booking information travelers have purchased in the past. In this way, the data collection unit can provide the most suitable collection method based on travelers' past interests.

[0116] The analysis unit can estimate the traveler's emotions and adjust the analysis method based on the estimated emotions. For example, if the traveler is excited, it can perform a detailed analysis and provide relevant information. If the traveler is relaxed, it can perform a concise analysis and provide only the essential information. Furthermore, if the traveler is tired, it can temporarily suspend the analysis and refrain from providing information during rest periods. In this way, the analysis unit can adjust the analysis method based on the traveler's emotions.

[0117] The generation unit can adjust the level of detail of the information it generates based on the traveler's interests. For example, if the traveler is interested in something, it can generate detailed information. Conversely, if the traveler is not very interested, it can generate concise information. Furthermore, if the traveler is interested in a particular field, it can generate information specific to that field. In this way, the generation unit can adjust the level of detail of the information it generates based on the traveler's interests.

[0118] The display unit can estimate the traveler's emotions and adjust its display method based on those emotions. For example, if the traveler is excited, it can provide a visually stimulating display. If the traveler is relaxed, it can display in a calm tone. Furthermore, if the traveler is tired, it can provide a simple and highly visible display. In this way, the display unit can adjust its display method based on the traveler's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The acquisition unit acquires the traveler's location information. This information includes, for example, GPS data, Wi-Fi location information, and beacons. The acquisition unit uses this data to determine the traveler's current location. For example, it acquires GPS data in real time to determine the traveler's location. Wi-Fi location information determines the location based on the signal strength of surrounding Wi-Fi access points. Beacons can determine the location within a specific area with high accuracy. Step 2: The data collection unit gathers information about travelers' interests. This includes, for example, surveys, social media data, and past activity history. The data collection unit uses this data to identify travelers' interests. For example, surveys are used to gather information about travelers' interests. Social media data is used to identify interests by analyzing travelers' posts and the number of likes they receive. Past activity history is used to identify interests based on places travelers have visited and events they have attended in the past. Step 3: The analysis unit analyzes the information obtained by the acquisition and collection units. Analysis includes, for example, data mining, machine learning algorithms, and statistical analysis. The analysis unit uses these techniques to analyze the data and extract useful information. For example, it uses data mining techniques to extract useful information from large amounts of data. Machine learning algorithms learn data patterns and perform predictions and classifications. Statistical analysis analyzes the distribution and correlations of the data. Step 4: The generation unit generates personalized information based on the analysis results obtained by the analysis unit. For generation, for example, a generation AI (e.g., a text generation AI or a multimodal generation AI) is used. The generation unit uses these technologies to generate information suitable for travelers. For example, a text generation AI is used to generate information on tourist spots suitable for travelers. A multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI generates relevant information based on the traveler's interests. Step 5: The display unit displays the information generated by the generation unit using AR technology. The display includes, for example, AR glasses, smartphone apps, and tablet devices. The display unit displays the information generated using these devices. For example, AR glasses overlay the information onto the traveler's field of view. A smartphone app displays the information on the traveler's smartphone screen. A tablet device can display the information on a larger screen.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit identifies the traveler's location using GPS data and Wi-Fi location information from the smart device 14. The collection unit collects the traveler's interests by analyzing the survey function and social media data of the smart device 14. The analysis unit analyzes the information using data mining and machine learning algorithms by the identification processing unit 290 of the data processing unit 12. The generation unit generates personalized information using generation AI by the identification processing unit 290 of the data processing unit 12. The display unit displays the generated information using AR glasses or a smartphone application on the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit identifies the traveler's location using GPS data and Wi-Fi location information from the smart glasses 214. The collection unit collects the traveler's interests by analyzing the survey function and social media data of the smart glasses 214. The analysis unit analyzes the information using data mining and machine learning algorithms by the identification processing unit 290 of the data processing unit 12. The generation unit generates personalized information using generation AI by the identification processing unit 290 of the data processing unit 12. The display unit displays the generated information using the AR glasses of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit identifies the traveler's location using GPS data and Wi-Fi location information from the headset terminal 314. The collection unit collects the traveler's interests by analyzing the survey function and social media data of the headset terminal 314. The analysis unit analyzes the information using data mining and machine learning algorithms by the identification processing unit 290 of the data processing unit 12. The generation unit generates personalized information using generation AI by the identification processing unit 290 of the data processing unit 12. The display unit displays the generated information using the AR glasses of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit identifies the traveler's location using GPS data and Wi-Fi location information from the robot 414. The collection unit collects the traveler's interests by analyzing the robot 414's survey function and social media data. The analysis unit analyzes the information using data mining and machine learning algorithms via the identification processing unit 290 of the data processing unit 12. The generation unit generates personalized information using generation AI via the identification processing unit 290 of the data processing unit 12. The display unit displays the generated information using the robot 414's display. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A unit that acquires the traveler's location information, A collection department that gathers information on travelers' interests and concerns, An analysis unit that analyzes the information obtained from the acquisition unit and the collection unit, A generation unit that generates personalized information based on the analysis results obtained by the analysis unit, The system includes a display unit that displays the information generated by the generation unit using AR technology. A system characterized by the following features. (Note 2) The generating unit is Generate information on relevant tourist attractions and events based on travelers' interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Displaying information available 24 hours a day. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Providing navigation to help travelers enjoy sightseeing at their own pace. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate information that contributes to the promotion of tourist destinations and the improvement of tourist satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the traveler's emotions and adjusts the timing of location data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the traveler's past movement history to select the optimal method for obtaining location information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring location information, filtering is performed based on the traveler's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the traveler's emotions and determines the priority of location data to acquire based on the estimated traveler's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring location information, the system prioritizes acquiring highly relevant information by considering the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring location information, the traveler's social media activity is analyzed to obtain relevant location data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate travelers' emotions and adjust how we gather their interests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We analyze travelers' past interests and preferences to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting information on interests, filtering is performed based on the traveler's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the traveler's emotions and determines the priority of interests to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When gathering information on interests, the system prioritizes collecting highly relevant information by considering the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When gathering information on interests, analyze travelers' social media activity to collect relevant interests. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the traveler's emotions and adjust the analysis method based on the estimated traveler's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the traveler's past data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the traveler's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the traveler's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the traveler's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, we will analyze travelers' social media activity and use relevant data for the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is We estimate travelers' emotions and adjust how the information generated is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, adjust the level of detail of the information generated based on the traveler's interests. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, different generation algorithms are applied depending on the traveler's category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the traveler's emotions and determines the priority of information to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the system prioritizes generating highly relevant information by considering the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the traveler's social media activity is analyzed to generate relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is It estimates the traveler's sentiment and adjusts the display method based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the traveler's past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying information, the content is customized based on the traveler's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is It estimates the traveler's sentiment and determines the priority of the information displayed based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is When displaying information, the system selects the optimal display method considering the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is When displaying information, the system analyzes the traveler's social media activity and shows relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit that acquires the traveler's location information, A collection department that gathers information on travelers' interests and concerns, An analysis unit that analyzes the information obtained from the acquisition unit and the collection unit, A generation unit that generates personalized information based on the analysis results obtained by the analysis unit, The system includes a display unit that displays the information generated by the generation unit using AR technology. A system characterized by the following features.

2. The generating unit is Generate information on relevant tourist attractions and events based on travelers' interests. The system according to feature 1.

3. The aforementioned display unit is Displaying information available 24 hours a day. The system according to feature 1.

4. The aforementioned display unit is Providing navigation to help travelers enjoy sightseeing at their own pace. The system according to feature 1.

5. The generating unit is Generate information that contributes to the promotion of tourist destinations and the improvement of tourist satisfaction. The system according to feature 1.

6. The acquisition unit is, The system estimates the traveler's emotions and adjusts the timing of location data acquisition based on those estimated emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the traveler's past movement history to select the optimal method for obtaining location information. The system according to feature 1.

8. The acquisition unit is, When acquiring location information, filtering is performed based on the traveler's current activities and areas of interest. The system according to feature 1.

9. The acquisition unit is, It estimates the traveler's emotions and determines the priority of location data to acquire based on the estimated traveler's emotions. The system according to feature 1.

10. The acquisition unit is, When acquiring location information, the system prioritizes acquiring highly relevant information by considering the traveler's geographical location. The system according to feature 1.

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