system

Abstract

The system according to the embodiment aims to enable travelers to obtain personalized tourist information based on their current location. [Solution] A system according to an embodiment includes a location identification unit, an information providing unit, a feedback collecting unit, a personalization unit, and an information management unit. The location identification unit identifies the current location of a traveler. The information providing unit provides information related to the location identified by the location identification unit. The feedback collecting unit collects feedback from the traveler based on the information provided by the information providing unit. The personalization unit personalizes information based on the feedback collected by the feedback collecting unit. The information management unit manages the reliability of the information personalized by the personalization unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has presented challenges in that it is difficult for travelers to obtain appropriate tourist information based on their current location, resulting in a lack of improvement in the quality of their travel experience.

[0005] The system according to the embodiment aims to enable travelers to obtain personalized tourist information based on their current location. [Means for solving the problem]

[0006] The system according to this embodiment comprises a location identification unit, an information provision unit, a feedback collection unit, a personalization unit, and an information management unit. The location identification unit identifies the traveler's current location. The information provision unit provides information about the location identified by the location identification unit. The feedback collection unit collects feedback from the traveler based on the information provided by the information provision unit. The personalization unit personalizes the information based on the feedback collected by the feedback collection unit. The information management unit manages the reliability of the information personalized by the personalization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to obtain personalized tourist information based on their current location. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel support system according to an embodiment of the present invention identifies a traveler's current location and provides information about historical sites, subculture locations, cultural spots, gourmet food, and other information in the surrounding area. This travel support system allows travelers to focus on local attractions, not just famous tourist destinations. For example, if a traveler is in Tokyo, the system identifies hidden gems, historical sites, subculture locations, and other information in the surrounding area and provides detailed information, access methods, and nearby tourist attractions. The system also collects feedback about the places the traveler has visited and provides useful information to other travelers. This helps spread the appeal of the region and further boost inbound tourism demand. For example, if a traveler is in Tokyo, the system can visit not only famous tourist destinations such as Asakusa and Shibuya, but also historical sites in the downtown area and locations featured in anime. This allows travelers to gain a deeper understanding of Japan's diverse attractions and contributes to the revitalization of the region. The travel support system thus provides appropriate information based on the traveler's current location, collects feedback to personalize the information, and manages reliability, thereby providing travelers with an optimal travel experience.

[0029] The travel support system according to this embodiment comprises a location identification unit, an information provision unit, a feedback collection unit, a personalization unit, and an information management unit. The location identification unit identifies the traveler's current location. For example, the location identification unit can identify the traveler's current location using GPS or location information services. GPS receives signals from multiple satellites and calculates the traveler's precise location. Location information services can identify the traveler's current location using Wi-Fi or cell tower location information. The information provision unit provides information about the location identified by the location identification unit. For example, the information provision unit can provide detailed information about the identified location, access methods, and nearby tourist attractions. Detailed information may include historical background, opening hours, and price information. Access methods may include how to use public transport, walking routes, and parking information. Nearby tourist attractions may include popularity, historical value, and natural scenery. The feedback collection unit collects traveler feedback based on the information provided by the information provision unit. For example, the feedback collection unit can collect traveler impressions and photos of places visited. Impressions may include text reviews, star ratings, and voice messages. The photo documentation includes JPEG, PNG, and resolution criteria. The personalization unit personalizes information based on feedback collected by the feedback collection unit. For example, the personalization unit can analyze a traveler's past visit history and interests and provide optimal information based on that. Visit history includes past location data, visit date and time, and length of stay. Interests include past search history, browsing history, and feedback content. The information management unit manages the reliability of the information personalized by the personalization unit. For example, the information management unit manages the sources of the information provided and can provide reliable information. Information sources include official websites, user reviews, and evaluations from third-party organizations. As a result, the travel support system according to the embodiment can provide travelers with the optimal travel experience by providing appropriate information based on the traveler's current location, collecting feedback to personalize the information, and managing reliability.

[0030] The location determination unit can determine the current location of the traveler using GPS or a location information service. GPS receives signals from multiple satellites and calculates the traveler's exact location. For example, the accuracy of GPS varies depending on the number of satellites used and the frequency of location information updates. Location information services can determine the current location of the traveler using location information from Wi-Fi or cell towers. For example, location information services can use Google® Maps API or Apple® Location Information Services. This allows the traveler's current location to be accurately determined using GPS or a location information service. Some or all of the above-described processing in the location determination unit may be performed using, for example, AI, or may be performed without AI. For example, the location determination unit can input GPS data or location information service data into the generation AI and have the generation AI determine the traveler's current location.

[0031] The information providing unit can provide detailed information about the identified location, access methods, and surrounding tourist attractions. The detailed information includes historical background, business hours, and fee information. For example, the information providing unit can provide the historical background of the identified location. The information providing unit can also provide business hours and fee information for the identified location. Access methods include how to use public transportation, walking routes, parking information, and the like. For example, the information providing unit can provide how to use public transportation to the identified location. The information providing unit can also provide walking routes and parking information to the identified location. The surrounding tourist attractions include popularity, historical value, natural scenery, and the like. For example, the information providing unit can provide popular tourist attractions near the identified location. The information providing unit can also provide spots of historical value and natural scenery near the identified location. This makes it easier for travelers to choose places to visit by providing detailed information to them. Some or all of the above-described processing by the information providing unit may be performed using, for example, AI, or without AI. For example, the information providing unit can input information about the identified location into the generation AI and cause the generation AI to provide detailed information.

[0032] The feedback collection unit can collect impressions or photos of places visited by travelers. Impressions include text reviews, star ratings, voice messages, etc. For example, the feedback collection unit can collect text reviews of places visited by travelers. The feedback collection unit can also collect star ratings of places visited by travelers. The feedback collection unit can also collect voice messages of places visited by travelers. The photos can include JPEG, PNG, resolution standards, etc. For example, the feedback collection unit can collect photos of places visited by travelers in JPEG format. The feedback collection unit can also collect photos of places visited by travelers in PNG format. The feedback collection unit can also collect photos of places visited by travelers based on their resolution. In this way, collecting travelers' feedback can provide useful information for other travelers. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input travelers' impressions and photos into a generation AI and cause the generation AI to collect feedback.

[0033] The personalization unit can analyze a traveler's past visit history and interests and provide appropriate information based thereon. The visit history includes past location information data, visit dates and times, and stay durations. For example, the personalization unit can analyze a traveler's past location information data and provide appropriate information based thereon. The personalization unit can also analyze a traveler's past visit dates and stay durations and provide appropriate information based thereon. Interests include past search history, browsing history, feedback content, and the like. For example, the personalization unit can analyze a traveler's past search history and provide appropriate information based thereon. The personalization unit can also analyze a traveler's past browsing history and feedback content and provide appropriate information based thereon. This allows the traveler to personalize information based on the traveler's past visit history and interests, thereby providing optimal information for the traveler. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without AI. For example, the personalization unit can input a traveler's past visit history and interests into a generation AI and have the generation AI personalize the information.

[0034] The information management unit manages the sources of the information to be provided and can provide reliable information. Information sources include official websites, user reviews, and evaluations by third-party organizations. For example, the information management unit can prioritize providing information from official websites. The information management unit can also provide reliable information based on user reviews. Furthermore, the information management unit can also provide reliable information based on evaluations by third-party organizations. This allows travelers to use the information with peace of mind by providing reliable information. Some or all of the above-mentioned processing in the information management unit may be performed using, for example, AI, or may be performed without using AI. For example, the information management unit can input the source of the information into the generation AI and have the generation AI evaluate the reliability of the information.

[0035] The location identification unit can analyze a traveler's past travel history and select the optimal location identification method. For example, the location identification unit can analyze patterns of places the traveler has visited in the past and select the optimal location identification method. The location identification unit can also select a location identification method that avoids congestion based on the traveler's past travel history. Furthermore, the location identification unit can select an efficient location identification method based on the traveler's past travel history. In this way, the optimal location identification method can be selected by analyzing past travel history. Travel history includes past location information data, travel routes, and length of stay. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input the traveler's past travel history data into a generating AI and have the generating AI select the optimal location identification method.

[0036] The location identification unit can perform filtering based on the traveler's current activity status when determining their location. For example, if the traveler is sightseeing, the location identification unit will prioritize identifying location information related to tourist spots. Similarly, if the traveler is eating, the location identification unit can prioritize identifying location information related to restaurants. Furthermore, if the traveler is resting, the location identification unit can prioritize identifying location information related to resting places. This allows the system to provide highly relevant information by filtering based on the traveler's current activity status. Activity status includes current speed of movement, type of place visited, and duration of stay. Some or all of the above processing in the location identification unit may be performed using AI, or without AI. For example, the location identification unit can input the traveler's current activity status data into a generating AI and have the generating AI perform the filtering.

[0037] When determining the location, the location identification unit can prioritize identifying highly relevant locations based on the traveler's geographical location information. For example, if the traveler is in a tourist destination, the location identification unit can prioritize identifying nearby tourist spots. Furthermore, if the traveler is in a shopping area, the location identification unit can prioritize identifying nearby shopping spots. Furthermore, if the traveler is in a historical location, the location identification unit can prioritize identifying nearby historical spots. In this way, by taking the traveler's geographical location information into consideration, highly relevant locations can be prioritized. Geographical location information includes GPS data, map information, location information services, etc. Some or all of the above-mentioned processing in the location identification unit may be performed using, or without, AI. For example, the location identification unit can input the traveler's geographical location information data into the generation AI and cause the generation AI to identify highly relevant locations.

[0038] When determining the location, the location identification unit can identify relevant locations by analyzing the traveler's social media activity. For example, the location identification unit can identify relevant locations based on locations where the traveler has checked in on social media. The location identification unit can also identify relevant locations based on photos the traveler has shared on social media. Furthermore, the location identification unit can identify relevant locations based on posts from accounts the traveler follows on social media. In this way, by analyzing social media activity, it is possible to identify places that are highly relevant to the traveler. Social media activity includes the content of posts, the number of likes, and reactions from followers. Some or all of the above-described processing in the location identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the location identification unit can input the traveler's social media activity data into a generation AI and cause the generation AI to identify relevant locations.

[0039] The information providing unit can adjust the level of detail of the information based on the importance of the location when providing the information. For example, the information providing unit provides detailed information for important tourist spots. The information providing unit can also provide concise information for lesser-known spots. Furthermore, the information providing unit can adjust the level of detail of the information according to the traveler's interests. By adjusting the level of detail of the information based on the importance of the location, it is possible to provide optimal information for the traveler. The importance of a location includes the popularity of a tourist destination, historical value, ease of access, etc. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input location importance data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0040] When providing information, the information providing unit can apply different information provision algorithms depending on the location category. For example, for historical locations, the information providing unit can provide information that emphasizes historical background and episodes. For subculture locations, the information providing unit can also provide information on related works and characters. For gourmet spots, the information providing unit can also provide information that emphasizes menus and word-of-mouth information. By applying different information provision algorithms depending on the location category, it is possible to provide optimal information to travelers. Location categories include historical buildings, natural landscapes, shopping areas, etc. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input location category data into the generation AI and cause the generation AI to apply the information provision algorithm.

[0041] The information provision department can prioritize information based on the time of visit to a location when providing information. For example, for seasonal spots, the information provision department can prioritize information according to the time of visit. The information provision department can also provide information for locations where events are held according to the timing of the events. Furthermore, the information provision department can provide optimal information tailored to the traveler's visit timing. This makes it possible to provide travelers with the most suitable information by prioritizing information based on the time of visit to a location. The time of visit includes season, event duration, and business hours. Some or all of the above processing in the information provision department may be performed using AI, for example, or not. For example, the information provision department can input visit timing data into a generating AI and have the generating AI perform the determination of information prioritization.

[0042] The information provision unit can adjust the order of information based on location relevance when providing information. For example, the information provision unit can provide information starting with locations closest to the traveler's current location. It can also prioritize providing information on locations related to the traveler's interests. Furthermore, the information provision unit can prioritize providing information on highly relevant locations based on the traveler's visit history. This allows for the provision of optimal information to travelers by adjusting the order of information based on location relevance. Location relevance includes geographical proximity, theme matching, and past visit history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input location relevance data into a generating AI and have the generating AI perform the adjustment of the information order.

[0043] The feedback collection unit can analyze a traveler's past feedback history to select the optimal collection method when collecting feedback. For example, the feedback collection unit can analyze patterns in feedback previously provided by the traveler and select the optimal collection method. The feedback collection unit can also determine whether to request detailed or brief feedback based on the traveler's past feedback history. Furthermore, the feedback collection unit can select an efficient collection method based on the traveler's past feedback history. In this way, the optimal feedback collection method can be selected by analyzing past feedback history. Feedback history includes past review content, rating scores, comments, etc. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or not using AI. For example, the feedback collection unit can input the traveler's past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0044] The feedback collection unit can customize the means of collecting feedback based on the traveler's current activity status. For example, if the traveler is sightseeing, the feedback collection unit can collect feedback in the form of a simple questionnaire. It can also request feedback about the meal if the traveler is eating. Furthermore, it can request detailed feedback if the traveler is resting. This allows for optimal feedback collection by customizing the means of collection based on the traveler's current activity status. Activity status includes current travel speed, types of places visited, and length of stay. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input the traveler's current activity status data into a generating AI and have the generating AI customize the means of collection.

[0045] When collecting feedback, the feedback collection unit can select the optimal collection method by taking into account the traveler's geographical location information. For example, if the traveler is in a tourist destination, the feedback collection unit can request feedback related to the location. Also, if the traveler is in a shopping area, the feedback collection unit can request feedback related to shopping. Furthermore, if the traveler is in a historical location, the feedback collection unit can request feedback related to the location. In this way, the optimal feedback collection method can be selected by taking into account the traveler's geographical location information. Geographical location information includes GPS data, map information, location information services, etc. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the traveler's geographical location information data into the generation AI and cause the generation AI to select the optimal collection method.

[0046] When collecting feedback, the feedback collection unit can analyze the traveler's social media activity and suggest collection methods. For example, the feedback collection unit may request feedback on places the traveler has checked in on social media. The feedback collection unit may also request feedback on photos the traveler has shared on social media. The feedback collection unit may also request feedback on posts from accounts the traveler follows on social media. This allows the optimal feedback collection method to be suggested by analyzing social media activity. Social media activity includes the content of posts, the number of likes, and reactions from followers. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the traveler's social media activity data into a generation AI and have the generation AI suggest collection methods.

[0047] During personalization, the personalization unit can analyze the traveler's past visit history to select the optimal personalization method. For example, the personalization unit analyzes the patterns of places the traveler has visited in the past and selects the optimal personalization method. The personalization unit can also provide information tailored to the traveler's interests based on the traveler's past visit history. Furthermore, the personalization unit can select an efficient personalization method based on the traveler's past visit history. In this way, the optimal personalization method can be selected by analyzing the past visit history. The visit history includes past location information data, visit dates and times, duration of stay, etc. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the traveler's past visit history data into the generation AI and have the generation AI select the optimal personalization method.

[0048] During personalization, the personalization unit can customize the personalization means based on the traveler's current interests. For example, the personalization unit can prioritize providing locations related to anime in which the traveler is currently interested. The personalization unit can also prioritize providing information about historical locations in which the traveler is currently interested. Furthermore, the personalization unit can prioritize providing information about gourmet spots in which the traveler is currently interested. This enables optimal information to be provided by customizing the personalization means based on the traveler's current interests. Interests include past search history, browsing history, feedback content, etc. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without AI. For example, the personalization unit can input the traveler's current interest data into the generation AI and have the generation AI customize the personalization means.

[0049] The personalization unit can select the optimal personalization method by considering the traveler's geographical location information during the personalization process. For example, if the traveler is in a tourist area, the personalization unit can prioritize providing information relevant to that location. Similarly, if the traveler is in a shopping area, the personalization unit can prioritize providing information relevant to that location. Furthermore, if the traveler is in a historical site, the personalization unit can prioritize providing information relevant to that site. This allows the personalization unit to select the optimal personalization method by considering geographical location information. Geographical location information includes GPS data, map information, and location-based services. Some or all of the processing described above in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the traveler's geographical location data into a generating AI and have the generating AI select the optimal personalization method.

[0050] The personalization unit can analyze the traveler's social media activity during the personalization process and suggest methods for personalization. For example, the personalization unit can provide information related to places the traveler has checked in to on social media. It can also provide information related to photos the traveler has shared on social media. Furthermore, the personalization unit can provide information related to posts from accounts the traveler follows on social media. By analyzing social media activity, it can suggest the most suitable personalization method. Social media activity includes post content, the number of likes, and follower reactions. Some or all of the above processing in the personalization unit may be performed using AI, for example, or not. For example, the personalization unit can input the traveler's social media activity data into a generating AI and have the generating AI suggest personalization methods.

[0051] During information management, the information management unit can select the optimal management method by referring to past data for evaluating the reliability of information. For example, the information management unit selects a highly reliable information source based on past data. The information management unit can also adjust the update frequency of information based on past data. Furthermore, the information management unit can evaluate the reliability of information and select an appropriate management method based on past data. This allows the optimal information management method to be selected by referring to past data. Past data includes past evaluation results, feedback content, usage history, etc. Some or all of the above-described processing in the information management unit may be performed using, or without, AI. For example, the information management unit can input past data into a generation AI and have the generation AI evaluate the reliability of information and select a management method.

[0052] The information management unit can apply different management methods depending on the category of information when managing information. For example, the information management unit can prioritize using highly reliable information sources for historical information. The information management unit can also prioritize using the latest information for subculture information. Furthermore, the information management unit can apply a management method that emphasizes word-of-mouth and reviews for gourmet information. This enables optimal information management by applying different management methods depending on the category of information. Information categories include historical buildings, natural landscapes, shopping areas, etc. Some or all of the above-mentioned processing in the information management unit may be performed using, or without, AI. For example, the information management unit can input information category data into a generation AI and have the generation AI apply the management method.

[0053] The information management unit can determine management priorities based on the time of information provision when managing information. For example, the information management unit prioritizes management of seasonal information according to the time of provision. The information management unit can also prioritize management of event information according to the time of event. Furthermore, the information management unit can determine information management priorities according to the time of traveler visit. This enables optimal information provision by determining management priorities based on the time of information provision. The time of provision includes the season, event period, business hours, etc. Some or all of the above-mentioned processing in the information management unit may be performed using AI, for example, or may be performed without using AI. For example, the information management unit can input provision time data into a generation AI and have the generation AI determine management priorities.

[0054] The information management unit can adjust the management method based on the relevance of the information during information management. For example, the information management unit prioritizes management of information related to the traveler's current location. The information management unit can also prioritize management of information related to the traveler's interests. Furthermore, the information management unit can prioritize management of highly relevant information based on the traveler's visit history. This enables optimal information provision by adjusting the management method based on the relevance of the information. The relevance of the information includes geographical proximity, thematic similarity, past visit history, etc. Some or all of the above-mentioned processing in the information management unit may be performed using, for example, AI, or may be performed without using AI. For example, the information management unit can input information relevance data into a generation AI and have the generation AI adjust the management method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The travel assistance system may further include an audio guidance unit. The audio guidance unit provides audio guidance based on the traveler's current location. For example, when the traveler approaches a particular tourist spot, the audio guidance unit may provide audio explanations of the place's history and highlights. The audio guidance unit may also provide audio guidance on the optimal route if the traveler gets lost. Furthermore, the audio guidance unit may provide audio answers when the traveler asks a specific question. This allows the traveler to obtain not only visual information but also audio information, thereby providing a more fulfilling travel experience.

[0057] The location identification unit can monitor the traveler's health condition and adjust the frequency of location identification based on the health condition. For example, if the traveler is tired, the frequency of location identification can be reduced and information encouraging the traveler to take a rest can be provided. Also, if the traveler is in good health, the frequency of location identification can be increased and active tourist spots can be suggested. Furthermore, if the traveler is feeling unwell, information on the nearest medical facilities can be provided. This makes it possible to provide optimal information according to the traveler's health condition.

[0058] The information management unit can analyze user feedback in real time to evaluate the reliability of the information to be provided. For example, the information management unit re-evaluates the reliability of the information based on the feedback provided by the traveler. The information management unit can also update the information based on the content of the feedback. Furthermore, the information management unit can prioritize the provision of reliable information based on the feedback evaluation score. This makes it possible to always provide the latest and most reliable information.

[0059] The location identification unit can analyze the traveler's past movement history and select the optimal location identification method. For example, it analyzes the patterns of places the traveler has visited in the past and selects the optimal location identification method. The location identification unit can also select a location identification method that avoids congestion based on the traveler's past movement history. Furthermore, the location identification unit can also select an efficient location identification method based on the traveler's past movement history. In this way, the optimal location identification method can be selected by analyzing the past movement history.

[0060] When providing information, the information providing unit can adjust the level of detail of the information based on the importance of the location. For example, detailed information is provided for important tourist spots. The information providing unit can also provide concise information for lesser-known spots. Furthermore, the information providing unit can adjust the level of detail of the information according to the interests of the traveler. In this way, by adjusting the level of detail of the information based on the importance of the location, it is possible to provide optimal information for the traveler.

[0061] When collecting feedback, the feedback collection unit can analyze the traveler's past feedback history and select the optimal collection method. For example, the feedback collection unit can analyze the patterns of feedback provided by the traveler in the past and select the optimal collection method. The feedback collection unit can also determine whether to request detailed feedback or simple feedback from the traveler's past feedback history. Furthermore, the feedback collection unit can select an efficient collection method based on the traveler's past feedback history. In this way, the optimal feedback collection method can be selected by analyzing the past feedback history.

[0062] During personalization, the personalization unit can customize the personalization means based on the traveler's current interests. For example, places related to anime, which the traveler is currently interested in, can be provided preferentially. The personalization unit can also provide information on historical places, which the traveler is currently interested in, preferentially. Furthermore, the personalization unit can also provide information on gourmet spots, which the traveler is currently interested in preferentially. In this way, by customizing the personalization means based on the traveler's current interests, it is possible to provide optimal information.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The location determination unit determines the traveler's current location. For example, the location determination unit can use GPS or location services to determine the traveler's current location. GPS receives signals from multiple satellites and calculates the traveler's precise location. Location services can use Wi-Fi or cell tower location information to determine the traveler's current location. Step 2: The information providing unit provides information about the location identified by the location identifying unit. For example, the information providing unit can provide detailed information about the identified location, access methods, surrounding tourist attractions, etc. The detailed information includes historical background, business hours, fare information, etc. The access methods include how to use public transportation, walking routes, parking information, etc. The surrounding tourist attractions include popularity, historical value, natural scenery, etc. Step 3: The feedback collection unit collects traveler feedback based on the information provided by the information provision unit. For example, the feedback collection unit can collect traveler impressions and photos of places visited by the traveler. Impressions include text reviews, star ratings, voice messages, etc. Photos include JPEG, PNG, resolution standards, etc. Step 4: The personalization unit personalizes information based on the feedback collected by the feedback collection unit. For example, the personalization unit can analyze the traveler's past visit history and interests and provide optimal information based on that. Visit history includes past location information data, visit date and time, and duration of stay. Interests include past search history, browsing history, feedback content, etc. Step 5: The information management unit manages the reliability of the information personalized by the personalization unit. For example, the information management unit manages the sources of the information to be provided, enabling the provision of reliable information. Information sources include official websites, user reviews, and ratings from third-party organizations.

[0065] (Example 2) A travel support system according to an embodiment of the present invention identifies a traveler's current location and provides information about historical sites, subculture locations, cultural spots, gourmet food, and other information in the surrounding area. This travel support system allows travelers to focus on local attractions, not just famous tourist destinations. For example, if a traveler is in Tokyo, the system identifies hidden gems, historical sites, subculture locations, and other information in the surrounding area and provides detailed information, access methods, and nearby tourist attractions. The system also collects feedback about the places the traveler has visited and provides useful information to other travelers. This helps spread the appeal of the region and further boost inbound tourism demand. For example, if a traveler is in Tokyo, the system can visit not only famous tourist destinations such as Asakusa and Shibuya, but also historical sites in the downtown area and locations featured in anime. This allows travelers to gain a deeper understanding of Japan's diverse attractions and contributes to the revitalization of the region. The travel support system thus provides appropriate information based on the traveler's current location, collects feedback to personalize the information, and manages reliability, thereby providing travelers with an optimal travel experience.

[0066] The travel support system according to this embodiment comprises a location identification unit, an information provision unit, a feedback collection unit, a personalization unit, and an information management unit. The location identification unit identifies the traveler's current location. For example, the location identification unit can identify the traveler's current location using GPS or location information services. GPS receives signals from multiple satellites and calculates the traveler's precise location. Location information services can identify the traveler's current location using Wi-Fi or cell tower location information. The information provision unit provides information about the location identified by the location identification unit. For example, the information provision unit can provide detailed information about the identified location, access methods, and nearby tourist attractions. Detailed information may include historical background, opening hours, and price information. Access methods may include how to use public transport, walking routes, and parking information. Nearby tourist attractions may include popularity, historical value, and natural scenery. The feedback collection unit collects traveler feedback based on the information provided by the information provision unit. For example, the feedback collection unit can collect traveler impressions and photos of places visited. Impressions may include text reviews, star ratings, and voice messages. The photo documentation includes JPEG, PNG, and resolution criteria. The personalization unit personalizes information based on feedback collected by the feedback collection unit. For example, the personalization unit can analyze a traveler's past visit history and interests and provide optimal information based on that. Visit history includes past location data, visit date and time, and length of stay. Interests include past search history, browsing history, and feedback content. The information management unit manages the reliability of the information personalized by the personalization unit. For example, the information management unit manages the sources of the information provided and can provide reliable information. Information sources include official websites, user reviews, and evaluations from third-party organizations. As a result, the travel support system according to the embodiment can provide travelers with the optimal travel experience by providing appropriate information based on the traveler's current location, collecting feedback to personalize the information, and managing reliability.

[0067] The location identification unit can determine the traveler's current location using GPS or location services. GPS receives signals from multiple satellites and calculates the traveler's precise location. For example, the accuracy of GPS varies depending on the number of satellites used and the frequency of location information updates. Location services can determine the traveler's current location using Wi-Fi or cell tower location information. For example, location services can utilize Google Maps API or Apple's location services. This allows for accurate determination of the traveler's current location using GPS or location services. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input GPS data or location service data into a generating AI and have the generating AI perform the determination of the traveler's current location.

[0068] The information providing unit can provide detailed information about the identified location, access methods, and surrounding tourist attractions. The detailed information includes historical background, business hours, and fee information. For example, the information providing unit can provide the historical background of the identified location. The information providing unit can also provide business hours and fee information for the identified location. Access methods include how to use public transportation, walking routes, parking information, and the like. For example, the information providing unit can provide how to use public transportation to the identified location. The information providing unit can also provide walking routes and parking information to the identified location. The surrounding tourist attractions include popularity, historical value, natural scenery, and the like. For example, the information providing unit can provide popular tourist attractions near the identified location. The information providing unit can also provide spots of historical value and natural scenery near the identified location. This makes it easier for travelers to choose places to visit by providing detailed information to them. Some or all of the above-described processing by the information providing unit may be performed using, for example, AI, or without AI. For example, the information providing unit can input information about the identified location into the generation AI and cause the generation AI to provide detailed information.

[0069] The feedback collection unit can collect impressions or photos of places visited by travelers. Impressions include text reviews, star ratings, voice messages, etc. For example, the feedback collection unit can collect text reviews of places visited by travelers. The feedback collection unit can also collect star ratings of places visited by travelers. The feedback collection unit can also collect voice messages of places visited by travelers. The photos can include JPEG, PNG, resolution standards, etc. For example, the feedback collection unit can collect photos of places visited by travelers in JPEG format. The feedback collection unit can also collect photos of places visited by travelers in PNG format. The feedback collection unit can also collect photos of places visited by travelers based on their resolution. In this way, collecting travelers' feedback can provide useful information for other travelers. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input travelers' impressions and photos into a generation AI and cause the generation AI to collect feedback.

[0070] The personalization unit can analyze a traveler's past visit history and interests and provide appropriate information based thereon. The visit history includes past location information data, visit dates and times, and stay durations. For example, the personalization unit can analyze a traveler's past location information data and provide appropriate information based thereon. The personalization unit can also analyze a traveler's past visit dates and stay durations and provide appropriate information based thereon. Interests include past search history, browsing history, feedback content, and the like. For example, the personalization unit can analyze a traveler's past search history and provide appropriate information based thereon. The personalization unit can also analyze a traveler's past browsing history and feedback content and provide appropriate information based thereon. This allows the traveler to personalize information based on the traveler's past visit history and interests, thereby providing optimal information for the traveler. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without AI. For example, the personalization unit can input a traveler's past visit history and interests into a generation AI and have the generation AI personalize the information.

[0071] The information management unit manages the sources of the information to be provided and can provide reliable information. Information sources include official websites, user reviews, and evaluations by third-party organizations. For example, the information management unit can prioritize providing information from official websites. The information management unit can also provide reliable information based on user reviews. Furthermore, the information management unit can also provide reliable information based on evaluations by third-party organizations. This allows travelers to use the information with peace of mind by providing reliable information. Some or all of the above-mentioned processing in the information management unit may be performed using, for example, AI, or may be performed without using AI. For example, the information management unit can input the source of the information into the generation AI and have the generation AI evaluate the reliability of the information.

[0072] The location identification unit can estimate the traveler's emotions and change the frequency of location identification based on the estimated traveler's emotions. For example, if the traveler is excited, the location identification unit can frequently identify the traveler's location and provide information in real time. Furthermore, if the traveler is relaxed, the location identification unit can reduce the frequency of location identification and provide information only when necessary. Furthermore, if the traveler is tired, the location identification unit can minimize the frequency of location identification and refrain from providing information during breaks. This allows optimal information to be provided to the traveler by adjusting the frequency of location identification according to 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the location identification unit can be performed using, for example, AI, or without AI. For example, the location identification unit can input the traveler's emotion data into the generation AI and cause the generation AI to adjust the frequency of location identification based on the emotion.

[0073] The location identification unit can analyze a traveler's past travel history and select the optimal location identification method. For example, the location identification unit can analyze patterns of places the traveler has visited in the past and select the optimal location identification method. The location identification unit can also select a location identification method that avoids congestion based on the traveler's past travel history. Furthermore, the location identification unit can select an efficient location identification method based on the traveler's past travel history. In this way, the optimal location identification method can be selected by analyzing past travel history. Travel history includes past location information data, travel routes, and length of stay. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input the traveler's past travel history data into a generating AI and have the generating AI select the optimal location identification method.

[0074] The location identification unit can perform filtering based on the traveler's current activity status when determining their location. For example, if the traveler is sightseeing, the location identification unit will prioritize identifying location information related to tourist spots. Similarly, if the traveler is eating, the location identification unit can prioritize identifying location information related to restaurants. Furthermore, if the traveler is resting, the location identification unit can prioritize identifying location information related to resting places. This allows the system to provide highly relevant information by filtering based on the traveler's current activity status. Activity status includes current speed of movement, type of place visited, and duration of stay. Some or all of the above processing in the location identification unit may be performed using AI, or without AI. For example, the location identification unit can input the traveler's current activity status data into a generating AI and have the generating AI perform the filtering.

[0075] The location tracking unit can estimate the traveler's emotions and adjust the accuracy of location tracking based on the estimated emotions. For example, if the traveler is excited, the location tracking unit can increase the accuracy of location tracking to provide more detailed information. Conversely, if the traveler is relaxed, the location tracking unit can decrease the accuracy of location tracking to provide more general information. Furthermore, if the traveler is tired, the location tracking unit can minimize the accuracy of location tracking and provide only the necessary information. This allows for optimal information provision for the traveler by adjusting the accuracy of location tracking according to their 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 location tracking unit may be performed using AI, or not. For example, the location tracking unit can input the traveler's emotion data into the generative AI and have the generative AI perform emotion-based adjustments to the accuracy of location tracking.

[0076] When determining the location, the location identification unit can prioritize identifying highly relevant locations based on the traveler's geographical location information. For example, if the traveler is in a tourist destination, the location identification unit can prioritize identifying nearby tourist spots. Furthermore, if the traveler is in a shopping area, the location identification unit can prioritize identifying nearby shopping spots. Furthermore, if the traveler is in a historical location, the location identification unit can prioritize identifying nearby historical spots. In this way, by taking the traveler's geographical location information into consideration, highly relevant locations can be prioritized. Geographical location information includes GPS data, map information, location information services, etc. Some or all of the above-mentioned processing in the location identification unit may be performed using, or without, AI. For example, the location identification unit can input the traveler's geographical location information data into the generation AI and cause the generation AI to identify highly relevant locations.

[0077] When determining the location, the location identification unit can identify relevant locations by analyzing the traveler's social media activity. For example, the location identification unit can identify relevant locations based on locations where the traveler has checked in on social media. The location identification unit can also identify relevant locations based on photos the traveler has shared on social media. Furthermore, the location identification unit can identify relevant locations based on posts from accounts the traveler follows on social media. In this way, by analyzing social media activity, it is possible to identify places that are highly relevant to the traveler. Social media activity includes the content of posts, the number of likes, and reactions from followers. Some or all of the above-described processing in the location identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the location identification unit can input the traveler's social media activity data into a generation AI and cause the generation AI to identify relevant locations.

[0078] The information providing unit can estimate the traveler's emotions and adjust the way information is presented based on the estimated traveler's emotions. For example, if the traveler is excited, the information providing unit can provide visually stimulating information. Furthermore, if the traveler is relaxed, the information providing unit can provide information in a calm tone. Furthermore, if the traveler is tired, the information providing unit can provide concise and easy-to-understand information. By adjusting the way information is presented based on the traveler's emotions, optimal information can be provided to the traveler. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or without AI. For example, the information providing unit can input the traveler's emotion data into the generation AI and have the generation AI adjust the way information is presented based on the emotion.

[0079] The information providing unit can adjust the level of detail of the information based on the importance of the location when providing the information. For example, the information providing unit provides detailed information for important tourist spots. The information providing unit can also provide concise information for lesser-known spots. Furthermore, the information providing unit can adjust the level of detail of the information according to the traveler's interests. By adjusting the level of detail of the information based on the importance of the location, it is possible to provide optimal information for the traveler. The importance of a location includes the popularity of a tourist destination, historical value, ease of access, etc. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input location importance data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0080] When providing information, the information providing unit can apply different information provision algorithms depending on the location category. For example, for historical locations, the information providing unit can provide information that emphasizes historical background and episodes. For subculture locations, the information providing unit can also provide information on related works and characters. For gourmet spots, the information providing unit can also provide information that emphasizes menus and word-of-mouth information. By applying different information provision algorithms depending on the location category, it is possible to provide optimal information to travelers. Location categories include historical buildings, natural landscapes, shopping areas, etc. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input location category data into the generation AI and cause the generation AI to apply the information provision algorithm.

[0081] The information providing unit can estimate the traveler's emotions and adjust the length of information based on the estimated traveler's emotions. For example, if the traveler is in a hurry, the information providing unit can provide short, concise information. Furthermore, if the traveler is relaxed, the information providing unit can provide detailed information. Furthermore, if the traveler is excited, the information providing unit can provide visually stimulating information. By adjusting the length of information according to the traveler's emotions, optimal information can be provided to the traveler. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or without AI. For example, the information providing unit can input the traveler's emotion data into the generation AI and have the generation AI adjust the length of information based on the emotion.

[0082] The information provision department can prioritize information based on the time of visit to a location when providing information. For example, for seasonal spots, the information provision department can prioritize information according to the time of visit. The information provision department can also provide information for locations where events are held according to the timing of the events. Furthermore, the information provision department can provide optimal information tailored to the traveler's visit timing. This makes it possible to provide travelers with the most suitable information by prioritizing information based on the time of visit to a location. The time of visit includes season, event duration, and business hours. Some or all of the above processing in the information provision department may be performed using AI, for example, or not. For example, the information provision department can input visit timing data into a generating AI and have the generating AI perform the determination of information prioritization.

[0083] The information provision unit can adjust the order of information based on location relevance when providing information. For example, the information provision unit can provide information starting with locations closest to the traveler's current location. It can also prioritize providing information on locations related to the traveler's interests. Furthermore, the information provision unit can prioritize providing information on highly relevant locations based on the traveler's visit history. This allows for the provision of optimal information to travelers by adjusting the order of information based on location relevance. Location relevance includes geographical proximity, theme matching, and past visit history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input location relevance data into a generating AI and have the generating AI perform the adjustment of the information order.

[0084] The feedback collection unit can estimate the traveler's emotions and adjust the feedback collection method based on the estimated traveler's emotions. For example, if the traveler is excited, the feedback collection unit collects feedback in the form of a simple questionnaire. Furthermore, if the traveler is relaxed, the feedback collection unit can also request detailed feedback. Furthermore, if the traveler is tired, the feedback collection unit can refrain from collecting feedback. This allows optimal feedback collection for the traveler by adjusting the feedback collection method according to the traveler's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback collection unit can input traveler's emotion data into the generation AI and have the generation AI adjust the feedback collection method based on the emotion.

[0085] The feedback collection unit can analyze a traveler's past feedback history to select the optimal collection method when collecting feedback. For example, the feedback collection unit can analyze patterns in feedback previously provided by the traveler and select the optimal collection method. The feedback collection unit can also determine whether to request detailed or brief feedback based on the traveler's past feedback history. Furthermore, the feedback collection unit can select an efficient collection method based on the traveler's past feedback history. In this way, the optimal feedback collection method can be selected by analyzing past feedback history. Feedback history includes past review content, rating scores, comments, etc. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or not using AI. For example, the feedback collection unit can input the traveler's past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0086] The feedback collection unit can customize the means of collecting feedback based on the traveler's current activity status. For example, if the traveler is sightseeing, the feedback collection unit can collect feedback in the form of a simple questionnaire. It can also request feedback about the meal if the traveler is eating. Furthermore, it can request detailed feedback if the traveler is resting. This allows for optimal feedback collection by customizing the means of collection based on the traveler's current activity status. Activity status includes current travel speed, types of places visited, and length of stay. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input the traveler's current activity status data into a generating AI and have the generating AI customize the means of collection.

[0087] The feedback collection unit can estimate the traveler's emotions and determine the priority of feedback collection based on the estimated traveler's emotions. For example, if the traveler is excited, the feedback collection unit can set the priority of feedback collection to high. If the traveler is relaxed, the feedback collection unit can also set the priority of feedback collection to medium. If the traveler is tired, the feedback collection unit can also set the priority of feedback collection to low. This enables optimal feedback collection by determining the priority of feedback collection according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit can be performed using, for example, AI, or without AI. For example, the feedback collection unit can input traveler's emotion data into the generation AI and cause the generation AI to determine the priority of feedback collection based on emotions.

[0088] When collecting feedback, the feedback collection unit can select the optimal collection method by taking into account the traveler's geographical location information. For example, if the traveler is in a tourist destination, the feedback collection unit can request feedback related to the location. Also, if the traveler is in a shopping area, the feedback collection unit can request feedback related to shopping. Furthermore, if the traveler is in a historical location, the feedback collection unit can request feedback related to the location. In this way, the optimal feedback collection method can be selected by taking into account the traveler's geographical location information. Geographical location information includes GPS data, map information, location information services, etc. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the traveler's geographical location information data into the generation AI and cause the generation AI to select the optimal collection method.

[0089] When collecting feedback, the feedback collection unit can analyze the traveler's social media activity and suggest collection methods. For example, the feedback collection unit may request feedback on places the traveler has checked in on social media. The feedback collection unit may also request feedback on photos the traveler has shared on social media. The feedback collection unit may also request feedback on posts from accounts the traveler follows on social media. This allows the optimal feedback collection method to be suggested by analyzing social media activity. Social media activity includes the content of posts, the number of likes, and reactions from followers. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the traveler's social media activity data into a generation AI and have the generation AI suggest collection methods.

[0090] The personalization unit can estimate the traveler's emotions and adjust the personalization method based on the estimated emotions. For example, if the traveler is excited, the personalization unit can provide visually stimulating information. If the traveler is relaxed, the personalization unit can also provide information in a calm tone. Furthermore, if the traveler is tired, the personalization unit can provide concise and easy-to-understand information. This allows for optimal information delivery by adjusting the personalization method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input the traveler's emotion data into the generative AI and have the generative AI perform adjustments to the personalization method based on the emotions.

[0091] During personalization, the personalization unit can analyze the traveler's past visit history to select the optimal personalization method. For example, the personalization unit analyzes the patterns of places the traveler has visited in the past and selects the optimal personalization method. The personalization unit can also provide information tailored to the traveler's interests based on the traveler's past visit history. Furthermore, the personalization unit can select an efficient personalization method based on the traveler's past visit history. In this way, the optimal personalization method can be selected by analyzing the past visit history. The visit history includes past location information data, visit dates and times, duration of stay, etc. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the traveler's past visit history data into the generation AI and have the generation AI select the optimal personalization method.

[0092] During personalization, the personalization unit can customize the personalization means based on the traveler's current interests. For example, the personalization unit can prioritize providing locations related to anime in which the traveler is currently interested. The personalization unit can also prioritize providing information about historical locations in which the traveler is currently interested. Furthermore, the personalization unit can prioritize providing information about gourmet spots in which the traveler is currently interested. This enables optimal information to be provided by customizing the personalization means based on the traveler's current interests. Interests include past search history, browsing history, feedback content, etc. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without AI. For example, the personalization unit can input the traveler's current interest data into the generation AI and have the generation AI customize the personalization means.

[0093] The personalization unit can estimate the traveler's emotions and determine the priority of personalization based on the estimated traveler's emotions. For example, if the traveler is excited, the personalization unit can set the personalization priority to high. If the traveler is relaxed, the personalization unit can also set the personalization priority to medium. If the traveler is tired, the personalization unit can also set the personalization priority to low. This enables optimal information provision by determining the personalization priority according to 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the personalization unit can be performed using, for example, AI, or without AI. For example, the personalization unit can input the traveler's emotion data into the generation AI and have the generation AI determine the personalization priority based on the emotion.

[0094] The personalization unit can select the optimal personalization method by considering the traveler's geographical location information during the personalization process. For example, if the traveler is in a tourist area, the personalization unit can prioritize providing information relevant to that location. Similarly, if the traveler is in a shopping area, the personalization unit can prioritize providing information relevant to that location. Furthermore, if the traveler is in a historical site, the personalization unit can prioritize providing information relevant to that site. This allows the personalization unit to select the optimal personalization method by considering geographical location information. Geographical location information includes GPS data, map information, and location-based services. Some or all of the processing described above in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the traveler's geographical location data into a generating AI and have the generating AI select the optimal personalization method.

[0095] The personalization unit can analyze the traveler's social media activity during the personalization process and suggest methods for personalization. For example, the personalization unit can provide information related to places the traveler has checked in to on social media. It can also provide information related to photos the traveler has shared on social media. Furthermore, the personalization unit can provide information related to posts from accounts the traveler follows on social media. By analyzing social media activity, it can suggest the most suitable personalization method. Social media activity includes post content, the number of likes, and follower reactions. Some or all of the above processing in the personalization unit may be performed using AI, for example, or not. For example, the personalization unit can input the traveler's social media activity data into a generating AI and have the generating AI suggest personalization methods.

[0096] The information management unit can estimate the traveler's emotions and adjust the information management method based on the estimated traveler's emotions. For example, if the traveler is excited, the information management unit can increase the information update frequency and provide information in real time. Furthermore, if the traveler is relaxed, the information management unit can reduce the information update frequency and provide information only when necessary. Furthermore, if the traveler is tired, the information management unit can minimize the information update frequency and refrain from providing information during breaks. This allows optimal information provision by adjusting the information management method according to 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information management unit can be performed using, for example, AI, or without AI. For example, the information management unit can input the traveler's emotion data into the generation AI and have the generation AI adjust the information management method based on the emotion.

[0097] During information management, the information management unit can select the optimal management method by referring to past data for evaluating the reliability of information. For example, the information management unit selects a highly reliable information source based on past data. The information management unit can also adjust the update frequency of information based on past data. Furthermore, the information management unit can evaluate the reliability of information and select an appropriate management method based on past data. This allows the optimal information management method to be selected by referring to past data. Past data includes past evaluation results, feedback content, usage history, etc. Some or all of the above-described processing in the information management unit may be performed using, or without, AI. For example, the information management unit can input past data into a generation AI and have the generation AI evaluate the reliability of information and select a management method.

[0098] The information management unit can apply different management methods depending on the category of information when managing information. For example, the information management unit can prioritize using highly reliable information sources for historical information. The information management unit can also prioritize using the latest information for subculture information. Furthermore, the information management unit can apply a management method that emphasizes word-of-mouth and reviews for gourmet information. This enables optimal information management by applying different management methods depending on the category of information. Information categories include historical buildings, natural landscapes, shopping areas, etc. Some or all of the above-mentioned processing in the information management unit may be performed using, or without, AI. For example, the information management unit can input information category data into a generation AI and have the generation AI apply the management method.

[0099] The information management unit can estimate the traveler's emotions and determine the priority of information management based on the estimated traveler's emotions. For example, if the traveler is excited, the information management unit can set the priority of information management to high. Furthermore, if the traveler is relaxed, the information management unit can also set the priority of information management to medium. Furthermore, if the traveler is tired, the information management unit can also set the priority of information management to low. This enables optimal information provision by determining the priority of information management according to 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information management unit can be performed using, for example, AI, or without AI. For example, the information management unit can input the traveler's emotion data into the generation AI and have the generation AI determine the priority of information management based on emotions.

[0100] The information management unit can determine management priorities based on the time of information provision when managing information. For example, the information management unit prioritizes management of seasonal information according to the time of provision. The information management unit can also prioritize management of event information according to the time of event. Furthermore, the information management unit can determine information management priorities according to the time of traveler visit. This enables optimal information provision by determining management priorities based on the time of information provision. The time of provision includes the season, event period, business hours, etc. Some or all of the above-mentioned processing in the information management unit may be performed using AI, for example, or may be performed without using AI. For example, the information management unit can input provision time data into a generation AI and have the generation AI determine management priorities.

[0101] The information management unit can adjust the management method based on the relevance of the information during information management. For example, the information management unit prioritizes management of information related to the traveler's current location. The information management unit can also prioritize management of information related to the traveler's interests. Furthermore, the information management unit can prioritize management of highly relevant information based on the traveler's visit history. This enables optimal information provision by adjusting the management method based on the relevance of the information. The relevance of the information includes geographical proximity, thematic similarity, past visit history, etc. Some or all of the above-mentioned processing in the information management unit may be performed using, for example, AI, or may be performed without using AI. For example, the information management unit can input information relevance data into a generation AI and have the generation AI adjust the management method. === Hard Collateral 1-1 === Each of the multiple elements, including the location identification unit, information provision unit, feedback collection unit, personalization unit, and information management unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the location identification unit identifies the traveler's current location using the GPS or location information service of the smart device 14. The information provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides detailed information about the identified location, access methods, surrounding tourist attractions, etc. The feedback collection unit is implemented, for example, by the control unit 46A of the smart device 14 and collects the traveler's impressions and photos. The personalization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and personalizes information based on the collected feedback. The information management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the reliability of the information provided. === Hard Collateral 1-2 === Each of the multiple elements, including the location identification unit, information provision unit, feedback collection unit, personalization unit, and information management unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the location identification unit identifies the traveler's current location using the GPS or location information service of the smart glasses 214. The information provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and provides detailed information about the identified location, access methods, nearby tourist attractions, etc. The feedback collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collects the traveler's impressions and photos. The personalization unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and personalizes information based on the collected feedback. The information management unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and manages the reliability of the information to be provided. === Hard Collateral 1-3 === Each of the multiple elements described above, including the location identification unit, information provision unit, feedback collection unit, personalization unit, and information management unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the location identification unit uses the GPS and location information services of the headset terminal 314 to identify the traveler's current location. The information provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides detailed information about the identified location, access methods, and nearby tourist attractions. The feedback collection unit is implemented, for example, by the control unit 46A of the headset terminal 314, and collects the traveler's impressions and photos. The personalization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and personalizes the information based on the collected feedback. The information management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and manages the reliability of the information provided. === Hard Collateral 1-4 === Each of the multiple elements described above, including the location identification unit, information provision unit, feedback collection unit, personalization unit, and information management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the location identification unit uses the robot 414's GPS or location information services to identify the traveler's current location. The information provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides detailed information about the identified location, access methods, and nearby tourist attractions. The feedback collection unit is implemented, for example, by the control unit 46A of the robot 414, and collects the traveler's impressions and photos. The personalization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and personalizes the information based on the collected feedback. The information management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and manages the reliability of the information provided.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The travel assistance system may further include an audio guidance unit. The audio guidance unit provides audio guidance based on the traveler's current location. For example, when the traveler approaches a particular tourist spot, the audio guidance unit may provide audio explanations of the place's history and highlights. The audio guidance unit may also provide audio guidance on the optimal route if the traveler gets lost. Furthermore, the audio guidance unit may provide audio answers when the traveler asks a specific question. This allows the traveler to obtain not only visual information but also audio information, thereby providing a more fulfilling travel experience.

[0104] The location identification unit can monitor the traveler's health condition and adjust the frequency of location identification based on the health condition. For example, if the traveler is tired, the frequency of location identification can be reduced and information encouraging the traveler to take a rest can be provided. Also, if the traveler is in good health, the frequency of location identification can be increased and active tourist spots can be suggested. Furthermore, if the traveler is feeling unwell, information on the nearest medical facilities can be provided. This makes it possible to provide optimal information according to the traveler's health condition.

[0105] The information providing unit can estimate the traveler's emotions and adjust the priority of information based on the estimated traveler's emotions. For example, if the traveler is excited, information about active tourist spots can be provided preferentially. If the traveler is relaxed, information about quiet places and spots where they can relax can be provided preferentially. Furthermore, if the traveler is tired, information about rest areas and cafes can be provided preferentially. This makes it possible to provide optimal information according to the traveler's emotions.

[0106] The feedback collection unit can estimate the traveler's emotions and adjust the form of feedback based on the estimated traveler's emotions. For example, if the traveler is excited, feedback can be collected in the form of a simple questionnaire. If the traveler is relaxed, detailed feedback can be requested. Furthermore, if the traveler is tired, feedback collection can be refrained from. This makes it possible to collect optimal feedback according to the traveler's emotions.

[0107] The personalization unit can estimate the traveler's emotions and adjust the personalization method based on the estimated traveler's emotions. For example, if the traveler is excited, visually stimulating information can be provided. If the traveler is relaxed, information can be provided in a calm tone. Furthermore, if the traveler is tired, concise and easy-to-understand information can be provided. In this way, by adjusting the personalization method according to the traveler's emotions, it is possible to provide optimal information.

[0108] The information management unit can analyze user feedback in real time to evaluate the reliability of the information to be provided. For example, the information management unit re-evaluates the reliability of the information based on the feedback provided by the traveler. The information management unit can also update the information based on the content of the feedback. Furthermore, the information management unit can prioritize the provision of reliable information based on the feedback evaluation score. This makes it possible to always provide the latest and most reliable information.

[0109] The location identification unit can analyze the traveler's past movement history and select the optimal location identification method. For example, it analyzes the patterns of places the traveler has visited in the past and selects the optimal location identification method. The location identification unit can also select a location identification method that avoids congestion based on the traveler's past movement history. Furthermore, the location identification unit can also select an efficient location identification method based on the traveler's past movement history. In this way, the optimal location identification method can be selected by analyzing the past movement history.

[0110] When providing information, the information providing unit can adjust the level of detail of the information based on the importance of the location. For example, detailed information is provided for important tourist spots. The information providing unit can also provide concise information for lesser-known spots. Furthermore, the information providing unit can adjust the level of detail of the information according to the interests of the traveler. In this way, by adjusting the level of detail of the information based on the importance of the location, it is possible to provide optimal information for the traveler.

[0111] When collecting feedback, the feedback collection unit can analyze the traveler's past feedback history and select the optimal collection method. For example, the feedback collection unit can analyze the patterns of feedback provided by the traveler in the past and select the optimal collection method. The feedback collection unit can also determine whether to request detailed feedback or simple feedback from the traveler's past feedback history. Furthermore, the feedback collection unit can select an efficient collection method based on the traveler's past feedback history. In this way, the optimal feedback collection method can be selected by analyzing the past feedback history.

[0112] During personalization, the personalization unit can customize the personalization means based on the traveler's current interests. For example, places related to anime, which the traveler is currently interested in, can be provided preferentially. The personalization unit can also provide information on historical places, which the traveler is currently interested in, preferentially. Furthermore, the personalization unit can also provide information on gourmet spots, which the traveler is currently interested in preferentially. In this way, by customizing the personalization means based on the traveler's current interests, it is possible to provide optimal information.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The location determination unit determines the traveler's current location. For example, the location determination unit can use GPS or location services to determine the traveler's current location. GPS receives signals from multiple satellites and calculates the traveler's precise location. Location services can use Wi-Fi or cell tower location information to determine the traveler's current location. Step 2: The information providing unit provides information about the location identified by the location identifying unit. For example, the information providing unit can provide detailed information about the identified location, access methods, surrounding tourist attractions, etc. The detailed information includes historical background, business hours, fare information, etc. The access methods include how to use public transportation, walking routes, parking information, etc. The surrounding tourist attractions include popularity, historical value, natural scenery, etc. Step 3: The feedback collection unit collects traveler feedback based on the information provided by the information provision unit. For example, the feedback collection unit can collect traveler impressions and photos of places visited by the traveler. Impressions include text reviews, star ratings, voice messages, etc. Photos include JPEG, PNG, resolution standards, etc. Step 4: The personalization unit personalizes information based on the feedback collected by the feedback collection unit. For example, the personalization unit can analyze the traveler's past visit history and interests and provide optimal information based on that. Visit history includes past location information data, visit date and time, and duration of stay. Interests include past search history, browsing history, feedback content, etc. Step 5: The information management unit manages the reliability of the information personalized by the personalization unit. For example, the information management unit manages the sources of the information to be provided, enabling the provision of reliable information. Information sources include official websites, user reviews, and ratings from third-party organizations.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a location identification unit that identifies the current location of the traveler; an information providing unit that provides information about the location identified by the location identifying unit; a feedback collection unit that collects feedback from travelers based on the information provided by the information provision unit; a personalization unit that personalizes information based on the feedback collected by the feedback collection unit; an information management unit that manages the reliability of the information personalized by the personalization unit; A system characterized by:

2. The position identification unit Use GPS or location services to determine traveler location 2. The system of claim 1.

3. The information providing unit Providing detailed information about identified locations, directions, and nearby attractions 2. The system of claim 1.

4. The feedback collection unit: Collect travellers' impressions or photos of the places they visit 2. The system of claim 1.

5. The personalization unit Analyze travelers' past visit history and interests and provide them with appropriate information based on that information 2. The system of claim 1.

6. The information management unit Manage the source of information provided and provide reliable information 2. The system of claim 1.

7. The position identification unit Estimating traveler sentiment and changing the frequency of location determination based on the estimated traveler sentiment 2. The system of claim 1.

8. The position identification unit Analyze traveler's past movements and select the appropriate location method 2. The system of claim 1.

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