System

Abstract

An object of a system according to an embodiment is to enable a traveler to obtain the latest and detailed sightseeing information in real time.SOLUTION: A system includes a collection unit, a learning unit, an acquisition unit, a provision unit, and a customization unit. The collection unit collects sightseeing information. The learning unit learns the sightseeing information collected by the collection unit. The acquisition unit acquires a current location of a user. The providing unit provides relevant sightseeing information based on the current location acquired by the acquisition unit. The customization unit customizes the sightseeing information provided by the providing unit based on the user's preference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for travelers to obtain the latest and detailed tourist information in real time.

[0005] The system according to the embodiment aims to enable travelers to obtain the latest and detailed tourist information in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, an acquisition unit, a provision unit, and a customization unit. The collection unit collects tourist information. The learning unit learns the tourist information collected by the collection unit. The acquisition unit acquires a user's current location. The provision unit provides related tourist information based on the current location acquired by the acquisition unit. The customization unit customizes the tourist information provided by the provision unit based on the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to obtain the latest and detailed tourist information in real time. [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 real-time navigation system according to an embodiment of the present invention collects tourist information, uses a generation AI to learn, provides relevant tourist information based on a user's current location, and customizes the information based on the user's preferences. The real-time navigation system collects tourist information, uses a generation AI to learn, and provides relevant tourist information based on the user's current location, thereby customizing the information to suit the user's preferences. For example, the real-time navigation system collects tourist information from publicly available data on the Internet and tourist guidebooks. The real-time navigation system then trains the collected tourist information using a generation AI. The generation AI analyzes the tourist information and generates a tourist guide. The real-time navigation system then acquires the user's current location, for example, using GPS or Wi-Fi location information. The real-time navigation system then provides relevant tourist information based on the acquired current location. For example, the real-time navigation system provides detailed information about tourist attractions the user has approached, as well as information about recommended restaurants and shopping centers. The real-time navigation system then customizes the provided tourist information based on the user's preferences. For example, the real-time navigation system customizes the tourist information based on the user's preferences and past activity history. This allows travelers to obtain the latest and most detailed tourist information in real time, facilitating smooth travel planning and on-site activities. This allows the real-time navigation system to provide travelers with the latest and most detailed tourist information in real time, making trip planning and on-site activities smoother. For example, real-time information on tourist spot congestion and weather conditions is provided, allowing travelers to enjoy sightseeing at the optimal time.

[0029] A real-time navigation system according to an embodiment includes a collection unit, a learning unit, an acquisition unit, a provision unit, and a customization unit. The collection unit collects tourist information. The tourist information includes, but is not limited to, information about tourist attractions, restaurants, and shopping centers. The collection unit collects tourist information from, for example, public data on the Internet and tourist guidebooks. The collection unit can also collect detailed information about tourist attractions, such as their history, characteristics, opening hours, and admission fees. For example, the collection unit collects tourist information from tourist websites, blogs, and social media. The learning unit uses a generation AI to learn the tourist information collected by the collection unit. The learning unit analyzes the tourist information using, for example, a text generation AI (e.g., LLM) and generates a tourist guide. The learning unit can also analyze the tourist information using a multimodal generation AI. The learning unit can also extract and learn important parts of the tourist information using the generation AI. For example, the text generation AI learns large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, such as text, images, and audio, in addition to text. The generation AI uses keyword extraction technology to pick out particularly important information from tourist information and generates a tourist guide based on that information. The acquisition unit acquires the user's current location. The acquisition unit acquires the current location using, for example, GPS or Wi-Fi location information. The acquisition unit can also acquire the current location using mobile base station information. For example, the acquisition unit acquires the user's current location with high accuracy using GPS. Wi-Fi location information estimates the current location based on the location of Wi-Fi access points. Mobile base station information estimates the current location based on the location of mobile phone base stations. The provision unit provides related tourist information based on the current location acquired by the acquisition unit. For example, the provision unit provides detailed information about tourist spots the user has approached, as well as information about recommended restaurants and shopping centers. The provision unit can also provide information related to areas the user plans to visit. For example, the provision unit provides advance information about tourist spots and restaurants the user plans to visit next.The customization unit customizes the tourist information provided by the provision unit based on the user's preferences. The customization unit customizes the tourist information based on, for example, the user's preferences and past behavioral history. The customization unit can also customize the tourist information based on user feedback. For example, if the user prefers restaurants of a specific genre, the customization unit can preferentially provide restaurant information of that genre. The customization unit can also provide information on tourist spots and activities that the user may be interested in based on the user's past behavioral history. As a result, the real-time navigation system according to the embodiment can provide tourist information in real time based on the user's current location and customize it to suit the user's preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's preferences and past behavioral history into the generation AI and cause the generation AI to customize the tourist information.

[0030] The collection unit can collect tourism information from public data on the Internet or tourist guidebooks. The collection unit, for example, collects tourism information from public data on the Internet. For example, the collection unit collects tourism information from tourist sites, blogs, social media, etc. The collection unit can also collect tourism information from tourist guidebooks. For example, the collection unit collects tourism information from paper tourist guidebooks or electronic tourist guidebooks. By collecting tourism information from public data on the Internet or tourist guidebooks, it is possible to provide the latest and most detailed information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input tourism information collected from tourist sites, blogs, social media, etc. into the generation AI and cause the generation AI to collect tourism information.

[0031] The learning unit can analyze the collected tourist information and generate a tourist guide. The learning unit, for example, analyzes the collected tourist information using text mining technology. For example, the learning unit extracts important keywords and phrases from the tourist information. The learning unit can also analyze the tourist information using data mining technology. For example, the learning unit discovers patterns and trends from the tourist information. The learning unit can also analyze the tourist information using a machine learning algorithm. For example, the learning unit classifies the tourist information and generates a tourist guide. In this way, by analyzing the collected tourist information and generating a tourist guide, appropriate tourist information can be provided to the user. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the collected tourist information into a generation AI and cause the generation AI to generate a tourist guide.

[0032] The acquisition unit can acquire the user's current location in real time. The acquisition unit acquires the user's current location in real time, for example, using GPS. For example, the acquisition unit receives a GPS signal and acquires the user's current location with high accuracy. The acquisition unit can also acquire the user's current location in real time using Wi-Fi location information. For example, the acquisition unit estimates the current location based on the location of a Wi-Fi access point. The acquisition unit can also acquire the user's current location in real time using mobile base station information. For example, the acquisition unit estimates the current location based on the location of a mobile phone base station. This allows the user's current location to be acquired in real time, thereby providing the latest tourist information. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input a GPS signal to the generation AI and cause the generation AI to acquire the current location.

[0033] The providing unit can provide detailed information about tourist spots approached by the user or information about recommended restaurants and shopping centers. The providing unit provides, for example, detailed information about tourist spots approached by the user. For example, the providing unit provides detailed information such as the history and characteristics of the tourist spots, business hours, and admission fees. The providing unit can also provide information about recommended restaurants. For example, the providing unit provides information such as restaurant ratings and reviews and the types of cuisine. The providing unit can also provide information about recommended shopping centers. For example, the providing unit provides information such as a list of stores in the shopping center, sale information, and access methods. This makes it possible to provide useful information to the user in real time by providing detailed information about tourist spots approached by the user or information about recommended restaurants and shopping centers. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input detailed information about tourist spots and information about restaurants and shopping centers into the generation AI and cause the generation AI to provide the information.

[0034] The customization unit can customize the tourist information based on the user's preferences and past activity history. The customization unit customizes the tourist information based on, for example, the user's preferences. For example, if the user prefers restaurants of a particular genre, the customization unit prioritizes providing restaurant information of that genre. The customization unit can also customize the tourist information based on the user's past activity history. For example, the customization unit provides information on tourist spots and activities that the user is likely to be interested in based on the user's past visit history and search history. The customization unit can also customize the tourist information based on user feedback. For example, the customization unit prioritizes providing information on tourist spots that the user has given a high rating. This allows the tourist information to be customized based on the user's preferences and past activity history, thereby providing optimal information for the user. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's preferences and past activity history into the generation AI and cause the generation AI to customize the tourist information.

[0035] When collecting tourist information, the collection unit can analyze the user's past travel history and select a collection method. The collection unit, for example, uses data mining technology to analyze the user's past travel history. For example, the collection unit can prioritize collecting information on tourist spots that the user has visited in the past. The collection unit can also collect information on restaurants that the user has previously preferred and provide information on similar restaurants. The collection unit can also collect information on shopping centers in areas that the user has previously visited and provide the latest information. This allows more relevant tourist information to be collected by analyzing the user's past travel history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past travel history data into a generation AI and have the generation AI select a collection method.

[0036] When collecting tourist information, the collection unit can filter the tourist information based on the user's current areas of interest. The collection unit, for example, uses text mining technology to identify the user's current areas of interest. For example, the collection unit analyzes the user's search history and social media activity to identify the user's current areas of interest. The collection unit also filters the tourist information based on the user's current areas of interest. For example, the collection unit prioritizes collecting information about tourist spots in which the user is currently interested. The collection unit can also collect and provide information about restaurants in which the user is currently interested. The collection unit can also collect and provide information about shopping centers in which the user is currently interested. In this way, by filtering information based on the user's current areas of interest, more interesting information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's search history data into a generation AI and have the generation AI identify the user's areas of interest and filter the information.

[0037] When collecting tourist information, the collection unit can select the optimal collection means depending on the user's input method. The collection unit, for example, uses voice recognition technology to identify the user's input method. For example, if the user uses voice input, the collection unit collects tourist information using voice recognition technology. The collection unit can also identify the user's input method using text analysis technology. For example, if the user uses text input, the collection unit collects tourist information using text analysis technology. The collection unit can also identify the user's input method using image recognition technology. For example, if the user uses image input, the collection unit collects tourist information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data into a generation AI and cause the generation AI to collect tourist information.

[0038] When collecting tourist information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the collection unit receives GPS signals and acquires the user's current location with high accuracy. The collection unit can also acquire the user's geographical location information by using Wi-Fi location information. For example, the collection unit estimates the current location based on the location of a Wi-Fi access point. The collection unit can also acquire the user's geographical location information by using mobile base station information. For example, the collection unit estimates the current location based on the location of a mobile phone base station. Next, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about tourist spots close to the user's current location. The collection unit can also prioritize collecting information about restaurants close to the user's current location. The collection unit can also prioritize collecting information about shopping centers close to the user's current location. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0039] The collection unit can analyze the user's social media activities and collect related information when collecting tourist information. The collection unit, for example, uses text mining technology to analyze the user's social media activities. For example, the collection unit collects information on places where the user has checked in on social media. The collection unit can also analyze the user's social media posts to collect information on related tourist spots. The collection unit can also refer to the activities of the user's friends on social media to collect related tourist information. This allows for analysis of the user's social media activities to provide more relevant information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0040] When collecting tourist information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses data mining technology to analyze the user's past feedback. For example, the collection unit prioritizes collecting information about tourist spots that the user has previously rated highly. The collection unit can also avoid collecting information about tourist spots that the user has previously rated poorly. The collection unit can also adjust the categories of information to be collected based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During learning, the learning unit can adjust the level of detail of the learning based on the importance of the tourist information. The learning unit, for example, uses data mining technology to evaluate the importance of the tourist information. For example, the learning unit extracts important information from the tourist information and studies it in detail. The learning unit can also simply study tourist information with low importance. For example, the learning unit studies information about tourist spots that are of high interest to the user in detail. The learning unit can also simply study information about tourist spots that are of low interest to the user. This allows more appropriate information to be provided by adjusting the level of detail of the learning based on the importance of the tourist information. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input tourist information importance data into the generation AI and cause the generation AI to adjust the level of detail of the learning.

[0042] During learning, the learning unit can apply different learning algorithms depending on the category of tourism information. The learning unit, for example, uses text mining technology to classify the categories of tourism information. For example, the learning unit classifies tourism information into categories such as tourist attractions, restaurants, and shopping centers. The learning unit also applies different learning algorithms depending on the category of tourism information. For example, the learning unit applies an image recognition algorithm to tourist attraction information to learn. The learning unit can also apply a text analysis algorithm to restaurant information to learn. The learning unit can also use a geographic information system (GIS) to learn shopping center information. In this way, by applying different learning algorithms depending on the category of tourism information, more appropriate information can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input category data of tourism information into the generation AI and cause the generation AI to apply a learning algorithm.

[0043] During learning, the learning unit can improve the accuracy of learning by referring to the user's past learning results. The learning unit, for example, uses data mining technology to analyze the user's past learning results. For example, the learning unit prioritizes learning information about tourist spots that the user has previously rated highly. The learning unit can also avoid learning information about tourist spots that the user has previously rated poorly. The learning unit can also adjust the learning algorithm based on the user's past learning results. This allows the accuracy of learning to be improved by referring to the user's past learning results. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past learning result data into the generation AI and cause the generation AI to improve the accuracy of learning.

[0044] During learning, the learning unit can determine learning priorities based on the time when tourist information was collected. The learning unit, for example, uses data mining technology to evaluate the time when tourist information was collected. For example, the learning unit prioritizes learning of recently collected tourist information. The learning unit can also postpone learning of older tourist information. The learning unit can also prioritize learning of the latest information on areas in which the user is interested. In this way, by determining learning priorities based on the time when tourist information was collected, it is possible to provide the latest information. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data on the time when tourist information was collected into the generation AI and have the generation AI determine the learning priorities.

[0045] During learning, the learning unit can adjust the learning order based on the relevance of the tourist information. The learning unit, for example, uses data mining technology to evaluate the relevance of the tourist information. For example, the learning unit prioritizes learning information about tourist spots in which the user is interested. The learning unit can also postpone learning information about tourist spots in which the user is not interested. The learning unit can also prioritize learning tourist information related to the user's current location. In this way, by adjusting the learning order based on the relevance of the tourist information, more relevant information can be provided. Some or all of the above-described processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input relevance data of tourist information into the generation AI and cause the generation AI to adjust the learning order.

[0046] During learning, the learning unit can adjust the use of technical terms in the learning depending on the user's level of expertise. The learning unit, for example, uses survey results to evaluate the user's level of expertise. For example, if the user has technical expertise, the learning unit may use a lot of technical terms in the learning. Furthermore, if the user does not have technical expertise, the learning unit may avoid technical terms in the learning. Furthermore, the learning unit may select and learn appropriate terms depending on the user's level of expertise. This allows for the provision of more understandable information by adjusting the use of technical terms in the learning depending on the user's level of expertise. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] When acquiring the current location, the acquisition unit can analyze the user's past movement history and select the optimal acquisition method. The acquisition unit, for example, uses data mining technology to analyze the user's past movement history. For example, the acquisition unit selects the optimal current location acquisition method based on information about places the user has visited in the past. The acquisition unit can also analyze the user's past movement patterns and select an efficient current location acquisition method. The acquisition unit can also adjust the timing of current location acquisition by referring to the user's past movement history. In this way, the current location can be acquired more efficiently by analyzing the user's past movement history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past movement history data into the generation AI and cause the generation AI to select the optimal acquisition method.

[0048] When acquiring the current location, the acquisition unit can perform filtering based on the user's current activity status. The acquisition unit, for example, uses sensor technology to identify the user's current activity status. For example, the acquisition unit detects the user's activity status, such as walking, driving, or resting, using a sensor. The acquisition unit also filters the current location information based on the user's current activity status. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring current location information related to tourist spots. If the user is eating, the acquisition unit can also prioritize acquiring current location information related to restaurants. If the user is shopping, the acquisition unit can also prioritize acquiring current location information related to shopping centers. This allows for filtering based on the user's current activity status to provide more relevant information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's activity status data to the generation AI and cause the generation AI to filter the current location information.

[0049] When acquiring the current location, the acquisition unit can select the optimal acquisition means depending on the user's input method. The acquisition unit, for example, uses voice recognition technology to identify the user's input method. For example, if the user is using voice input, the acquisition unit acquires the current location using voice recognition technology. The acquisition unit can also identify the user's input method using text analysis technology. For example, if the user is using text input, the acquisition unit acquires the current location using text analysis technology. The acquisition unit can also identify the user's input method using image recognition technology. For example, if the user is using image input, the acquisition unit acquires the current location using image recognition technology. This allows the current location to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's voice data to a generation AI and cause the generation AI to acquire the current location.

[0050] When acquiring the user's current location, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The acquisition unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the acquisition unit receives GPS signals and acquires the user's current location with high accuracy. The acquisition unit can also acquire the user's geographical location information by using Wi-Fi location information. For example, the acquisition unit estimates the user's current location based on the location of a Wi-Fi access point. The acquisition unit can also acquire the user's geographical location information by using mobile base station information. For example, the acquisition unit estimates the user's current location based on the location of a mobile base station. Next, the acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information about tourist spots close to the user's current location. The acquisition unit can also prioritize acquiring information about restaurants close to the user's current location. The acquisition unit can also prioritize acquiring information about shopping centers close to the user's current location. This makes it possible to provide more relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0051] When acquiring the current location, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, uses text mining technology to analyze the user's social media activity. For example, the acquisition unit acquires information on locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire information on related tourist spots. The acquisition unit can also acquire related tourist information by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0052] When acquiring the current location, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit, for example, uses data mining technology to analyze the user's past feedback. For example, the acquisition unit prioritizes acquiring information about tourist spots that the user has previously rated highly. The acquisition unit can also avoid acquiring information about tourist spots that the user has previously rated poorly. The acquisition unit can also adjust the categories of information to be acquired based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0053] The providing unit can adjust the level of detail of the provided information based on the importance of the tourist information when providing it. The providing unit, for example, uses data mining technology to evaluate the importance of the tourist information. For example, the providing unit can provide detailed information about important tourist spots. The providing unit can also provide simplified information about tourist spots with low importance. The providing unit can also provide detailed information about tourist spots that are of high interest to the user. In this way, by adjusting the level of detail of the provided information based on the importance of the tourist information, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input importance data of the tourist information into the generation AI and cause the generation AI to adjust the level of detail of the provided information.

[0054] The providing unit can apply different provision algorithms depending on the category of the tourism information when providing the information. The providing unit, for example, uses text mining technology to classify the category of the tourism information. For example, the providing unit classifies the tourism information according to categories such as tourist spots, restaurants, and shopping centers. The providing unit also applies different provision algorithms depending on the category of the tourism information. For example, the providing unit applies an image recognition algorithm to provide information about tourist spots. The providing unit can also apply a text analysis algorithm to provide information about restaurants. The providing unit can also use a geographic information system (GIS) to provide information about shopping centers. In this way, by applying different provision algorithms depending on the category of the tourism information, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input category data of the tourism information into the generation AI and cause the generation AI to apply the provision algorithm.

[0055] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results. The providing unit, for example, uses data mining technology to analyze the user's past provision results. For example, the providing unit may preferentially provide information about tourist spots that the user has previously rated highly. The providing unit may also avoid providing information about tourist spots that the user has previously rated poorly. The providing unit may also adjust the providing algorithm based on the user's past provision results. This allows the accuracy of the information provided to be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit may input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0056] The providing unit can determine the priority of provision based on the time when the tourist information was collected when providing the information. The providing unit, for example, uses data mining technology to evaluate the time when the tourist information was collected. For example, the providing unit can prioritize providing recently collected tourist information. The providing unit can also postpone providing older tourist information. The providing unit can also prioritize providing the latest information on areas in which the user is interested. In this way, the latest information can be provided by determining the priority of provision based on the time when the tourist information was collected. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input data on the time when the tourist information was collected into the generation AI and have the generation AI determine the priority of provision.

[0057] The providing unit can adjust the order of providing tourist information based on the relevance of the tourist information when providing the information. The providing unit, for example, uses data mining technology to evaluate the relevance of the tourist information. For example, the providing unit can prioritize providing information about tourist spots in which the user is interested. The providing unit can also postpone providing information about tourist spots in which the user is not interested. The providing unit can also prioritize providing tourist information related to the user's current location. In this way, by adjusting the order of providing the tourist information based on the relevance, more relevant information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input relevance data of the tourist information into the generation AI and cause the generation AI to adjust the order of providing the information.

[0058] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. The providing unit, for example, uses survey results to evaluate the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information that avoids technical terminology. The providing unit can also select and provide appropriate terminology according to the user's level of expertise. This allows for the provision of more understandable information by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0059] During customization, the customization unit can analyze the user's past behavioral history and select the optimal customization method. The customization unit, for example, uses data mining technology to analyze the user's past behavioral history. For example, the customization unit prioritizes customization of information about tourist spots that the user has previously rated highly. The customization unit can also avoid customizing information about tourist spots that the user has previously rated poorly. The customization unit can also adjust the content of customization based on the user's past behavioral history. This makes it possible to provide more appropriate information by analyzing the user's past behavioral history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past behavioral history data into the generation AI and have the generation AI select the optimal customization method.

[0060] During customization, the customization unit can customize the customization means based on the user's current areas of interest. The customization unit, for example, uses text mining technology to identify the user's current areas of interest. For example, the customization unit analyzes the user's search history and social media activity to identify the user's current areas of interest. The customization unit also customizes tourist information based on the user's current areas of interest. For example, the customization unit prioritizes customization of information about tourist spots in which the user is currently interested. The customization unit can also customize and provide information about restaurants in which the user is currently interested. The customization unit can also customize and provide information about shopping centers in which the user is currently interested. This allows the customization unit to customize the customization means based on the user's current areas of interest, thereby providing more appropriate information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's search history data into the generation AI and cause the generation AI to identify the user's areas of interest and customize the information.

[0061] During customization, the customization unit can improve the customization method by reflecting user feedback. The customization unit, for example, uses data mining technology to analyze user feedback. For example, the customization unit prioritizes customization of information about tourist spots that the user has previously rated highly. The customization unit can also avoid customizing information about tourist spots that the user has previously rated poorly. The customization unit can also adjust the content of customization based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting user feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into the generation AI and cause the generation AI to improve the customization method.

[0062] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. The customization unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the customization unit receives GPS signals and acquires the user's current location with high accuracy. The customization unit can also acquire the user's geographical location information by using Wi-Fi location information. For example, the customization unit estimates the current location based on the location of a Wi-Fi access point. The customization unit can also acquire the user's geographical location information by using mobile base station information. For example, the customization unit estimates the current location based on the location of a mobile base station. Next, the customization unit selects the optimal customization method by taking into account the user's geographical location information. For example, the customization unit can prioritize customizing information about tourist spots close to the user's current location. The customization unit can also prioritize customizing information about restaurants close to the user's current location. The customization unit can also prioritize customizing information about shopping centers close to the user's current location. This allows more appropriate information to be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's geographical location information to the generation AI and cause the generation AI to select the optimal customization method.

[0063] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit, for example, uses text mining technology to analyze the user's social media activity. For example, the customization unit prioritizes customization of information about places where the user has checked in on social media. The customization unit can also analyze the user's social media posts to customize information about related tourist spots. The customization unit can also customize related tourist information based on the activity of the user's friends on social media. This allows for more appropriate information to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media data into the generation AI and have the generation AI suggest customization methods.

[0064] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit, for example, uses data mining technology to analyze the user's past feedback. For example, the customization unit prioritizes customization of information about tourist spots that the user has previously rated highly. The customization unit can also avoid customizing information about tourist spots that the user has previously rated poorly. The customization unit can also adjust the content of the customization based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the customization unit may be performed, for example, using AI or without AI. For example, the customization unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the customization method.

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

[0066] The real-time navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, number of steps, and calorie consumption, and monitors the user's health condition in real time. For example, the health management unit may provide a notification urging the user to take a break if the user has been walking for a long time. Also, if the user's heart rate is high, the health management unit may suggest tourist spots where the user can relax. Furthermore, the health management unit may provide appropriate dietary and exercise advice based on the user's health data. This allows the user to enjoy sightseeing while maintaining their health even during their trip.

[0067] The real-time navigation system may further include a traffic management unit that takes into account the user's mode of transportation. The traffic management unit provides the optimal route based on the mode of transportation used by the user (e.g., walking, cycling, car, public transportation, etc.). For example, if the user is using a bicycle, the traffic management unit may provide information on bicycle lanes and bicycle parking areas. Also, if the user is using public transportation, the traffic management unit may provide bus and train timetables and transfer information. Furthermore, the traffic management unit may provide traffic congestion and construction information in real time to help the user travel smoothly. This allows the user to efficiently tour tourist spots.

[0068] The real-time navigation system may further include a language management unit that supports multiple languages ​​based on the user's language setting. The language management unit provides tourist information based on the user's language setting. For example, if the user selects English, the language management unit may provide tourist information in English. Also, if the user selects Japanese, the language management unit may provide tourist information in Japanese. Furthermore, the language management unit may provide simple phrases and words for learning the local language of the tourist destination. This allows the user to enjoy sightseeing smoothly without experiencing language barriers.

[0069] The real-time navigation system can further include a gamification unit that introduces gamification elements to attract users' interest. The gamification unit provides a mechanism that allows users to earn points when touring tourist spots. For example, users are awarded points when they visit specific tourist spots, and by collecting points, they can receive special benefits or discounts. The gamification unit can also provide a ranking function that allows users to compete with each other. Furthermore, users can earn badges and titles by completing specific missions at tourist spots. This allows users to enjoy touring tourist spots.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The collection unit collects tourist information. This information includes information on tourist attractions, restaurants, shopping centers, etc. The collection unit collects tourist information from publicly available data on the Internet, tourist guidebooks, tourist sites, blogs, social media, etc. It also collects detailed information about tourist attractions, such as their history and characteristics, opening hours, and admission fees. Step 2: The learning unit uses the generation AI to learn the tourist information collected by the collection unit. The learning unit analyzes the tourist information using text generation AI (e.g., LLM) or multimodal generation AI to generate a tourist guide. The generation AI is also used to extract and learn important parts of the tourist information. Step 3: The acquisition unit acquires the user's current location. The acquisition unit acquires the current location using GPS, Wi-Fi location information, and mobile base station information. For example, the acquisition unit acquires the current location with high accuracy using GPS, and estimates the current location using Wi-Fi location information and mobile base station information. Step 4: The providing unit provides relevant tourist information based on the current location acquired by the acquiring unit. The providing unit provides detailed information about tourist spots the user is approaching, information about recommended restaurants and shopping centers, and information related to areas the user plans to visit. Step 5: The customization unit customizes the tourist information provided by the provision unit based on the user's preferences. The customization unit customizes the tourist information based on the user's preferences, past behavior history, and feedback. For example, it may provide preferentially information about restaurants in a specific genre, or information about tourist spots and activities that the user may be interested in.

[0072] (Example 2) A real-time navigation system according to an embodiment of the present invention collects tourist information, uses a generation AI to learn, provides relevant tourist information based on a user's current location, and customizes the information based on the user's preferences. The real-time navigation system collects tourist information, uses a generation AI to learn, and provides relevant tourist information based on the user's current location, thereby customizing the information to suit the user's preferences. For example, the real-time navigation system collects tourist information from publicly available data on the Internet and tourist guidebooks. The real-time navigation system then trains the collected tourist information using a generation AI. The generation AI analyzes the tourist information and generates a tourist guide. The real-time navigation system then acquires the user's current location, for example, using GPS or Wi-Fi location information. The real-time navigation system then provides relevant tourist information based on the acquired current location. For example, the real-time navigation system provides detailed information about tourist attractions the user has approached, as well as information about recommended restaurants and shopping centers. The real-time navigation system then customizes the provided tourist information based on the user's preferences. For example, the real-time navigation system customizes the tourist information based on the user's preferences and past activity history. This allows travelers to obtain the latest and most detailed tourist information in real time, facilitating smooth travel planning and on-site activities. This allows the real-time navigation system to provide travelers with the latest and most detailed tourist information in real time, making trip planning and on-site activities smoother. For example, real-time information on tourist spot congestion and weather conditions is provided, allowing travelers to enjoy sightseeing at the optimal time.

[0073] A real-time navigation system according to an embodiment includes a collection unit, a learning unit, an acquisition unit, a provision unit, and a customization unit. The collection unit collects tourist information. The tourist information includes, but is not limited to, information about tourist attractions, restaurants, and shopping centers. The collection unit collects tourist information from, for example, public data on the Internet and tourist guidebooks. The collection unit can also collect detailed information about tourist attractions, such as their history, characteristics, opening hours, and admission fees. For example, the collection unit collects tourist information from tourist websites, blogs, and social media. The learning unit uses a generation AI to learn the tourist information collected by the collection unit. The learning unit analyzes the tourist information using, for example, a text generation AI (e.g., LLM) and generates a tourist guide. The learning unit can also analyze the tourist information using a multimodal generation AI. The learning unit can also extract and learn important parts of the tourist information using the generation AI. For example, the text generation AI learns large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, such as text, images, and audio, in addition to text. The generation AI uses keyword extraction technology to pick out particularly important information from tourist information and generates a tourist guide based on that information. The acquisition unit acquires the user's current location. The acquisition unit acquires the current location using, for example, GPS or Wi-Fi location information. The acquisition unit can also acquire the current location using mobile base station information. For example, the acquisition unit acquires the user's current location with high accuracy using GPS. Wi-Fi location information estimates the current location based on the location of Wi-Fi access points. Mobile base station information estimates the current location based on the location of mobile phone base stations. The provision unit provides related tourist information based on the current location acquired by the acquisition unit. For example, the provision unit provides detailed information about tourist spots the user has approached, as well as information about recommended restaurants and shopping centers. The provision unit can also provide information related to areas the user plans to visit. For example, the provision unit provides advance information about tourist spots and restaurants the user plans to visit next.The customization unit customizes the tourist information provided by the provision unit based on the user's preferences. The customization unit customizes the tourist information based on, for example, the user's preferences and past behavioral history. The customization unit can also customize the tourist information based on user feedback. For example, if the user prefers restaurants of a specific genre, the customization unit can preferentially provide restaurant information of that genre. The customization unit can also provide information on tourist spots and activities that the user may be interested in based on the user's past behavioral history. As a result, the real-time navigation system according to the embodiment can provide tourist information in real time based on the user's current location and customize it to suit the user's preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's preferences and past behavioral history into the generation AI and cause the generation AI to customize the tourist information.

[0074] The collection unit can collect tourism information from public data on the Internet or tourist guidebooks. The collection unit, for example, collects tourism information from public data on the Internet. For example, the collection unit collects tourism information from tourist sites, blogs, social media, etc. The collection unit can also collect tourism information from tourist guidebooks. For example, the collection unit collects tourism information from paper tourist guidebooks or electronic tourist guidebooks. By collecting tourism information from public data on the Internet or tourist guidebooks, it is possible to provide the latest and most detailed information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input tourism information collected from tourist sites, blogs, social media, etc. into the generation AI and cause the generation AI to collect tourism information.

[0075] The learning unit can analyze the collected tourist information and generate a tourist guide. The learning unit, for example, analyzes the collected tourist information using text mining technology. For example, the learning unit extracts important keywords and phrases from the tourist information. The learning unit can also analyze the tourist information using data mining technology. For example, the learning unit discovers patterns and trends from the tourist information. The learning unit can also analyze the tourist information using a machine learning algorithm. For example, the learning unit classifies the tourist information and generates a tourist guide. In this way, by analyzing the collected tourist information and generating a tourist guide, appropriate tourist information can be provided to the user. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the collected tourist information into a generation AI and cause the generation AI to generate a tourist guide.

[0076] The acquisition unit can acquire the user's current location in real time. The acquisition unit acquires the user's current location in real time, for example, using GPS. For example, the acquisition unit receives a GPS signal and acquires the user's current location with high accuracy. The acquisition unit can also acquire the user's current location in real time using Wi-Fi location information. For example, the acquisition unit estimates the current location based on the location of a Wi-Fi access point. The acquisition unit can also acquire the user's current location in real time using mobile base station information. For example, the acquisition unit estimates the current location based on the location of a mobile phone base station. This allows the user's current location to be acquired in real time, thereby providing the latest tourist information. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input a GPS signal to the generation AI and cause the generation AI to acquire the current location.

[0077] The providing unit can provide detailed information about tourist spots approached by the user or information about recommended restaurants and shopping centers. The providing unit provides, for example, detailed information about tourist spots approached by the user. For example, the providing unit provides detailed information such as the history and characteristics of the tourist spots, business hours, and admission fees. The providing unit can also provide information about recommended restaurants. For example, the providing unit provides information such as restaurant ratings and reviews and the types of cuisine. The providing unit can also provide information about recommended shopping centers. For example, the providing unit provides information such as a list of stores in the shopping center, sale information, and access methods. This makes it possible to provide useful information to the user in real time by providing detailed information about tourist spots approached by the user or information about recommended restaurants and shopping centers. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input detailed information about tourist spots and information about restaurants and shopping centers into the generation AI and cause the generation AI to provide the information.

[0078] The customization unit can customize the tourist information based on the user's preferences and past activity history. The customization unit customizes the tourist information based on, for example, the user's preferences. For example, if the user prefers restaurants of a particular genre, the customization unit prioritizes providing restaurant information of that genre. The customization unit can also customize the tourist information based on the user's past activity history. For example, the customization unit provides information on tourist spots and activities that the user is likely to be interested in based on the user's past visit history and search history. The customization unit can also customize the tourist information based on user feedback. For example, the customization unit prioritizes providing information on tourist spots that the user has given a high rating. This allows the tourist information to be customized based on the user's preferences and past activity history, thereby providing optimal information for the user. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's preferences and past activity history into the generation AI and cause the generation AI to customize the tourist information.

[0079] The collection unit can estimate the user's emotions and adjust the timing of collecting tourist information based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice and calculates an emotion score. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes the user's text messages and estimates the emotions. Next, the collection unit adjusts the timing of collecting tourist information based on the estimated user's emotions. For example, if the user is excited, new tourist information can be collected in real time and provided immediately. Alternatively, if the user is relaxed, tourist information can be collected periodically and provided at appropriate times. Alternatively, if the user is tired, the collection frequency can be reduced and only the minimum necessary information can be provided. In this way, by adjusting the timing of collecting tourist information according to the user's emotions, information can be provided at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0080] When collecting tourist information, the collection unit can analyze the user's past travel history and select a collection method. The collection unit, for example, uses data mining technology to analyze the user's past travel history. For example, the collection unit can prioritize collecting information on tourist spots that the user has visited in the past. The collection unit can also collect information on restaurants that the user has previously preferred and provide information on similar restaurants. The collection unit can also collect information on shopping centers in areas that the user has previously visited and provide the latest information. This allows more relevant tourist information to be collected by analyzing the user's past travel history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past travel history data into a generation AI and have the generation AI select a collection method.

[0081] When collecting tourist information, the collection unit can filter the tourist information based on the user's current areas of interest. The collection unit, for example, uses text mining technology to identify the user's current areas of interest. For example, the collection unit analyzes the user's search history and social media activity to identify the user's current areas of interest. The collection unit also filters the tourist information based on the user's current areas of interest. For example, the collection unit prioritizes collecting information about tourist spots in which the user is currently interested. The collection unit can also collect and provide information about restaurants in which the user is currently interested. The collection unit can also collect and provide information about shopping centers in which the user is currently interested. In this way, by filtering information based on the user's current areas of interest, more interesting information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's search history data into a generation AI and have the generation AI identify the user's areas of interest and filter the information.

[0082] When collecting tourist information, the collection unit can select the optimal collection means depending on the user's input method. The collection unit, for example, uses voice recognition technology to identify the user's input method. For example, if the user uses voice input, the collection unit collects tourist information using voice recognition technology. The collection unit can also identify the user's input method using text analysis technology. For example, if the user uses text input, the collection unit collects tourist information using text analysis technology. The collection unit can also identify the user's input method using image recognition technology. For example, if the user uses image input, the collection unit collects tourist information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data into a generation AI and cause the generation AI to collect tourist information.

[0083] The collection unit can estimate the user's emotions and determine the priority of tourist information to be collected based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to calculate an emotion score. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes the user's text messages to estimate the emotions. Next, the collection unit determines the priority of tourist information to be collected based on the estimated user's emotions. For example, if the user is excited, the collection unit can prioritize collecting information about popular tourist spots. If the user is relaxed, the collection unit can prioritize collecting information about quiet places and relaxation spots. If the user is tired, the collection unit can prioritize collecting information about places where users can take a break and refresh themselves. This allows the user to provide more appropriate information by prioritizing tourist information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and have the generation AI estimate emotions and determine the priority of tourist information.

[0084] When collecting tourist information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the collection unit receives GPS signals and acquires the user's current location with high accuracy. The collection unit can also acquire the user's geographical location information by using Wi-Fi location information. For example, the collection unit estimates the current location based on the location of a Wi-Fi access point. The collection unit can also acquire the user's geographical location information by using mobile base station information. For example, the collection unit estimates the current location based on the location of a mobile phone base station. Next, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about tourist spots close to the user's current location. The collection unit can also prioritize collecting information about restaurants close to the user's current location. The collection unit can also prioritize collecting information about shopping centers close to the user's current location. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0085] The collection unit can analyze the user's social media activities and collect related information when collecting tourist information. The collection unit, for example, uses text mining technology to analyze the user's social media activities. For example, the collection unit collects information on places where the user has checked in on social media. The collection unit can also analyze the user's social media posts to collect information on related tourist spots. The collection unit can also refer to the activities of the user's friends on social media to collect related tourist information. This allows for analysis of the user's social media activities to provide more relevant information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0086] When collecting tourist information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses data mining technology to analyze the user's past feedback. For example, the collection unit prioritizes collecting information about tourist spots that the user has previously rated highly. The collection unit can also avoid collecting information about tourist spots that the user has previously rated poorly. The collection unit can also adjust the categories of information to be collected based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0087] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit analyzes the tone and speed of the user's voice and calculates an emotion score. The learning unit can also estimate the user's emotions using text analysis technology. For example, the learning unit analyzes the user's text messages and estimates the emotions. Next, the learning unit selects training data based on the estimated user emotions. For example, if the user is excited, the learning unit can prioritize learning information about popular tourist spots. If the user is relaxed, the learning unit can prioritize learning information about quiet places and relaxation spots. If the user is tired, the learning unit can prioritize learning information about places where users can take a break or refresh themselves. This allows the system to provide more appropriate information by selecting training data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, or without, the generation AI. For example, the learning unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and select learning data.

[0088] During learning, the learning unit can adjust the level of detail of the learning based on the importance of the tourist information. The learning unit, for example, uses data mining technology to evaluate the importance of the tourist information. For example, the learning unit extracts important information from the tourist information and studies it in detail. The learning unit can also simply study tourist information with low importance. For example, the learning unit studies information about tourist spots that are of high interest to the user in detail. The learning unit can also simply study information about tourist spots that are of low interest to the user. This allows more appropriate information to be provided by adjusting the level of detail of the learning based on the importance of the tourist information. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input tourist information importance data into the generation AI and cause the generation AI to adjust the level of detail of the learning.

[0089] During learning, the learning unit can apply different learning algorithms depending on the category of tourism information. The learning unit, for example, uses text mining technology to classify the categories of tourism information. For example, the learning unit classifies tourism information into categories such as tourist attractions, restaurants, and shopping centers. The learning unit also applies different learning algorithms depending on the category of tourism information. For example, the learning unit applies an image recognition algorithm to tourist attraction information to learn. The learning unit can also apply a text analysis algorithm to restaurant information to learn. The learning unit can also use a geographic information system (GIS) to learn shopping center information. In this way, by applying different learning algorithms depending on the category of tourism information, more appropriate information can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input category data of tourism information into the generation AI and cause the generation AI to apply a learning algorithm.

[0090] During learning, the learning unit can improve the accuracy of learning by referring to the user's past learning results. The learning unit, for example, uses data mining technology to analyze the user's past learning results. For example, the learning unit prioritizes learning information about tourist spots that the user has previously rated highly. The learning unit can also avoid learning information about tourist spots that the user has previously rated poorly. The learning unit can also adjust the learning algorithm based on the user's past learning results. This allows the accuracy of learning to be improved by referring to the user's past learning results. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past learning result data into the generation AI and cause the generation AI to improve the accuracy of learning.

[0091] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user's emotions. The learning unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit analyzes the user's tone and speed of voice to calculate an emotion score. The learning unit can also estimate the user's emotions using text analysis technology. For example, the learning unit analyzes the user's text messages to estimate the emotions. The learning unit then adjusts the learning frequency based on the estimated user's emotions. For example, if the user is excited, the learning frequency can be increased to provide the latest information. If the user is relaxed, the learning frequency can be kept moderate. If the user is tired, the learning frequency can be reduced to provide the minimum necessary information. By adjusting the learning frequency based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the frequency of learning.

[0092] During learning, the learning unit can determine learning priorities based on the time when tourist information was collected. The learning unit, for example, uses data mining technology to evaluate the time when tourist information was collected. For example, the learning unit prioritizes learning of recently collected tourist information. The learning unit can also postpone learning of older tourist information. The learning unit can also prioritize learning of the latest information on areas in which the user is interested. In this way, by determining learning priorities based on the time when tourist information was collected, it is possible to provide the latest information. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data on the time when tourist information was collected into the generation AI and have the generation AI determine the learning priorities.

[0093] During learning, the learning unit can adjust the learning order based on the relevance of the tourist information. The learning unit, for example, uses data mining technology to evaluate the relevance of the tourist information. For example, the learning unit prioritizes learning information about tourist spots in which the user is interested. The learning unit can also postpone learning information about tourist spots in which the user is not interested. The learning unit can also prioritize learning tourist information related to the user's current location. In this way, by adjusting the learning order based on the relevance of the tourist information, more relevant information can be provided. Some or all of the above-described processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input relevance data of tourist information into the generation AI and cause the generation AI to adjust the learning order.

[0094] During learning, the learning unit can adjust the use of technical terms in the learning depending on the user's level of expertise. The learning unit, for example, uses survey results to evaluate the user's level of expertise. For example, if the user has technical expertise, the learning unit may use a lot of technical terms in the learning. Furthermore, if the user does not have technical expertise, the learning unit may avoid technical terms in the learning. Furthermore, the learning unit may select and learn appropriate terms depending on the user's level of expertise. This allows for the provision of more understandable information by adjusting the use of technical terms in the learning depending on the user's level of expertise. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0095] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the current location based on the estimated user's emotion. The acquisition unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The acquisition unit can also estimate the user's emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the user's voice to calculate an emotion score. The acquisition unit can also estimate the user's emotion using text analysis technology. For example, the acquisition unit analyzes the user's text messages to estimate the emotion. Next, the acquisition unit adjusts the timing of acquiring the current location based on the estimated user's emotion. For example, if the user is excited, the current location can be acquired frequently to provide the latest information. If the user is relaxed, the current location can be acquired at an appropriate time. If the user is tired, the frequency of acquiring the current location can be reduced to provide the minimum necessary information. In this way, by adjusting the timing of acquiring the current location based on the user's emotion, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the timing of acquiring the current location.

[0096] When acquiring the current location, the acquisition unit can analyze the user's past movement history and select the optimal acquisition method. The acquisition unit, for example, uses data mining technology to analyze the user's past movement history. For example, the acquisition unit selects the optimal current location acquisition method based on information about places the user has visited in the past. The acquisition unit can also analyze the user's past movement patterns and select an efficient current location acquisition method. The acquisition unit can also adjust the timing of current location acquisition by referring to the user's past movement history. In this way, the current location can be acquired more efficiently by analyzing the user's past movement history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past movement history data into the generation AI and cause the generation AI to select the optimal acquisition method.

[0097] When acquiring the current location, the acquisition unit can perform filtering based on the user's current activity status. The acquisition unit, for example, uses sensor technology to identify the user's current activity status. For example, the acquisition unit detects the user's activity status, such as walking, driving, or resting, using a sensor. The acquisition unit also filters the current location information based on the user's current activity status. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring current location information related to tourist spots. If the user is eating, the acquisition unit can also prioritize acquiring current location information related to restaurants. If the user is shopping, the acquisition unit can also prioritize acquiring current location information related to shopping centers. This allows for filtering based on the user's current activity status to provide more relevant information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's activity status data to the generation AI and cause the generation AI to filter the current location information.

[0098] When acquiring the current location, the acquisition unit can select the optimal acquisition means depending on the user's input method. The acquisition unit, for example, uses voice recognition technology to identify the user's input method. For example, if the user is using voice input, the acquisition unit acquires the current location using voice recognition technology. The acquisition unit can also identify the user's input method using text analysis technology. For example, if the user is using text input, the acquisition unit acquires the current location using text analysis technology. The acquisition unit can also identify the user's input method using image recognition technology. For example, if the user is using image input, the acquisition unit acquires the current location using image recognition technology. This allows the current location to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's voice data to a generation AI and cause the generation AI to acquire the current location.

[0099] The acquisition unit can estimate the user's emotion and determine the priority of current locations to be acquired based on the estimated user's emotion. The acquisition unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The acquisition unit can also estimate the user's emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the user's voice to calculate an emotion score. The acquisition unit can also estimate the user's emotion using text analysis technology. For example, the acquisition unit analyzes the user's text messages to estimate the emotion. Next, the acquisition unit determines the priority of current locations to be acquired based on the estimated user's emotion. For example, if the user is excited, the acquisition unit can prioritize acquiring current location information of tourist spots. If the user is relaxed, the acquisition unit can prioritize acquiring current location information of places where the user can rest. If the user is tired, the acquisition unit can prioritize acquiring current location information of the nearest rest spot. This allows the provision of more appropriate information by prioritizing current locations based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and determine the priority of the current location.

[0100] When acquiring the user's current location, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The acquisition unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the acquisition unit receives GPS signals and acquires the user's current location with high accuracy. The acquisition unit can also acquire the user's geographical location information by using Wi-Fi location information. For example, the acquisition unit estimates the user's current location based on the location of a Wi-Fi access point. The acquisition unit can also acquire the user's geographical location information by using mobile base station information. For example, the acquisition unit estimates the user's current location based on the location of a mobile base station. Next, the acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information about tourist spots close to the user's current location. The acquisition unit can also prioritize acquiring information about restaurants close to the user's current location. The acquisition unit can also prioritize acquiring information about shopping centers close to the user's current location. This makes it possible to provide more relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0101] When acquiring the current location, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, uses text mining technology to analyze the user's social media activity. For example, the acquisition unit acquires information on locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire information on related tourist spots. The acquisition unit can also acquire related tourist information by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0102] When acquiring the current location, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit, for example, uses data mining technology to analyze the user's past feedback. For example, the acquisition unit prioritizes acquiring information about tourist spots that the user has previously rated highly. The acquisition unit can also avoid acquiring information about tourist spots that the user has previously rated poorly. The acquisition unit can also adjust the categories of information to be acquired based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0103] The providing unit can estimate the user's emotions and adjust the presentation of the tourist information to be provided based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. The providing unit can also estimate the user's emotions using text analysis technology. For example, the providing unit analyzes the user's text messages and estimates the emotions. Next, the providing unit adjusts the presentation of the tourist information to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide tourist information with visually stimulating effects. If the user is relaxed, the providing unit can provide tourist information in a calm tone. If the user is tired, the providing unit can provide tourist information that is simple and easy to read. This allows the providing unit to provide more appropriate information by adjusting the presentation of the tourist information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the way tourist information is presented.

[0104] The providing unit can adjust the level of detail of the provided information based on the importance of the tourist information when providing it. The providing unit, for example, uses data mining technology to evaluate the importance of the tourist information. For example, the providing unit can provide detailed information about important tourist spots. The providing unit can also provide simplified information about tourist spots with low importance. The providing unit can also provide detailed information about tourist spots that are of high interest to the user. In this way, by adjusting the level of detail of the provided information based on the importance of the tourist information, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input importance data of the tourist information into the generation AI and cause the generation AI to adjust the level of detail of the provided information.

[0105] The providing unit can apply different provision algorithms depending on the category of the tourism information when providing the information. The providing unit, for example, uses text mining technology to classify the category of the tourism information. For example, the providing unit classifies the tourism information according to categories such as tourist spots, restaurants, and shopping centers. The providing unit also applies different provision algorithms depending on the category of the tourism information. For example, the providing unit applies an image recognition algorithm to provide information about tourist spots. The providing unit can also apply a text analysis algorithm to provide information about restaurants. The providing unit can also use a geographic information system (GIS) to provide information about shopping centers. In this way, by applying different provision algorithms depending on the category of the tourism information, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input category data of the tourism information into the generation AI and cause the generation AI to apply the provision algorithm.

[0106] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results. The providing unit, for example, uses data mining technology to analyze the user's past provision results. For example, the providing unit may preferentially provide information about tourist spots that the user has previously rated highly. The providing unit may also avoid providing information about tourist spots that the user has previously rated poorly. The providing unit may also adjust the providing algorithm based on the user's past provision results. This allows the accuracy of the information provided to be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit may input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0107] The providing unit can estimate the user's emotions and adjust the length of the tourist information to be provided based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. The providing unit can also estimate the user's emotions using text analysis technology. For example, the providing unit analyzes the user's text messages and estimates the emotions. Next, the providing unit adjusts the length of the tourist information to be provided based on the estimated user's emotions. For example, if the user is in a hurry, short and to the point tourist information can be provided. If the user is relaxed, longer tourist information with detailed explanations can be provided. If the user is excited, tourist information with visually stimulating effects can be provided. In this way, by adjusting the length of the tourist information based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may 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 providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the length of the tourist information.

[0108] The providing unit can determine the priority of provision based on the time when the tourist information was collected when providing the information. The providing unit, for example, uses data mining technology to evaluate the time when the tourist information was collected. For example, the providing unit can prioritize providing recently collected tourist information. The providing unit can also postpone providing older tourist information. The providing unit can also prioritize providing the latest information on areas in which the user is interested. In this way, the latest information can be provided by determining the priority of provision based on the time when the tourist information was collected. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input data on the time when the tourist information was collected into the generation AI and have the generation AI determine the priority of provision.

[0109] The providing unit can adjust the order of providing tourist information based on the relevance of the tourist information when providing the information. The providing unit, for example, uses data mining technology to evaluate the relevance of the tourist information. For example, the providing unit can prioritize providing information about tourist spots in which the user is interested. The providing unit can also postpone providing information about tourist spots in which the user is not interested. The providing unit can also prioritize providing tourist information related to the user's current location. In this way, by adjusting the order of providing the tourist information based on the relevance, more relevant information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input relevance data of the tourist information into the generation AI and cause the generation AI to adjust the order of providing the information.

[0110] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. The providing unit, for example, uses survey results to evaluate the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information that avoids technical terminology. The providing unit can also select and provide appropriate terminology according to the user's level of expertise. This allows for the provision of more understandable information by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0111] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user's emotions. The customization unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The customization unit can also estimate the user's emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the user's voice to calculate an emotion score. The customization unit can also estimate the user's emotions using text analysis technology. For example, the customization unit analyzes the user's text messages to estimate the emotions. Next, the customization unit adjusts the customization method based on the estimated user's emotions. For example, if the user is excited, customization can be performed with visually stimulating effects. If the user is relaxed, customization can be performed with a calm tone. If the user is tired, customization can be performed with a simple, highly visible tone. This allows the customization method to be adjusted based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the customization method.

[0112] During customization, the customization unit can analyze the user's past behavioral history and select the optimal customization method. The customization unit, for example, uses data mining technology to analyze the user's past behavioral history. For example, the customization unit prioritizes customization of information about tourist spots that the user has previously rated highly. The customization unit can also avoid customizing information about tourist spots that the user has previously rated poorly. The customization unit can also adjust the content of customization based on the user's past behavioral history. This makes it possible to provide more appropriate information by analyzing the user's past behavioral history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past behavioral history data into the generation AI and have the generation AI select the optimal customization method.

[0113] During customization, the customization unit can customize the customization means based on the user's current areas of interest. The customization unit, for example, uses text mining technology to identify the user's current areas of interest. For example, the customization unit analyzes the user's search history and social media activity to identify the user's current areas of interest. The customization unit also customizes tourist information based on the user's current areas of interest. For example, the customization unit prioritizes customization of information about tourist spots in which the user is currently interested. The customization unit can also customize and provide information about restaurants in which the user is currently interested. The customization unit can also customize and provide information about shopping centers in which the user is currently interested. This allows the customization unit to customize the customization means based on the user's current areas of interest, thereby providing more appropriate information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's search history data into the generation AI and cause the generation AI to identify the user's areas of interest and customize the information.

[0114] During customization, the customization unit can improve the customization method by reflecting user feedback. The customization unit, for example, uses data mining technology to analyze user feedback. For example, the customization unit prioritizes customization of information about tourist spots that the user has previously rated highly. The customization unit can also avoid customizing information about tourist spots that the user has previously rated poorly. The customization unit can also adjust the content of customization based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting user feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into the generation AI and cause the generation AI to improve the customization method.

[0115] The customization unit can estimate the user's emotions and prioritize customization based on the estimated user emotions. The customization unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The customization unit can also estimate the user's emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the user's voice to calculate an emotion score. The customization unit can also estimate the user's emotions using text analysis technology. For example, the customization unit analyzes the user's text messages to estimate the emotions. Next, the customization unit prioritizes customization based on the estimated user emotions. For example, if the user is excited, the customization unit can prioritize customization with visually stimulating effects. If the user is relaxed, the customization unit can prioritize customization with a calm tone. If the user is tired, the customization unit can prioritize customization with simple and high visibility. This allows for more appropriate information to be provided by prioritizing customization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and determine customization priorities.

[0116] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. The customization unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the customization unit receives GPS signals and acquires the user's current location with high accuracy. The customization unit can also acquire the user's geographical location information by using Wi-Fi location information. For example, the customization unit estimates the current location based on the location of a Wi-Fi access point. The customization unit can also acquire the user's geographical location information by using mobile base station information. For example, the customization unit estimates the current location based on the location of a mobile base station. Next, the customization unit selects the optimal customization method by taking into account the user's geographical location information. For example, the customization unit can prioritize customizing information about tourist spots close to the user's current location. The customization unit can also prioritize customizing information about restaurants close to the user's current location. The customization unit can also prioritize customizing information about shopping centers close to the user's current location. This allows more appropriate information to be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's geographical location information to the generation AI and cause the generation AI to select the optimal customization method.

[0117] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit, for example, uses text mining technology to analyze the user's social media activity. For example, the customization unit prioritizes customization of information about places where the user has checked in on social media. The customization unit can also analyze the user's social media posts to customize information about related tourist spots. The customization unit can also customize related tourist information based on the activity of the user's friends on social media. This allows for more appropriate information to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media data into the generation AI and have the generation AI suggest customization methods.

[0118] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit, for example, uses data mining technology to analyze the user's past feedback. For example, the customization unit prioritizes customization of information about tourist spots that the user has previously rated highly. The customization unit can also avoid customizing information about tourist spots that the user has previously rated poorly. The customization unit can also adjust the content of the customization based on the user's past feedback. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the customization unit may be performed, for example, using AI or without AI. For example, the customization unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the customization method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, acquisition unit, provision unit, and customization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects tourist information using the camera 42 and microphone 38B of the smart device 14 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The learning unit learns the tourist information using AI generated by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires the user's current location using GPS or Wi-Fi location information of the smart device 14. The provision unit provides tourist information based on the current location acquired by the specific processing unit 290 of the data processing device 12. The customization unit customizes the tourist information provided by the control unit 46A of the smart device 14 based on the user's preferences. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, learning unit, acquisition unit, provision unit, and customization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects tourist information using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The learning unit learns the tourist information using AI generated by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires the user's current location using GPS or Wi-Fi location information of the smart glasses 214. The provision unit provides tourist information based on the current location acquired by the specific processing unit 290 of the data processing device 12. The customization unit customizes the tourist information provided by the control unit 46A of the smart glasses 214 based on the user's preferences. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, acquisition unit, provision unit, and customization unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects tourist information using the camera 42 and microphone 238 of the headset type terminal 314 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The learning unit learns the tourist information using AI generated by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires the user's current location using GPS or Wi-Fi location information of the headset type terminal 314. The provision unit provides tourist information based on the current location acquired by the specific processing unit 290 of the data processing device 12. The customization unit customizes the tourist information provided by the control unit 46A of the headset type terminal 314 based on the user's preferences. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, acquisition unit, provision unit, and customization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects tourist information using the camera 42 and microphone 238 of the robot 414 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The learning unit learns the tourist information using AI generated by the specific processing unit 290 of the data processing device 12. The acquisition unit acquires the user's current location using GPS or Wi-Fi location information of the robot 414. The provision unit provides tourist information based on the current location acquired by the specific processing unit 290 of the data processing device 12. The customization unit customizes the tourist information provided by the control unit 46A of the robot 414 based on the user's preferences.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The real-time navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, number of steps, and calorie consumption, and monitors the user's health condition in real time. For example, the health management unit may provide a notification urging the user to take a break if the user has been walking for a long time. Also, if the user's heart rate is high, the health management unit may suggest tourist spots where the user can relax. Furthermore, the health management unit may provide appropriate dietary and exercise advice based on the user's health data. This allows the user to enjoy sightseeing while maintaining their health even during their trip.

[0121] The real-time navigation system may further include a traffic management unit that takes into account the user's mode of transportation. The traffic management unit provides the optimal route based on the mode of transportation used by the user (e.g., walking, cycling, car, public transportation, etc.). For example, if the user is using a bicycle, the traffic management unit may provide information on bicycle lanes and bicycle parking areas. Also, if the user is using public transportation, the traffic management unit may provide bus and train timetables and transfer information. Furthermore, the traffic management unit may provide traffic congestion and construction information in real time to help the user travel smoothly. This allows the user to efficiently tour tourist spots.

[0122] The real-time navigation system may further include a language management unit that supports multiple languages ​​based on the user's language setting. The language management unit provides tourist information based on the user's language setting. For example, if the user selects English, the language management unit may provide tourist information in English. Also, if the user selects Japanese, the language management unit may provide tourist information in Japanese. Furthermore, the language management unit may provide simple phrases and words for learning the local language of the tourist destination. This allows the user to enjoy sightseeing smoothly without experiencing language barriers.

[0123] The real-time navigation system can further include a gamification unit that introduces gamification elements to attract users' interest. The gamification unit provides a mechanism that allows users to earn points when touring tourist spots. For example, users are awarded points when they visit specific tourist spots, and by collecting points, they can receive special benefits or discounts. The gamification unit can also provide a ranking function that allows users to compete with each other. Furthermore, users can earn badges and titles by completing specific missions at tourist spots. This allows users to enjoy touring tourist spots.

[0124] The real-time navigation system can further estimate the user's emotions and adjust the way in which tourist information is provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide tourist information with visually stimulating effects. If the user is relaxed, the providing unit can provide tourist information in a calm tone. Furthermore, if the user is tired, the providing unit can provide simple, highly visible tourist information. In this way, by adjusting the way in which tourist information is provided based on the user's emotions, more appropriate information can be provided.

[0125] The real-time navigation system can further estimate the user's emotions and determine the priority of tourist information based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information about popular tourist spots with priority. If the user is relaxed, the providing unit can provide information about quiet places and spots where the user can relax with priority. If the user is tired, the providing unit can provide information about places where the user can rest and refresh with priority. In this way, by determining the priority of tourist information according to the user's emotions, more appropriate information can be provided.

[0126] The real-time navigation system can further estimate the user's emotions and adjust the timing of collecting tourist information based on the estimated user emotions. For example, if the user is excited, the collection unit can collect new tourist information in real time and provide it immediately. If the user is relaxed, the collection unit can collect tourist information periodically and provide it at an appropriate time. Furthermore, if the user is tired, the collection frequency can be reduced and only the minimum necessary information can be provided. In this way, by adjusting the timing of collecting tourist information according to the user's emotions, information can be provided at a more appropriate time.

[0127] The real-time navigation system can further estimate the user's emotions and adjust the way in which tourist information is presented based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide tourist information with visually stimulating effects. If the user is relaxed, the providing unit can provide tourist information in a calm tone. If the user is tired, the providing unit can provide tourist information that is simple and highly visible. In this way, more appropriate information can be provided by adjusting the way in which tourist information is presented based on the user's emotions.

[0128] The real-time navigation system can further estimate the user's emotions and adjust the length of the tourist information based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point tourist information. If the user is relaxed, the providing unit can provide longer tourist information with detailed explanations. Furthermore, if the user is excited, the providing unit can provide tourist information with visually stimulating effects. In this way, by adjusting the length of the tourist information based on the user's emotions, more appropriate information can be provided.

[0129] The real-time navigation system can further estimate the user's emotions and adjust the customization method for tourist information based on the estimated user's emotions. For example, if the user is excited, the customization unit can customize the tourist information by adding visually stimulating effects. If the user is relaxed, the customization unit can customize the tourist information in a calm tone. Furthermore, if the user is tired, the customization unit can customize the tourist information in a simple, highly visible manner. In this way, by adjusting the customization method based on the user's emotions, more appropriate information can be provided.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The collection unit collects tourist information. This information includes information on tourist attractions, restaurants, shopping centers, etc. The collection unit collects tourist information from publicly available data on the Internet, tourist guidebooks, tourist sites, blogs, social media, etc. It also collects detailed information about tourist attractions, such as their history and characteristics, opening hours, and admission fees. Step 2: The learning unit uses the generation AI to learn the tourist information collected by the collection unit. The learning unit analyzes the tourist information using text generation AI (e.g., LLM) or multimodal generation AI to generate a tourist guide. The generation AI is also used to extract and learn important parts of the tourist information. Step 3: The acquisition unit acquires the user's current location. The acquisition unit acquires the current location using GPS, Wi-Fi location information, and mobile base station information. For example, the acquisition unit acquires the current location with high accuracy using GPS, and estimates the current location using Wi-Fi location information and mobile base station information. Step 4: The providing unit provides relevant tourist information based on the current location acquired by the acquiring unit. The providing unit provides detailed information about tourist spots the user is approaching, information about recommended restaurants and shopping centers, and information related to areas the user plans to visit. Step 5: The customization unit customizes the tourist information provided by the provision unit based on the user's preferences. The customization unit customizes the tourist information based on the user's preferences, past behavior history, and feedback. For example, it may provide preferentially information about restaurants in a specific genre, or information about tourist spots and activities that the user may be interested in.

[0132] 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.

[0133] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[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 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.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

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

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 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 collection department that collects tourist information; a learning unit that learns the tourist information collected by the collection unit; an acquisition unit that acquires a user's current location; a providing unit that provides related tourist information based on the current location acquired by the acquiring unit; a customization unit that customizes the tourist information provided by the provision unit based on the user's preferences. A system characterized by:

2. The collecting unit Collect tourist information from publicly available data on the Internet or tourist guidebooks 2. The system of claim 1.

3. The learning unit Analyze collected tourist information and generate tourist guides 2. The system of claim 1.

4. The acquisition unit Get the user's location in real time 2. The system of claim 1.

5. The providing unit Providing detailed information about tourist spots the user is approaching, or information about recommended restaurants and shopping centers 2. The system of claim 1.

6. The customization unit Customize tourist information based on user preferences and past behavior 2. The system of claim 1.

7. The collecting unit Estimating user emotions and adjusting the timing of collecting tourist information based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit When collecting tourist information, analyze the user's past travel history and select the collection method.

2. The system of claim 1.

Images