System

Abstract

To provide a system for easily and accurately collecting and providing sightseeing information at an overseas travel destination.SOLUTION: A system includes an information collection unit, an analysis unit, and a provision unit. The information collection part collects sightseeing information of a travel destination. The analysis unit analyzes the information collected by the information collection unit. The providing unit provides the user with the information analyzed by the analysis unit.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 to easily and accurately collect tourist information about overseas travel destinations.

[0005] The system according to the embodiment aims to easily and accurately collect and provide tourist information on overseas travel destinations. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects tourist information about a travel destination. The analysis unit analyzes the information collected by the information collection unit. The provision unit provides the information analyzed by the analysis unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can easily and accurately collect and provide tourist information on overseas travel destinations. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The tourist information providing system according to an embodiment of the present invention is a system that automatically collects tourist information about a travel destination, analyzes it using a generation AI, and provides it to users. This allows travelers to easily obtain useful and accurate tourist information.

[0029] A tourist information providing system according to an embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects tourist information about a travel destination. For example, the information collection unit collects information about tourist attractions using web scraping technology. The information collection unit can also acquire event information about tourist attractions using an API. The information collection unit can also collect user posts. For example, the information collection unit collects information from official websites of tourist attractions. The API is used to acquire event information about tourist attractions in real time. User posts can be collected from social networking sites and review sites. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes the popularity of tourist attractions using data mining technology. The analysis unit can also analyze user reviews using natural language processing technology. The analysis unit can also predict trends in tourist attractions using machine learning algorithms. For example, the analysis unit analyzes the number of visitors to tourist attractions using data mining technology. The analysis unit extracts positive and negative opinions from user reviews using natural language processing technology. The analysis unit predicts trends in tourist attractions and identifies future popular spots using machine learning algorithms. The providing unit provides the user with the information analyzed by the analyzing unit. For example, the providing unit provides tourist information in text format. The providing unit can also provide information visually using images and videos. The providing unit can also provide location information of tourist destinations using an interactive map. For example, the providing unit displays detailed information about tourist spots in text format. The attractions of tourist destinations are visually conveyed using images and videos. Location information of tourist destinations is provided to the user using an interactive map. In this way, the tourist information providing system according to the embodiment allows travelers to easily obtain useful and accurate tourist information. For example, travelers can easily obtain detailed information about tourist spots. The provision of visual information allows travelers to intuitively understand the attractions of tourist destinations. The use of an interactive map makes it easier to understand the location of tourist destinations.

[0030] The analysis unit can analyze the congestion situation at tourist destinations in real time and suggest the optimal visit time to the user. For example, the analysis unit uses generation AI to analyze the real-time congestion situation at tourist destinations and suggest the optimal visit time to the user. For example, it predicts congestion based on past data and the current number of visitors. The analysis unit also monitors the congestion situation at tourist destinations in real time and suggests visit times to the user to avoid congestion. For example, it recommends less crowded times or days of the week. The analysis unit also suggests changes to the visit time to the user based on the results of the congestion analysis. For example, if congestion is expected, it suggests a different time or date. This allows the user to enjoy sightseeing while avoiding crowds.

[0031] The analysis unit can dig deep into the historical background and cultural significance of tourist destinations and provide users with detailed explanations. For example, the analysis unit uses generative AI to analyze the historical background and cultural significance of tourist destinations and provide users with detailed explanations. For example, it collects information about the history and culture of tourist destinations and provides explanations in text or audio. The analysis unit also digs deep into the historical background and cultural significance of tourist destinations and provides users with interactive explanations. For example, it displays detailed information based on topics that interest the user. The analysis unit also visualizes information about the historical background and cultural significance and provides users with explanations that are easy to understand visually. For example, it shows historical events and cultural symbols in diagrams and illustrations. This allows users to gain a deeper understanding of the history and culture of the tourist destination.

[0032] The provision unit can provide local weather forecasts and climate information in addition to tourist information to support travel planning. The provision unit, for example, uses generation AI to collect weather forecasts and climate information for tourist destinations and provide it to the user. For example, it displays weather forecasts and seasonal climate information tailored to the travel schedule. The provision unit also provides travel planning advice to the user based on the local weather forecasts and climate information. For example, it suggests indoor tourist spots on rainy days. The provision unit also updates the weather forecasts and climate information in real time to provide the user with the latest information. For example, it suggests changes to travel plans in response to sudden changes in the weather. This allows the user to make travel plans that suit the weather and climate.

[0033] The providing unit can provide information about tourist spots as a video guide customized according to the user's interests. The providing unit generates a customized video guide according to the user's interests, for example, using a generation AI. For example, it creates video content based on tourist spots and themes that interest the user. The providing unit also analyzes the user's preferences and interests and provides a customized video guide based on the analysis. For example, a user who likes history can be provided with a video guide of historical tourist spots. The providing unit also makes the video guide interactive, adding a function that provides detailed explanations of parts that interest the user. For example, clicking on a specific point in the video displays detailed information. This allows the user to visually understand information about tourist spots that interest them as a video guide.

[0034] The information collection unit can analyze past travelers' reviews of transportation methods and suggest the most comfortable transportation method. For example, the information collection unit uses generation AI to analyze reviews of transportation methods posted by past travelers and suggest the most comfortable transportation method. For example, it extracts information about comfort and convenience from the review text. The information collection unit also analyzes reviews of transportation methods and displays comfortable transportation methods to the user in a ranking format. For example, transportation methods with high comfort ratings are displayed at the top. The information collection unit also evaluates the comfort and convenience of transportation methods based on past travelers' reviews and suggests the most suitable transportation method for the user. For example, it evaluates transportation methods based on the emotional score of the reviews. This allows the user to select a comfortable transportation method.

[0035] The analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, using generation AI, the analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, it can display a list of the travel time and fare for each means of transportation. The analysis unit can also suggest the optimal means of transportation to the user based on the travel time and cost of each means of transportation. For example, it can suggest the shortest means of transportation to a user who values ​​time. The analysis unit can also display the travel option to the user in a ranking format based on the comparison of travel time and cost. For example, it can display the most cost-effective means of transportation at the top. This allows the user to select the optimal means of transportation taking travel time and cost into consideration.

[0036] The providing unit can provide a troubleshooting guide for transportation and suggest countermeasures in emergencies. The providing unit provides a troubleshooting guide for transportation, for example, using a generation AI. For example, it suggests countermeasures in the event of delays or cancellations of public transportation. The providing unit also provides the troubleshooting guide for transportation to the user and suggests countermeasures in emergencies. For example, it suggests alternative means of transportation when a taxi cannot be caught. The providing unit also provides emergency countermeasures to the user in real time based on the troubleshooting guide. For example, it immediately displays countermeasures when a problem occurs with the transportation. This allows the user to quickly respond to problems with the transportation.

[0037] The providing unit can provide information on local traffic rules and manners in addition to information on means of transportation. The providing unit, for example, uses a generation AI to provide information on local traffic rules and manners in addition to information on means of transportation. For example, it explains local traffic rules and driving manners. The providing unit also provides information on local traffic rules and manners to the user and gives useful advice when using means of transportation. For example, it explains manners for using public transportation. The providing unit also suggests options for means of transportation to the user based on the information on traffic rules and manners. For example, it recommends means of transportation that are suitable for local traffic rules. This allows the user to understand local traffic rules and manners and use means of transportation appropriately.

[0038] The provision unit can enable users to complete transportation reservations and ticket purchases within the app. The provision unit, for example, uses generation AI to build a system that enables users to complete transportation reservations and ticket purchases within the app. For example, reservations for public transportation tickets and taxis are made within the app. The provision unit also enables users to complete transportation reservations and ticket purchases within the app, providing convenience to users. For example, rental car reservations and payments are made within the app. The provision unit also develops a system that enables users to complete transportation reservations and ticket purchases within the app, providing users with smooth transportation arrangements. For example, airport transfer reservations are made within the app. This allows users to smoothly make transportation reservations and ticket purchases within the app.

[0039] The information gathering unit can analyze social media posts and blogs posted by local residents and provide the latest local information. For example, the information gathering unit uses generative AI to analyze social media posts and blogs posted by local residents and provide the latest local information. For example, it collects information on local events and new restaurants. The information gathering unit also analyzes social media posts and blogs posted by local residents and provides users with reliable local information. For example, it introduces tourist spots and activities recommended by locals. The information gathering unit also builds a system that provides the latest information unique to local residents in real time based on social media and blog posts. For example, it displays the latest news and trends shared by locals. This allows users to obtain the latest local information.

[0040] The providing unit can customize and provide recommended spots by local residents based on the user's interests and preferences. For example, the providing unit uses generation AI to customize and provide recommended spots by local residents based on the user's interests and preferences. For example, it selects spots based on themes that interest the user. The providing unit also analyzes the user's preferences and interests and suggests recommended spots by local residents based on that analysis. For example, it introduces popular local restaurants to a user who loves food. The providing unit also builds a system that provides customized recommended spots in real time and dynamically adjusts them according to the user's interests. For example, it suggests spots based on the user's search history and reviews. This makes it possible to customize and provide spots that interest the user.

[0041] The provision unit can provide a detailed guide on local culture and customs to help travelers understand local etiquette. The provision unit, for example, uses a generative AI to provide a detailed guide on local culture and customs. For example, it explains local greetings, dining etiquette, religious customs, etc. The provision unit also provides information on local culture and customs to users to help travelers understand local etiquette. For example, it provides advice on behavior and clothing in public places. The provision unit also visualizes the guide on culture and customs to provide users with information that is visually easy to understand. For example, it uses illustrations and videos to explain cultural customs. This allows users to understand local culture and customs and behave appropriately.

[0042] The providing unit can provide information on local events and festivals in addition to information on local residents. The providing unit, for example, uses a generation AI to provide information on local events and festivals in addition to information on local residents. For example, it collects information on local festivals, concerts, and sporting events. The providing unit also provides information on local events and festivals to users to help with travel planning. For example, it displays event information that matches travel dates and times. The providing unit also builds a system that updates event and festival information in real time and provides users with the latest information. For example, it displays the date, time, and location of an event, as well as how to participate. This allows users to obtain information on local events and festivals.

[0043] The provision unit can plan social events with local residents within the app, providing travelers with opportunities to interact directly with local people. For example, the provision unit uses generation AI to plan social events with local residents within the app, providing travelers with opportunities to interact directly with local people. For example, it plans dinner parties and tours with locals. The provision unit also announces social events with local residents within the app so travelers can participate. For example, it introduces local cultural experiences and workshops. The provision unit also builds a system that improves the content of the events based on feedback from participants in the social events and provides better opportunities for interaction. For example, it collects participants' impressions and evaluations and reflects them in the next event. This allows users to have opportunities to interact directly with local people.

[0044] The analysis unit can analyze a user's past travel history and reviews and provide an individually optimized sightseeing plan. The analysis unit, for example, uses generation AI to analyze a user's past travel history and reviews and provide an individually optimized sightseeing plan. For example, it creates a plan based on tourist spots visited in the past and the content of reviews. The analysis unit also suggests sightseeing plans based on the user's preferences and interests based on the user's travel history and reviews. For example, a user who loves history can be offered a plan centered around historical tourist spots. The analysis unit also analyzes past travel data and builds a system that suggests optimal sightseeing routes and schedules to users. For example, it suggests efficient sightseeing routes based on past travel patterns. This allows users to obtain the optimal sightseeing plan based on their past travel history and reviews.

[0045] The analysis unit can dynamically suggest optimal sightseeing routes based on the user's real-time location information. The analysis unit, for example, uses generation AI to build a system that dynamically suggests optimal sightseeing routes based on the user's real-time location information. For example, it suggests the tourist spots closest to the current location. The analysis unit also analyzes the user's location information in real time and dynamically adjusts the sightseeing route based on the results. For example, it changes the route taking into account traffic conditions and congestion during travel. The analysis unit also suggests efficient sightseeing routes to the user based on the real-time location information. For example, it displays the optimal order for visiting multiple tourist spots from the current location. This allows the user to obtain the optimal sightseeing route based on their real-time location information.

[0046] The analysis unit can collect data on the user's behavior during the trip and reflect it in the next travel plan. The analysis unit, for example, uses generative AI to build a system that collects data on the user's behavior during the trip and reflects it in the next travel plan. For example, it collects data on tourist spots visited and means of transportation used. The analysis unit also analyzes the user's behavior data and optimizes the next travel plan based on the results. For example, it suggests tourist spots that the user might be interested in based on past behavior patterns. The analysis unit also collects data on the user's behavior during the trip in real time and develops a system that reflects it in the next travel plan. For example, it adjusts the plan based on the user's movement history and length of stay during the trip. This allows the user to optimize their next travel plan based on their past behavior data.

[0047] The provision unit can provide local shopping information and special offer information in addition to individually optimized sightseeing plans. The provision unit, for example, uses generation AI to build a system that provides local shopping information and special offer information in addition to individually optimized sightseeing plans. For example, it displays shopping spots and discount information that match the user's interests. The provision unit also analyzes the user's preferences and interests and provides shopping information and special offer information based on that. For example, it introduces popular local shops and sale information to a fashion-loving user. The provision unit also develops a system that updates shopping information and special offer information in real time to provide users with the latest information. For example, it instantly displays new promotions and discount information. This allows users to obtain local shopping information and special offer information.

[0048] The provision unit can enable users to complete reservations for local activities and tours within the app based on their preferences. The provision unit, for example, uses generative AI to build a system that enables users to complete reservations for local activities and tours within the app based on their preferences. For example, it suggests activities that match the user's interests and makes reservations. The provision unit also analyzes the user's preferences and interests and enables users to complete reservations for activities and tours within the app based on those analyses. For example, it suggests outdoor activities for users who like adventure. The provision unit also develops a system that enables users to complete reservations for activities and tours within the app, providing users with a smooth reservation process. For example, reservation confirmation and payment can be made within the app. This allows users to smoothly make reservations for local activities and tours within the app.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The providing unit can support travel planning by providing local weather forecasts and climate information in addition to tourist information. For example, it displays weather forecasts and seasonal climate information tailored to travel dates. The providing unit also provides travel planning advice to users based on local weather forecasts and climate information. For example, it suggests indoor tourist spots on rainy days. The providing unit also updates the weather forecasts and climate information in real time to provide users with the latest information. For example, it suggests changes to travel plans in response to sudden changes in the weather. This allows users to make travel plans that suit the weather and climate.

[0051] The providing unit can provide information about tourist spots as a video guide customized according to the user's interests. For example, it creates video content based on tourist spots and themes that interest the user. The providing unit also analyzes the user's preferences and interests and provides a customized video guide based on the analysis. For example, a video guide of historical tourist spots is provided to a user who loves history. The providing unit also makes the video guide interactive, adding a function that provides detailed explanations of parts that interest the user. For example, clicking on a specific point in the video displays detailed information. This allows the user to visually understand information about tourist spots that interest them as a video guide.

[0052] The information collection unit can analyze past travelers' reviews of transportation methods and suggest the most comfortable transportation method. For example, using a generation AI, it can analyze reviews of transportation methods posted by past travelers and suggest the most comfortable transportation method. For example, it can extract information about comfort and convenience from the review text. The information collection unit also analyzes reviews of transportation methods and displays a ranking of comfortable transportation methods to the user. For example, transportation methods with high comfort ratings are displayed at the top. The information collection unit also evaluates the comfort and convenience of transportation methods based on past travelers' reviews and suggests the most suitable transportation method for the user. For example, it evaluates transportation methods based on the emotional score of the reviews. This allows the user to select a comfortable transportation method.

[0053] The analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, using generation AI, the analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, it can display a list of the travel time and fare for each means of transportation. The analysis unit can also suggest the optimal means of transportation to the user based on the travel time and cost of each means of transportation. For example, it can suggest the shortest means of transportation to a user who values ​​time. The analysis unit can also display the travel option rankings to the user based on the comparison of travel time and cost. For example, it can display the most cost-effective means of transportation at the top. This allows the user to select the optimal means of transportation taking travel time and cost into consideration.

[0054] The providing unit can provide a troubleshooting guide for transportation and suggest emergency measures. For example, the troubleshooting guide for transportation is provided using a generation AI. For example, it suggests measures to take when public transportation is delayed or canceled. The providing unit also provides the troubleshooting guide for transportation to the user and suggests emergency measures. For example, it suggests alternative means of transportation when a taxi cannot be caught. The providing unit also provides emergency measures to the user in real time based on the troubleshooting guide. For example, it immediately displays measures to take when a problem occurs with transportation. This allows the user to quickly respond to problems with transportation.

[0055] The information gathering unit can analyze social media posts and blogs posted by local residents and provide the latest local information. For example, generative AI can be used to analyze social media posts and blogs posted by local residents and provide the latest local information. For example, it can collect information on local events and new restaurants. The information gathering unit can also analyze social media posts and blogs posted by local residents and provide users with reliable local information. For example, it can introduce tourist spots and activities recommended by locals. The information gathering unit can also build a system that provides the latest information unique to local residents in real time based on social media and blog posts. For example, it can display the latest news and trends shared by locals. This allows users to obtain the latest local information.

[0056] The provision unit can customize and provide recommended spots by local residents based on the user's interests and preferences. For example, using generation AI, the unit can customize and provide recommended spots by local residents based on the user's interests and preferences. For example, it can select spots based on themes that interest the user. The provision unit can also analyze the user's preferences and interests and suggest recommended spots by local residents based on that analysis. For example, it can introduce popular local restaurants to a user who loves food. The provision unit can also provide customized recommended spots in real time and build a system that dynamically adjusts them according to the user's interests. For example, it can suggest spots based on the user's search history and reviews. This allows the unit to customize and provide spots that interest the user.

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

[0058] Step 1: The information collection unit collects tourist information about the travel destination. For example, the information collection unit may use web scraping technology to collect information about tourist spots. It may also use an API to obtain event information about tourist spots. Furthermore, the information collection unit may collect user posts from social media and review sites. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses data mining technology to analyze the popularity of tourist spots. It also uses natural language processing technology to analyze user reviews and extract positive and negative opinions. It also uses machine learning algorithms to predict trends in tourist destinations and identify future popular spots. Step 3: The providing unit provides the user with the information analyzed by the analyzing unit. For example, the providing unit may provide tourist information in text format. It may also provide information visually using images or videos. It may also provide location information of tourist spots using an interactive map.

[0059] (Example 2) The tourist information providing system according to an embodiment of the present invention is a system that automatically collects tourist information about a travel destination, analyzes it using a generation AI, and provides it to users. This allows travelers to easily obtain useful and accurate tourist information.

[0060] A tourist information providing system according to an embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects tourist information about a travel destination. For example, the information collection unit collects information about tourist attractions using web scraping technology. The information collection unit can also acquire event information about tourist attractions using an API. The information collection unit can also collect user posts. For example, the information collection unit collects information from official websites of tourist attractions. The API is used to acquire event information about tourist attractions in real time. User posts can be collected from social networking sites and review sites. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes the popularity of tourist attractions using data mining technology. The analysis unit can also analyze user reviews using natural language processing technology. The analysis unit can also predict trends in tourist attractions using machine learning algorithms. For example, the analysis unit analyzes the number of visitors to tourist attractions using data mining technology. The analysis unit extracts positive and negative opinions from user reviews using natural language processing technology. The analysis unit predicts trends in tourist attractions and identifies future popular spots using machine learning algorithms. The providing unit provides the user with the information analyzed by the analyzing unit. For example, the providing unit provides tourist information in text format. The providing unit can also provide information visually using images and videos. The providing unit can also provide location information of tourist destinations using an interactive map. For example, the providing unit displays detailed information about tourist spots in text format. The attractions of tourist destinations are visually conveyed using images and videos. Location information of tourist destinations is provided to the user using an interactive map. In this way, the tourist information providing system according to the embodiment allows travelers to easily obtain useful and accurate tourist information. For example, travelers can easily obtain detailed information about tourist spots. The provision of visual information allows travelers to intuitively understand the attractions of tourist destinations. The use of an interactive map makes it easier to understand the location of tourist destinations.

[0061] The information collection unit can analyze reviews and photos of past visitors to a tourist destination, infer the visitors' emotions, and reflect this in the information. For example, the information collection unit uses a generative AI to analyze reviews and photos posted by past visitors to the tourist destination and infer the visitors' emotions. For example, it extracts positive and negative emotions from the review text and evaluates the tourist destination based on the emotion score. The information collection unit also analyzes photos posted by past visitors and evaluates the facial expressions and beauty of the scenery in the photos. For example, it gives a high rating to tourist destinations that have many photos with many smiling faces or beautiful scenery. The information collection unit also infers emotions from the visitors' reviews and photos and displays the attractiveness of tourist destinations in a ranking format based on the results. For example, tourist destinations with many positive emotions are displayed at the top. This allows the system to provide information that reflects the visitors' emotions, thereby providing more useful tourist information to users.

[0062] The analysis unit can analyze the congestion situation at tourist destinations in real time and suggest the optimal visit time to the user. For example, the analysis unit uses generation AI to analyze the real-time congestion situation at tourist destinations and suggest the optimal visit time to the user. For example, it predicts congestion based on past data and the current number of visitors. The analysis unit also monitors the congestion situation at tourist destinations in real time and suggests visit times to the user to avoid congestion. For example, it recommends less crowded times or days of the week. The analysis unit also suggests changes to the visit time to the user based on the results of the congestion analysis. For example, if congestion is expected, it suggests a different time or date. This allows the user to enjoy sightseeing while avoiding crowds.

[0063] The analysis unit can dig deep into the historical background and cultural significance of tourist destinations and provide users with detailed explanations. For example, the analysis unit uses generative AI to analyze the historical background and cultural significance of tourist destinations and provide users with detailed explanations. For example, it collects information about the history and culture of tourist destinations and provides explanations in text or audio. The analysis unit also digs deep into the historical background and cultural significance of tourist destinations and provides users with interactive explanations. For example, it displays detailed information based on topics that interest the user. The analysis unit also visualizes information about the historical background and cultural significance and provides users with explanations that are easy to understand visually. For example, it shows historical events and cultural symbols in diagrams and illustrations. This allows users to gain a deeper understanding of the history and culture of the tourist destination.

[0064] The provision unit can provide local weather forecasts and climate information in addition to tourist information to support travel planning. The provision unit, for example, uses generation AI to collect weather forecasts and climate information for tourist destinations and provide it to the user. For example, it displays weather forecasts and seasonal climate information tailored to the travel schedule. The provision unit also provides travel planning advice to the user based on the local weather forecasts and climate information. For example, it suggests indoor tourist spots on rainy days. The provision unit also updates the weather forecasts and climate information in real time to provide the user with the latest information. For example, it suggests changes to travel plans in response to sudden changes in the weather. This allows the user to make travel plans that suit the weather and climate.

[0065] The providing unit can provide information about tourist spots as a video guide customized according to the user's interests. The providing unit generates a customized video guide according to the user's interests, for example, using a generation AI. For example, it creates video content based on tourist spots and themes that interest the user. The providing unit also analyzes the user's preferences and interests and provides a customized video guide based on the analysis. For example, a user who likes history can be provided with a video guide of historical tourist spots. The providing unit also makes the video guide interactive, adding a function that provides detailed explanations of parts that interest the user. For example, clicking on a specific point in the video displays detailed information. This allows the user to visually understand information about tourist spots that interest them as a video guide.

[0066] The providing unit can use the emotion estimation function to prioritize displaying tourist destinations that are most likely to interest the user. For example, the providing unit uses the emotion estimation function to build a system that prioritizes displaying tourist destinations that are likely to be of most interest to the user. For example, the providing unit analyzes the user's past search history and reviews to display tourist destinations that are of high interest to the user. The providing unit also analyzes the user's emotional responses in real time and suggests tourist destinations that are likely to be of interest based on the results. For example, tourist destinations for which the user expressed positive emotions are prioritized to be displayed. The providing unit also develops a system that dynamically displays tourist destinations that are likely to be of most interest to the user based on the emotion estimation data. For example, the display content is adjusted according to changes in the user's emotions. This prioritizes displaying tourist destinations that are likely to be of interest to the user, thereby improving travel satisfaction.

[0067] The information collection unit can analyze past travelers' reviews of transportation methods and suggest the most comfortable transportation method. For example, the information collection unit uses generation AI to analyze reviews of transportation methods posted by past travelers and suggest the most comfortable transportation method. For example, it extracts information about comfort and convenience from the review text. The information collection unit also analyzes reviews of transportation methods and displays comfortable transportation methods to the user in a ranking format. For example, transportation methods with high comfort ratings are displayed at the top. The information collection unit also evaluates the comfort and convenience of transportation methods based on past travelers' reviews and suggests the most suitable transportation method for the user. For example, it evaluates transportation methods based on the emotional score of the reviews. This allows the user to select a comfortable transportation method.

[0068] The analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, using generation AI, the analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, it can display a list of the travel time and fare for each means of transportation. The analysis unit can also suggest the optimal means of transportation to the user based on the travel time and cost of each means of transportation. For example, it can suggest the shortest means of transportation to a user who values ​​time. The analysis unit can also display the travel option to the user in a ranking format based on the comparison of travel time and cost. For example, it can display the most cost-effective means of transportation at the top. This allows the user to select the optimal means of transportation taking travel time and cost into consideration.

[0069] The providing unit can provide a troubleshooting guide for transportation and suggest countermeasures in emergencies. The providing unit provides a troubleshooting guide for transportation, for example, using a generation AI. For example, it suggests countermeasures in the event of delays or cancellations of public transportation. The providing unit also provides the troubleshooting guide for transportation to the user and suggests countermeasures in emergencies. For example, it suggests alternative means of transportation when a taxi cannot be caught. The providing unit also provides emergency countermeasures to the user in real time based on the troubleshooting guide. For example, it immediately displays countermeasures when a problem occurs with the transportation. This allows the user to quickly respond to problems with the transportation.

[0070] The providing unit can provide information on local traffic rules and manners in addition to information on means of transportation. The providing unit, for example, uses a generation AI to provide information on local traffic rules and manners in addition to information on means of transportation. For example, it explains local traffic rules and driving manners. The providing unit also provides information on local traffic rules and manners to the user and gives useful advice when using means of transportation. For example, it explains manners for using public transportation. The providing unit also suggests options for means of transportation to the user based on the information on traffic rules and manners. For example, it recommends means of transportation that are suitable for local traffic rules. This allows the user to understand local traffic rules and manners and use means of transportation appropriately.

[0071] The provision unit can enable users to complete transportation reservations and ticket purchases within the app. The provision unit, for example, uses generation AI to build a system that enables users to complete transportation reservations and ticket purchases within the app. For example, reservations for public transportation tickets and taxis are made within the app. The provision unit also enables users to complete transportation reservations and ticket purchases within the app, providing convenience to users. For example, rental car reservations and payments are made within the app. The provision unit also develops a system that enables users to complete transportation reservations and ticket purchases within the app, providing users with smooth transportation arrangements. For example, airport transfer reservations are made within the app. This allows users to smoothly make transportation reservations and ticket purchases within the app.

[0072] The providing unit can use the emotion estimation function to suggest the transportation means that the user can use with the greatest confidence. The providing unit, for example, uses the emotion estimation function to build a system that suggests the transportation means that the user can use with the greatest confidence. For example, it suggests transportation means with a high degree of confidence based on the user's emotion score. The providing unit also analyzes the user's emotional response in real time and suggests transportation means that the user can use with confidence based on the results. For example, it preferentially displays transportation means for which the user has expressed positive emotions. The providing unit also develops a system that dynamically displays the transportation means that the user can use with the greatest confidence based on the emotion estimation data. For example, it adjusts the display content according to changes in the user's emotions. This allows the user to select transportation means that the user can use with confidence.

[0073] The information gathering unit can analyze social media posts and blogs posted by local residents and provide the latest local information. For example, the information gathering unit uses generative AI to analyze social media posts and blogs posted by local residents and provide the latest local information. For example, it collects information on local events and new restaurants. The information gathering unit also analyzes social media posts and blogs posted by local residents and provides users with reliable local information. For example, it introduces tourist spots and activities recommended by locals. The information gathering unit also builds a system that provides the latest information unique to local residents in real time based on social media and blog posts. For example, it displays the latest news and trends shared by locals. This allows users to obtain the latest local information.

[0074] The providing unit can customize and provide recommended spots by local residents based on the user's interests and preferences. For example, the providing unit uses generation AI to customize and provide recommended spots by local residents based on the user's interests and preferences. For example, it selects spots based on themes that interest the user. The providing unit also analyzes the user's preferences and interests and suggests recommended spots by local residents based on that analysis. For example, it introduces popular local restaurants to a user who loves food. The providing unit also builds a system that provides customized recommended spots in real time and dynamically adjusts them according to the user's interests. For example, it suggests spots based on the user's search history and reviews. This makes it possible to customize and provide spots that interest the user.

[0075] The provision unit can provide a detailed guide on local culture and customs to help travelers understand local etiquette. The provision unit, for example, uses a generative AI to provide a detailed guide on local culture and customs. For example, it explains local greetings, dining etiquette, religious customs, etc. The provision unit also provides information on local culture and customs to users to help travelers understand local etiquette. For example, it provides advice on behavior and clothing in public places. The provision unit also visualizes the guide on culture and customs to provide users with information that is visually easy to understand. For example, it uses illustrations and videos to explain cultural customs. This allows users to understand local culture and customs and behave appropriately.

[0076] The providing unit can provide information on local events and festivals in addition to information on local residents. The providing unit, for example, uses a generation AI to provide information on local events and festivals in addition to information on local residents. For example, it collects information on local festivals, concerts, and sporting events. The providing unit also provides information on local events and festivals to users to help with travel planning. For example, it displays event information that matches travel dates and times. The providing unit also builds a system that updates event and festival information in real time and provides users with the latest information. For example, it displays the date, time, and location of an event, as well as how to participate. This allows users to obtain information on local events and festivals.

[0077] The provision unit can plan social events with local residents within the app, providing travelers with opportunities to interact directly with local people. For example, the provision unit uses generation AI to plan social events with local residents within the app, providing travelers with opportunities to interact directly with local people. For example, it plans dinner parties and tours with locals. The provision unit also announces social events with local residents within the app so travelers can participate. For example, it introduces local cultural experiences and workshops. The provision unit also builds a system that improves the content of the events based on feedback from participants in the social events and provides better opportunities for interaction. For example, it collects participants' impressions and evaluations and reflects them in the next event. This allows users to have opportunities to interact directly with local people.

[0078] The providing unit can use the emotion estimation function to prioritize displaying local information that is most interesting to the user. For example, the providing unit uses the emotion estimation function to build a system that prioritizes displaying local information that is likely to be of most interest to the user. For example, it displays information that is of high interest to the user based on the user's emotion score. The providing unit also analyzes the user's emotional response in real time and suggests local information that is likely to be of interest based on the results. For example, it prioritizes displaying information for which the user expressed positive emotions. The providing unit also develops a system that dynamically displays local information that is likely to be of most interest to the user based on the emotion estimation data. For example, it adjusts the display content according to changes in the user's emotions. This prioritizes displaying local information that is likely to be of interest to the user, thereby improving travel satisfaction.

[0079] The analysis unit can analyze a user's past travel history and reviews and provide an individually optimized sightseeing plan. The analysis unit, for example, uses generation AI to analyze a user's past travel history and reviews and provide an individually optimized sightseeing plan. For example, it creates a plan based on tourist spots visited in the past and the content of reviews. The analysis unit also suggests sightseeing plans based on the user's preferences and interests based on the user's travel history and reviews. For example, a user who loves history can be offered a plan centered around historical tourist spots. The analysis unit also analyzes past travel data and builds a system that suggests optimal sightseeing routes and schedules to users. For example, it suggests efficient sightseeing routes based on past travel patterns. This allows users to obtain the optimal sightseeing plan based on their past travel history and reviews.

[0080] The analysis unit can dynamically suggest optimal sightseeing routes based on the user's real-time location information. The analysis unit, for example, uses generation AI to build a system that dynamically suggests optimal sightseeing routes based on the user's real-time location information. For example, it suggests the tourist spots closest to the current location. The analysis unit also analyzes the user's location information in real time and dynamically adjusts the sightseeing route based on the results. For example, it changes the route taking into account traffic conditions and congestion during travel. The analysis unit also suggests efficient sightseeing routes to the user based on the real-time location information. For example, it displays the optimal order for visiting multiple tourist spots from the current location. This allows the user to obtain the optimal sightseeing route based on their real-time location information.

[0081] The analysis unit can collect data on the user's behavior during the trip and reflect it in the next travel plan. The analysis unit, for example, uses generative AI to build a system that collects data on the user's behavior during the trip and reflects it in the next travel plan. For example, it collects data on tourist spots visited and means of transportation used. The analysis unit also analyzes the user's behavior data and optimizes the next travel plan based on the results. For example, it suggests tourist spots that the user might be interested in based on past behavior patterns. The analysis unit also collects data on the user's behavior during the trip in real time and develops a system that reflects it in the next travel plan. For example, it adjusts the plan based on the user's movement history and length of stay during the trip. This allows the user to optimize their next travel plan based on their past behavior data.

[0082] The provision unit can provide local shopping information and special offer information in addition to individually optimized sightseeing plans. The provision unit, for example, uses generation AI to build a system that provides local shopping information and special offer information in addition to individually optimized sightseeing plans. For example, it displays shopping spots and discount information that match the user's interests. The provision unit also analyzes the user's preferences and interests and provides shopping information and special offer information based on that. For example, it introduces popular local shops and sale information to a fashion-loving user. The provision unit also develops a system that updates shopping information and special offer information in real time to provide users with the latest information. For example, it instantly displays new promotions and discount information. This allows users to obtain local shopping information and special offer information.

[0083] The provision unit can enable users to complete reservations for local activities and tours within the app based on their preferences. The provision unit, for example, uses generative AI to build a system that enables users to complete reservations for local activities and tours within the app based on their preferences. For example, it suggests activities that match the user's interests and makes reservations. The provision unit also analyzes the user's preferences and interests and enables users to complete reservations for activities and tours within the app based on those analyses. For example, it suggests outdoor activities for users who like adventure. The provision unit also develops a system that enables users to complete reservations for activities and tours within the app, providing users with a smooth reservation process. For example, reservation confirmation and payment can be made within the app. This allows users to smoothly make reservations for local activities and tours within the app.

[0084] The providing unit can use the emotion estimation function to propose a sightseeing plan that will most satisfy the user. The providing unit, for example, uses the emotion estimation function to build a system that proposes a sightseeing plan that will most satisfy the user. For example, it proposes a plan that will provide high satisfaction based on the user's emotion score. The providing unit also analyzes the user's emotional response in real time and proposes a sightseeing plan that will provide high satisfaction based on the results. For example, it preferentially displays plans for which the user has expressed positive emotions. The providing unit also develops a system that dynamically displays a sightseeing plan that will most satisfy the user based on the emotion estimation data. For example, it adjusts the plan according to changes in the user's emotions. This allows the user to obtain a sightseeing plan that will most satisfy them.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The providing unit can support travel planning by providing local weather forecasts and climate information in addition to tourist information. For example, it displays weather forecasts and seasonal climate information tailored to travel dates. The providing unit also provides travel planning advice to users based on local weather forecasts and climate information. For example, it suggests indoor tourist spots on rainy days. The providing unit also updates the weather forecasts and climate information in real time to provide users with the latest information. For example, it suggests changes to travel plans in response to sudden changes in the weather. This allows users to make travel plans that suit the weather and climate.

[0087] The providing unit can provide information about tourist spots as a video guide customized according to the user's interests. For example, it creates video content based on tourist spots and themes that interest the user. The providing unit also analyzes the user's preferences and interests and provides a customized video guide based on the analysis. For example, a video guide of historical tourist spots is provided to a user who loves history. The providing unit also makes the video guide interactive, adding a function that provides detailed explanations of parts that interest the user. For example, clicking on a specific point in the video displays detailed information. This allows the user to visually understand information about tourist spots that interest them as a video guide.

[0088] The providing unit can use the emotion estimation function to prioritize displaying tourist spots that the user is most interested in. For example, it can analyze the user's past search history and reviews to display tourist spots that interest them. The providing unit can also analyze the user's emotional reactions in real time and suggest tourist spots that are likely to interest the user based on the results. For example, it can prioritize displaying tourist spots for which the user has expressed positive emotions. The providing unit can also develop a system that dynamically displays tourist spots that are likely to interest the user based on emotion estimation data. For example, it can adjust the display content according to changes in the user's emotions. This can improve travel satisfaction by prioritizing displaying tourist spots that are likely to interest the user.

[0089] The information collection unit can analyze past travelers' reviews of transportation methods and suggest the most comfortable transportation method. For example, using a generation AI, it can analyze reviews of transportation methods posted by past travelers and suggest the most comfortable transportation method. For example, it can extract information about comfort and convenience from the review text. The information collection unit also analyzes reviews of transportation methods and displays a ranking of comfortable transportation methods to the user. For example, transportation methods with high comfort ratings are displayed at the top. The information collection unit also evaluates the comfort and convenience of transportation methods based on past travelers' reviews and suggests the most suitable transportation method for the user. For example, it evaluates transportation methods based on the emotional score of the reviews. This allows the user to select a comfortable transportation method.

[0090] The analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, using generation AI, the analysis unit can compare the travel time and cost of each means of transportation in detail and provide the user with the optimal option. For example, it can display a list of the travel time and fare for each means of transportation. The analysis unit can also suggest the optimal means of transportation to the user based on the travel time and cost of each means of transportation. For example, it can suggest the shortest means of transportation to a user who values ​​time. The analysis unit can also display the travel option rankings to the user based on the comparison of travel time and cost. For example, it can display the most cost-effective means of transportation at the top. This allows the user to select the optimal means of transportation taking travel time and cost into consideration.

[0091] The providing unit can provide a troubleshooting guide for transportation and suggest emergency measures. For example, the troubleshooting guide for transportation is provided using a generation AI. For example, it suggests measures to take when public transportation is delayed or canceled. The providing unit also provides the troubleshooting guide for transportation to the user and suggests emergency measures. For example, it suggests alternative means of transportation when a taxi cannot be caught. The providing unit also provides emergency measures to the user in real time based on the troubleshooting guide. For example, it immediately displays measures to take when a problem occurs with transportation. This allows the user to quickly respond to problems with transportation.

[0092] The providing unit can use the emotion estimation function to suggest the means of transportation that the user can use with the greatest confidence. For example, a system is constructed using the emotion estimation function to suggest the means of transportation that the user can use with the greatest confidence. For example, a means of transportation with a high degree of confidence is suggested based on the user's emotion score. The providing unit also analyzes the user's emotional response in real time and suggests means of transportation that the user can use with confidence based on the results. For example, means of transportation for which the user has shown positive emotions are preferentially displayed. The providing unit also develops a system that dynamically displays the means of transportation that the user can use with the greatest confidence based on the emotion estimation data. For example, the display content is adjusted according to changes in the user's emotions. This allows the user to select a means of transportation that they can use with confidence.

[0093] The information gathering unit can analyze social media posts and blogs posted by local residents and provide the latest local information. For example, generative AI can be used to analyze social media posts and blogs posted by local residents and provide the latest local information. For example, it can collect information on local events and new restaurants. The information gathering unit can also analyze social media posts and blogs posted by local residents and provide users with reliable local information. For example, it can introduce tourist spots and activities recommended by locals. The information gathering unit can also build a system that provides the latest information unique to local residents in real time based on social media and blog posts. For example, it can display the latest news and trends shared by locals. This allows users to obtain the latest local information.

[0094] The provision unit can use the emotion estimation function to propose a sightseeing plan that will most satisfy the user. For example, a system is constructed that uses the emotion estimation function to propose a sightseeing plan that will most satisfy the user. For example, a plan with a high level of satisfaction is proposed based on the user's emotion score. The provision unit also analyzes the user's emotional response in real time and proposes a sightseeing plan with a high level of satisfaction based on the results. For example, plans for which the user expressed positive emotions are displayed preferentially. The provision unit also develops a system that dynamically displays a sightseeing plan that will most satisfy the user based on the emotion estimation data. For example, the plan is adjusted according to changes in the user's emotions. This makes it possible to obtain a sightseeing plan that will most satisfy the user.

[0095] The provision unit can customize and provide recommended spots by local residents based on the user's interests and preferences. For example, using generation AI, the unit can customize and provide recommended spots by local residents based on the user's interests and preferences. For example, it can select spots based on themes that interest the user. The provision unit can also analyze the user's preferences and interests and suggest recommended spots by local residents based on that analysis. For example, it can introduce popular local restaurants to a user who loves food. The provision unit can also provide customized recommended spots in real time and build a system that dynamically adjusts them according to the user's interests. For example, it can suggest spots based on the user's search history and reviews. This allows the unit to customize and provide spots that interest the user.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The information collection unit collects tourist information about the travel destination. For example, the information collection unit may use web scraping technology to collect information about tourist spots. It may also use an API to obtain event information about tourist spots. Furthermore, the information collection unit may collect user posts from social media and review sites. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses data mining technology to analyze the popularity of tourist spots. It also uses natural language processing technology to analyze user reviews and extract positive and negative opinions. It also uses machine learning algorithms to predict trends in tourist destinations and identify future popular spots. Step 3: The providing unit provides the user with the information analyzed by the analyzing unit. For example, the providing unit may provide tourist information in text format. It may also provide information visually using images or videos. It may also provide location information of tourist spots using an interactive map.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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. [Explanation of symbols]

[0165] 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. an information gathering department that collects tourist information about travel destinations; an analysis unit that analyzes the information collected by the information collection unit; a providing unit that provides the information analyzed by the analyzing unit to a user. A system characterized by:

2. The information collecting unit Analyzing reviews and photos from past visitors to tourist spots, inferring visitor sentiment and reflecting it in the information 2. The system of claim 1.

3. The analysis unit Analyzes the congestion situation at tourist spots in real time and suggests optimal visit times to users 2. The system of claim 1.

4. The providing unit In addition to tourist information, it also provides local weather forecasts and climate information to help with travel planning.

2. The system of claim 1.

5. The information collecting unit Analyzes travel reviews from past travelers and suggests the most comfortable travel options 2. The system of claim 1.

6. The providing unit Providing a mobility troubleshooting guide and emergency response plans 2. The system of claim 1.

7. The information collecting unit Analyzing social media posts and blogs from local residents to provide the latest local information 2. The system of claim 1.

8. The analysis unit Analyzes users' past travel history and reviews to provide individually optimized sightseeing plans 2. The system of claim 1.

Images