System

Abstract

An object of a system according to an embodiment is to enable a tourist to efficiently collect information on a specific sightseeing spot and perform navigation.SOLUTION: A system includes a collection unit, a provision unit, and a navigation unit. The collection part collects information on sightseeing spots. The providing unit provides the information collected by the collecting unit. The navigation unit performs navigation based on the information provided by the providing 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 for tourists to efficiently gather information about specific tourist spots and navigate there.

[0005] The system according to the embodiment aims to enable tourists to efficiently collect information about specific tourist spots and navigate there. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a provision unit, and a navigation unit. The collection unit collects information about tourist spots. The provision unit provides the information collected by the collection unit. The navigation unit performs navigation based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows tourists to efficiently collect information about specific tourist spots and navigate there. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A tourist guide system according to an embodiment of the present invention automatically collects and provides information about tourist spots and provides navigation. By collecting and providing information about tourist spots and providing navigation, the tourist guide system allows users to enjoy sightseeing without getting lost. For example, when a user launches an app, the tourist guide system displays information about nearby otaku spots based on the user's current location. The tourist guide system then provides detailed information about each spot and displays user reviews and ratings. Furthermore, the tourist guide system is equipped with a navigation function. When a user selects a spot they want to visit, the system displays directions from the user's current location to that spot. This tourist guide system supports multiple languages, providing information in English, Chinese, Korean, and other languages. The tourist guide system also provides event information, allowing users to keep up with the latest event information and plan their sightseeing. This allows the tourist guide system to ensure users enjoy sightseeing without getting lost. For example, the tourist guide system provides information that other users can refer to based on user reviews and ratings. The tourist guide system also displays directions to the spots the user wants to visit, allowing users to reach their destination without getting lost. Furthermore, the tourist guide system provides information in multiple languages, making it easy for foreign users to use.

[0029] A tourist guide system according to an embodiment includes a collection unit, a provision unit, and a navigation unit. The collection unit collects information about tourist attractions. The information about tourist attractions includes, for example, the name, location, business hours, admission fee, and highlights of the tourist destination, but is not limited to these examples. The collection unit collects information about tourist attractions using, for example, web scraping technology. The collection unit can also obtain information about tourist attractions using an API. The collection unit can also collect information about tourist attractions based on user input. For example, the collection unit stores the information about tourist attractions entered by the user in a database and provides it to other users. The provision unit provides the information collected by the collection unit. The provision unit provides the information by, for example, text display, audio guidance, provision of images and videos, etc. For example, the provision unit displays detailed information about tourist attractions in text. The provision unit can also provide audio guidance about the information about tourist attractions. The provision unit can also provide images and videos of the tourist attractions. The navigation unit performs navigation based on the information provided by the provision unit. The navigation unit performs navigation by, for example, map display, audio guidance, real-time location information tracking, etc. For example, the navigation unit displays routes from the user's current location to the destination on a map. The navigation unit can also provide route guidance by voice. The navigation unit can also track the user's location information in real time and provide the optimal route. This enables the tourist guide system according to the embodiment to efficiently collect, provide, and navigate information about tourist spots.

[0030] The collection unit can collect detailed information about tourist attractions. Examples of detailed information include, but are not limited to, the history, cultural background, access methods, and surrounding facilities of the tourist attraction. For example, the collection unit collects information about the history and cultural background of the tourist attraction. The collection unit can also collect information about how to access the tourist attraction. The collection unit can also collect information about the surrounding facilities of the tourist attraction. For example, the collection unit collects information about the history and cultural background of the tourist attraction using web scraping technology. The collection unit can also obtain information about how to access the tourist attraction using an API. The collection unit can also collect information about the surrounding facilities of the tourist attraction based on user input. By collecting detailed information about tourist attractions, detailed information can be provided to users.

[0031] The providing unit can provide detailed information about tourist spots based on the collected information. Examples of detailed information include, but are not limited to, the history, cultural background, access methods, and surrounding facilities of the tourist spot. For example, the providing unit provides information about the history and cultural background of the tourist spot. The providing unit can also provide information about how to access the tourist spot. The providing unit can also provide information about surrounding facilities of the tourist spot. For example, the providing unit displays information about the history and cultural background of the tourist spot in text. The providing unit can also provide audio information about how to access the tourist spot. The providing unit can also provide information about surrounding facilities of the tourist spot in the form of images or videos. This improves the user's sightseeing experience by providing detailed information based on the collected information.

[0032] The providing unit can provide user reviews or ratings. Examples of reviews or ratings include, but are not limited to, star ratings, comments, and reviews with photos. For example, the providing unit displays star ratings given by users to tourist attractions. The providing unit can also display comments written by users about tourist attractions. The providing unit can also display reviews with photos taken by users at tourist attractions. For example, the providing unit displays star ratings given by users to tourist attractions in text form. The providing unit can also provide audio guidance for comments written by users about tourist attractions. The providing unit can also provide reviews with photos taken by users at tourist attractions in the form of images or videos. In this way, providing user reviews and ratings allows other users to refer to them.

[0033] The navigation unit can provide directions from the user's current location to the destination. Examples of directions include, but are not limited to, walking routes, car routes, and methods for using public transportation. For example, the navigation unit can provide a walking route from the user's current location to the destination. The navigation unit can also provide a car route from the user's current location to the destination. The navigation unit can also provide methods for using public transportation from the user's current location to the destination. For example, the navigation unit can display a walking route from the user's current location to the destination on a map. The navigation unit can also provide voice guidance for the car route from the user's current location to the destination. The navigation unit can also track the methods for using public transportation from the user's current location to the destination in real time and provide the optimal route. By providing directions from the user's current location to the destination, the user can reach the destination without getting lost.

[0034] The providing unit can provide event information. Event information includes, for example, the date and time of the event, the location, the participation fee, and details of the event, but is not limited to these examples. For example, the providing unit provides the date and time of the event. The providing unit can also provide the location of the event. The providing unit can also provide the participation fee of the event. For example, the providing unit displays the date and time of the event in text. The providing unit can also provide audio guidance of the location of the event. The providing unit can also provide the participation fee of the event in the form of an image or video. In this way, by providing event information, the user can keep up with the latest event information.

[0035] The providing unit can provide information in multiple languages. Multilingual support includes, for example, the types of supported languages, the accuracy of translation, and multilingual support for audio guidance, but is not limited to these examples. The providing unit can provide information in English, for example. The providing unit can also provide information in Chinese. The providing unit can also provide information in Korean. For example, the providing unit can display information in English as text. The providing unit can also provide information by audio guidance in Chinese. The providing unit can also provide information in Korean as images or videos. In this way, providing information in multiple languages ​​makes it easier for foreign users to use.

[0036] The collection unit can analyze the user's past visit history of tourist spots and select an appropriate information collection method. For example, the collection unit can analyze the trends of spots the user has visited in the past and prioritize collecting information about similar spots. The collection unit can also focus on collecting information about spots that the user has previously rated highly. The collection unit can also adjust the frequency of information collection based on the frequency of visits to spots the user has previously visited. This allows the user to be provided with optimal information by analyzing the user's past visit history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's visit history data into a generation AI and have the generation AI select the optimal information collection method.

[0037] When collecting information on tourist spots, the collection unit can filter the information based on the user's current interests. For example, if the user is interested in anime, the collection unit can prioritize collecting information on anime-related spots. Furthermore, if the user is interested in cosplay, the collection unit can prioritize collecting information on cosplay shops. Furthermore, if the user is interested in figurines, the collection unit can prioritize collecting information on figurine shops. By filtering information based on the user's interests, more relevant information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's interest data into a generation AI and have the generation AI perform the filtering.

[0038] When collecting information about tourist attractions, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. If the user uses text input, the collection unit can also collect information using keyword search. If the user uses image input, the collection unit can also collect related information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input voice data, text data, and image data into a generation AI and have the generation AI select the optimal collection means.

[0039] When collecting information about tourist spots, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in Akihabara, the collection unit can prioritize collecting spot information around Akihabara. Furthermore, if the user is in Shinjuku, the collection unit can prioritize collecting spot information around Shinjuku. Furthermore, if the user is in Ikebukuro, the collection unit can prioritize collecting spot information around Ikebukuro. This makes it possible to provide highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0040] When collecting information about tourist attractions, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect information about related spots. The collection unit can also refer to the activities of the user's friends on social media to collect information about related spots. In this way, highly relevant information can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0041] When collecting information on tourist spots, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting information on spots that the user has previously rated highly. The collection unit can also avoid collecting information on spots that the user has previously rated poorly. The collection unit can also adjust the information collection algorithm based on the user's past feedback. This improves the accuracy of information collection by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI customize the collection method.

[0042] When providing information, the providing unit can adjust the level of detail of the information provided based on the priority of the tourist spot. For example, the providing unit provides detailed information for popular spots. The providing unit can also provide only basic information for lesser-known spots. The providing unit can also adjust the level of detail of the information according to the user's level of interest. This allows the user to be provided with optimal information by adjusting the level of detail of the information based on the importance of the tourist spot. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input priority data of tourist spots into the generating AI and have the generating AI adjust the level of detail of the information.

[0043] When providing information, the providing unit can apply different providing algorithms depending on the category of the tourist spot. For example, the providing unit can provide information for anime fans to anime-related spots. The providing unit can also provide information for cosplay enthusiasts to cosplay shops. The providing unit can also provide information for figure collectors to figure shops. In this way, by applying the providing algorithm depending on the category of the tourist spot, it is possible to provide optimal information to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input tourist spot category data into the generating AI and cause the generating AI to apply the providing algorithm.

[0044] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. For example, the providing unit preferentially uses information provision methods that the user has previously rated highly. The providing unit can also avoid information provision methods that the user has previously rated poorly. The providing unit can also adjust the information provision algorithm based on the user's past feedback. This improves the accuracy of the information provided by referring to the user's past information provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past information provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0045] When providing information, the providing unit can determine the priority of provision based on the time of visiting tourist spots. For example, the providing unit can prioritize providing information about seasonal spots. The providing unit can also prioritize providing information about spots where events are being held. The providing unit can also provide optimal spot information according to the user's planned visit date. In this way, by determining the priority of provision based on the time of visiting tourist spots, optimal information can be provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data about the time of visiting tourist spots into the generating AI and cause the generating AI to determine the priority of provision.

[0046] When providing information, the providing unit can adjust the order of provision based on the relevance of tourist spots. For example, the providing unit can prioritize providing spot information of a category in which the user is interested. The providing unit can also prioritize providing information that is highly relevant to spots the user has previously visited. The providing unit can also prioritize providing spot information that is close to the user's current location. In this way, by adjusting the order of provision based on the relevance of tourist spots, optimal information can be provided to the user. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input tourist spot relevance data into the generating AI and cause the generating AI to adjust the order of provision.

[0047] When providing information, the providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, the providing unit can provide easy-to-understand information by avoiding technical terminology for beginners. The providing unit can also provide information using appropriate technical terminology for intermediate users. The providing unit can also provide detailed information by using a lot of technical terminology for advanced users. In this way, by adjusting the use of technical terminology according to the user's level of expertise, more understandable information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology.

[0048] During navigation, the navigation unit can optimize current navigation by referring to past navigation data. The navigation unit, for example, suggests an optimal navigation method based on routes the user has used in the past. The navigation unit can also suggest routes that avoid congestion based on the user's past navigation history. The navigation unit can also analyze the user's past navigation history and suggest the most efficient route. In this way, current navigation can be optimized by referring to past navigation data. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input past navigation data into a generation AI and have the generation AI optimize the current navigation.

[0049] During navigation, the navigation unit can provide an appropriate route by taking into account the congestion status of tourist spots. The navigation unit, for example, can propose a route that avoids congestion based on real-time congestion information. The navigation unit can also propose a route that avoids time periods when congestion is expected. The navigation unit can also propose a detour route depending on the congestion status. This makes it possible to provide an optimal route that avoids congestion by taking into account the congestion status of tourist spots. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input congestion status data into a generation AI and have the generation AI provide an appropriate route.

[0050] During navigation, the navigation unit can select an optimal route depending on the user's means of transportation. For example, if the user is walking, the navigation unit can suggest a pedestrian-only route. Furthermore, if the user is cycling, the navigation unit can also suggest a bicycle-only route. Furthermore, if the user is driving, the navigation unit can also suggest a car-only route. This allows for more efficient navigation by selecting an optimal route depending on the user's means of transportation. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit can input data on the user's means of transportation into a generation AI and have the generation AI select an optimal route.

[0051] During navigation, the navigation unit can provide an optimal route by taking into account the user's geographical location information. For example, if the user is in Akihabara, the navigation unit can provide an optimal route to spots around Akihabara. Furthermore, if the user is in Shinjuku, the navigation unit can provide an optimal route to spots around Shinjuku. Furthermore, if the user is in Ikebukuro, the navigation unit can provide an optimal route to spots around Ikebukuro. In this way, the optimal route can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's geographical location information into a generation AI and cause the generation AI to provide an optimal route.

[0052] During navigation, the navigation unit can analyze the user's social media activity and provide related routes. For example, the navigation unit can provide routes to places where the user has checked in on social media. The navigation unit can also analyze the content of the user's social media posts and provide routes to related spots. The navigation unit can also provide routes to related spots by referring to the activity of the user's friends on social media. In this way, related routes can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's social media data into a generation AI and cause the generation AI to provide related routes.

[0053] The navigation unit can customize the navigation method by reflecting the user's past feedback during navigation. For example, the navigation unit can preferentially use navigation methods that the user has previously rated highly. The navigation unit can also avoid navigation methods that the user has previously rated poorly. The navigation unit can also adjust the navigation algorithm based on the user's past feedback. This improves the accuracy of navigation by reflecting the user's past feedback. Some or all of the above-described processing in the navigation unit can be performed using, for example, AI, or can be performed without using AI. For example, the navigation unit can input user feedback data into a generation AI and have the generation AI customize the navigation method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The tourist guide system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and calories burned, and monitors the user's health condition in real time. The health management unit may also suggest rest spots if the user is tired. Furthermore, the health management unit may suggest shortening the tourist route if the user is overly tired. This allows for a safer and more comfortable tourist experience by providing a tourist plan tailored to the user's health condition.

[0056] The tourist guide system can further include a plan generation unit that generates a customized sightseeing plan based on the user's preferences. The plan generation unit can suggest tourist spots that match the user's preferences, for example, based on the user's past visits and ratings of those spots. The plan generation unit can also customize sightseeing routes based on the user's current interests and concerns. Furthermore, the plan generation unit can generate an optimal sightseeing plan that matches the user's schedule. This allows for a more satisfying sightseeing experience by providing a sightseeing plan customized to the user's preferences.

[0057] The tourist guide system may further include a social analysis unit that analyzes the user's social media activity and suggests related tourist spots. The social analysis unit may, for example, analyze the places where the user has checked in on social media and the content of posts to suggest related tourist spots. The social analysis unit may also suggest related spots based on the activities of the user's friends. Furthermore, the social analysis unit may suggest popular spots based on the user's ratings on social media. In this way, more relevant tourist spots can be suggested by analyzing the user's social media activity.

[0058] The tourist guide system can further include a congestion prediction unit that analyzes the user's past behavior data and predicts congestion at tourist spots. The congestion prediction unit predicts the congestion level for a specific time period or date based on, for example, past visit data. The congestion prediction unit can also predict the current congestion level based on real-time data. Furthermore, the congestion prediction unit can suggest the optimal time to visit to the user based on the predicted congestion level. This allows for a more comfortable tourist experience by providing a sightseeing plan that avoids crowds.

[0059] The tourist guide system can further include an evaluation unit that evaluates tourist spots based on the user's past feedback. For example, the evaluation unit can prioritize displaying information about spots that the user has previously rated highly. The evaluation unit can also prevent the display of information about spots that the user has previously rated poorly. Furthermore, the evaluation unit can adjust the evaluation algorithm for tourist spots based on the user's past feedback. This allows the system to provide more accurate evaluations by reflecting the user's past feedback.

[0060] The tourist guide system can further include a route generation unit that generates recommended routes to tourist spots based on the user's past visit history. The route generation unit, for example, analyzes the trends of spots that the user has visited in the past and proposes routes that include similar spots. The route generation unit can also propose routes that prioritize spots that the user has previously given high ratings to. Furthermore, the route generation unit can also propose optimal routes based on the user's frequency of visits. This makes it possible to provide a more satisfying tourist experience by providing recommended routes based on the user's past visit history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects tourist attraction information. This information includes the name, location, opening hours, admission fees, and attractions of the tourist attraction. The collection unit can collect information using web scraping technology or API. It can also collect information based on user input and store it in a database. Step 2: The providing unit provides the information collected by the collecting unit. The providing unit provides the information by methods such as displaying text, providing audio guidance, and providing images and videos. For example, the providing unit can display detailed information about tourist spots in text, provide audio guidance, or provide images and videos. Step 3: The navigation unit performs navigation based on the information provided by the providing unit. The navigation unit performs navigation by methods such as map display, voice guidance, and real-time location information tracking. For example, the navigation unit can display the route from the user's current location to the destination on a map, provide voice guidance, and track the user's location information in real time to provide the optimal route.

[0063] (Example 2) A tourist guide system according to an embodiment of the present invention automatically collects and provides information about tourist spots and provides navigation. By collecting and providing information about tourist spots and providing navigation, the tourist guide system allows users to enjoy sightseeing without getting lost. For example, when a user launches an app, the tourist guide system displays information about nearby otaku spots based on the user's current location. The tourist guide system then provides detailed information about each spot and displays user reviews and ratings. Furthermore, the tourist guide system is equipped with a navigation function. When a user selects a spot they want to visit, the system displays directions from the user's current location to that spot. This tourist guide system supports multiple languages, providing information in English, Chinese, Korean, and other languages. The tourist guide system also provides event information, allowing users to keep up with the latest event information and plan their sightseeing. This allows the tourist guide system to ensure users enjoy sightseeing without getting lost. For example, the tourist guide system provides information that other users can refer to based on user reviews and ratings. The tourist guide system also displays directions to the spots the user wants to visit, allowing users to reach their destination without getting lost. Furthermore, the tourist guide system provides information in multiple languages, making it easy for foreign users to use.

[0064] A tourist guide system according to an embodiment includes a collection unit, a provision unit, and a navigation unit. The collection unit collects information about tourist attractions. The information about tourist attractions includes, for example, the name, location, business hours, admission fee, and highlights of the tourist destination, but is not limited to these examples. The collection unit collects information about tourist attractions using, for example, web scraping technology. The collection unit can also obtain information about tourist attractions using an API. The collection unit can also collect information about tourist attractions based on user input. For example, the collection unit stores the information about tourist attractions entered by the user in a database and provides it to other users. The provision unit provides the information collected by the collection unit. The provision unit provides the information by, for example, text display, audio guidance, provision of images and videos, etc. For example, the provision unit displays detailed information about tourist attractions in text. The provision unit can also provide audio guidance about the information about tourist attractions. The provision unit can also provide images and videos of the tourist attractions. The navigation unit performs navigation based on the information provided by the provision unit. The navigation unit performs navigation by, for example, map display, audio guidance, real-time location information tracking, etc. For example, the navigation unit displays routes from the user's current location to the destination on a map. The navigation unit can also provide route guidance by voice. The navigation unit can also track the user's location information in real time and provide the optimal route. This enables the tourist guide system according to the embodiment to efficiently collect, provide, and navigate information about tourist spots.

[0065] The collection unit can collect detailed information about tourist attractions. Examples of detailed information include, but are not limited to, the history, cultural background, access methods, and surrounding facilities of the tourist attraction. For example, the collection unit collects information about the history and cultural background of the tourist attraction. The collection unit can also collect information about how to access the tourist attraction. The collection unit can also collect information about the surrounding facilities of the tourist attraction. For example, the collection unit collects information about the history and cultural background of the tourist attraction using web scraping technology. The collection unit can also obtain information about how to access the tourist attraction using an API. The collection unit can also collect information about the surrounding facilities of the tourist attraction based on user input. By collecting detailed information about tourist attractions, detailed information can be provided to users.

[0066] The providing unit can provide detailed information about tourist spots based on the collected information. Examples of detailed information include, but are not limited to, the history, cultural background, access methods, and surrounding facilities of the tourist spot. For example, the providing unit provides information about the history and cultural background of the tourist spot. The providing unit can also provide information about how to access the tourist spot. The providing unit can also provide information about surrounding facilities of the tourist spot. For example, the providing unit displays information about the history and cultural background of the tourist spot in text. The providing unit can also provide audio information about how to access the tourist spot. The providing unit can also provide information about surrounding facilities of the tourist spot in the form of images or videos. This improves the user's sightseeing experience by providing detailed information based on the collected information.

[0067] The providing unit can provide user reviews or ratings. Examples of reviews or ratings include, but are not limited to, star ratings, comments, and reviews with photos. For example, the providing unit displays star ratings given by users to tourist attractions. The providing unit can also display comments written by users about tourist attractions. The providing unit can also display reviews with photos taken by users at tourist attractions. For example, the providing unit displays star ratings given by users to tourist attractions in text form. The providing unit can also provide audio guidance for comments written by users about tourist attractions. The providing unit can also provide reviews with photos taken by users at tourist attractions in the form of images or videos. In this way, providing user reviews and ratings allows other users to refer to them.

[0068] The navigation unit can provide directions from the user's current location to the destination. Examples of directions include, but are not limited to, walking routes, car routes, and methods for using public transportation. For example, the navigation unit can provide a walking route from the user's current location to the destination. The navigation unit can also provide a car route from the user's current location to the destination. The navigation unit can also provide methods for using public transportation from the user's current location to the destination. For example, the navigation unit can display a walking route from the user's current location to the destination on a map. The navigation unit can also provide voice guidance for the car route from the user's current location to the destination. The navigation unit can also track the methods for using public transportation from the user's current location to the destination in real time and provide the optimal route. By providing directions from the user's current location to the destination, the user can reach the destination without getting lost.

[0069] The providing unit can provide event information. Event information includes, for example, the date and time of the event, the location, the participation fee, and details of the event, but is not limited to these examples. For example, the providing unit provides the date and time of the event. The providing unit can also provide the location of the event. The providing unit can also provide the participation fee of the event. For example, the providing unit displays the date and time of the event in text. The providing unit can also provide audio guidance of the location of the event. The providing unit can also provide the participation fee of the event in the form of an image or video. In this way, by providing event information, the user can keep up with the latest event information.

[0070] The providing unit can provide information in multiple languages. Multilingual support includes, for example, the types of supported languages, the accuracy of translation, and multilingual support for audio guidance, but is not limited to these examples. The providing unit can provide information in English, for example. The providing unit can also provide information in Chinese. The providing unit can also provide information in Korean. For example, the providing unit can display information in English as text. The providing unit can also provide information by audio guidance in Chinese. The providing unit can also provide information in Korean as images or videos. In this way, providing information in multiple languages ​​makes it easier for foreign users to use.

[0071] The collection unit can estimate the user's emotions and adjust the timing of information collection for tourist spots based on the estimated user emotions. For example, when the user is excited, the collection unit can collect information about new tourist spots in real time. Furthermore, when the user is tired, the collection unit can reduce the frequency of information collection and collect only the minimum amount of information necessary. Furthermore, when the user is relaxed, the collection unit can periodically collect information and provide the latest information. This allows for adjusting the timing of information collection according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection based on the emotion.

[0072] The collection unit can analyze the user's past visit history of tourist spots and select an appropriate information collection method. For example, the collection unit can analyze the trends of spots the user has visited in the past and prioritize collecting information about similar spots. The collection unit can also focus on collecting information about spots that the user has previously rated highly. The collection unit can also adjust the frequency of information collection based on the frequency of visits to spots the user has previously visited. This allows the user to be provided with optimal information by analyzing the user's past visit history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's visit history data into a generation AI and have the generation AI select the optimal information collection method.

[0073] When collecting information on tourist spots, the collection unit can filter the information based on the user's current interests. For example, if the user is interested in anime, the collection unit can prioritize collecting information on anime-related spots. Furthermore, if the user is interested in cosplay, the collection unit can prioritize collecting information on cosplay shops. Furthermore, if the user is interested in figurines, the collection unit can prioritize collecting information on figurine shops. By filtering information based on the user's interests, more relevant information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's interest data into a generation AI and have the generation AI perform the filtering.

[0074] When collecting information about tourist attractions, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. If the user uses text input, the collection unit can also collect information using keyword search. If the user uses image input, the collection unit can also collect related information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input voice data, text data, and image data into a generation AI and have the generation AI select the optimal collection means.

[0075] The collection unit can estimate the user's emotions and determine the priority of tourist spot information to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting information about popular spots. Furthermore, if the user is relaxed, the collection unit can prioritize collecting information about quiet spots. Furthermore, if the user is tired, the collection unit can prioritize collecting information about spots where the user can rest. This allows for information priority to be determined based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the information.

[0076] When collecting information about tourist spots, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in Akihabara, the collection unit can prioritize collecting spot information around Akihabara. Furthermore, if the user is in Shinjuku, the collection unit can prioritize collecting spot information around Shinjuku. Furthermore, if the user is in Ikebukuro, the collection unit can prioritize collecting spot information around Ikebukuro. This makes it possible to provide highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0077] When collecting information about tourist attractions, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect information about related spots. The collection unit can also refer to the activities of the user's friends on social media to collect information about related spots. In this way, highly relevant information can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0078] When collecting information on tourist spots, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting information on spots that the user has previously rated highly. The collection unit can also avoid collecting information on spots that the user has previously rated poorly. The collection unit can also adjust the information collection algorithm based on the user's past feedback. This improves the accuracy of information collection by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI customize the collection method.

[0079] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information with a visually stimulating design. Furthermore, if the user is relaxed, the providing unit can provide information with a calming design. Furthermore, if the user is tired, the providing unit can provide information with a simple, highly visible design. This allows for more appropriate information to be provided by adjusting the way information is presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way information is presented.

[0080] When providing information, the providing unit can adjust the level of detail of the information provided based on the priority of the tourist spot. For example, the providing unit provides detailed information for popular spots. The providing unit can also provide only basic information for lesser-known spots. The providing unit can also adjust the level of detail of the information according to the user's level of interest. This allows the user to be provided with optimal information by adjusting the level of detail of the information based on the importance of the tourist spot. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input priority data of tourist spots into the generating AI and have the generating AI adjust the level of detail of the information.

[0081] When providing information, the providing unit can apply different providing algorithms depending on the category of the tourist spot. For example, the providing unit can provide information for anime fans to anime-related spots. The providing unit can also provide information for cosplay enthusiasts to cosplay shops. The providing unit can also provide information for figure collectors to figure shops. In this way, by applying the providing algorithm depending on the category of the tourist spot, it is possible to provide optimal information to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input tourist spot category data into the generating AI and cause the generating AI to apply the providing algorithm.

[0082] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. For example, the providing unit preferentially uses information provision methods that the user has previously rated highly. The providing unit can also avoid information provision methods that the user has previously rated poorly. The providing unit can also adjust the information provision algorithm based on the user's past feedback. This improves the accuracy of the information provided by referring to the user's past information provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past information provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0083] The providing unit can estimate the user's emotions and adjust the length of information provided based on the estimated user emotions. For example, when the user is excited, the providing unit can provide detailed information. When the user is relaxed, the providing unit can also provide information of appropriate length. When the user is tired, the providing unit can also provide short, to-the-point information. This allows for adjusting the length of information provided based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of information provided.

[0084] When providing information, the providing unit can determine the priority of provision based on the time of visiting tourist spots. For example, the providing unit can prioritize providing information about seasonal spots. The providing unit can also prioritize providing information about spots where events are being held. The providing unit can also provide optimal spot information according to the user's planned visit date. In this way, by determining the priority of provision based on the time of visiting tourist spots, optimal information can be provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data about the time of visiting tourist spots into the generating AI and cause the generating AI to determine the priority of provision.

[0085] When providing information, the providing unit can adjust the order of provision based on the relevance of tourist spots. For example, the providing unit can prioritize providing spot information of a category in which the user is interested. The providing unit can also prioritize providing information that is highly relevant to spots the user has previously visited. The providing unit can also prioritize providing spot information that is close to the user's current location. In this way, by adjusting the order of provision based on the relevance of tourist spots, optimal information can be provided to the user. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input tourist spot relevance data into the generating AI and cause the generating AI to adjust the order of provision.

[0086] When providing information, the providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, the providing unit can provide easy-to-understand information by avoiding technical terminology for beginners. The providing unit can also provide information using appropriate technical terminology for intermediate users. The providing unit can also provide detailed information by using a lot of technical terminology for advanced users. In this way, by adjusting the use of technical terminology according to the user's level of expertise, more understandable information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology.

[0087] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. For example, if the user is excited, the navigation unit can provide navigation with a visually stimulating design. Furthermore, if the user is relaxed, the navigation unit can provide navigation with a calming design. Furthermore, if the user is tired, the navigation unit can provide navigation with a simple, highly visible design. This allows for more appropriate navigation by adjusting the navigation display method based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the navigation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the navigation unit can input user emotion data into the generation AI and have the generation AI adjust the navigation display method.

[0088] During navigation, the navigation unit can optimize current navigation by referring to past navigation data. The navigation unit, for example, suggests an optimal navigation method based on routes the user has used in the past. The navigation unit can also suggest routes that avoid congestion based on the user's past navigation history. The navigation unit can also analyze the user's past navigation history and suggest the most efficient route. In this way, current navigation can be optimized by referring to past navigation data. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input past navigation data into a generation AI and have the generation AI optimize the current navigation.

[0089] During navigation, the navigation unit can provide an appropriate route by taking into account the congestion status of tourist spots. The navigation unit, for example, can propose a route that avoids congestion based on real-time congestion information. The navigation unit can also propose a route that avoids time periods when congestion is expected. The navigation unit can also propose a detour route depending on the congestion status. This makes it possible to provide an optimal route that avoids congestion by taking into account the congestion status of tourist spots. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input congestion status data into a generation AI and have the generation AI provide an appropriate route.

[0090] During navigation, the navigation unit can select an optimal route depending on the user's means of transportation. For example, if the user is walking, the navigation unit can suggest a pedestrian-only route. Furthermore, if the user is cycling, the navigation unit can also suggest a bicycle-only route. Furthermore, if the user is driving, the navigation unit can also suggest a car-only route. This allows for more efficient navigation by selecting an optimal route depending on the user's means of transportation. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit can input data on the user's means of transportation into a generation AI and have the generation AI select an optimal route.

[0091] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated user emotions. For example, if the user is excited, the navigation unit can prioritize navigation to popular spots. Furthermore, if the user is relaxed, the navigation unit can prioritize navigation to quiet spots. Furthermore, if the user is tired, the navigation unit can prioritize navigation to spots where the user can rest. Thus, by determining navigation priorities based on the user's emotions, more appropriate navigation can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI determine the navigation priorities.

[0092] During navigation, the navigation unit can provide an optimal route by taking into account the user's geographical location information. For example, if the user is in Akihabara, the navigation unit can provide an optimal route to spots around Akihabara. Furthermore, if the user is in Shinjuku, the navigation unit can provide an optimal route to spots around Shinjuku. Furthermore, if the user is in Ikebukuro, the navigation unit can provide an optimal route to spots around Ikebukuro. In this way, the optimal route can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's geographical location information into a generation AI and cause the generation AI to provide an optimal route.

[0093] During navigation, the navigation unit can analyze the user's social media activity and provide related routes. For example, the navigation unit can provide routes to places where the user has checked in on social media. The navigation unit can also analyze the content of the user's social media posts and provide routes to related spots. The navigation unit can also provide routes to related spots by referring to the activity of the user's friends on social media. In this way, related routes can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's social media data into a generation AI and cause the generation AI to provide related routes.

[0094] The navigation unit can customize the navigation method by reflecting the user's past feedback during navigation. For example, the navigation unit can preferentially use navigation methods that the user has previously rated highly. The navigation unit can also avoid navigation methods that the user has previously rated poorly. The navigation unit can also adjust the navigation algorithm based on the user's past feedback. This improves the accuracy of navigation by reflecting the user's past feedback. Some or all of the above-described processing in the navigation unit can be performed using, for example, AI, or can be performed without using AI. For example, the navigation unit can input user feedback data into a generation AI and have the generation AI customize the navigation method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, the provision unit, and the navigation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information about tourist spots using the camera 42 and the microphone 38B of the smart device 14. The provision unit can provide information using the display 40A and the speaker 40B of the smart device 14. The navigation unit can display a map and provide audio guidance using the control unit 46A of the smart device 14. The collection unit can also collect information about tourist spots using the specific processing unit 290 of the data processing device 12. The provision unit can provide information using the specific processing unit 290 of the data processing device 12. The navigation unit can perform navigation using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, the provision unit, and the navigation unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information about tourist spots using the camera 42 and the microphone 238 of the smart glasses 214. The provision unit can provide information using, for example, the speaker 240 of the smart glasses 214. The navigation unit can display a map and provide audio guidance using, for example, the control unit 46A of the smart glasses 214. The collection unit can also collect information about tourist spots using, for example, the specific processing unit 290 of the data processing device 12. The provision unit can also provide information using, for example, the specific processing unit 290 of the data processing device 12. The navigation unit can also perform navigation using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, provision unit, and navigation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect information about tourist spots using the camera 42 and microphone 238 of the headset terminal 314. The provision unit can provide information using the speaker 240 of the headset terminal 314. For example, the navigation unit can display a map and provide audio guidance using the control unit 46A of the headset terminal 314. The collection unit can also collect information about tourist spots using the specific processing unit 290 of the data processing device 12. The provision unit can provide information using the specific processing unit 290 of the data processing device 12. The navigation unit can perform navigation using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, the provision unit, and the navigation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information about tourist spots using the camera 42 and the microphone 238 of the robot 414. The provision unit can provide information using the speaker 240 of the robot 414. For example, the navigation unit can display a map and provide audio guidance using the control unit 46A of the robot 414. The collection unit can also collect information about tourist spots using the specific processing unit 290 of the data processing device 12, for example. The provision unit can provide information using the specific processing unit 290 of the data processing device 12, for example. The navigation unit can perform navigation using the specific processing unit 290 of the data processing device 12, for example.

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

[0096] The tourist guide system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and calories burned, and monitors the user's health condition in real time. The health management unit may also suggest rest spots if the user is tired. Furthermore, the health management unit may suggest shortening the tourist route if the user is overly tired. This allows for a safer and more comfortable tourist experience by providing a tourist plan tailored to the user's health condition.

[0097] The tourist guide system can further include a plan generation unit that generates a customized sightseeing plan based on the user's preferences. The plan generation unit can suggest tourist spots that match the user's preferences, for example, based on the user's past visits and ratings of those spots. The plan generation unit can also customize sightseeing routes based on the user's current interests and concerns. Furthermore, the plan generation unit can generate an optimal sightseeing plan that matches the user's schedule. This allows for a more satisfying sightseeing experience by providing a sightseeing plan customized to the user's preferences.

[0098] The tourist guide system may further include a suggestion unit that estimates the user's emotions and suggests tourist spots based on the estimated user emotions. For example, the suggestion unit may suggest active spots if the user is excited. The suggestion unit may also suggest quiet spots if the user is relaxed. Furthermore, the suggestion unit may also suggest rest spots if the user is tired. This allows the system to provide a more appropriate tourist experience by suggesting tourist spots according to the user's emotions.

[0099] The tourist guide system may further include a social analysis unit that analyzes the user's social media activity and suggests related tourist spots. The social analysis unit may, for example, analyze the places where the user has checked in on social media and the content of posts to suggest related tourist spots. The social analysis unit may also suggest related spots based on the activities of the user's friends. Furthermore, the social analysis unit may suggest popular spots based on the user's ratings on social media. In this way, more relevant tourist spots can be suggested by analyzing the user's social media activity.

[0100] The tourist guide system may further include a review management unit that estimates the user's emotions and filters reviews of tourist spots based on the estimated user emotions. For example, the review management unit may preferentially display positive reviews when the user is excited. The review management unit may also display balanced reviews when the user is relaxed. Furthermore, the review management unit may display brief, to-the-point reviews when the user is tired. In this way, by filtering reviews according to the user's emotions, more appropriate information can be provided.

[0101] The tourist guide system can further include a congestion prediction unit that analyzes the user's past behavior data and predicts congestion at tourist spots. The congestion prediction unit predicts the congestion level for a specific time period or date based on, for example, past visit data. The congestion prediction unit can also predict the current congestion level based on real-time data. Furthermore, the congestion prediction unit can suggest the optimal time to visit to the user based on the predicted congestion level. This allows for a more comfortable tourist experience by providing a sightseeing plan that avoids crowds.

[0102] The tourist guide system can further include a media providing unit that estimates the user's emotions and provides images and videos of tourist spots based on the estimated user emotions. For example, if the user is excited, the media providing unit can provide visually stimulating images and videos. If the user is relaxed, the media providing unit can also provide images and videos with a calming atmosphere. Furthermore, if the user is tired, the media providing unit can provide simple, highly visible images and videos. This allows the system to provide more appropriate information by providing media according to the user's emotions.

[0103] The tourist guide system can further include an evaluation unit that evaluates tourist spots based on the user's past feedback. For example, the evaluation unit can prioritize displaying information about spots that the user has previously rated highly. The evaluation unit can also prevent the display of information about spots that the user has previously rated poorly. Furthermore, the evaluation unit can adjust the evaluation algorithm for tourist spots based on the user's past feedback. This allows the system to provide more accurate evaluations by reflecting the user's past feedback.

[0104] The tourist guide system may further include a navigation adjustment unit that estimates the user's emotions and navigates tourist spots based on the estimated user emotions. For example, if the user is excited, the navigation adjustment unit may provide navigation with a visually stimulating design. If the user is relaxed, the navigation adjustment unit may also provide navigation with a calming design. Furthermore, if the user is tired, the navigation adjustment unit may also provide navigation with a simple, highly visible design. In this way, more appropriate navigation can be provided by adjusting the navigation display method based on the user's emotions.

[0105] The tourist guide system can further include a route generation unit that generates recommended routes to tourist spots based on the user's past visit history. The route generation unit, for example, analyzes the trends of spots that the user has visited in the past and proposes routes that include similar spots. The route generation unit can also propose routes that prioritize spots that the user has previously given high ratings to. Furthermore, the route generation unit can also propose optimal routes based on the user's frequency of visits. This makes it possible to provide a more satisfying tourist experience by providing recommended routes based on the user's past visit history.

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

[0107] Step 1: The collection unit collects tourist attraction information. This information includes the name, location, opening hours, admission fees, and attractions of the tourist attraction. The collection unit can collect information using web scraping technology or API. It can also collect information based on user input and store it in a database. Step 2: The providing unit provides the information collected by the collecting unit. The providing unit provides the information by methods such as displaying text, providing audio guidance, and providing images and videos. For example, the providing unit can display detailed information about tourist spots in text, provide audio guidance, or provide images and videos. Step 3: The navigation unit performs navigation based on the information provided by the providing unit. The navigation unit performs navigation by methods such as map display, voice guidance, and real-time location information tracking. For example, the navigation unit can display the route from the user's current location to the destination on a map, provide voice guidance, and track the user's location information in real time to provide the optimal route.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

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

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

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

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

[0166] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0170] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0171] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0173] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

[0179] [Explanation of symbols]

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

Claims

1. a collection department that collects information on tourist spots; a providing unit that provides the information collected by the collecting unit; a navigation unit that performs navigation based on the information provided by the providing unit. A system characterized by:

2. The collecting unit Collect detailed information about tourist attractions 2. The system of claim 1.

3. The providing unit Providing detailed information about tourist attractions based on collected information 2. The system of claim 1.

4. The providing unit Providing user reviews or ratings 2. The system of claim 1.

5. The navigation unit Provide directions from your current location to your destination 2. The system of claim 1.

6. The providing unit Providing event information 2. The system of claim 1.

7. The providing unit Providing information in multiple languages 2. The system of claim 1.

8. The collecting unit Estimates the user's emotions and adjusts the timing of collecting information about tourist spots based on the estimated user emotions.

2. The system of claim 1.

Images