System

Abstract

An object of the system according to the embodiment is to extract high-quality sightseeing information from a large amount of sightseeing data and review data and propose an optimum sightseeing route to each traveler.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects sightseeing data and review data. The analysis unit analyzes the data collected by the collection unit and extracts sightseeing information based on specific criteria. The generation unit generates a sightseeing route based on the information extracted by the analysis unit. The providing unit provides the user with the sightseeing route generated by the generating 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 technology has faced the challenge of efficiently extracting high-quality tourist information from large amounts of tourist data and review data, and proposing optimal tourist routes to individual travelers.

[0005] The system according to the embodiment aims to extract high-quality tourist information from large amounts of tourist data and review data, and to propose optimal tourist routes for individual travelers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects tourism data and review data. The analysis unit analyzes the data collected by the collection unit and extracts tourism information based on specific criteria. The generation unit generates a tourist route based on the information extracted by the analysis unit. The provision unit provides the tourist route generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can extract high-quality tourist information from large amounts of tourist data and review data, and can propose optimal tourist routes for individual travelers. [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 information providing system according to an embodiment of the present invention collects and analyzes tourist data and review data, generates tourist routes, and provides them to users. This tourist information providing system collects and analyzes tourist data and review data, and proposes tourist routes optimized for each traveler, taking advantage of the local culture and characteristics. Furthermore, once a user's desired itinerary is confirmed, the system automatically generates a plan incorporating local information (such as festivals and events) relevant to that period. For example, the tourist information providing system applies the user's past behavioral data and preferences to propose a personalized tourist plan for each user. The tourist information providing system also provides a concierge service in which AI handles things like making reservations at local restaurants and arranging activities. This allows the tourist information providing system to provide high-quality tourist information and propose a personalized tourist plan tailored to the user's wishes and preferences. Furthermore, because AI also handles things like making reservations at local restaurants and arranging activities, users can enjoy sightseeing without any hassle. This allows the tourist information providing system to provide high-quality tourist information and propose a personalized tourist plan tailored to the user's wishes and preferences. In addition, AI will also handle local restaurant reservations and activity arrangements, allowing users to enjoy sightseeing without any hassle.

[0029] A tourist information providing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects tourism data and review data. Tourism data includes, for example, tourist destination information and visitor statistics, but is not limited to, examples thereof. The collection unit collects, for example, tourist destination ratings and reviews from online review sites. The collection unit can also collect visitor impressions and survey results. For example, the collection unit collects tourist destination ratings as star ratings and comments. The analysis unit analyzes the collected data and extracts tourism information based on specific criteria. Specific criteria include, for example, rating criteria and filtering criteria, but are not limited to, examples thereof. For example, the analysis unit analyzes the collected data using natural language processing technology to evaluate the reliability of the tourist destination ratings and reviews. The analysis unit can also analyze the collected data using a machine learning algorithm to evaluate the popularity of the tourist destination and visitor satisfaction. The generation unit generates a tourist route based on the information extracted by the analysis unit. The generation of a tourist route includes, for example, criteria such as the shortest route and the most popular route, but is not limited to, examples thereof. The generation unit generates an optimal sightseeing route for the user based on, for example, ratings and reviews of tourist destinations. The generation unit can also generate a personalized sightseeing route based on the user's desired itinerary and past behavior data. The provision unit provides the user with the sightseeing route generated by the generation unit. The provision unit, for example, displays the generated sightseeing route to the user via a web application or a mobile application. The provision unit can also send the generated sightseeing route to the user by email. Furthermore, once the user's desired itinerary is confirmed, the provision unit automatically generates a plan that incorporates local information (festivals, events, etc.) of that period. For example, if the user is planning a trip on specific dates, the provision unit can incorporate information about festivals and events held during that period into the plan. This allows the tourist information provision system according to the embodiment to consistently perform processes from collecting tourism data to analyzing it, generating routes, and providing the information.

[0030] The collection unit can collect data on tourist destination ratings, reviews, and visitor impressions. The collection unit, for example, collects tourist destination ratings as star ratings and comments. For example, the collection unit collects tourist destination ratings and reviews from online review sites. The collection unit can also collect visitor impressions and survey results. For example, the collection unit collects tourist destination ratings as star ratings and comments. By collecting data such as tourist destination ratings, reviews, and visitor impressions, it is possible to provide high-quality tourist information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected from online review sites into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and extract tourist information based on specific criteria. For example, the analysis unit can analyze the collected data using natural language processing technology to evaluate the reliability of tourist destination ratings and reviews. For example, the analysis unit can analyze the collected data using a machine learning algorithm to evaluate the popularity of tourist destinations and visitor satisfaction. The analysis unit can also analyze the collected data based on filtering criteria to extract high-quality tourist information. For example, the analysis unit can score the ratings of tourist destinations based on evaluation criteria and prioritize analysis of highly reliable data. This allows high-quality tourist information to be extracted by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI extract tourist information.

[0032] The generation unit can generate a tourist route based on specific criteria using the extracted information. The generation unit generates an optimal tourist route for the user, for example, based on ratings and reviews of tourist destinations. For example, the generation unit generates a tourist route based on criteria such as the shortest route or a popular route. The generation unit can also generate a personalized tourist route based on the user's desired itinerary and past behavior data. For example, the generation unit customizes the tourist route taking into account the user's past visit history and preferences. This allows the optimal tourist route to be provided to the user by generating a tourist route based on the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the extracted information into a generation AI and cause the generation AI to generate a tourist route.

[0033] The providing unit can provide the generated tourist route to the user. For example, the providing unit displays the generated tourist route to the user through a web application or a mobile application. For example, the providing unit sends the generated tourist route to the user by email. The providing unit can also automatically generate a plan incorporating local information (such as festivals and events) of that period once the user's desired itinerary is confirmed. For example, if the user is planning a trip on a specific date, the providing unit can incorporate information about festivals and events held during that period into the plan. This allows the user to enjoy sightseeing by providing the generated tourist route to the user. 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 generated tourist route into a generation AI and have the generation AI provide it to the user.

[0034] The provision unit can automatically generate a plan incorporating local information based on a specific time period once the user's desired itinerary is confirmed. For example, if the user is planning a trip based on specific dates, the provision unit can incorporate information about festivals and events held during that period into the plan. For example, the provision unit automatically generates a plan that allows the user to enjoy local culture and characteristics based on the user's desired itinerary. The provision unit can also generate a plan incorporating seasonal tourist spots and event information based on the user's desired itinerary. For example, the provision unit generates a plan that includes cherry blossom viewing spots in spring and a plan that includes beaches in summer. This allows the user to enjoy local culture and characteristics by automatically generating a plan incorporating local information based on the user's desired itinerary. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit can input the user's desired itinerary into a generation AI and cause the generation AI to generate a plan incorporating local information.

[0035] The provision unit can apply the user's past behavioral data and preferences to propose a sightseeing plan tailored to each individual user. The provision unit, for example, customizes the sightseeing plan by taking into account the user's past visit history and preferences. For example, the provision unit proposes a sightseeing plan optimized for the user based on data on tourist spots the user has visited in the past and data on activities the user prefers. The provision unit can also analyze the user's past behavioral data and generate a sightseeing plan tailored to the user's preferences. For example, the provision unit proposes a sightseeing plan optimized for the user based on the user's past search history and review ratings. This allows the user to enjoy a sightseeing plan tailored to their preferences by proposing a personalized sightseeing plan based on the user's past behavioral data and preferences. Some or all of the above-described processing by the provision unit may be performed using, or without, AI. For example, the provision unit can input the user's past behavioral data into a generation AI and cause the generation AI to generate a personalized sightseeing plan.

[0036] The providing unit can also make reservations for local restaurants and arrange activities. For example, when a user requests a reservation for a restaurant included in a sightseeing plan, the providing unit can have the AI ​​automatically make the reservation. For example, when a user requests an arrangement for an activity included in the sightseeing plan, the providing unit can have the AI ​​automatically make the arrangement. The providing unit can also provide information on local restaurants and activities according to the user's requests. For example, the providing unit can provide the user with the menus and business hours of restaurants included in the sightseeing plan. This allows the user to enjoy sightseeing without hassle by making reservations for local restaurants and arranging activities. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input information about the user's desired restaurants and activities into the generating AI and have the generating AI make the reservations and arrangements.

[0037] The collection unit can evaluate the reliability of ratings and reviews of tourist destinations and prioritize the collection of reliable data based on specific criteria. For example, the collection unit prioritizes the collection of data on tourist destinations with high ratings and a large number of reviews. For example, the collection unit prioritizes the collection of data from reliable reviewers. The collection unit can also prioritize the collection of ratings and reviews that match past data. For example, the collection unit evaluates the reliability of ratings and reviews of tourist destinations and prioritizes the collection of reliable data. This prioritizes the collection of reliable data, making it possible to provide high-quality tourist information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the reliability of ratings and reviews of tourist destinations into a generation AI and have the generation AI perform a reliability evaluation.

[0038] The collection unit diversifies the types of data to be collected based on specific types and can collect not only text data but also image and video data. The collection unit, for example, collects photos and videos of tourist spots to provide visual information. For example, the collection unit collects images and videos posted by users on social media. The collection unit can also collect images and videos from the official websites of tourist spots. For example, the collection unit diversifies the types of data to be collected and collects not only text data but also image and video data. This allows visual information to be provided by collecting not only text data but also image and video data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input photos and videos of tourist spots into the generation AI and cause the generation AI to collect visual information.

[0039] The collection unit can expand the scope of data it collects to include local news and blog articles. For example, the collection unit collects articles about tourist destinations from local news sites. For example, the collection unit collects articles about tourist destinations written by local bloggers. The collection unit can also collect data from sites that provide local event information. For example, the collection unit expands the scope of data it collects to include local news and blog articles as collection targets. By including local news and blog articles as collection targets, more diverse 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 local news and blog articles into the generation AI and cause the generation AI to collect data.

[0040] The collection unit can expand the area of ​​data to be collected and collect information not only on tourist destinations but also on specific surrounding areas. The collection unit, for example, collects information on restaurants and accommodations around tourist destinations. For example, the collection unit collects transportation information around tourist destinations. The collection unit can also collect event information around tourist destinations. For example, the collection unit expands the area of ​​data to be collected and collects information on not only tourist destinations but also on surrounding areas. This allows for the provision of more extensive information by collecting information on not only tourist destinations but also surrounding areas. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the area around tourist destinations into the generation AI and have the generation AI collect data.

[0041] The collection unit can make the language of the collected data multilingual based on a specific language and collect data in different languages. The collection unit collects tourism data in major foreign languages, such as English and Chinese. For example, the collection unit collects data from multilingual review sites. The collection unit can also collect data from multilingual pages on official tourist destination websites. For example, the collection unit can make the language of the collected data multilingual and collect data in different languages. By collecting data in different languages, it is possible to provide tourism information in multiple languages. 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 data in different languages ​​into a generation AI and have the generation AI collect the data.

[0042] The collection unit diversifies the format of the data to be collected based on specific types and can also collect voice data and sensor data. The collection unit, for example, collects audio guide data for tourist attractions. For example, the collection unit collects audio reviews recorded by users. The collection unit can also collect sensor data (temperature, humidity, etc.) for tourist attractions. For example, the collection unit diversifies the format of the data to be collected and collects voice data and sensor data. By collecting voice data and sensor data, more diverse 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 voice data and sensor data to a generation AI and have the generation AI collect the data.

[0043] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analysis of reliable data based on specific criteria. For example, the analysis unit prioritizes analysis of data on tourist destinations with high ratings and a large number of reviews. For example, the analysis unit prioritizes analysis of data from reliable reviewers. The analysis unit can also prioritize analysis of ratings and reviews that match past data. For example, the analysis unit evaluates the reliability of tourist destination ratings and reviews and prioritizes analysis of reliable data. This prioritizes analysis of reliable data, making it possible to provide high-quality tourist information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the reliability of tourist destination ratings and reviews into a generation AI and have the generation AI evaluate the reliability.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships of data. The analysis unit, for example, analyzes the correlation between tourist destination ratings and the number of visitors. For example, the analysis unit analyzes the correlation between review content and rating points. The analysis unit can also analyze seasonal fluctuations in tourist destination ratings. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the interrelationships of data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can apply a specific analysis method depending on the data category. For example, the analysis unit applies natural language processing to text data to analyze the review content. For example, the analysis unit applies image recognition technology to image data to analyze the characteristics of tourist destinations. The analysis unit can also apply video analysis technology to video data to analyze the atmosphere of tourist destinations. For example, the analysis unit applies different analysis methods depending on the data category. In this way, the accuracy of the analysis can be improved by applying different analysis methods depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis method.

[0046] During analysis, the analysis unit can perform analysis based on the regional distribution of data. The analysis unit, for example, analyzes the evaluation of tourist destinations by region. For example, the analysis unit analyzes fluctuations in the number of visitors by region. The analysis unit can also analyze event information by region. For example, the analysis unit performs analysis taking into account the regional distribution of data. In this way, by taking into account the regional distribution of data, analysis results that reflect the characteristics of each region can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input regional distribution data into a generation AI and have the generation AI perform the analysis.

[0047] During analysis, the analysis unit can perform analysis based on time-series changes in data. The analysis unit, for example, analyzes time-series changes in ratings of tourist destinations. For example, the analysis unit analyzes time-series changes in review content. The analysis unit can also analyze time-series changes in the number of visitors. For example, the analysis unit performs analysis taking into account time-series changes in data. By taking time-series changes in data into consideration, analysis results that reflect temporal fluctuations can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input time-series data into a generation AI and have the generation AI perform the analysis.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis based on literature related to the data. The analysis unit, for example, performs the analysis by referring to academic papers on tourist destinations. For example, the analysis unit performs the analysis by referring to tourism industry reports. The analysis unit can also perform the analysis by referring to literature on the history and culture of the tourist destination. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the data. In this way, the accuracy of the analysis can be improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0049] At the time of generation, the generation unit can generate a route based on specific criteria based on the congestion status of tourist destinations. The generation unit, for example, generates a route that avoids tourist destinations that are expected to be crowded. For example, the generation unit generates a route that visits tourist destinations during times when they are less crowded. The generation unit can also generate a route that adjusts the order of visits depending on the congestion status. For example, the generation unit generates an optimal route taking into account the congestion status of tourist destinations. This makes it possible to provide an optimal route that avoids congestion by taking into account the congestion status of tourist destinations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input congestion status data into the generation AI and have the generation AI generate a route.

[0050] The generation unit can optimize the route based on the opening and closing times of tourist attractions during generation. For example, the generation unit generates a route that adjusts the visiting order to match the opening and closing times of tourist attractions. For example, the generation unit generates a route that efficiently visits tourist attractions with different opening and closing times. The generation unit can also generate a route that suggests optimal visiting times based on the opening and closing times of tourist attractions. For example, the generation unit optimizes the route taking into account the opening and closing times of tourist attractions. This makes it possible to provide an efficient tourist route by taking into account the opening and closing times of tourist attractions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input opening and closing time data of tourist attractions into the generation AI and cause the generation AI to optimize the route.

[0051] The generation unit can customize the route according to the user's means of transportation when generating the route. For example, if the user travels on foot, the generation unit generates a route suitable for walking. For example, if the user travels by bicycle, the generation unit generates a route suitable for bicycles. Furthermore, if the user travels by car, the generation unit can also generate a route suitable for cars. For example, the generation unit customizes the route according to the user's means of transportation. In this way, by customizing the route according to the user's means of transportation, a more appropriate tourist route can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's means of transportation data into the generation AI and cause the generation AI to customize the route.

[0052] During generation, the generation unit can generate a route based on seasonal characteristics of tourist destinations. For example, in spring, the generation unit generates a route that includes famous cherry blossom viewing spots. For example, in summer, the generation unit generates a route that includes beaches. The generation unit can also generate a route that includes famous autumn foliage viewing spots in autumn. For example, the generation unit generates a route taking into account seasonal characteristics of tourist destinations. This makes it possible to provide optimal tourist routes according to the season by taking into account seasonal characteristics of tourist destinations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input seasonal characteristic data into the generation AI and have the generation AI generate a route.

[0053] The generation unit can generate a route based on special event information of tourist destinations at the time of generation. The generation unit generates a route based on, for example, the date on which a specific festival or event is held. For example, the generation unit generates a route that includes the location of the event. The generation unit can also generate a route that adjusts the visiting order to match the start time of the event. For example, the generation unit generates a route taking into account special event information of tourist destinations. In this way, by taking into account the special event information of tourist destinations, it is possible to provide an optimal tourist route tailored to the event. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input special event information into the generation AI and cause the generation AI to generate a route.

[0054] During generation, the generation unit can adjust the route based on specific criteria in accordance with the user's budget. For example, the generation unit generates a route that includes many free tourist spots in accordance with the user's budget. For example, the generation unit generates a route that includes tourist spots with high cost performance in accordance with the user's budget. The generation unit can also generate a route that adjusts accommodation and restaurant options in accordance with the user's budget. For example, the generation unit customizes the route in accordance with the user's budget. In this way, by customizing the route in accordance with the user's budget, it is possible to provide an optimal tourist route that fits the budget. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's budget data into the generation AI and have the generation AI adjust the route.

[0055] When providing information, the providing unit can adjust the content to be provided based on specific criteria by reflecting the user's past feedback. For example, the providing unit can prioritize providing tourist spots that the user has previously rated highly. For example, the providing unit can exclude tourist spots that the user has previously complained about. The providing unit can also optimize the content to be provided based on the user's past feedback. For example, the providing unit can customize the content to be provided by reflecting the user's past feedback. In this way, more appropriate information can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to adjust the content to be provided.

[0056] The providing unit can select the optimal delivery method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the providing unit selects a delivery method that matches the screen size. For example, if the user is using a tablet, the providing unit selects a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select a delivery method that is concise and highly visible. For example, the providing unit selects the optimal delivery method taking into account the user's device information. In this way, the optimal delivery method can be selected by taking into account the user's device information. 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 device information into the generation AI and cause the generation AI to select the delivery method.

[0057] The providing unit can adjust the provided content based on the user's current location information when providing the information. For example, the providing unit prioritizes providing tourist spots close to the user's current location. For example, the providing unit adjusts the provided content taking into account the travel time from the user's current location. The providing unit can also adjust the provided content to match the time of day the user is in their current location. For example, the providing unit adjusts the provided content taking into account the user's current location information. This makes it possible to provide more appropriate information by taking into account the user's current location information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's location information data into a generating AI and cause the generating AI to adjust the provided content.

[0058] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the providing unit provides a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the display content in that language. For example, the providing unit makes the display content multilingual according to the user's language setting. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to perform multilingual support for the display content.

[0059] The providing unit can adjust the display method based on specific criteria in accordance with the user's visual and auditory characteristics when providing the information. For example, the providing unit can prioritize providing audio guidance to a visually impaired user. For example, the providing unit can prioritize providing visual guidance to a hearing impaired user. The providing unit can also customize the display method in accordance with the user's visual and auditory characteristics. For example, the providing unit can adjust the display method in accordance with the user's visual and auditory characteristics. This allows for customizing the display method in accordance with the user's visual and auditory characteristics to provide more appropriate information. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's visual and auditory characteristic data into the generation AI and cause the generation AI to adjust the display method.

[0060] The providing unit can select the optimal timing for providing information based on the user's schedule information when providing the information. The providing unit, for example, adjusts the timing for providing the tourist route to match the user's schedule. For example, the providing unit provides the tourist route to match the time period when the user is free. The providing unit can also select the optimal timing for providing information based on the user's schedule information. For example, the providing unit selects the optimal timing for providing information based on the user's schedule information. This makes it possible to provide information at the optimal timing by taking the user's schedule information into consideration. 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 the user's schedule information to the generation AI and cause the generation AI to select the timing for providing information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can evaluate the reliability of ratings and reviews of tourist destinations and prioritize the collection of reliable data based on specific criteria. For example, it can prioritize the collection of data on tourist destinations with high ratings and a large number of reviews. It can also prioritize the collection of data from reliable reviewers. It can also prioritize the collection of ratings and reviews that match past data. This allows for the preferential collection of reliable data, thereby providing high-quality tourist information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the reliability of ratings and reviews of tourist destinations into a generation AI and have the generation AI perform a reliability evaluation.

[0063] The collection unit can diversify the types of data to be collected based on specific types, and can collect not only text data but also image and video data. For example, it collects photos and videos of tourist spots to provide visual information. It can collect images and videos posted by users on social media. It can also collect images and videos from the official websites of tourist spots. This allows it to provide visual information by collecting not only text data but also image and video data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input photos and videos of tourist spots into the generation AI and have the generation AI collect visual information.

[0064] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analysis of reliable data based on specific criteria. For example, it can prioritize analysis of data on tourist destinations with high ratings and a large number of reviews. It can prioritize analysis of data from reliable reviewers. It can also prioritize analysis of ratings and reviews that match past data. This allows for the provision of high-quality tourist information by prioritizing analysis of reliable data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the reliability of tourist destination ratings and reviews into the generation AI and have the generation AI perform a reliability evaluation.

[0065] At the time of generation, the generation unit can generate a route based on specific criteria based on the congestion status of tourist spots. For example, it can generate a route that avoids tourist spots that are expected to be crowded. It can generate a route that visits tourist spots during times when they are less crowded. It can also generate a route that adjusts the order of visits 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 generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input congestion status data into the generation AI and have the generation AI generate a route.

[0066] At the time of provision, the provision unit can select the optimal provision method based on the user's device information. For example, if the user is using a smartphone, the provision unit selects a provision method that matches the screen size. If the user is using a tablet, the provision unit selects a provision method optimized for a large screen. Furthermore, if the user is using a smartwatch, the provision unit can select a provision method that is simple and highly visible. This allows the optimal provision method to be selected by taking the user's device information into consideration. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input the user's device information into the generation AI and have the generation AI select the provision method.

[0067] The providing unit can adjust the content to be provided based on the user's current location information when providing the information. For example, tourist spots close to the user's current location can be provided preferentially. The content to be provided can be adjusted taking into account the travel time from the user's current location. The content to be provided can also be adjusted to match the time of day the user is in their current location. This allows more appropriate information to be provided by taking into account the user's current location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data into the generating AI and have the generating AI adjust the content to be provided.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects tourism data and review data. Tourism data includes information about tourist destinations and visitor statistics. The collection unit collects ratings and reviews of tourist destinations from online review sites, as well as visitor impressions and survey results. For example, tourist destination ratings are collected as star ratings and comments. Step 2: The analysis unit analyzes the collected data and extracts tourist information based on specific criteria, including rating and filtering criteria. The analysis unit uses natural language processing technology to evaluate the reliability of tourist destination ratings and reviews, and machine learning algorithms to evaluate the popularity of tourist destinations and visitor satisfaction. Step 3: The generation unit generates a tourist route based on the information extracted by the analysis unit. The generation of a tourist route includes criteria such as the shortest route and the most popular route. The generation unit generates the optimal tourist route for the user based on the ratings and reviews of tourist spots, and can also generate a personalized tourist route based on the user's desired itinerary and past behavior data. Step 4: The providing unit provides the user with the tourist route generated by the generating unit. The providing unit displays the generated tourist route to the user via a web application or a mobile application, and can also send it by email. Furthermore, once the user's desired itinerary is confirmed, the providing unit automatically generates a plan that incorporates local information (such as festivals and events) for that period.

[0070] (Example 2) A tourist information providing system according to an embodiment of the present invention collects and analyzes tourist data and review data, generates tourist routes, and provides them to users. This tourist information providing system collects and analyzes tourist data and review data, and proposes tourist routes optimized for each traveler, taking advantage of the local culture and characteristics. Furthermore, once a user's desired itinerary is confirmed, the system automatically generates a plan incorporating local information (such as festivals and events) relevant to that period. For example, the tourist information providing system applies the user's past behavioral data and preferences to propose a personalized tourist plan for each user. The tourist information providing system also provides a concierge service in which AI handles things like making reservations at local restaurants and arranging activities. This allows the tourist information providing system to provide high-quality tourist information and propose a personalized tourist plan tailored to the user's wishes and preferences. Furthermore, because AI also handles things like making reservations at local restaurants and arranging activities, users can enjoy sightseeing without any hassle. This allows the tourist information providing system to provide high-quality tourist information and propose a personalized tourist plan tailored to the user's wishes and preferences. In addition, AI will also handle local restaurant reservations and activity arrangements, allowing users to enjoy sightseeing without any hassle.

[0071] A tourist information providing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects tourism data and review data. Tourism data includes, for example, tourist destination information and visitor statistics, but is not limited to, examples thereof. The collection unit collects, for example, tourist destination ratings and reviews from online review sites. The collection unit can also collect visitor impressions and survey results. For example, the collection unit collects tourist destination ratings as star ratings and comments. The analysis unit analyzes the collected data and extracts tourism information based on specific criteria. Specific criteria include, for example, rating criteria and filtering criteria, but are not limited to, examples thereof. For example, the analysis unit analyzes the collected data using natural language processing technology to evaluate the reliability of the tourist destination ratings and reviews. The analysis unit can also analyze the collected data using a machine learning algorithm to evaluate the popularity of the tourist destination and visitor satisfaction. The generation unit generates a tourist route based on the information extracted by the analysis unit. The generation of a tourist route includes, for example, criteria such as the shortest route and the most popular route, but is not limited to, examples thereof. The generation unit generates an optimal sightseeing route for the user based on, for example, ratings and reviews of tourist destinations. The generation unit can also generate a personalized sightseeing route based on the user's desired itinerary and past behavior data. The provision unit provides the user with the sightseeing route generated by the generation unit. The provision unit, for example, displays the generated sightseeing route to the user via a web application or a mobile application. The provision unit can also send the generated sightseeing route to the user by email. Furthermore, once the user's desired itinerary is confirmed, the provision unit automatically generates a plan that incorporates local information (festivals, events, etc.) of that period. For example, if the user is planning a trip on specific dates, the provision unit can incorporate information about festivals and events held during that period into the plan. This allows the tourist information provision system according to the embodiment to consistently perform processes from collecting tourism data to analyzing it, generating routes, and providing the information.

[0072] The collection unit can collect data on tourist destination ratings, reviews, and visitor impressions. The collection unit, for example, collects tourist destination ratings as star ratings and comments. For example, the collection unit collects tourist destination ratings and reviews from online review sites. The collection unit can also collect visitor impressions and survey results. For example, the collection unit collects tourist destination ratings as star ratings and comments. By collecting data such as tourist destination ratings, reviews, and visitor impressions, it is possible to provide high-quality tourist information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected from online review sites into a generation AI and have the generation AI analyze the data.

[0073] The analysis unit can analyze the collected data and extract tourist information based on specific criteria. For example, the analysis unit can analyze the collected data using natural language processing technology to evaluate the reliability of tourist destination ratings and reviews. For example, the analysis unit can analyze the collected data using a machine learning algorithm to evaluate the popularity of tourist destinations and visitor satisfaction. The analysis unit can also analyze the collected data based on filtering criteria to extract high-quality tourist information. For example, the analysis unit can score the ratings of tourist destinations based on evaluation criteria and prioritize analysis of highly reliable data. This allows high-quality tourist information to be extracted by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI extract tourist information.

[0074] The generation unit can generate a tourist route based on specific criteria using the extracted information. The generation unit generates an optimal tourist route for the user, for example, based on ratings and reviews of tourist destinations. For example, the generation unit generates a tourist route based on criteria such as the shortest route or a popular route. The generation unit can also generate a personalized tourist route based on the user's desired itinerary and past behavior data. For example, the generation unit customizes the tourist route taking into account the user's past visit history and preferences. This allows the optimal tourist route to be provided to the user by generating a tourist route based on the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the extracted information into a generation AI and cause the generation AI to generate a tourist route.

[0075] The providing unit can provide the generated tourist route to the user. For example, the providing unit displays the generated tourist route to the user through a web application or a mobile application. For example, the providing unit sends the generated tourist route to the user by email. The providing unit can also automatically generate a plan incorporating local information (such as festivals and events) of that period once the user's desired itinerary is confirmed. For example, if the user is planning a trip on a specific date, the providing unit can incorporate information about festivals and events held during that period into the plan. This allows the user to enjoy sightseeing by providing the generated tourist route to the user. 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 generated tourist route into a generation AI and have the generation AI provide it to the user.

[0076] The provision unit can automatically generate a plan incorporating local information based on a specific time period once the user's desired itinerary is confirmed. For example, if the user is planning a trip based on specific dates, the provision unit can incorporate information about festivals and events held during that period into the plan. For example, the provision unit automatically generates a plan that allows the user to enjoy local culture and characteristics based on the user's desired itinerary. The provision unit can also generate a plan incorporating seasonal tourist spots and event information based on the user's desired itinerary. For example, the provision unit generates a plan that includes cherry blossom viewing spots in spring and a plan that includes beaches in summer. This allows the user to enjoy local culture and characteristics by automatically generating a plan incorporating local information based on the user's desired itinerary. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit can input the user's desired itinerary into a generation AI and cause the generation AI to generate a plan incorporating local information.

[0077] The provision unit can apply the user's past behavioral data and preferences to propose a sightseeing plan tailored to each individual user. The provision unit, for example, customizes the sightseeing plan by taking into account the user's past visit history and preferences. For example, the provision unit proposes a sightseeing plan optimized for the user based on data on tourist spots the user has visited in the past and data on activities the user prefers. The provision unit can also analyze the user's past behavioral data and generate a sightseeing plan tailored to the user's preferences. For example, the provision unit proposes a sightseeing plan optimized for the user based on the user's past search history and review ratings. This allows the user to enjoy a sightseeing plan tailored to their preferences by proposing a personalized sightseeing plan based on the user's past behavioral data and preferences. Some or all of the above-described processing by the provision unit may be performed using, or without, AI. For example, the provision unit can input the user's past behavioral data into a generation AI and cause the generation AI to generate a personalized sightseeing plan.

[0078] The providing unit can also make reservations for local restaurants and arrange activities. For example, when a user requests a reservation for a restaurant included in a sightseeing plan, the providing unit can have the AI ​​automatically make the reservation. For example, when a user requests an arrangement for an activity included in the sightseeing plan, the providing unit can have the AI ​​automatically make the arrangement. The providing unit can also provide information on local restaurants and activities according to the user's requests. For example, the providing unit can provide the user with the menus and business hours of restaurants included in the sightseeing plan. This allows the user to enjoy sightseeing without hassle by making reservations for local restaurants and arranging activities. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input information about the user's desired restaurants and activities into the generating AI and have the generating AI make the reservations and arrangements.

[0079] The collection unit can estimate the user's emotions and adjust the timing of collecting tourism data based on the estimated user emotions. For example, when the user is excited, the collection unit collects the latest tourism data in real time. For example, when the user is relaxed, the collection unit periodically collects tourism data and reduces the update frequency. Furthermore, when the user is stressed, the collection unit can prioritize collecting only important tourism data. For example, the collection unit estimates the user's emotions and adjusts the timing of collecting tourism data based on the estimated emotions. This allows data to be collected at a more appropriate time by adjusting the timing of collecting tourism data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of collecting tourism data.

[0080] The collection unit can evaluate the reliability of ratings and reviews of tourist destinations and prioritize the collection of reliable data based on specific criteria. For example, the collection unit prioritizes the collection of data on tourist destinations with high ratings and a large number of reviews. For example, the collection unit prioritizes the collection of data from reliable reviewers. The collection unit can also prioritize the collection of ratings and reviews that match past data. For example, the collection unit evaluates the reliability of ratings and reviews of tourist destinations and prioritizes the collection of reliable data. This prioritizes the collection of reliable data, making it possible to provide high-quality tourist information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the reliability of ratings and reviews of tourist destinations into a generation AI and have the generation AI perform a reliability evaluation.

[0081] The collection unit diversifies the types of data to be collected based on specific types and can collect not only text data but also image and video data. The collection unit, for example, collects photos and videos of tourist spots to provide visual information. For example, the collection unit collects images and videos posted by users on social media. The collection unit can also collect images and videos from the official websites of tourist spots. For example, the collection unit diversifies the types of data to be collected and collects not only text data but also image and video data. This allows visual information to be provided by collecting not only text data but also image and video data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input photos and videos of tourist spots into the generation AI and cause the generation AI to collect visual information.

[0082] The collection unit can expand the scope of data it collects to include local news and blog articles. For example, the collection unit collects articles about tourist destinations from local news sites. For example, the collection unit collects articles about tourist destinations written by local bloggers. The collection unit can also collect data from sites that provide local event information. For example, the collection unit expands the scope of data it collects to include local news and blog articles as collection targets. By including local news and blog articles as collection targets, more diverse 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 local news and blog articles into the generation AI and cause the generation AI to collect data.

[0083] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest event information. For example, if the user is relaxed, the collection unit prioritizes collecting detailed information about tourist spots. Furthermore, if the user is stressed, the collection unit can prioritize collecting concise and important information. For example, the collection unit estimates the user's emotions and prioritizes the data to be collected based on the estimated emotions. This allows for more appropriate data to be collected by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 a generation AI and have the generation AI determine the data priorities.

[0084] The collection unit can expand the area of ​​data to be collected and collect information not only on tourist destinations but also on specific surrounding areas. The collection unit, for example, collects information on restaurants and accommodations around tourist destinations. For example, the collection unit collects transportation information around tourist destinations. The collection unit can also collect event information around tourist destinations. For example, the collection unit expands the area of ​​data to be collected and collects information on not only tourist destinations but also on surrounding areas. This allows for the provision of more extensive information by collecting information on not only tourist destinations but also surrounding areas. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the area around tourist destinations into the generation AI and have the generation AI collect data.

[0085] The collection unit can make the language of the collected data multilingual based on a specific language and collect data in different languages. The collection unit collects tourism data in major foreign languages, such as English and Chinese. For example, the collection unit collects data from multilingual review sites. The collection unit can also collect data from multilingual pages on official tourist destination websites. For example, the collection unit can make the language of the collected data multilingual and collect data in different languages. By collecting data in different languages, it is possible to provide tourism information in multiple languages. 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 data in different languages ​​into a generation AI and have the generation AI collect the data.

[0086] The collection unit diversifies the format of the data to be collected based on specific types and can also collect voice data and sensor data. The collection unit, for example, collects audio guide data for tourist attractions. For example, the collection unit collects audio reviews recorded by users. The collection unit can also collect sensor data (temperature, humidity, etc.) for tourist attractions. For example, the collection unit diversifies the format of the data to be collected and collects voice data and sensor data. By collecting voice data and sensor data, more diverse 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 voice data and sensor data to a generation AI and have the generation AI collect the data.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is excited, the analysis unit applies an algorithm that emphasizes positive reviews. For example, if the user is relaxed, the analysis unit applies an algorithm that emphasizes detailed information. Furthermore, if the user is stressed, the analysis unit can apply an algorithm that emphasizes concise and important information. For example, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. This allows for adjusting the analysis algorithm according to the user's emotions to obtain more appropriate analysis results. 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0088] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analysis of reliable data based on specific criteria. For example, the analysis unit prioritizes analysis of data on tourist destinations with high ratings and a large number of reviews. For example, the analysis unit prioritizes analysis of data from reliable reviewers. The analysis unit can also prioritize analysis of ratings and reviews that match past data. For example, the analysis unit evaluates the reliability of tourist destination ratings and reviews and prioritizes analysis of reliable data. This prioritizes analysis of reliable data, making it possible to provide high-quality tourist information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the reliability of tourist destination ratings and reviews into a generation AI and have the generation AI evaluate the reliability.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships of data. The analysis unit, for example, analyzes the correlation between tourist destination ratings and the number of visitors. For example, the analysis unit analyzes the correlation between review content and rating points. The analysis unit can also analyze seasonal fluctuations in tourist destination ratings. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the interrelationships of data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] During analysis, the analysis unit can apply a specific analysis method depending on the data category. For example, the analysis unit applies natural language processing to text data to analyze the review content. For example, the analysis unit applies image recognition technology to image data to analyze the characteristics of tourist destinations. The analysis unit can also apply video analysis technology to video data to analyze the atmosphere of tourist destinations. For example, the analysis unit applies different analysis methods depending on the data category. In this way, the accuracy of the analysis can be improved by applying different analysis methods depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis method.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can emphasize and display positive information. For example, if the user is relaxed, the analysis unit can display detailed information. Furthermore, if the user is stressed, the analysis unit can display concise but important information. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0092] During analysis, the analysis unit can perform analysis based on the regional distribution of data. The analysis unit, for example, analyzes the evaluation of tourist destinations by region. For example, the analysis unit analyzes fluctuations in the number of visitors by region. The analysis unit can also analyze event information by region. For example, the analysis unit performs analysis taking into account the regional distribution of data. In this way, by taking into account the regional distribution of data, analysis results that reflect the characteristics of each region can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input regional distribution data into a generation AI and have the generation AI perform the analysis.

[0093] During analysis, the analysis unit can perform analysis based on time-series changes in data. The analysis unit, for example, analyzes time-series changes in ratings of tourist destinations. For example, the analysis unit analyzes time-series changes in review content. The analysis unit can also analyze time-series changes in the number of visitors. For example, the analysis unit performs analysis taking into account time-series changes in data. By taking time-series changes in data into consideration, analysis results that reflect temporal fluctuations can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input time-series data into a generation AI and have the generation AI perform the analysis.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis based on literature related to the data. The analysis unit, for example, performs the analysis by referring to academic papers on tourist destinations. For example, the analysis unit performs the analysis by referring to tourism industry reports. The analysis unit can also perform the analysis by referring to literature on the history and culture of the tourist destination. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the data. In this way, the accuracy of the analysis can be improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0095] The generation unit can estimate the user's emotions and adjust the tourist route generation method based on the estimated user emotions. For example, if the user is excited, the generation unit generates a route that includes many active tourist spots. For example, if the user is relaxed, the generation unit generates a route that includes many quiet tourist spots. Furthermore, if the user is stressed, the generation unit can generate a route that can be completed in a short time. For example, the generation unit can estimate the user's emotions and adjust the tourist route generation method based on the estimated emotions. This allows for providing a more appropriate tourist route by adjusting the tourist route generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the tourist route generation method.

[0096] At the time of generation, the generation unit can generate a route based on specific criteria based on the congestion status of tourist destinations. The generation unit, for example, generates a route that avoids tourist destinations that are expected to be crowded. For example, the generation unit generates a route that visits tourist destinations during times when they are less crowded. The generation unit can also generate a route that adjusts the order of visits depending on the congestion status. For example, the generation unit generates an optimal route taking into account the congestion status of tourist destinations. This makes it possible to provide an optimal route that avoids congestion by taking into account the congestion status of tourist destinations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input congestion status data into the generation AI and have the generation AI generate a route.

[0097] The generation unit can optimize the route based on the opening and closing times of tourist attractions during generation. For example, the generation unit generates a route that adjusts the visiting order to match the opening and closing times of tourist attractions. For example, the generation unit generates a route that efficiently visits tourist attractions with different opening and closing times. The generation unit can also generate a route that suggests optimal visiting times based on the opening and closing times of tourist attractions. For example, the generation unit optimizes the route taking into account the opening and closing times of tourist attractions. This makes it possible to provide an efficient tourist route by taking into account the opening and closing times of tourist attractions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input opening and closing time data of tourist attractions into the generation AI and cause the generation AI to optimize the route.

[0098] The generation unit can customize the route according to the user's means of transportation when generating the route. For example, if the user travels on foot, the generation unit generates a route suitable for walking. For example, if the user travels by bicycle, the generation unit generates a route suitable for bicycles. Furthermore, if the user travels by car, the generation unit can also generate a route suitable for cars. For example, the generation unit customizes the route according to the user's means of transportation. In this way, by customizing the route according to the user's means of transportation, a more appropriate tourist route can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's means of transportation data into the generation AI and cause the generation AI to customize the route.

[0099] The generation unit can estimate the user's emotions and prioritize sightseeing routes based on the estimated user emotions. For example, if the user is excited, the generation unit prioritizes active tourist spots. For example, if the user is relaxed, the generation unit prioritizes quiet tourist spots. Furthermore, if the user is stressed, the generation unit can prioritize tourist spots that can be completed in a short time. For example, the generation unit estimates the user's emotions and prioritizes sightseeing routes based on the estimated emotions. This allows for more appropriate sightseeing routes to be provided by prioritizing sightseeing routes according to the user's emotions. 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 generation unit may be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of sightseeing routes.

[0100] During generation, the generation unit can generate a route based on seasonal characteristics of tourist destinations. For example, in spring, the generation unit generates a route that includes famous cherry blossom viewing spots. For example, in summer, the generation unit generates a route that includes beaches. The generation unit can also generate a route that includes famous autumn foliage viewing spots in autumn. For example, the generation unit generates a route taking into account seasonal characteristics of tourist destinations. This makes it possible to provide optimal tourist routes according to the season by taking into account seasonal characteristics of tourist destinations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input seasonal characteristic data into the generation AI and have the generation AI generate a route.

[0101] The generation unit can generate a route based on special event information of tourist destinations at the time of generation. The generation unit generates a route based on, for example, the date on which a specific festival or event is held. For example, the generation unit generates a route that includes the location of the event. The generation unit can also generate a route that adjusts the visiting order to match the start time of the event. For example, the generation unit generates a route taking into account special event information of tourist destinations. In this way, by taking into account the special event information of tourist destinations, it is possible to provide an optimal tourist route tailored to the event. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input special event information into the generation AI and cause the generation AI to generate a route.

[0102] During generation, the generation unit can adjust the route based on specific criteria in accordance with the user's budget. For example, the generation unit generates a route that includes many free tourist spots in accordance with the user's budget. For example, the generation unit generates a route that includes tourist spots with high cost performance in accordance with the user's budget. The generation unit can also generate a route that adjusts accommodation and restaurant options in accordance with the user's budget. For example, the generation unit customizes the route in accordance with the user's budget. In this way, by customizing the route in accordance with the user's budget, it is possible to provide an optimal tourist route that fits the budget. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's budget data into the generation AI and have the generation AI adjust the route.

[0103] The providing unit can estimate the user's emotions and adjust the way the tourist route is presented based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide the route with a visually stimulating interface. For example, if the user is relaxed, the providing unit can provide the route with a calming interface. Furthermore, if the user is stressed, the providing unit can provide the route with a simple and intuitive interface. For example, the providing unit can estimate the user's emotions and adjust the way the tourist route is presented based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the way the tourist route is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method.

[0104] When providing information, the providing unit can adjust the content to be provided based on specific criteria by reflecting the user's past feedback. For example, the providing unit can prioritize providing tourist spots that the user has previously rated highly. For example, the providing unit can exclude tourist spots that the user has previously complained about. The providing unit can also optimize the content to be provided based on the user's past feedback. For example, the providing unit can customize the content to be provided by reflecting the user's past feedback. In this way, more appropriate information can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to adjust the content to be provided.

[0105] The providing unit can select the optimal delivery method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the providing unit selects a delivery method that matches the screen size. For example, if the user is using a tablet, the providing unit selects a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select a delivery method that is concise and highly visible. For example, the providing unit selects the optimal delivery method taking into account the user's device information. In this way, the optimal delivery method can be selected by taking into account the user's device information. 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 device information into the generation AI and cause the generation AI to select the delivery method.

[0106] The providing unit can adjust the provided content based on the user's current location information when providing the information. For example, the providing unit prioritizes providing tourist spots close to the user's current location. For example, the providing unit adjusts the provided content taking into account the travel time from the user's current location. The providing unit can also adjust the provided content to match the time of day the user is in their current location. For example, the providing unit adjusts the provided content taking into account the user's current location information. This makes it possible to provide more appropriate information by taking into account the user's current location information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's location information data into a generating AI and cause the generating AI to adjust the provided content.

[0107] The providing unit can estimate the user's emotions and adjust the display method of the tourist route based on the estimated user emotions. For example, if the user is excited, the providing unit provides a visually stimulating display method. For example, if the user is relaxed, the providing unit provides a display method with a calm design. Furthermore, if the user is stressed, the providing unit can also provide a simple and intuitive display method. For example, the providing unit can estimate the user's emotions and adjust the display method of the tourist route based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the display method of the tourist route according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0108] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the providing unit provides a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the display content in that language. For example, the providing unit makes the display content multilingual according to the user's language setting. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to perform multilingual support for the display content.

[0109] The providing unit can adjust the display method based on specific criteria in accordance with the user's visual and auditory characteristics when providing the information. For example, the providing unit can prioritize providing audio guidance to a visually impaired user. For example, the providing unit can prioritize providing visual guidance to a hearing impaired user. The providing unit can also customize the display method in accordance with the user's visual and auditory characteristics. For example, the providing unit can adjust the display method in accordance with the user's visual and auditory characteristics. This allows for customizing the display method in accordance with the user's visual and auditory characteristics to provide more appropriate information. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's visual and auditory characteristic data into the generation AI and cause the generation AI to adjust the display method.

[0110] The providing unit can select the optimal timing for providing information based on the user's schedule information when providing the information. The providing unit, for example, adjusts the timing for providing the tourist route to match the user's schedule. For example, the providing unit provides the tourist route to match the time period when the user is free. The providing unit can also select the optimal timing for providing information based on the user's schedule information. For example, the providing unit selects the optimal timing for providing information based on the user's schedule information. This makes it possible to provide information at the optimal timing by taking the user's schedule information into consideration. 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 the user's schedule information to the generation AI and cause the generation AI to select the timing for providing information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a tourist route based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated tourist route to the user. The collection unit can also estimate the user's emotions and adjust the timing of collecting tourist data based on the estimated emotions. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and microphone 238 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a tourist route based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated tourist route to the user. The collection unit can also estimate the user's emotions and adjust the timing of collecting tourist data based on the estimated emotions. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and microphone 238 of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a tourist route based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides the generated tourist route to the user. The collection unit can also estimate the user's emotions and adjust the timing of collecting tourist data based on the estimated emotions. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and microphone 238 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a tourist route based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides the generated tourist route to the user. The collection unit can also estimate the user's emotions and adjust the timing of collecting tourist data based on the estimated emotions. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is excited, positive reviews are prioritized for analysis. If the user is relaxed, detailed information is emphasized for analysis. Also, if the user is stressed, concise and important information can be prioritized for analysis. This allows for more appropriate analysis results to be obtained by adjusting the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis priority.

[0113] The collection unit can evaluate the reliability of ratings and reviews of tourist destinations and prioritize the collection of reliable data based on specific criteria. For example, it can prioritize the collection of data on tourist destinations with high ratings and a large number of reviews. It can also prioritize the collection of data from reliable reviewers. It can also prioritize the collection of ratings and reviews that match past data. This allows for the preferential collection of reliable data, thereby providing high-quality tourist information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the reliability of ratings and reviews of tourist destinations into a generation AI and have the generation AI perform a reliability evaluation.

[0114] The generation unit can estimate the user's emotions and adjust the method for generating a tourist route based on the estimated emotions. For example, if the user is excited, the generation unit generates a route that includes many active tourist spots. If the user is relaxed, the generation unit generates a route that includes many quiet tourist spots. Also, if the user is stressed, the generation unit can generate a route that can be completed in a short time. This allows the user to provide a more appropriate tourist route by adjusting the method for generating a tourist route according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the method for generating a tourist route.

[0115] The providing unit can estimate the user's emotions and adjust the way in which the tourist route is provided based on the estimated emotions. For example, if the user is excited, the route can be provided with a visually stimulating interface. If the user is relaxed, the route can be provided with a calming interface. Also, if the user is stressed, the route can be provided with a simple and intuitive interface. This allows for more appropriate information to be provided by adjusting the way in which the tourist route is provided according to the user's emotions. Emotion estimation is achieved using 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 providing unit can be performed using AI or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way in which the route is provided.

[0116] The collection unit can diversify the types of data to be collected based on specific types, and can collect not only text data but also image and video data. For example, it collects photos and videos of tourist spots to provide visual information. It can collect images and videos posted by users on social media. It can also collect images and videos from the official websites of tourist spots. This allows it to provide visual information by collecting not only text data but also image and video data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input photos and videos of tourist spots into the generation AI and have the generation AI collect visual information.

[0117] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analysis of reliable data based on specific criteria. For example, it can prioritize analysis of data on tourist destinations with high ratings and a large number of reviews. It can prioritize analysis of data from reliable reviewers. It can also prioritize analysis of ratings and reviews that match past data. This allows for the provision of high-quality tourist information by prioritizing analysis of reliable data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the reliability of tourist destination ratings and reviews into the generation AI and have the generation AI perform a reliability evaluation.

[0118] At the time of generation, the generation unit can generate a route based on specific criteria based on the congestion status of tourist spots. For example, it can generate a route that avoids tourist spots that are expected to be crowded. It can generate a route that visits tourist spots during times when they are less crowded. It can also generate a route that adjusts the order of visits 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 generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input congestion status data into the generation AI and have the generation AI generate a route.

[0119] At the time of provision, the provision unit can select the optimal provision method based on the user's device information. For example, if the user is using a smartphone, the provision unit selects a provision method that matches the screen size. If the user is using a tablet, the provision unit selects a provision method optimized for a large screen. Furthermore, if the user is using a smartwatch, the provision unit can select a provision method that is simple and highly visible. This allows the optimal provision method to be selected by taking the user's device information into consideration. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input the user's device information into the generation AI and have the generation AI select the provision method.

[0120] The collection unit can estimate the user's emotions and adjust the timing of collecting tourist data based on the estimated user emotions. For example, if the user is excited, the latest tourist data can be collected in real time. If the user is relaxed, tourist data can be collected periodically with a lower update frequency. Furthermore, if the user is stressed, only important tourist data can be collected preferentially. This allows the timing of tourist data collection to be adjusted according to the user's emotions, enabling data to be collected at a more appropriate time. Emotion estimation is achieved using 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 AI or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of collecting tourist data.

[0121] The providing unit can adjust the content to be provided based on the user's current location information when providing the information. For example, tourist spots close to the user's current location can be provided preferentially. The content to be provided can be adjusted taking into account the travel time from the user's current location. The content to be provided can also be adjusted to match the time of day the user is in their current location. This allows more appropriate information to be provided by taking into account the user's current location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data into the generating AI and have the generating AI adjust the content to be provided.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The collection unit collects tourism data and review data. Tourism data includes information about tourist destinations and visitor statistics. The collection unit collects ratings and reviews of tourist destinations from online review sites, as well as visitor impressions and survey results. For example, tourist destination ratings are collected as star ratings and comments. Step 2: The analysis unit analyzes the collected data and extracts tourist information based on specific criteria, including rating and filtering criteria. The analysis unit uses natural language processing technology to evaluate the reliability of tourist destination ratings and reviews, and machine learning algorithms to evaluate the popularity of tourist destinations and visitor satisfaction. Step 3: The generation unit generates a tourist route based on the information extracted by the analysis unit. The generation of a tourist route includes criteria such as the shortest route and the most popular route. The generation unit generates the optimal tourist route for the user based on the ratings and reviews of tourist spots, and can also generate a personalized tourist route based on the user's desired itinerary and past behavior data. Step 4: The providing unit provides the user with the tourist route generated by the generating unit. The providing unit displays the generated tourist route to the user via a web application or a mobile application, and can also send it by email. Furthermore, once the user's desired itinerary is confirmed, the providing unit automatically generates a plan that incorporates local information (such as festivals and events) for that period.

[0124] 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.

[0125] 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.

[0126] 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.

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

[0129] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0142] 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.

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

[0145] 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.

[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 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.

[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 (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).

[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] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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 unit that collects tourist data and review data; an analysis unit that analyzes the data collected by the collection unit and extracts tourist information based on specific criteria; a generation unit that generates a tourist route based on the information extracted by the analysis unit; a providing unit that provides the tourist route generated by the generating unit to a user. A system characterized by:

2. The collecting unit Collecting destination ratings, reviews, and visitor feedback data 2. The system of claim 1.

3. The analysis unit Analyze the collected data and extract tourism information based on specific criteria 2. The system of claim 1.

4. The generation unit Generate tourist routes based on specific criteria using extracted information 2. The system of claim 1.

5. The providing unit Providing the generated tourist route to the user 2. The system of claim 1.

6. The providing unit Once the user's desired dates are confirmed, a plan incorporating local information based on the specific time period is automatically generated.

2. The system of claim 1.

7. The providing unit Applying user past behavior data and preferences to propose personalized sightseeing plans for each individual user 2. The system of claim 1.

8. The providing unit We also make reservations for local restaurants and arrange activities.

2. The system of claim 1.

Images