System

Abstract

An object of a system according to an embodiment is to provide information on a tourist spot, make a reservation, and propose congestion avoidance in an integrated manner.SOLUTION: A system includes an acquisition unit, an analysis unit, a reservation unit, and a re-proposal unit. The acquisition unit acquires information by scanning a two dimensional code. The analysis unit analyzes the information acquired by the acquisition unit and proposes or explains a tourist spot. The reservation part reserves the tourist resort on the basis of the information obtained by the analysis part. The re-suggestion unit makes a re-suggestion in consideration of the congestion situation of the tourist spot reserved by the reservation 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 did not provide information about tourist destinations, reservations, or suggestions for avoiding crowds in a centralized manner, which resulted in low convenience for tourists.

[0005] The system according to the embodiment aims to provide information on tourist destinations, make reservations, and suggest ways to avoid crowds in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a reservation unit, and a re-proposal unit. The acquisition unit acquires information by scanning a two-dimensional code. The analysis unit analyzes the information acquired by the acquisition unit and proposes and explains tourist destinations. The reservation unit makes reservations for tourist destinations based on the information acquired by the analysis unit. The re-proposal unit makes re-proposals taking into account the congestion status of the tourist destination reserved by the reservation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information about tourist spots, make reservations, and suggest ways to avoid crowds in an integrated manner. [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 tourism support system according to an embodiment of the present invention uses two-dimensional codes (e.g., QR codes (registered trademark)) placed at tourist destinations to automatically generate recommendations for tourist destinations and explanations of events that occurred at those destinations. The tourism support system not only allows tourists to visit tourist destinations but also allows them to gain a deeper understanding of the events and history of those destinations. For example, when a tourist scans a two-dimensional code placed on a monument or other object with a smartphone, recommendations for nearby tourist destinations and explanations of events that occurred at those destinations are automatically generated and displayed on the smartphone. Furthermore, if a tourist wants to visit a specific tourist destination, they can make a reservation right away. The system also has a re-recommendation function that allows tourists to set a time when they can visit the destination and avoid crowds. This allows the tourism support system to efficiently tour tourist destinations and gain a deeper understanding of the history and events of the tourist destinations. Furthermore, by avoiding crowds, a comfortable sightseeing experience can be provided.

[0029] A tourism support system according to an embodiment includes an acquisition unit, an analysis unit, a reservation unit, and a re-suggestion unit. The acquisition unit acquires information by scanning a two-dimensional code. For example, the acquisition unit may scan the two-dimensional code using a smartphone camera to acquire the information. Alternatively, the acquisition unit may scan the two-dimensional code using a dedicated scanner. The analysis unit analyzes the information acquired by the acquisition unit to suggest and explain tourist destinations. For example, the analysis unit may analyze the acquired information in the form of text, audio, images, video, or the like to suggest and explain tourist destinations. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. The reservation unit makes reservations for tourist destinations based on the information acquired by the analysis unit. For example, the reservation unit may make reservations for tourist destinations by online reservation, telephone reservation, on-site reservation, or other methods. The re-suggestion unit makes re-suggestions taking into account the congestion status of the tourist destination reserved by the reservation unit. For example, the re-suggestion unit may re-suggest an optimal visiting time based on real-time congestion data and past congestion data. As a result, the tourism support system according to the embodiment improves convenience for tourists by automatically suggesting, explaining, booking, and re-suggesting tourist spots.

[0030] The tourism support system is equipped with a management unit that determines the installation location and management of 2D codes. The management unit determines the installation location and management of 2D codes. For example, the management unit installs 2D codes at the entrances to tourist destinations and major tourist spots. The management unit also sets selection criteria for the installation location of 2D codes and can install 2D codes in appropriate locations. Furthermore, the management unit regularly checks the installation status of 2D codes and performs maintenance on the 2D codes as necessary. In this way, by determining the installation location and management of 2D codes, the operational efficiency of the tourism support system is improved.

[0031] The analysis unit can be equipped with a mechanism for periodically updating the tourist destination information. The analysis unit is equipped with a mechanism for periodically updating the tourist destination information. For example, the analysis unit automatically updates the tourist destination information daily, weekly, or monthly. The analysis unit can also manually update the information. Furthermore, the analysis unit can update the tourist destination information in real time to provide the latest information. By periodically updating the tourist destination information, the latest information can be provided.

[0032] The re-suggestion unit can re-suggest an optimal visiting time based on the user's behavioral history. The re-suggestion unit re-suggests an optimal visiting time based on the user's behavioral history. For example, the re-suggestion unit analyzes the user's behavioral history based on GPS data, past visit history, and app usage history, and re-suggests an optimal visiting time. The re-suggestion unit can also suggest an optimal visiting time to avoid crowds based on the user's behavioral history. This makes it possible to avoid crowds when sightseeing by re-suggesting an optimal visiting time based on the user's behavioral history.

[0033] The acquisition unit can analyze the user's past sightseeing history and select the optimal information acquisition method. The acquisition unit analyzes the user's past sightseeing history and selects the optimal information acquisition method. For example, the acquisition unit prioritizes acquiring related information based on data on tourist spots the user has visited in the past. The acquisition unit can also select and provide information that is likely to be of interest to the user from the user's past sightseeing history. Furthermore, the acquisition unit can also prioritize selecting information acquisition methods (audio, text, etc.) that the user has used in the past. In this way, the optimal information acquisition method can be provided by analyzing the user's past sightseeing history.

[0034] The acquisition unit can perform filtering based on the user's current interests and concerns when scanning a two-dimensional code. The acquisition unit can perform filtering based on the user's current interests and concerns when scanning a two-dimensional code. For example, the acquisition unit can preferentially display information related to themes in which the user is currently interested. The acquisition unit can also filter unnecessary information based on the user's current interests. Furthermore, the acquisition unit can provide information on related tourist spots and events according to the user's interests. This makes it possible to provide highly relevant information by filtering information based on the user's current interests and concerns.

[0035] When scanning a two-dimensional code, the acquisition unit can select the optimal acquisition means depending on the user's input method. When scanning a two-dimensional code, the acquisition unit selects the optimal acquisition means depending on the user's input method. For example, if the user selects voice input, the acquisition unit can acquire information using voice recognition technology. Also, if the user selects text input, the acquisition unit can acquire information using text analysis technology. Furthermore, if the user selects image input, the acquisition unit can acquire information using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, the efficiency of information acquisition is improved.

[0036] When scanning a two-dimensional code, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. When scanning a two-dimensional code, the acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit can prioritize providing information about tourist spots close to the user's current location. The acquisition unit can also prioritize providing information about places that are easily accessible from the user's current location. Furthermore, the acquisition unit can prioritize providing information about historical events related to the user's current location. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0037] The acquisition unit can analyze the user's social media activity and acquire related information when the two-dimensional code is scanned. The acquisition unit can analyze the user's social media activity and acquire related information when the two-dimensional code is scanned. For example, the acquisition unit can provide information related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and provide information on related tourist spots. Furthermore, the acquisition unit can provide information on related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activity.

[0038] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when scanning a two-dimensional code. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when scanning a two-dimensional code. For example, the acquisition unit customizes the information acquisition method based on feedback provided by the user in the past. The acquisition unit can also select a preferred information acquisition means based on the user's past feedback. Furthermore, the acquisition unit can adjust the information display method by referring to the user's past feedback. In this way, the information acquisition method can be customized by reflecting the user's past feedback.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the tourist destination during the analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the tourist destination during the analysis. For example, the analysis unit performs a detailed analysis for an important tourist destination. The analysis unit can also perform a brief analysis for a less important tourist destination. Furthermore, the analysis unit can adjust the depth of the analysis based on the historical importance of the tourist destination. In this way, by adjusting the level of detail of the analysis based on the importance of the tourist destination, appropriate information can be provided.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the tourist destination during analysis. The analysis unit can apply different analysis algorithms depending on the category of the tourist destination during analysis. For example, in the case of a historical tourist destination, the analysis unit applies an analysis algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the analysis unit can also apply an analysis algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the analysis unit can also apply an analysis algorithm that emphasizes cultural background. In this way, by applying an analysis algorithm depending on the category of the tourist destination, appropriate information can be provided.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also set parameters for performing highly accurate analysis from the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0042] The analysis unit can determine the priority of analysis based on the time of visiting tourist spots during analysis. The analysis unit determines the priority of analysis based on the time of visiting tourist spots during analysis. For example, the analysis unit performs analysis preferentially if the time of visiting tourist spots is close. Also, the analysis unit can postpone analysis if the time of visiting tourist spots is far away. Furthermore, the analysis unit can determine the priority of analysis based on the time of visiting tourist spots. In this way, by determining the priority of analysis based on the time of visiting tourist spots, information can be provided at the appropriate time.

[0043] The analysis unit can adjust the order of analysis based on the relevance of tourist destinations during analysis. The analysis unit adjusts the order of analysis based on the relevance of tourist destinations during analysis. For example, the analysis unit prioritizes analysis of highly relevant tourist destinations. The analysis unit can also postpone analysis of less relevant tourist destinations. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of tourist destinations. In this way, by adjusting the order of analysis based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0044] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit uses a lot of technical terms. Also, if the user does not have technical knowledge, the analysis unit can avoid technical terms and use concise expressions. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0045] The reservation unit can adjust the level of detail of the reservation based on the importance of the tourist destination when making a reservation. The reservation unit can adjust the level of detail of the reservation based on the importance of the tourist destination when making a reservation. For example, the reservation unit provides detailed reservation information for important tourist destinations. The reservation unit can also provide concise reservation information for less important tourist destinations. Furthermore, the reservation unit can adjust the level of detail of the reservation based on the historical importance of the tourist destination. In this way, by adjusting the level of detail of the reservation based on the importance of the tourist destination, appropriate reservation information can be provided.

[0046] The reservation unit can apply different reservation algorithms depending on the category of the tourist destination when making a reservation. The reservation unit can apply different reservation algorithms depending on the category of the tourist destination when making a reservation. For example, in the case of a historical tourist destination, the reservation unit applies a reservation algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the reservation unit can also apply a reservation algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the reservation unit can also apply a reservation algorithm that emphasizes cultural background. In this way, by applying a reservation algorithm depending on the category of the tourist destination, appropriate reservation information can be provided.

[0047] The reservation unit can improve the accuracy of reservations by referring to the user's past reservation results when making a reservation. The reservation unit can improve the accuracy of reservations by referring to the user's past reservation results when making a reservation. For example, the reservation unit adjusts the reservation algorithm based on feedback provided by the user in the past. The reservation unit can also set parameters for making highly accurate reservations based on the user's past reservation results. Furthermore, the reservation unit can also improve the accuracy of reservations by referring to the user's past reservation results. In this way, the accuracy of reservations is improved by referring to the user's past reservation results.

[0048] The reservation unit can determine the priority of reservations based on the time of visiting a tourist attraction when making a reservation. The reservation unit determines the priority of reservations based on the time of visiting a tourist attraction when making a reservation. For example, the reservation unit will make reservations preferentially if the time of visiting a tourist attraction is approaching. The reservation unit can also make reservations at a later date if the time of visiting a tourist attraction is far away. Furthermore, the reservation unit can also determine the priority of reservations based on the time of visiting a tourist attraction. In this way, by determining the priority of reservations based on the time of visiting a tourist attraction, reservation information can be provided at the appropriate time.

[0049] The reservation unit can adjust the order of reservations based on the relevance of tourist destinations when making a reservation. The reservation unit adjusts the order of reservations based on the relevance of tourist destinations when making a reservation. For example, the reservation unit gives priority to reserving highly relevant tourist destinations. The reservation unit can also reserve less relevant tourist destinations later. Furthermore, the reservation unit can adjust the order of reservations based on the relevance of tourist destinations. In this way, by adjusting the order of reservations based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0050] The reservation unit can adjust the use of terminology for reservations according to the user's level of expertise when making a reservation. The reservation unit can adjust the use of terminology for reservations according to the user's level of expertise when making a reservation. For example, if the user has specialized knowledge, the reservation unit uses a lot of terminology. Also, if the user does not have specialized knowledge, the reservation unit can avoid using terminology and use concise expressions. Furthermore, the reservation unit can adjust the use of terminology for reservations according to the user's level of expertise. In this way, by adjusting the use of terminology according to the user's level of expertise, it is possible to provide reservation information that is easy for the user to understand.

[0051] The re-proposal unit can adjust the level of detail of the re-proposal based on the congestion status of the tourist destination when re-proposal. The re-proposal unit adjusts the level of detail of the re-proposal based on the congestion status of the tourist destination when re-proposal. For example, the re-proposal unit makes a detailed re-proposal in the case of a crowded tourist destination. Also, the re-proposal unit can make a concise re-proposal in the case of a uncrowded tourist destination. Furthermore, the re-proposal unit can adjust the level of detail of the re-proposal based on the congestion status of the tourist destination. In this way, by adjusting the level of detail of the re-proposal based on the congestion status of the tourist destination, appropriate re-proposal information can be provided.

[0052] The re-proposal unit can apply different re-proposal algorithms depending on the category of the tourist destination when re-proposal. The re-proposal unit applies different re-proposal algorithms depending on the category of the tourist destination when re-proposal. For example, in the case of a historical tourist destination, the re-proposal unit applies a re-proposal algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the re-proposal unit can also apply a re-proposal algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the re-proposal unit can also apply a re-proposal algorithm that emphasizes cultural background. In this way, by applying a re-proposal algorithm depending on the category of the tourist destination, appropriate re-proposal information can be provided.

[0053] The re-proposal unit can improve the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. The re-proposal unit improves the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. For example, the re-proposal unit adjusts the re-proposal algorithm based on feedback provided by the user in the past. The re-proposal unit can also set parameters for making highly accurate re-proposals based on the user's past re-proposal results. Furthermore, the re-proposal unit can also improve the accuracy of the re-proposal by referring to the user's past re-proposal results. In this way, the accuracy of the re-proposal is improved by referring to the user's past re-proposal results.

[0054] When re-proposing, the re-proposing unit can determine the priority of re-proposing based on the time of visiting the tourist destination. When re-proposing, the re-proposing unit determines the priority of re-proposing based on the time of visiting the tourist destination. For example, if the time of visiting the tourist destination is close, the re-proposing unit will prioritize re-proposing. Also, if the time of visiting the tourist destination is far away, the re-proposing unit can postpone re-proposing. Furthermore, the re-proposing unit can also determine the priority of re-proposing based on the time of visiting the tourist destination. In this way, by determining the priority of re-proposing based on the time of visiting the tourist destination, re-proposing information can be provided at an appropriate time.

[0055] The re-proposal unit can adjust the order of re-proposal based on the relevance of tourist destinations when re-proposal is performed. The re-proposal unit adjusts the order of re-proposal based on the relevance of tourist destinations when re-proposal is performed. For example, the re-proposal unit prioritizes re-proposing highly relevant tourist destinations. The re-proposal unit can also postpone re-proposing less relevant tourist destinations. Furthermore, the re-proposal unit can adjust the order of re-proposal based on the relevance of tourist destinations. In this way, by adjusting the order of re-proposal based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0056] The re-suggestion unit can adjust the use of technical terms in the re-suggestion according to the user's level of expertise when re-suggesting. The re-suggestion unit adjusts the use of technical terms in the re-suggestion according to the user's level of expertise when re-suggesting. For example, if the user has technical knowledge, the re-suggestion unit uses a lot of technical terms. Also, if the user does not have technical knowledge, the re-suggestion unit can avoid technical terms and use concise expressions. Furthermore, the re-suggestion unit can adjust the use of technical terms in the re-suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide re-suggestion information that is easy for the user to understand.

[0057] The management department can select the location where the 2D code is to be installed based on the importance of the tourist destination. The management department selects the location where the 2D code is to be installed based on the importance of the tourist destination. For example, the management department installs the 2D code in a prominent location for an important tourist destination. The management department can also install the 2D code in a more concise location for a less important tourist destination. Furthermore, the management department can select the location where the 2D code is to be installed based on the historical importance of the tourist destination. In this way, by selecting the location where the 2D code is to be installed based on the importance of the tourist destination, 2D codes can be appropriately installed at important tourist destinations.

[0058] The management unit can apply different installation methods to the installation location of the 2D code depending on the category of the tourist attraction. The management unit applies different installation methods to the installation location of the 2D code depending on the category of the tourist attraction. For example, in the case of a historical tourist attraction, the management unit applies an installation method that takes into account the historical background. In addition, in the case of a tourist attraction with a natural landscape, the management unit can also apply an installation method that takes into account the natural environment. Furthermore, in the case of a cultural tourist attraction, the management unit can apply an installation method that takes into account the cultural background. In this way, by applying an installation method depending on the category of the tourist attraction, it is possible to install 2D codes in appropriate locations.

[0059] The management unit can select the location where the 2D code will be installed based on the time of visiting the tourist attraction. The management unit selects the location where the 2D code will be installed based on the time of visiting the tourist attraction. For example, if the time of visiting the tourist attraction is approaching, the management unit can prioritize installing the 2D code. In addition, if the time of visiting the tourist attraction is far away, the management unit can also install the 2D code at a later date. Furthermore, the management unit can select the location where the 2D code will be installed based on the time of visiting the tourist attraction. In this way, by selecting the location where the 2D code will be installed based on the time of visiting the tourist attraction, the 2D code can be installed at the appropriate time.

[0060] The management unit can select the location where the 2D code will be installed based on the relevance of the tourist destinations. The management unit selects the location where the 2D code will be installed based on the relevance of the tourist destinations. For example, the management unit can prioritize installing 2D codes at tourist destinations with high relevance. The management unit can also install 2D codes at tourist destinations with low relevance later. Furthermore, the management unit can also select the location where the 2D code will be installed based on the relevance of the tourist destinations. In this way, by selecting the location where the 2D code will be installed based on the relevance of the tourist destinations, 2D codes can be appropriately installed at tourist destinations with high relevance.

[0061] The information update function (included in the analysis unit) can adjust the level of detail of the update based on the importance of the tourist destination when updating information. The information update function adjusts the level of detail of the update based on the importance of the tourist destination when updating information. For example, the information update function frequently updates detailed information for important tourist destinations. The information update function can also update concise information at an appropriate frequency for less important tourist destinations. Furthermore, the information update function can adjust the level of detail of the information update based on the historical importance of the tourist destination. In this way, appropriate information can be provided by adjusting the level of detail of the update based on the importance of the tourist destination.

[0062] The information update function (included in the analysis unit) can apply different update algorithms depending on the category of tourist destination when updating information. The information update function applies different update algorithms depending on the category of tourist destination when updating information. For example, in the case of a historical tourist destination, the information update function applies an update algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the information update function can also apply an update algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the information update function can also apply an update algorithm that emphasizes cultural background. In this way, by applying an update algorithm depending on the category of tourist destination, appropriate information can be provided.

[0063] The information update function (included in the analysis unit) can determine the priority of updates when updating information based on the time of visiting a tourist destination. When updating information, the information update function determines the priority of updates based on the time of visiting a tourist destination. For example, if the time of visiting a tourist destination is approaching, the information update function will prioritize updating information. In addition, if the time of visiting a tourist destination is far away, the information update function can also postpone updating information. Furthermore, the information update function can also determine the priority of information updates based on the time of visiting a tourist destination. In this way, by determining the priority of updates based on the time of visiting a tourist destination, information can be provided at the appropriate time.

[0064] The information update function (included in the analysis unit) can adjust the order of updates based on the relevance of tourist destinations when updating information. The information update function adjusts the order of updates based on the relevance of tourist destinations when updating information. For example, the information update function prioritizes updating information about highly relevant tourist destinations. The information update function can also postpone updating information about less relevant tourist destinations. Furthermore, the information update function can also adjust the order of information updates based on the relevance of tourist destinations. In this way, by adjusting the order of updates based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0065] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can adjust the level of detail of the re-suggestion based on the user's past behavioral history when making a re-suggestion. The behavioral history-based re-suggestion function adjusts the level of detail of the re-suggestion based on the user's past behavioral history when making a re-suggestion. For example, the behavioral history-based re-suggestion function makes detailed re-suggestions based on data on tourist spots the user has visited in the past. The behavioral history-based re-suggestion function can also select and re-suggest information that is likely to be of interest to the user from the user's past behavioral history. Furthermore, the behavioral history-based re-suggestion function can also prioritize and re-suggest information that the user has used in the past (voice, text, etc.). In this way, by adjusting the level of detail of the re-suggestion based on the user's past behavioral history, it is possible to provide appropriate re-suggestion information.

[0066] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can apply different re-suggestion algorithms depending on the category of the user's behavioral history when making a re-suggestion. The behavioral history-based re-suggestion function applies different re-suggestion algorithms depending on the category of the user's behavioral history when making a re-suggestion. For example, the behavioral history-based re-suggestion function applies a re-suggestion algorithm that emphasizes historical data in the case of a historical tourist destination. Also, the behavioral history-based re-suggestion function can apply a re-suggestion algorithm that emphasizes data related to the natural environment in the case of a tourist destination with natural scenery. Furthermore, the behavioral history-based re-suggestion function can apply a re-suggestion algorithm that emphasizes cultural background in the case of a cultural tourist destination. In this way, by applying a re-suggestion algorithm depending on the category of the user's behavioral history, appropriate re-suggestion information can be provided.

[0067] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can determine the priority of re-suggestion based on the visit time in the user's behavioral history when re-suggesting. The behavioral history-based re-suggestion function determines the priority of re-suggestion based on the visit time in the user's behavioral history when re-suggesting. For example, the behavioral history-based re-suggestion function prioritizes re-suggestion when the visit time in the user's behavioral history is close. In addition, the behavioral history-based re-suggestion function can also postpone re-suggestion when the visit time in the user's behavioral history is far away. Furthermore, the behavioral history-based re-suggestion function can also determine the priority of re-suggestion based on the visit time in the user's behavioral history. In this way, by determining the priority of re-suggestion based on the visit time in the user's behavioral history, re-suggestion information can be provided at an appropriate time.

[0068] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can adjust the order of re-suggestion based on the relevance of the user's behavioral history when re-suggesting. The behavioral history-based re-suggestion function adjusts the order of re-suggestion based on the relevance of the user's behavioral history when re-suggesting. For example, the behavioral history-based re-suggestion function prioritizes re-suggestion of highly relevant behavioral history. The behavioral history-based re-suggestion function can also postpone re-suggestion of less relevant behavioral history. Furthermore, the behavioral history-based re-suggestion function can also adjust the order of re-suggestion based on the relevance of the user's behavioral history. As a result, by adjusting the order of re-suggestion based on the relevance of the user's behavioral history, highly relevant information can be provided preferentially.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The acquisition unit can monitor the user's current health condition and suggest optimal tourist spots. For example, the acquisition unit can acquire the user's heart rate and step count in real time, and if the user is highly fatigued, suggest rest spots or easy walking courses. The acquisition unit can also suggest active tourist spots or long-distance walking courses if the user is in good health. Furthermore, the acquisition unit can adjust the difficulty of activities at tourist spots based on the user's health condition. This makes it possible to suggest optimal tourist spots according to the user's health condition.

[0071] The analysis unit can analyze the user's past stay time at tourist attractions and suggest the optimal stay time. For example, the analysis unit can suggest the stay time at the next tourist attraction based on the user's average stay time at tourist attractions that they have visited in the past. The analysis unit can also suggest extending the stay time if the user revisits a tourist attraction that they previously visited for a short time. Furthermore, the analysis unit can also suggest the optimal stay time to avoid crowds based on the user's past stay time data. This makes it possible to suggest the optimal stay time based on the user's past stay time data.

[0072] The acquisition unit can analyze the content posted by the user on social media and provide information on related tourist spots. For example, the acquisition unit can analyze photos and comments shared by the user on social media and prioritize displaying information on related tourist spots. The acquisition unit can also provide information on tourist spots visited by the user's friends on social media, based on their posts. Furthermore, the acquisition unit can provide information on related events and activities based on the user's social media interests. This makes it possible to provide highly relevant information based on the user's social media activities.

[0073] The acquisition unit can acquire the user's current weather information and suggest optimal tourist destinations. For example, the acquisition unit can suggest indoor tourist destinations and activities when it is raining. The acquisition unit can also suggest outdoor tourist destinations and activities when it is sunny. Furthermore, the acquisition unit can also suggest tourist destinations and activities suitable for extreme temperatures (high or low). This makes it possible to suggest optimal tourist destinations based on current weather information.

[0074] The acquisition unit can analyze the user's past purchase history and provide information on related tourist spots and activities. For example, the acquisition unit can preferentially display information on tourist spots related to products or services the user has previously purchased. The acquisition unit can also select and provide tourist spots and activities that the user is likely to be interested in based on the user's purchase history. Furthermore, the acquisition unit can provide information on related events and benefits based on the user's purchase history. This makes it possible to provide highly relevant information based on the user's purchase history.

[0075] The re-suggestion unit can analyze the user's past feedback and provide optimal re-suggestion content. For example, the re-suggestion unit customizes the re-suggestion content based on the user's past feedback. The re-suggestion unit can also select and re-suggest preferred tourist spots and activities based on the user's past feedback. Furthermore, the re-suggestion unit can also adjust the display method of the re-suggestion by referring to the user's past feedback. This makes it possible to provide optimal re-suggestion content based on the user's past feedback.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The acquisition unit scans the two-dimensional code to acquire information. For example, the acquisition unit may use a smartphone camera to scan the two-dimensional code and acquire information. Alternatively, the acquisition unit may use a dedicated scanner to scan the two-dimensional code. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and suggests and explains tourist spots. For example, the analysis unit analyzes the acquired information in the form of text, audio, images, video, etc., and suggests and explains tourist spots. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. Step 3: The reservation unit makes a reservation for the tourist attraction based on the information obtained by the analysis unit. For example, the reservation unit makes a reservation for the tourist attraction by online reservation, telephone reservation, on-site reservation, or the like. Step 4: The re-proposal unit re-proposes the optimal visit time based on the congestion status of the tourist destination reserved by the reservation unit. For example, the re-proposal unit re-proposes the optimal visit time based on real-time congestion data and past congestion data.

[0078] (Example 2) A tourism support system according to an embodiment of the present invention uses two-dimensional codes placed at tourist destinations to automatically generate recommendations for tourist destinations and explanations of events that occurred at those destinations. The tourism support system not only allows tourists to visit tourist destinations but also allows them to gain a deeper understanding of the events and history of those destinations. For example, when a tourist scans a two-dimensional code placed on a monument or other landmark with their smartphone, recommendations for nearby tourist destinations and explanations of events that occurred at those destinations are automatically generated and displayed on the smartphone. Furthermore, if a tourist wants to visit a specific tourist destination, they can make a reservation right away. The system also has a re-recommendation function that allows tourists to set a time when they can visit the destination and avoid crowds. This allows the tourism support system to efficiently tour tourist destinations and gain a deeper understanding of the history and events of the destinations. Furthermore, by avoiding crowds, a comfortable sightseeing experience can be provided.

[0079] A tourism support system according to an embodiment includes an acquisition unit, an analysis unit, a reservation unit, and a re-suggestion unit. The acquisition unit acquires information by scanning a two-dimensional code. For example, the acquisition unit may scan the two-dimensional code using a smartphone camera to acquire the information. Alternatively, the acquisition unit may scan the two-dimensional code using a dedicated scanner. The analysis unit analyzes the information acquired by the acquisition unit to suggest and explain tourist destinations. For example, the analysis unit may analyze the acquired information in the form of text, audio, images, video, or the like to suggest and explain tourist destinations. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. The reservation unit makes reservations for tourist destinations based on the information acquired by the analysis unit. For example, the reservation unit may make reservations for tourist destinations by online reservation, telephone reservation, on-site reservation, or other methods. The re-suggestion unit makes re-suggestions taking into account the congestion status of the tourist destination reserved by the reservation unit. For example, the re-suggestion unit may re-suggest an optimal visiting time based on real-time congestion data and past congestion data. As a result, the tourism support system according to the embodiment improves convenience for tourists by automatically suggesting, explaining, booking, and re-suggesting tourist spots.

[0080] The tourism support system is equipped with a management unit that determines the installation location and management of 2D codes. The management unit determines the installation location and management of 2D codes. For example, the management unit installs 2D codes at the entrances to tourist destinations and major tourist spots. The management unit also sets selection criteria for the installation location of 2D codes and can install 2D codes in appropriate locations. Furthermore, the management unit regularly checks the installation status of 2D codes and performs maintenance on the 2D codes as necessary. In this way, by determining the installation location and management of 2D codes, the operational efficiency of the tourism support system is improved.

[0081] The analysis unit can be equipped with a mechanism for periodically updating the tourist destination information. The analysis unit is equipped with a mechanism for periodically updating the tourist destination information. For example, the analysis unit automatically updates the tourist destination information daily, weekly, or monthly. The analysis unit can also manually update the information. Furthermore, the analysis unit can update the tourist destination information in real time to provide the latest information. By periodically updating the tourist destination information, the latest information can be provided.

[0082] The re-suggestion unit can re-suggest an optimal visiting time based on the user's behavioral history. The re-suggestion unit re-suggests an optimal visiting time based on the user's behavioral history. For example, the re-suggestion unit analyzes the user's behavioral history based on GPS data, past visit history, and app usage history, and re-suggests an optimal visiting time. The re-suggestion unit can also suggest an optimal visiting time to avoid crowds based on the user's behavioral history. This makes it possible to avoid crowds when sightseeing by re-suggesting an optimal visiting time based on the user's behavioral history.

[0083] The acquisition unit can estimate the user's emotions and adjust the timing of scanning the 2D code based on the estimated user emotions. The acquisition unit can estimate the user's emotions and adjust the timing of scanning the 2D code based on the estimated user emotions. For example, if the acquisition unit is excited, it can prompt the user to scan the 2D code quickly. Alternatively, if the user is relaxed, it can prompt the user to scan the 2D code at a slower pace. Furthermore, if the user is stressed, the acquisition unit can simplify the scanning procedure to ensure smooth progress. This improves user convenience by adjusting the timing of scanning the 2D code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0084] The acquisition unit can analyze the user's past sightseeing history and select the optimal information acquisition method. The acquisition unit analyzes the user's past sightseeing history and selects the optimal information acquisition method. For example, the acquisition unit prioritizes acquiring related information based on data on tourist spots the user has visited in the past. The acquisition unit can also select and provide information that is likely to be of interest to the user from the user's past sightseeing history. Furthermore, the acquisition unit can also prioritize selecting information acquisition methods (audio, text, etc.) that the user has used in the past. In this way, the optimal information acquisition method can be provided by analyzing the user's past sightseeing history.

[0085] The acquisition unit can perform filtering based on the user's current interests and concerns when scanning a two-dimensional code. The acquisition unit can perform filtering based on the user's current interests and concerns when scanning a two-dimensional code. For example, the acquisition unit can preferentially display information related to themes in which the user is currently interested. The acquisition unit can also filter unnecessary information based on the user's current interests. Furthermore, the acquisition unit can provide information on related tourist spots and events according to the user's interests. This makes it possible to provide highly relevant information by filtering information based on the user's current interests and concerns.

[0086] When scanning a two-dimensional code, the acquisition unit can select the optimal acquisition means depending on the user's input method. When scanning a two-dimensional code, the acquisition unit selects the optimal acquisition means depending on the user's input method. For example, if the user selects voice input, the acquisition unit can acquire information using voice recognition technology. Also, if the user selects text input, the acquisition unit can acquire information using text analysis technology. Furthermore, if the user selects image input, the acquisition unit can acquire information using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, the efficiency of information acquisition is improved.

[0087] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. For example, when the user is excited, the acquisition unit can prioritize providing interesting information. When the user is relaxed, the acquisition unit can also prioritize providing detailed information. When the user is stressed, the acquisition unit can also prioritize providing concise and to-the-point information. In this way, by determining the priority of information based on the user's emotions, it is possible to prioritize providing information that is important to the user. Emotion estimation is realized using an emotion estimation function, for example, 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.

[0088] When scanning a two-dimensional code, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. When scanning a two-dimensional code, the acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit can prioritize providing information about tourist spots close to the user's current location. The acquisition unit can also prioritize providing information about places that are easily accessible from the user's current location. Furthermore, the acquisition unit can prioritize providing information about historical events related to the user's current location. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0089] The acquisition unit can analyze the user's social media activity and acquire related information when the two-dimensional code is scanned. The acquisition unit can analyze the user's social media activity and acquire related information when the two-dimensional code is scanned. For example, the acquisition unit can provide information related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and provide information on related tourist spots. Furthermore, the acquisition unit can provide information on related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activity.

[0090] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when scanning a two-dimensional code. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when scanning a two-dimensional code. For example, the acquisition unit customizes the information acquisition method based on feedback provided by the user in the past. The acquisition unit can also select a preferred information acquisition means based on the user's past feedback. Furthermore, the acquisition unit can adjust the information display method by referring to the user's past feedback. In this way, the information acquisition method can be customized by reflecting the user's past feedback.

[0091] The analysis unit can estimate the user's emotion and adjust the way the analysis results are presented based on the estimated user emotion. The analysis unit can estimate the user's emotion and adjust the way the analysis results are presented based on the estimated user emotion. For example, if the user is excited, the analysis unit uses a visually stimulating presentation method. If the user is relaxed, the analysis unit can also use a calm presentation method. Furthermore, if the user is stressed, the analysis unit can also use a simple, highly visible presentation method. By adjusting the way the analysis results are presented based on the user's emotion, it is possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the tourist destination during the analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the tourist destination during the analysis. For example, the analysis unit performs a detailed analysis for an important tourist destination. The analysis unit can also perform a brief analysis for a less important tourist destination. Furthermore, the analysis unit can adjust the depth of the analysis based on the historical importance of the tourist destination. In this way, by adjusting the level of detail of the analysis based on the importance of the tourist destination, appropriate information can be provided.

[0093] The analysis unit can apply different analysis algorithms depending on the category of the tourist destination during analysis. The analysis unit can apply different analysis algorithms depending on the category of the tourist destination during analysis. For example, in the case of a historical tourist destination, the analysis unit applies an analysis algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the analysis unit can also apply an analysis algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the analysis unit can also apply an analysis algorithm that emphasizes cultural background. In this way, by applying an analysis algorithm depending on the category of the tourist destination, appropriate information can be provided.

[0094] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also set parameters for performing highly accurate analysis from the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis result based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis result based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis result based on the user's emotions, appropriate information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, 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.

[0096] The analysis unit can determine the priority of analysis based on the time of visiting tourist spots during analysis. The analysis unit determines the priority of analysis based on the time of visiting tourist spots during analysis. For example, the analysis unit performs analysis preferentially if the time of visiting tourist spots is close. Also, the analysis unit can postpone analysis if the time of visiting tourist spots is far away. Furthermore, the analysis unit can determine the priority of analysis based on the time of visiting tourist spots. In this way, by determining the priority of analysis based on the time of visiting tourist spots, information can be provided at the appropriate time.

[0097] The analysis unit can adjust the order of analysis based on the relevance of tourist destinations during analysis. The analysis unit adjusts the order of analysis based on the relevance of tourist destinations during analysis. For example, the analysis unit prioritizes analysis of highly relevant tourist destinations. The analysis unit can also postpone analysis of less relevant tourist destinations. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of tourist destinations. In this way, by adjusting the order of analysis based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0098] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit uses a lot of technical terms. Also, if the user does not have technical knowledge, the analysis unit can avoid technical terms and use concise expressions. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0099] The reservation unit can estimate the user's emotions and adjust the way the reservation is presented based on the estimated user emotions. The reservation unit can estimate the user's emotions and adjust the way the reservation is presented based on the estimated user emotions. For example, if the user is excited, the reservation unit uses a visually stimulating presentation. If the user is relaxed, the reservation unit can also use a calm presentation. If the user is stressed, the reservation unit can also use a simple, highly visible presentation. By adjusting the way the reservation is presented based on the user's emotions, reservation information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The reservation unit can adjust the level of detail of the reservation based on the importance of the tourist destination when making a reservation. The reservation unit can adjust the level of detail of the reservation based on the importance of the tourist destination when making a reservation. For example, the reservation unit provides detailed reservation information for important tourist destinations. The reservation unit can also provide concise reservation information for less important tourist destinations. Furthermore, the reservation unit can adjust the level of detail of the reservation based on the historical importance of the tourist destination. In this way, by adjusting the level of detail of the reservation based on the importance of the tourist destination, appropriate reservation information can be provided.

[0101] The reservation unit can apply different reservation algorithms depending on the category of the tourist destination when making a reservation. The reservation unit can apply different reservation algorithms depending on the category of the tourist destination when making a reservation. For example, in the case of a historical tourist destination, the reservation unit applies a reservation algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the reservation unit can also apply a reservation algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the reservation unit can also apply a reservation algorithm that emphasizes cultural background. In this way, by applying a reservation algorithm depending on the category of the tourist destination, appropriate reservation information can be provided.

[0102] The reservation unit can improve the accuracy of reservations by referring to the user's past reservation results when making a reservation. The reservation unit can improve the accuracy of reservations by referring to the user's past reservation results when making a reservation. For example, the reservation unit adjusts the reservation algorithm based on feedback provided by the user in the past. The reservation unit can also set parameters for making highly accurate reservations based on the user's past reservation results. Furthermore, the reservation unit can also improve the accuracy of reservations by referring to the user's past reservation results. In this way, the accuracy of reservations is improved by referring to the user's past reservation results.

[0103] The reservation unit can estimate the user's emotions and adjust the length of the reservation based on the estimated user emotions. The reservation unit can estimate the user's emotions and adjust the length of the reservation based on the estimated user emotions. For example, if the user is in a hurry, the reservation unit can provide a short and concise reservation procedure. Alternatively, if the user is relaxed, the reservation unit can provide a detailed reservation procedure. Furthermore, if the user is excited, the reservation unit can provide a visually stimulating reservation procedure. In this way, by adjusting the length of the reservation based on the user's emotions, a reservation procedure appropriate for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The reservation unit can determine the priority of reservations based on the time of visiting a tourist attraction when making a reservation. The reservation unit determines the priority of reservations based on the time of visiting a tourist attraction when making a reservation. For example, the reservation unit will make reservations preferentially if the time of visiting a tourist attraction is approaching. The reservation unit can also make reservations at a later date if the time of visiting a tourist attraction is far away. Furthermore, the reservation unit can also determine the priority of reservations based on the time of visiting a tourist attraction. In this way, by determining the priority of reservations based on the time of visiting a tourist attraction, reservation information can be provided at the appropriate time.

[0105] The reservation unit can adjust the order of reservations based on the relevance of tourist destinations when making a reservation. The reservation unit adjusts the order of reservations based on the relevance of tourist destinations when making a reservation. For example, the reservation unit gives priority to reserving highly relevant tourist destinations. The reservation unit can also reserve less relevant tourist destinations later. Furthermore, the reservation unit can adjust the order of reservations based on the relevance of tourist destinations. In this way, by adjusting the order of reservations based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0106] The reservation unit can adjust the use of terminology for reservations according to the user's level of expertise when making a reservation. The reservation unit can adjust the use of terminology for reservations according to the user's level of expertise when making a reservation. For example, if the user has specialized knowledge, the reservation unit uses a lot of terminology. Also, if the user does not have specialized knowledge, the reservation unit can avoid using terminology and use concise expressions. Furthermore, the reservation unit can adjust the use of terminology for reservations according to the user's level of expertise. In this way, by adjusting the use of terminology according to the user's level of expertise, it is possible to provide reservation information that is easy for the user to understand.

[0107] The re-suggestion unit can estimate the user's emotion and adjust the expression method of the re-suggestion based on the estimated user's emotion. The re-suggestion unit can estimate the user's emotion and adjust the expression method of the re-suggestion based on the estimated user's emotion. For example, if the user is excited, the re-suggestion unit can use a visually stimulating expression method. Also, if the user is relaxed, the re-suggestion unit can use a calm expression method. Furthermore, if the user is stressed, the re-suggestion unit can use a simple, highly visible expression method. In this way, by adjusting the expression method of the re-suggestion based on the user's emotion, it is possible to provide the user with re-suggestion information that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The re-proposal unit can adjust the level of detail of the re-proposal based on the congestion status of the tourist destination when re-proposal. The re-proposal unit adjusts the level of detail of the re-proposal based on the congestion status of the tourist destination when re-proposal. For example, the re-proposal unit makes a detailed re-proposal in the case of a crowded tourist destination. Also, the re-proposal unit can make a concise re-proposal in the case of a uncrowded tourist destination. Furthermore, the re-proposal unit can adjust the level of detail of the re-proposal based on the congestion status of the tourist destination. In this way, by adjusting the level of detail of the re-proposal based on the congestion status of the tourist destination, appropriate re-proposal information can be provided.

[0109] The re-proposal unit can apply different re-proposal algorithms depending on the category of the tourist destination when re-proposal. The re-proposal unit applies different re-proposal algorithms depending on the category of the tourist destination when re-proposal. For example, in the case of a historical tourist destination, the re-proposal unit applies a re-proposal algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the re-proposal unit can also apply a re-proposal algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the re-proposal unit can also apply a re-proposal algorithm that emphasizes cultural background. In this way, by applying a re-proposal algorithm depending on the category of the tourist destination, appropriate re-proposal information can be provided.

[0110] The re-proposal unit can improve the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. The re-proposal unit improves the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. For example, the re-proposal unit adjusts the re-proposal algorithm based on feedback provided by the user in the past. The re-proposal unit can also set parameters for making highly accurate re-proposals based on the user's past re-proposal results. Furthermore, the re-proposal unit can also improve the accuracy of the re-proposal by referring to the user's past re-proposal results. In this way, the accuracy of the re-proposal is improved by referring to the user's past re-proposal results.

[0111] The re-suggestion unit can estimate the user's emotion and adjust the length of the re-suggestion based on the estimated user's emotion. The re-suggestion unit can estimate the user's emotion and adjust the length of the re-suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the re-suggestion unit can provide a short and to-the-point re-suggestion. Also, if the user is relaxed, the re-suggestion unit can provide a detailed re-suggestion. Furthermore, if the user is excited, the re-suggestion unit can provide a visually stimulating re-suggestion. In this way, by adjusting the length of the re-suggestion based on the user's emotion, appropriate re-suggestion information can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, 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.

[0112] When re-proposing, the re-proposing unit can determine the priority of re-proposing based on the time of visiting the tourist destination. When re-proposing, the re-proposing unit determines the priority of re-proposing based on the time of visiting the tourist destination. For example, if the time of visiting the tourist destination is close, the re-proposing unit will prioritize re-proposing. Also, if the time of visiting the tourist destination is far away, the re-proposing unit can postpone re-proposing. Furthermore, the re-proposing unit can also determine the priority of re-proposing based on the time of visiting the tourist destination. In this way, by determining the priority of re-proposing based on the time of visiting the tourist destination, re-proposing information can be provided at an appropriate time.

[0113] The re-proposal unit can adjust the order of re-proposal based on the relevance of tourist destinations when re-proposal is performed. The re-proposal unit adjusts the order of re-proposal based on the relevance of tourist destinations when re-proposal is performed. For example, the re-proposal unit prioritizes re-proposing highly relevant tourist destinations. The re-proposal unit can also postpone re-proposing less relevant tourist destinations. Furthermore, the re-proposal unit can adjust the order of re-proposal based on the relevance of tourist destinations. In this way, by adjusting the order of re-proposal based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0114] The re-suggestion unit can adjust the use of technical terms in the re-suggestion according to the user's level of expertise when re-suggesting. The re-suggestion unit adjusts the use of technical terms in the re-suggestion according to the user's level of expertise when re-suggesting. For example, if the user has technical knowledge, the re-suggestion unit uses a lot of technical terms. Also, if the user does not have technical knowledge, the re-suggestion unit can avoid technical terms and use concise expressions. Furthermore, the re-suggestion unit can adjust the use of technical terms in the re-suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide re-suggestion information that is easy for the user to understand.

[0115] The management unit can estimate the user's emotions and adjust the location of the 2D code based on the estimated user emotions. The management unit can estimate the user's emotions and adjust the location of the 2D code based on the estimated user emotions. For example, if the user is excited, the management unit can place the 2D code in a visually noticeable location. If the user is relaxed, the management unit can place the 2D code in a calm location. Furthermore, if the user is stressed, the management unit can place the 2D code in an easily accessible location. In this way, by adjusting the location of the 2D code based on the user's emotions, the code can be placed in a location that is easily accessible to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The management department can select the location where the 2D code is to be installed based on the importance of the tourist destination. The management department selects the location where the 2D code is to be installed based on the importance of the tourist destination. For example, the management department installs the 2D code in a prominent location for an important tourist destination. The management department can also install the 2D code in a more concise location for a less important tourist destination. Furthermore, the management department can select the location where the 2D code is to be installed based on the historical importance of the tourist destination. In this way, by selecting the location where the 2D code is to be installed based on the importance of the tourist destination, 2D codes can be appropriately installed at important tourist destinations.

[0117] The management unit can apply different installation methods to the installation location of the 2D code depending on the category of the tourist attraction. The management unit applies different installation methods to the installation location of the 2D code depending on the category of the tourist attraction. For example, in the case of a historical tourist attraction, the management unit applies an installation method that takes into account the historical background. In addition, in the case of a tourist attraction with a natural landscape, the management unit can also apply an installation method that takes into account the natural environment. Furthermore, in the case of a cultural tourist attraction, the management unit can apply an installation method that takes into account the cultural background. In this way, by applying an installation method depending on the category of the tourist attraction, it is possible to install 2D codes in appropriate locations.

[0118] The management unit can estimate the user's emotions and prioritize the locations where 2D codes should be placed based on the estimated user emotions. The management unit can estimate the user's emotions and prioritize the locations where 2D codes should be placed based on the estimated user emotions. For example, if the user is excited, the management unit can prioritize placing 2D codes in visually prominent locations. If the user is relaxed, the management unit can also prioritize placing 2D codes in calm locations. If the user is stressed, the management unit can also prioritize placing 2D codes in locations that are easy to access. In this way, by prioritizing the locations where 2D codes should be placed based on the user's emotions, 2D codes can be preferentially placed in locations that are easy for the user to access. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The management unit can select the location where the 2D code will be installed based on the time of visiting the tourist attraction. The management unit selects the location where the 2D code will be installed based on the time of visiting the tourist attraction. For example, if the time of visiting the tourist attraction is approaching, the management unit can prioritize installing the 2D code. In addition, if the time of visiting the tourist attraction is far away, the management unit can also install the 2D code at a later date. Furthermore, the management unit can select the location where the 2D code will be installed based on the time of visiting the tourist attraction. In this way, by selecting the location where the 2D code will be installed based on the time of visiting the tourist attraction, the 2D code can be installed at the appropriate time.

[0120] The management unit can select the location where the 2D code will be installed based on the relevance of the tourist destinations. The management unit selects the location where the 2D code will be installed based on the relevance of the tourist destinations. For example, the management unit can prioritize installing 2D codes at tourist destinations with high relevance. The management unit can also install 2D codes at tourist destinations with low relevance later. Furthermore, the management unit can also select the location where the 2D code will be installed based on the relevance of the tourist destinations. In this way, by selecting the location where the 2D code will be installed based on the relevance of the tourist destinations, 2D codes can be appropriately installed at tourist destinations with high relevance.

[0121] The information update function (included in the analysis unit) can estimate the user's emotions and adjust the frequency of information updates based on the estimated user emotions. The information update function estimates the user's emotions and adjusts the frequency of information updates based on the estimated user emotions. For example, the information update function updates information frequently when the user is excited. The information update function can also update information at an appropriate frequency when the user is relaxed. Furthermore, the information update function can update information at the minimum necessary frequency when the user is stressed. In this way, by adjusting the frequency of information updates based on the user's emotions, information can be updated at a frequency appropriate for the user. Emotion estimation is realized using the emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0122] The information update function (included in the analysis unit) can adjust the level of detail of the update based on the importance of the tourist destination when updating information. The information update function adjusts the level of detail of the update based on the importance of the tourist destination when updating information. For example, the information update function frequently updates detailed information for important tourist destinations. The information update function can also update concise information at an appropriate frequency for less important tourist destinations. Furthermore, the information update function can adjust the level of detail of the information update based on the historical importance of the tourist destination. In this way, appropriate information can be provided by adjusting the level of detail of the update based on the importance of the tourist destination.

[0123] The information update function (included in the analysis unit) can apply different update algorithms depending on the category of tourist destination when updating information. The information update function applies different update algorithms depending on the category of tourist destination when updating information. For example, in the case of a historical tourist destination, the information update function applies an update algorithm that emphasizes historical data. In addition, in the case of a tourist destination with natural scenery, the information update function can also apply an update algorithm that emphasizes data related to the natural environment. Furthermore, in the case of a cultural tourist destination, the information update function can also apply an update algorithm that emphasizes cultural background. In this way, by applying an update algorithm depending on the category of tourist destination, appropriate information can be provided.

[0124] The information update function (included in the analysis unit) can estimate the user's emotions and determine the priority of information updates based on the estimated user emotions. The information update function estimates the user's emotions and determines the priority of information updates based on the estimated user emotions. For example, if the user is excited, the information update function can prioritize updating interesting information. Also, if the user is relaxed, the information update function can prioritize updating detailed information. Furthermore, if the user is stressed, the information update function can prioritize updating concise and to-the-point information. In this way, by determining the priority of information updates based on the user's emotions, information important to the user can be prioritized for updating. Emotion estimation is realized using the 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.

[0125] The information update function (included in the analysis unit) can determine the priority of updates when updating information based on the time of visiting a tourist destination. When updating information, the information update function determines the priority of updates based on the time of visiting a tourist destination. For example, if the time of visiting a tourist destination is approaching, the information update function will prioritize updating information. In addition, if the time of visiting a tourist destination is far away, the information update function can also postpone updating information. Furthermore, the information update function can also determine the priority of information updates based on the time of visiting a tourist destination. In this way, by determining the priority of updates based on the time of visiting a tourist destination, information can be provided at the appropriate time.

[0126] The information update function (included in the analysis unit) can adjust the order of updates based on the relevance of tourist destinations when updating information. The information update function adjusts the order of updates based on the relevance of tourist destinations when updating information. For example, the information update function prioritizes updating information about highly relevant tourist destinations. The information update function can also postpone updating information about less relevant tourist destinations. Furthermore, the information update function can also adjust the order of information updates based on the relevance of tourist destinations. In this way, by adjusting the order of updates based on the relevance of tourist destinations, highly relevant information can be provided preferentially.

[0127] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can estimate the user's emotions and adjust the presentation of re-suggestions based on the estimated user emotions. The behavioral history-based re-suggestion function can estimate the user's emotions and adjust the presentation of re-suggestions based on the estimated user emotions. For example, the behavioral history-based re-suggestion function can use a visually stimulating presentation when the user is excited. The behavioral history-based re-suggestion function can also use a calm presentation when the user is relaxed. Furthermore, the behavioral history-based re-suggestion function can also use a simple, highly visible presentation when the user is stressed. This allows the presentation of re-suggestions to be adjusted based on the user's emotions, thereby providing re-suggestion information that is easy for the user to understand. Emotion estimation is realized 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.

[0128] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can adjust the level of detail of the re-suggestion based on the user's past behavioral history when making a re-suggestion. The behavioral history-based re-suggestion function adjusts the level of detail of the re-suggestion based on the user's past behavioral history when making a re-suggestion. For example, the behavioral history-based re-suggestion function makes detailed re-suggestions based on data on tourist spots the user has visited in the past. The behavioral history-based re-suggestion function can also select and re-suggest information that is likely to be of interest to the user from the user's past behavioral history. Furthermore, the behavioral history-based re-suggestion function can also prioritize and re-suggest information that the user has used in the past (voice, text, etc.). In this way, by adjusting the level of detail of the re-suggestion based on the user's past behavioral history, it is possible to provide appropriate re-suggestion information.

[0129] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can apply different re-suggestion algorithms depending on the category of the user's behavioral history when making a re-suggestion. The behavioral history-based re-suggestion function applies different re-suggestion algorithms depending on the category of the user's behavioral history when making a re-suggestion. For example, the behavioral history-based re-suggestion function applies a re-suggestion algorithm that emphasizes historical data in the case of a historical tourist destination. Also, the behavioral history-based re-suggestion function can apply a re-suggestion algorithm that emphasizes data related to the natural environment in the case of a tourist destination with natural scenery. Furthermore, the behavioral history-based re-suggestion function can apply a re-suggestion algorithm that emphasizes cultural background in the case of a cultural tourist destination. In this way, by applying a re-suggestion algorithm depending on the category of the user's behavioral history, appropriate re-suggestion information can be provided.

[0130] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can estimate the user's emotions and adjust the length of the re-suggestion based on the estimated user emotions. The behavioral history-based re-suggestion function can estimate the user's emotions and adjust the length of the re-suggestion based on the estimated user emotions. For example, the behavioral history-based re-suggestion function can provide short and to-the-point re-suggestions when the user is in a hurry. The behavioral history-based re-suggestion function can also provide detailed re-suggestions when the user is relaxed. Furthermore, the behavioral history-based re-suggestion function can also provide visually stimulating re-suggestions when the user is excited. In this way, by adjusting the length of the re-suggestion based on the user's emotions, appropriate re-suggestion information can be provided to the user. Emotion estimation is realized 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.

[0131] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can determine the priority of re-suggestion based on the visit time in the user's behavioral history when re-suggesting. The behavioral history-based re-suggestion function determines the priority of re-suggestion based on the visit time in the user's behavioral history when re-suggesting. For example, the behavioral history-based re-suggestion function prioritizes re-suggestion when the visit time in the user's behavioral history is close. In addition, the behavioral history-based re-suggestion function can also postpone re-suggestion when the visit time in the user's behavioral history is far away. Furthermore, the behavioral history-based re-suggestion function can also determine the priority of re-suggestion based on the visit time in the user's behavioral history. In this way, by determining the priority of re-suggestion based on the visit time in the user's behavioral history, re-suggestion information can be provided at an appropriate time.

[0132] The behavioral history-based re-suggestion function (included in the re-suggestion unit) can adjust the order of re-suggestion based on the relevance of the user's behavioral history when re-suggesting. The behavioral history-based re-suggestion function adjusts the order of re-suggestion based on the relevance of the user's behavioral history when re-suggesting. For example, the behavioral history-based re-suggestion function prioritizes re-suggestion of highly relevant behavioral history. The behavioral history-based re-suggestion function can also postpone re-suggestion of less relevant behavioral history. Furthermore, the behavioral history-based re-suggestion function can also adjust the order of re-suggestion based on the relevance of the user's behavioral history. As a result, by adjusting the order of re-suggestion based on the relevance of the user's behavioral history, highly relevant information can be provided preferentially. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, reservation unit, re-proposal unit, and management 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 acquisition unit scans a two-dimensional code using the camera 42 of the smart device 14 to acquire information. The acquisition unit can also analyze the information in the two-dimensional code using the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12, and analyzes the acquired information to propose and explain tourist destinations. The reservation unit makes reservations for tourist destinations using, for example, the identification processing unit 290 of the data processing device 12. The re-proposal unit makes re-proposals taking into account congestion, using, for example, the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12. The management unit determines and manages the installation locations of two-dimensional codes using, for example, the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, reservation unit, re-proposal unit, and management 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 acquisition unit scans a two-dimensional code using the camera 42 of the smart glasses 214 to acquire information. The acquisition unit can also analyze the information in the two-dimensional code using the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12, and analyzes the acquired information to propose and explain tourist destinations. The reservation unit makes reservations for tourist destinations using, for example, the identification processing unit 290 of the data processing device 12. The re-proposal unit makes re-proposals taking into account congestion status using, for example, the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The management unit determines and manages the installation locations of two-dimensional codes using, for example, the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, analysis unit, reservation unit, re-proposal unit, and management unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit scans a two-dimensional code using the camera 42 of the headset terminal 314 to acquire information. The acquisition unit can also analyze the information in the two-dimensional code using the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12, and analyzes the acquired information to propose and explain tourist destinations. The reservation unit makes reservations for tourist destinations using, for example, the identification processing unit 290 of the data processing device 12. The re-proposal unit makes re-proposals taking into account congestion, using, for example, the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. The management unit determines and manages the installation locations of two-dimensional codes using, for example, the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, analysis unit, reservation unit, re-proposal unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit scans a two-dimensional code using the camera 42 of the robot 414 to acquire information. The acquisition unit can also analyze the information in the two-dimensional code using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and analyzes the acquired information to propose and explain tourist destinations. The reservation unit makes reservations for tourist destinations using, for example, the specific processing unit 290 of the data processing device 12. The re-proposal unit makes re-proposals taking into account congestion, using, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The management unit determines and manages the installation locations of two-dimensional codes using, for example, the specific processing unit 290 of the data processing device 12.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The acquisition unit can monitor the user's current health condition and suggest optimal tourist spots. For example, the acquisition unit can acquire the user's heart rate and step count in real time, and if the user is highly fatigued, suggest rest spots or easy walking courses. The acquisition unit can also suggest active tourist spots or long-distance walking courses if the user is in good health. Furthermore, the acquisition unit can adjust the difficulty of activities at tourist spots based on the user's health condition. This makes it possible to suggest optimal tourist spots according to the user's health condition.

[0135] The analysis unit can analyze the user's past stay time at tourist attractions and suggest the optimal stay time. For example, the analysis unit can suggest the stay time at the next tourist attraction based on the user's average stay time at tourist attractions that they have visited in the past. The analysis unit can also suggest extending the stay time if the user revisits a tourist attraction that they previously visited for a short time. Furthermore, the analysis unit can also suggest the optimal stay time to avoid crowds based on the user's past stay time data. This makes it possible to suggest the optimal stay time based on the user's past stay time data.

[0136] The re-suggestion unit can estimate the user's emotions and adjust the content of the re-suggestion based on the estimated user's emotions. For example, if the user is excited, the re-suggestion unit can re-suggest active tourist spots and events. Also, if the user is relaxed, the re-suggestion unit can re-suggest quiet tourist spots and relaxing spots. Furthermore, if the user is stressed, the re-suggestion unit can re-suggest tourist spots and activities that are useful for relieving stress. This makes it possible to make optimal re-suggestions based on the user's emotions.

[0137] The acquisition unit can analyze the content posted by the user on social media and provide information on related tourist spots. For example, the acquisition unit can analyze photos and comments shared by the user on social media and prioritize displaying information on related tourist spots. The acquisition unit can also provide information on tourist spots visited by the user's friends on social media, based on their posts. Furthermore, the acquisition unit can provide information on related events and activities based on the user's social media interests. This makes it possible to provide highly relevant information based on the user's social media activities.

[0138] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user's emotions. For example, if the user is excited, the analysis unit can display the analysis results using visually stimulating graphics or animations. If the user is relaxed, the analysis unit can also display the analysis results in calm colors and a simple design. Furthermore, if the user is feeling stressed, the analysis unit can also display the analysis results in a concise, highly visible format. This makes it possible to provide the analysis results in an optimal display format according to the user's emotions.

[0139] The acquisition unit can acquire the user's current weather information and suggest optimal tourist destinations. For example, the acquisition unit can suggest indoor tourist destinations and activities when it is raining. The acquisition unit can also suggest outdoor tourist destinations and activities when it is sunny. Furthermore, the acquisition unit can also suggest tourist destinations and activities suitable for extreme temperatures (high or low). This makes it possible to suggest optimal tourist destinations based on current weather information.

[0140] The re-suggestion unit can estimate the user's emotions and adjust the timing of re-suggestion based on the estimated user's emotions. For example, if the user is excited, the re-suggestion unit can immediately suggest the next tourist spot. Also, if the user is relaxed, the re-suggestion unit can make re-suggestions at a slower pace. Furthermore, if the user is feeling stressed, the re-suggestion unit can delay the timing of re-suggestion to allow the user time to relax. This makes it possible to provide the optimal timing of re-suggestion according to the user's emotions.

[0141] The acquisition unit can analyze the user's past purchase history and provide information on related tourist spots and activities. For example, the acquisition unit can preferentially display information on tourist spots related to products or services the user has previously purchased. The acquisition unit can also select and provide tourist spots and activities that the user is likely to be interested in based on the user's purchase history. Furthermore, the acquisition unit can provide information on related events and benefits based on the user's purchase history. This makes it possible to provide highly relevant information based on the user's purchase history.

[0142] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is excited. The analysis unit can also provide concise analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is stressed. This makes it possible to provide an optimal level of detail of the analysis results based on the user's emotions.

[0143] The re-suggestion unit can analyze the user's past feedback and provide optimal re-suggestion content. For example, the re-suggestion unit customizes the re-suggestion content based on the user's past feedback. The re-suggestion unit can also select and re-suggest preferred tourist spots and activities based on the user's past feedback. Furthermore, the re-suggestion unit can also adjust the display method of the re-suggestion by referring to the user's past feedback. This makes it possible to provide optimal re-suggestion content based on the user's past feedback.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The acquisition unit scans the two-dimensional code to acquire information. For example, the acquisition unit may use a smartphone camera to scan the two-dimensional code and acquire information. Alternatively, the acquisition unit may use a dedicated scanner to scan the two-dimensional code. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and suggests and explains tourist spots. For example, the analysis unit analyzes the acquired information in the form of text, audio, images, video, etc., and suggests and explains tourist spots. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. Step 3: The reservation unit makes a reservation for the tourist attraction based on the information obtained by the analysis unit. For example, the reservation unit makes a reservation for the tourist attraction by online reservation, telephone reservation, on-site reservation, or the like. Step 4: The re-proposal unit re-proposes the optimal visit time based on the congestion status of the tourist destination reserved by the reservation unit. For example, the re-proposal unit re-proposes the optimal visit time based on real-time congestion data and past congestion data.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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, in order to avoid confusion and to 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.

[0216] 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.

[0217] [Explanation of symbols]

[0218] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that scans the two-dimensional code to acquire information; an analysis unit that analyzes the information acquired by the acquisition unit and proposes and explains tourist spots; a reservation unit that makes reservations for tourist attractions based on the information obtained by the analysis unit; a re-proposal unit that makes a re-proposal taking into consideration the congestion situation of the tourist spot reserved by the reservation unit; A system characterized by:

2. Equipped with a management department that manages the installation location and management of 2D codes 2. The system of claim 1.

3. The analysis unit Equipping the system to regularly update information on tourist destinations 2. The system of claim 1.

4. The re-proposal unit Re-suggesting optimal visit times based on user behavior history 2. The system of claim 1.

5. The acquisition unit The user's emotions are estimated, and the timing of scanning the two-dimensional code is adjusted based on the estimated user's emotions.

2. The system of claim 1.

6. The acquisition unit Analyze the user's past travel history and select the optimal information acquisition method 2. The system of claim 1.

7. The acquisition unit When scanning a 2D code, filtering is performed based on the user's current interests.

2. The system of claim 1.

8. The acquisition unit When scanning a 2D code, select the optimal acquisition method according to the user's input method.

2. The system of claim 1.

Images