system

Abstract

The system according to this embodiment aims to suggest the optimal route and accommodation for fans of anime, manga, and games when they go on pilgrimages to real-life locations, and to promote interaction among fans. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, an information provision unit, and an exchange unit. The reception unit receives input of the sacred site the user wishes to visit. The analysis unit analyzes the information received by the reception unit and proposes the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. The proposal unit provides information on the pilgrimage route and accommodation proposed by the analysis unit. The information provision unit provides information specifically for sacred site pilgrimages. The exchange unit facilitates interaction among fans.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when fans of anime, manga, and games go on a pilgrimage to sacred places, it is difficult to find an optimal route and accommodation facilities, and there is also a problem that means for promoting communication among fans are insufficient.

[0005] The system according to the embodiment aims to propose an optimal route and accommodation facilities and promote communication among fans when fans of anime, manga, and games go on a pilgrimage to sacred places.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, an information provision unit, and an interaction unit. The reception unit receives input of the sacred site the user wishes to visit. The analysis unit analyzes the information received by the reception unit and proposes the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. The proposal unit provides information on the pilgrimage route and accommodation proposed by the analysis unit. The information provision unit provides information specifically for sacred site pilgrimages. The interaction unit facilitates interaction among fans. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal route and accommodation for fans of anime, manga, and games when they go on a pilgrimage to a real-life location, and can also promote interaction among fans. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The pilgrimage platform according to an embodiment of the present invention is a system in which AI recommends the optimal route and accommodation when fans of anime, manga, and games visit the real-life locations featured in those works. This system allows users to input the locations they wish to visit, and the AI ​​analyzes the user's preferences and behavioral data to suggest the optimal pilgrimage route and accommodation. Furthermore, it provides information specifically tailored to pilgrimages and functions to facilitate interaction among fans. For example, if a user inputs, "I want to visit the shrine steps from the final scene of a specific movie," the AI ​​will suggest the optimal route and accommodation, and provide information on related tourist spots and events. This allows users to make their pilgrimages more fulfilling. Thus, the pilgrimage platform enables fans of anime, manga, and games to efficiently plan and enjoy their pilgrimages.

[0029] The pilgrimage platform according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, an information provision unit, and an interaction unit. The reception unit receives input from the user about the pilgrimage site they wish to visit. For example, a user can input, "I want to visit the steps of the shrine from the last scene of a specific movie." The reception unit transmits the information entered by the user to the AI. The analysis unit analyzes the information received by the reception unit and suggests the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. For example, the analysis unit analyzes data on pilgrimage sites and accommodations the user has visited in the past and selects the optimal route and accommodation for the user. The suggestion unit provides information on the pilgrimage route and accommodation suggested by the analysis unit. For example, the suggestion unit displays detailed information on the optimal route and accommodation to the user. The information provision unit provides information specifically for pilgrimage. For example, it provides information on tourist spots and events to visit, local restaurants, etc. The interaction unit promotes interaction among fans. For example, users can share photos of pilgrimage sites they have visited and interact with other fans. As a result, the pilgrimage platform according to this embodiment can consistently provide users with input on the sacred sites they wish to visit, suggest optimal pilgrimage routes and accommodations, offer information specifically tailored to pilgrimages, and facilitate interaction among fans.

[0030] The reception desk accepts input from users regarding the sacred sites they wish to visit. For example, a user might input, "I want to visit the steps of the shrine from the final scene of a specific movie." The reception desk then sends the user's input to the AI. Specifically, the reception desk uses natural language processing technology to analyze the text information entered by the user and identify the desired location. For example, if a user inputs "the shrine from the final scene of the movie," the AI ​​searches the database for the movie title and scene details to identify the relevant shrine. Furthermore, based on the user's input, the reception desk collects relevant information and provides the foundational data to create a detailed plan tailored to the user's wishes. This allows users to easily receive a detailed pilgrimage plan simply by entering information about the sacred sites they wish to visit. The reception desk can also save the user's input and use it for future planning and analysis. For example, if a user wishes to visit multiple sacred sites, the reception desk can suggest the optimal pilgrimage route based on past input data. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0031] The analysis department analyzes information received by the reception department and proposes the optimal pilgrimage route and accommodation based on user preferences and behavioral data. Specifically, the analysis department uses AI to analyze users' past visit history and evaluation data to understand their preferences. For example, based on data on sacred sites and accommodations that users have visited in the past, it extracts the characteristics of tourist spots and accommodations that users prefer. Furthermore, the analysis department considers information such as the user's current location, mode of transportation, and length of stay to calculate the optimal pilgrimage route. The AI ​​integrates this data and proposes the most efficient and satisfying route for the user. In addition, the analysis department can optimize the pilgrimage route based on real-time updated traffic and weather information. For example, if traffic congestion or bad weather is predicted, it will propose an alternative route to ensure smoother travel for the user. In this way, the analysis department can provide the optimal pilgrimage plan that meets the user's needs and improve the user experience.

[0032] The suggestion department provides information on pilgrimage routes and accommodations proposed by the analysis department. Specifically, the suggestion department displays detailed information on the optimal route and accommodations to the user. For example, the suggestion department displays information such as maps of the pilgrimage route, photos of accommodations, prices, and reviews on the user's smartphone or computer. The suggestion department can also be integrated with a booking system to allow users to easily book the suggested routes and accommodations. For example, the suggestion department can allow users to check accommodation availability and complete bookings online. Furthermore, the suggestion department collects user feedback and provides data to improve the accuracy and satisfaction of the suggestions. In this way, the suggestion department can provide users with detailed and convenient information and support their pilgrimage experience.

[0033] The Information Department provides information specifically tailored for pilgrimages to sacred sites. Specifically, it provides information on must-visit tourist spots, events, and local restaurants. For example, the Information Department updates and notifies users in real time about tourist spots and events around the sacred sites they plan to visit. It also provides information on local restaurants and specialty products, enabling users to fully enjoy the region's attractions. Furthermore, the Information Department can provide individually customized information based on users' preferences and past visit history. For instance, it can recommend new restaurants suited to a user's preferences based on their past restaurant reviews. This allows the Information Department to provide users with abundant and useful information, enriching their pilgrimage experience.

[0034] The Community Section promotes interaction among fans. Specifically, it allows users to share photos of pilgrimage sites they have visited and interact with other fans. For example, the Community Section provides a platform for users to upload and share photos they have taken of pilgrimage sites with other users. Users can also exchange opinions and share their thoughts with other fans through comments and "likes." Furthermore, the Community Section provides information on events and meetups where users can interact offline, offering opportunities to deepen the bonds between fans. In this way, the Community Section can revitalize communication among users and broaden the enjoyment of pilgrimage.

[0035] The data collection unit can collect user preferences and behavioral data. For example, the data collection unit can collect user survey results and past selection history. The data collection unit can also collect behavioral data such as user location information, browsing history, and purchase history. This allows the data collection unit to make more accurate suggestions by collecting user preferences and behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user behavioral data into AI, which can then analyze the data to identify user preferences.

[0036] The reservation department can make reservations for suggested routes and accommodations. For example, the reservation department can make reservations for accommodations through an online reservation system. The reservation department can also accept reservations by phone. The reservation department can also make reservations for transportation based on the suggested route. This allows the reservation department to consistently make reservations for suggested routes and accommodations. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's reservation information into the AI, which can then suggest the optimal reservation method.

[0037] The photo sharing section allows users to share photos of sacred sites they have visited. The photo sharing section supports photo sharing on social media, for example. Photos can also be shared through a dedicated app. The photo sharing section provides a platform for users to share photos they have taken with other fans. This facilitates interaction between users and other fans by allowing them to share photos of sacred sites they have visited. Some or all of the above-described processes in the photo sharing section may be performed using AI or not. For example, the photo sharing section can input photos taken by users into an AI, which can then tag and categorize the photos.

[0038] The interaction promotion department allows users to interact with other fans. For example, the interaction promotion department supports interaction between fans through online forums. The interaction promotion department can also promote interaction among fans by holding offline events. The interaction promotion department provides a platform for users to exchange opinions and share information with other fans. In this way, the interaction promotion department helps to form a community of fans through interaction with other fans. Some or all of the above processes in the interaction promotion department may be performed using AI or not. For example, the interaction promotion department can input a user's interaction history into AI, which can then suggest the most suitable method of interaction.

[0039] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display as suggestions the sacred sites that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest sacred sites to be used at a specific time of day based on the user's past input history. In this way, the reception desk makes user input easier by providing the optimal interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's input history data into AI, and the AI ​​can suggest the optimal input interface.

[0040] The reception desk can suggest input options based on the user's current interests when they input a pilgrimage site they wish to visit. For example, the reception desk can display relevant pilgrimage sites as suggestions based on information about anime or manga the user has recently searched for. The reception desk can also suggest relevant pilgrimage sites based on information about events or forums the user has attended. The reception desk can also suggest relevant pilgrimage sites based on information from newsletters or blogs the user subscribes to. This makes input more efficient by allowing the reception desk to suggest input options based on the user's current interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's interest data into AI, which can then suggest the most suitable input options.

[0041] The reception system can prioritize displaying highly relevant sacred sites when the user inputs the sacred site they wish to visit, taking into account the user's geographical location. For example, the reception system can prioritize displaying sacred sites close to the user's current location. The reception system can also suggest highly relevant sacred sites based on the user's past visit history. The reception system can also suggest sacred sites to visit based on the user's current location and related event information. In this way, the reception system can prioritize displaying highly relevant sacred sites by taking into account the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into AI, which can then suggest the most suitable sacred sites.

[0042] The reception desk can analyze the user's social media activity when they input the pilgrimage site they wish to visit and suggest relevant sites. For example, the reception desk can suggest relevant sites based on information about accounts the user follows on social media. The reception desk can also suggest relevant sites based on content the user has shared on social media. The reception desk can also suggest relevant sites based on information about events the user has participated in on social media. In this way, the reception desk can suggest relevant sites by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then suggest the most suitable pilgrimage site.

[0043] The analysis unit can select the optimal analysis method by referring to the user's past behavioral data during analysis. For example, the analysis unit can suggest the optimal pilgrimage route based on data of sacred sites the user has visited in the past. The analysis unit can also suggest the optimal accommodation based on the user's past accommodation selection history. The analysis unit can also analyze the user's past travel patterns and suggest the most efficient route. In this way, the analysis unit can select the optimal analysis method by referring to the user's past behavioral data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's behavioral data into AI, and the AI ​​can select the optimal analysis method.

[0044] The analysis department can customize the analysis results based on the user's current lifestyle. For example, if the user is busy with work, the analysis department can suggest a pilgrimage site that can be visited in a short time. If the user is on vacation, the analysis department can also suggest accommodations where they can stay for an extended period. If the user is traveling with family, the analysis department can also suggest family-friendly tourist spots. In this way, the analysis department can provide suggestions that meet the user's needs by offering analysis results based on the user's current lifestyle. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the user's lifestyle data into AI, which can then provide the optimal analysis results.

[0045] The analysis unit can select the optimal analysis method by considering the user's geographical location information during analysis. For example, the analysis unit may prioritize analyzing sacred sites close to the user's current location. The analysis unit can also analyze highly relevant sacred sites based on the user's past visit history. The analysis unit can also analyze sacred sites that the user should visit based on event information related to the user's current location. In this way, the analysis unit can select the optimal analysis method by considering the user's geographical location information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information into AI, and the AI ​​can select the optimal analysis method.

[0046] The analysis department can optimize the analysis results by analyzing the user's social media activity during the analysis process. For example, the analysis department can analyze relevant sacred sites based on information about accounts that the user follows on social media. The analysis department can also analyze relevant sacred sites based on content that the user has shared on social media. The analysis department can also analyze relevant sacred sites based on information about events that the user has participated in on social media. In this way, the analysis department can provide optimal analysis results by analyzing the user's social media activity. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the user's social media data into AI, which can then provide optimal analysis results.

[0047] The suggestion unit can select the optimal suggestion method by referring to the user's past selection history when making a suggestion. For example, the suggestion unit can suggest the optimal accommodation based on the user's past selection data for accommodations. The suggestion unit can also suggest the optimal pilgrimage route based on the user's past selection history for pilgrimage routes. The suggestion unit can also analyze the user's past selection history and select the most efficient suggestion method. In this way, the suggestion unit can select the optimal suggestion method by referring to the user's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's selection history data into AI, and the AI ​​can select the optimal suggestion method.

[0048] The suggestion function can customize its suggestions based on the user's current interests and preferences. For example, it can suggest relevant locations based on information about anime and manga the user has recently searched for. It can also suggest relevant locations based on information about events and forums the user has attended. It can also suggest relevant locations based on information from newsletters and blogs the user subscribes to. This allows the suggestion function to provide suggestions tailored to the user's current interests and preferences, thereby improving user satisfaction. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input user interest data into AI, which can then customize the most suitable suggestions.

[0049] The suggestion unit can select the optimal suggestion method when making suggestions, taking into account the user's geographical location. For example, the suggestion unit may prioritize suggesting sacred sites close to the user's current location. The suggestion unit can also suggest highly relevant sacred sites based on the user's past visit history. The suggestion unit can also suggest sacred sites to visit based on the user's current location and related event information. In this way, the suggestion unit can select the optimal suggestion method by taking into account the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into AI, which can then select the optimal suggestion method.

[0050] The suggestion department can optimize its suggestions by analyzing the user's social media activity. For example, it can suggest relevant sacred sites based on information about accounts the user follows on social media. It can also suggest relevant sacred sites based on content the user has shared on social media. It can also suggest relevant sacred sites based on information about events the user has participated in on social media. In this way, the suggestion department can provide optimal suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input the user's social media data into AI, which can then provide optimal suggestions.

[0051] The information provision unit can provide the most relevant information by referring to the user's past information browsing history when providing information. For example, the information provision unit can provide relevant information based on information about sacred sites that the user has previously viewed. The information provision unit can also provide information about new sacred sites that the user might be interested in, based on the user's past information browsing history. The information provision unit can also analyze the user's past information browsing history and provide the most relevant information. In this way, the information provision unit can provide the most relevant information by referring to the user's past information browsing history. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's browsing history data into AI, and the AI ​​can provide the most relevant information.

[0052] The information provider can customize the information content based on the user's current interests and preferences when providing information. For example, the information provider can provide information on related pilgrimage sites based on information about anime and manga that the user has recently searched for. The information provider can also provide information on related pilgrimage sites based on information about events and forums that the user has attended. The information provider can also provide information on related pilgrimage sites based on information from newsletters and blogs that the user subscribes to. This allows the information provider to improve user satisfaction by providing information content based on the user's current interests and preferences. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user interest data into AI, which can then customize the information content to be optimal.

[0053] The information provision unit can provide optimal information by considering the user's geographical location when providing information. For example, the information provision unit can prioritize providing information about sacred sites close to the user's current location. The information provision unit can also provide information about highly relevant sacred sites based on the user's past visit history. The information provision unit can also provide information about sacred sites to visit based on the user's current location and related event information. In this way, the information provision unit can provide optimal information by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's geographical location information into AI, and the AI ​​can provide optimal information.

[0054] The information provision department can optimize the information content by analyzing the user's social media activity when providing information. For example, the information provision department can provide information on relevant sacred sites based on information about accounts that the user follows on social media. The information provision department can also provide information on relevant sacred sites based on content that the user has shared on social media. The information provision department can also provide information on relevant sacred sites based on information about events that the user has participated in on social media. In this way, the information provision department can provide optimal information content by analyzing the user's social media activity. Some or all of the above processing in the information provision department may be performed using AI or not. For example, the information provision department can input the user's social media data into AI, and the AI ​​can provide optimal information content.

[0055] The interaction unit can provide the most suitable interaction method by referring to the user's past interaction history during interaction. For example, the interaction unit can provide relevant interaction methods based on information about events and forums the user has previously participated in. The interaction unit can also provide new interaction methods that might be of interest to the user based on their past interaction history. The interaction unit can also analyze the user's past interaction history and provide the most relevant interaction method. In this way, the interaction unit can provide the most suitable interaction method by referring to the user's past interaction history. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the user's interaction history data into AI, and the AI ​​can provide the most suitable interaction method.

[0056] The interaction function can customize interaction content based on the user's current interests and preferences. For example, the interaction function can provide relevant interaction methods based on information about anime and manga the user has recently searched for. It can also provide relevant interaction methods based on information about events and forums the user has participated in. It can also provide relevant interaction methods based on information about newsletters and blogs the user subscribes to. This improves user satisfaction by providing interaction content based on the user's current interests and preferences. Some or all of the above processing in the interaction function may be performed using AI or not. For example, the interaction function can input user interest data into AI, which can then customize the most suitable interaction content.

[0057] The interaction unit can provide the most suitable interaction method by considering the user's geographical location during interaction. For example, the interaction unit can prioritize providing events and forums close to the user's current location. The interaction unit can also provide highly relevant interaction methods based on the user's past visit history. The interaction unit can also provide interaction methods that the user should visit based on the user's current location and relevant event information. In this way, the interaction unit can provide the most suitable interaction method by considering the user's geographical location. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the user's geographical location information into AI, and the AI ​​can provide the most suitable interaction method.

[0058] The interaction unit can analyze a user's social media activity during interaction to optimize the content of the interaction. For example, the interaction unit can provide relevant interaction methods based on information about accounts the user follows on social media. The interaction unit can also provide relevant interaction methods based on content the user has shared on social media. The interaction unit can also provide relevant interaction methods based on information about events the user has participated in on social media. In this way, the interaction unit can provide optimal interaction content by analyzing the user's social media activity. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the user's social media data into AI, and the AI ​​can provide optimal interaction content.

[0059] The data collection unit can provide the optimal collection method by referring to the user's past data collection history when collecting data. For example, the data collection unit can prioritize collecting relevant data based on data the user has collected in the past. The data collection unit can also collect new data that is likely to be of interest to the user based on their past data collection history. The data collection unit can also analyze the user's past data collection history and collect the most relevant data. In this way, the data collection unit can provide the optimal collection method by referring to the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's data collection history into AI, and the AI ​​can provide the optimal collection method.

[0060] The data collection unit can collect optimal data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize collecting data that is close to the user's current location. The data collection unit can also collect highly relevant data based on the user's past visit history. The data collection unit can also collect data that the user should visit based on event information related to the user's current location. In this way, the data collection unit can collect optimal data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can collect optimal data.

[0061] The reservation department can provide the optimal reservation method by referring to the user's past reservation history at the time of booking. For example, the reservation department can prioritize suggesting relevant accommodations based on data of accommodations the user has previously booked. The reservation department can also suggest new accommodations that the user might be interested in based on their past reservation history. The reservation department can also analyze the user's past reservation history and suggest the most relevant accommodations. In this way, the reservation department can provide the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's reservation history data into AI, and the AI ​​can provide the optimal reservation method.

[0062] The reservation department can provide the optimal reservation method by considering the user's geographical location information at the time of booking. For example, the reservation department can prioritize suggesting accommodations close to the user's current location. The reservation department can also suggest highly relevant accommodations based on the user's past visit history. The reservation department can also suggest accommodations to visit based on the user's current location and related event information. In this way, the reservation department can provide the optimal reservation method by considering the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI or not. For example, the reservation department can input the user's geographical location information into AI, and the AI ​​can provide the optimal reservation method.

[0063] The photo sharing unit can provide the optimal sharing method by referring to the user's past photo sharing history when sharing photos. For example, the photo sharing unit can prioritize sharing related photos based on data of photos the user has shared in the past. The photo sharing unit can also share new photos that are likely to interest the user based on their past photo sharing history. The photo sharing unit can also analyze the user's past photo sharing history and share the most relevant photos. In this way, the photo sharing unit can provide the optimal sharing method by referring to the user's past photo sharing history. Some or all of the above processing in the photo sharing unit may be performed using AI or not. For example, the photo sharing unit can input the user's photo sharing history into AI, and the AI ​​can provide the optimal sharing method.

[0064] The photo sharing unit can share the most suitable photos by considering the user's geographical location information when sharing photos. For example, the photo sharing unit can prioritize sharing photos taken near the user's current location. The photo sharing unit can also share highly relevant photos based on the user's past visit history. The photo sharing unit can also share photos taken at places the user should visit based on event information related to the user's current location. In this way, the photo sharing unit can share the most suitable photos by considering the user's geographical location information. Some or all of the above processing in the photo sharing unit may be performed using AI or not. For example, the photo sharing unit can input the user's geographical location information into AI, and the AI ​​can share the most suitable photos.

[0065] The interaction promotion unit can provide the most suitable interaction promotion method by referring to the user's past interaction history when promoting interaction. For example, the interaction promotion unit can provide relevant interaction promotion methods based on information about events and forums the user has previously participated in. The interaction promotion unit can also provide new interaction promotion methods that the user might be interested in, based on the user's past interaction history. The interaction promotion unit can also analyze the user's past interaction history and provide the most relevant interaction promotion method. In this way, the interaction promotion unit can provide the most suitable interaction promotion method by referring to the user's past interaction history. Some or all of the above processing in the interaction promotion unit may be performed using AI or not. For example, the interaction promotion unit can input the user's interaction history data into AI, and the AI ​​can provide the most suitable interaction promotion method.

[0066] The interaction promotion unit can provide the most suitable interaction promotion method when promoting interaction, taking into account the user's geographical location information. For example, the interaction promotion unit can prioritize providing events and forums close to the user's current location. The interaction promotion unit can also provide highly relevant interaction promotion methods based on the user's past visit history. The interaction promotion unit can also provide interaction promotion methods that the user should visit based on the user's current location and relevant event information. In this way, the interaction promotion unit can provide the most suitable interaction promotion method by taking into account the user's geographical location information. Some or all of the above processing in the interaction promotion unit may be performed using AI or not. For example, the interaction promotion unit can input the user's geographical location information into AI, and the AI ​​can provide the most suitable interaction promotion method.

[0067] The interaction promotion unit can analyze a user's social media activity and optimize the content of interactions during the interaction promotion process. For example, the interaction promotion unit can provide relevant interaction methods based on information about accounts that a user follows on social media. The interaction promotion unit can also provide relevant interaction methods based on content that a user has shared on social media. The interaction promotion unit can also provide relevant interaction methods based on information about events that a user has participated in on social media. In this way, the interaction promotion unit can provide optimal interaction content by analyzing the user's social media activity. Some or all of the above processing in the interaction promotion unit may be performed using AI or not. For example, the interaction promotion unit can input the user's social media data into AI, and the AI ​​can provide optimal interaction content.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The pilgrimage platform can analyze a user's past visit history and suggest the most suitable destinations. For example, it can suggest new related pilgrimage sites based on data of sites the user has visited in the past. It can also suggest similar accommodations based on data of facilities the user has stayed at in the past. It can also analyze a user's past travel patterns and suggest the most efficient route. In this way, the pilgrimage platform improves user convenience by providing optimal suggestions based on the user's past visit history. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's visit history data into AI, which can then make optimal suggestions.

[0070] The pilgrimage platform can customize destination suggestions based on the user's current interests. For example, it can suggest relevant pilgrimage sites based on information about anime and manga the user has recently searched for. It can also suggest relevant pilgrimage sites based on information about events and forums the user has attended. It can also suggest relevant pilgrimage sites based on information from newsletters and blogs the user subscribes to. In this way, the pilgrimage platform improves user satisfaction by providing suggestions based on the user's current interests. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user interest data into AI, which can then make optimal suggestions.

[0071] The pilgrimage platform can suggest destinations considering the user's geographical location. For example, it can prioritize suggesting pilgrimage sites close to the user's current location. It can also suggest highly relevant pilgrimage sites based on the user's past visit history. It can also suggest pilgrimage sites to visit based on the user's current location and related event information. In this way, the pilgrimage platform can suggest the optimal destination by considering the user's geographical location. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's geographical location information into AI, which can then make the optimal suggestion.

[0072] The pilgrimage platform can analyze a user's social media activity and suggest relevant pilgrimage sites. For example, it can suggest relevant pilgrimage sites based on information about accounts the user follows on social media. It can also suggest relevant pilgrimage sites based on content the user shares on social media. It can also suggest relevant pilgrimage sites based on information about events the user has participated in on social media. In this way, the pilgrimage platform can provide optimal suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's social media data into AI, which can then make optimal suggestions.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reception desk receives input from the user about the sacred site they wish to visit. For example, a user might input, "I want to visit the steps of the shrine from the final scene of a specific movie." The reception desk then sends the information entered by the user to the AI. Step 2: The analysis department analyzes the information received by the reception department and proposes the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. For example, the analysis department analyzes data on sacred sites and accommodations the user has visited in the past to select the most suitable route and accommodation for the user. Step 3: The suggestion section provides information on pilgrimage routes and accommodations suggested by the analysis section. For example, the suggestion section displays detailed information on the optimal route and accommodations to the user. Step 4: The information department provides information specifically for pilgrimages to sacred sites. For example, it provides information on tourist spots to visit, events, and local restaurants. Step 5: The interaction section facilitates interaction among fans. For example, users can share photos of places they have visited and interact with other fans.

[0075] (Example of form 2) The pilgrimage platform according to an embodiment of the present invention is a system in which AI recommends the optimal route and accommodation when fans of anime, manga, and games visit the real-life locations featured in those works. This system allows users to input the locations they wish to visit, and the AI ​​analyzes the user's preferences and behavioral data to suggest the optimal pilgrimage route and accommodation. Furthermore, it provides information specifically tailored to pilgrimages and functions to facilitate interaction among fans. For example, if a user inputs, "I want to visit the shrine steps from the final scene of a specific movie," the AI ​​will suggest the optimal route and accommodation, and provide information on related tourist spots and events. This allows users to make their pilgrimages more fulfilling. Thus, the pilgrimage platform enables fans of anime, manga, and games to efficiently plan and enjoy their pilgrimages.

[0076] The pilgrimage platform according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, an information provision unit, and an interaction unit. The reception unit receives input from the user about the pilgrimage site they wish to visit. For example, a user can input, "I want to visit the steps of the shrine from the last scene of a specific movie." The reception unit transmits the information entered by the user to the AI. The analysis unit analyzes the information received by the reception unit and suggests the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. For example, the analysis unit analyzes data on pilgrimage sites and accommodations the user has visited in the past and selects the optimal route and accommodation for the user. The suggestion unit provides information on the pilgrimage route and accommodation suggested by the analysis unit. For example, the suggestion unit displays detailed information on the optimal route and accommodation to the user. The information provision unit provides information specifically for pilgrimage. For example, it provides information on tourist spots and events to visit, local restaurants, etc. The interaction unit promotes interaction among fans. For example, users can share photos of pilgrimage sites they have visited and interact with other fans. As a result, the pilgrimage platform according to this embodiment can consistently provide users with input on the sacred sites they wish to visit, suggest optimal pilgrimage routes and accommodations, offer information specifically tailored to pilgrimages, and facilitate interaction among fans.

[0077] The reception desk accepts input from users regarding the sacred sites they wish to visit. For example, a user might input, "I want to visit the steps of the shrine from the final scene of a specific movie." The reception desk then sends the user's input to the AI. Specifically, the reception desk uses natural language processing technology to analyze the text information entered by the user and identify the desired location. For example, if a user inputs "the shrine from the final scene of the movie," the AI ​​searches the database for the movie title and scene details to identify the relevant shrine. Furthermore, based on the user's input, the reception desk collects relevant information and provides the foundational data to create a detailed plan tailored to the user's wishes. This allows users to easily receive a detailed pilgrimage plan simply by entering information about the sacred sites they wish to visit. The reception desk can also save the user's input and use it for future planning and analysis. For example, if a user wishes to visit multiple sacred sites, the reception desk can suggest the optimal pilgrimage route based on past input data. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0078] The analysis department analyzes information received by the reception department and proposes the optimal pilgrimage route and accommodation based on user preferences and behavioral data. Specifically, the analysis department uses AI to analyze users' past visit history and evaluation data to understand their preferences. For example, based on data on sacred sites and accommodations that users have visited in the past, it extracts the characteristics of tourist spots and accommodations that users prefer. Furthermore, the analysis department considers information such as the user's current location, mode of transportation, and length of stay to calculate the optimal pilgrimage route. The AI ​​integrates this data and proposes the most efficient and satisfying route for the user. In addition, the analysis department can optimize the pilgrimage route based on real-time updated traffic and weather information. For example, if traffic congestion or bad weather is predicted, it will propose an alternative route to ensure smoother travel for the user. In this way, the analysis department can provide the optimal pilgrimage plan that meets the user's needs and improve the user experience.

[0079] The suggestion department provides information on pilgrimage routes and accommodations proposed by the analysis department. Specifically, the suggestion department displays detailed information on the optimal route and accommodations to the user. For example, the suggestion department displays information such as maps of the pilgrimage route, photos of accommodations, prices, and reviews on the user's smartphone or computer. The suggestion department can also be integrated with a booking system to allow users to easily book the suggested routes and accommodations. For example, the suggestion department can allow users to check accommodation availability and complete bookings online. Furthermore, the suggestion department collects user feedback and provides data to improve the accuracy and satisfaction of the suggestions. In this way, the suggestion department can provide users with detailed and convenient information and support their pilgrimage experience.

[0080] The Information Department provides information specifically tailored for pilgrimages to sacred sites. Specifically, it provides information on must-visit tourist spots, events, and local restaurants. For example, the Information Department updates and notifies users in real time about tourist spots and events around the sacred sites they plan to visit. It also provides information on local restaurants and specialty products, enabling users to fully enjoy the region's attractions. Furthermore, the Information Department can provide individually customized information based on users' preferences and past visit history. For instance, it can recommend new restaurants suited to a user's preferences based on their past restaurant reviews. This allows the Information Department to provide users with abundant and useful information, enriching their pilgrimage experience.

[0081] The Community Section promotes interaction among fans. Specifically, it allows users to share photos of pilgrimage sites they have visited and interact with other fans. For example, the Community Section provides a platform for users to upload and share photos they have taken of pilgrimage sites with other users. Users can also exchange opinions and share their thoughts with other fans through comments and "likes." Furthermore, the Community Section provides information on events and meetups where users can interact offline, offering opportunities to deepen the bonds between fans. In this way, the Community Section can revitalize communication among users and broaden the enjoyment of pilgrimage.

[0082] The data collection unit can collect user preferences and behavioral data. For example, the data collection unit can collect user survey results and past selection history. The data collection unit can also collect behavioral data such as user location information, browsing history, and purchase history. This allows the data collection unit to make more accurate suggestions by collecting user preferences and behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user behavioral data into AI, which can then analyze the data to identify user preferences.

[0083] The reservation department can make reservations for suggested routes and accommodations. For example, the reservation department can make reservations for accommodations through an online reservation system. The reservation department can also accept reservations by phone. The reservation department can also make reservations for transportation based on the suggested route. This allows the reservation department to consistently make reservations for suggested routes and accommodations. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's reservation information into the AI, which can then suggest the optimal reservation method.

[0084] The photo sharing section allows users to share photos of sacred sites they have visited. The photo sharing section supports photo sharing on social media, for example. Photos can also be shared through a dedicated app. The photo sharing section provides a platform for users to share photos they have taken with other fans. This facilitates interaction between users and other fans by allowing them to share photos of sacred sites they have visited. Some or all of the above-described processes in the photo sharing section may be performed using AI or not. For example, the photo sharing section can input photos taken by users into an AI, which can then tag and categorize the photos.

[0085] The interaction promotion department allows users to interact with other fans. For example, the interaction promotion department supports interaction between fans through online forums. The interaction promotion department can also promote interaction among fans by holding offline events. The interaction promotion department provides a platform for users to exchange opinions and share information with other fans. In this way, the interaction promotion department helps to form a community of fans through interaction with other fans. Some or all of the above processes in the interaction promotion department may be performed using AI or not. For example, the interaction promotion department can input a user's interaction history into AI, which can then suggest the most suitable method of interaction.

[0086] The reception desk can estimate the user's emotions and adjust the input method for the sacred site the user wishes to visit based on the estimated emotions. For example, if the user is excited, the reception desk can make the interface colorful and fun to use. If the user is tired, the reception desk can also provide a simple and intuitive interface to make input easier. If the user is feeling anxious, the reception desk can display guide messages to support the input process. In this way, the reception desk improves user convenience by providing an input method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0087] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display as suggestions the sacred sites that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest sacred sites to be used at a specific time of day based on the user's past input history. In this way, the reception desk makes user input easier by providing the optimal interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's input history data into AI, and the AI ​​can suggest the optimal input interface.

[0088] The reception desk can suggest input options based on the user's current interests when they input a pilgrimage site they wish to visit. For example, the reception desk can display relevant pilgrimage sites as suggestions based on information about anime or manga the user has recently searched for. The reception desk can also suggest relevant pilgrimage sites based on information about events or forums the user has attended. The reception desk can also suggest relevant pilgrimage sites based on information from newsletters or blogs the user subscribes to. This makes input more efficient by allowing the reception desk to suggest input options based on the user's current interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's interest data into AI, which can then suggest the most suitable input options.

[0089] The reception desk can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is in a hurry, the reception desk may prioritize inputting the most important information. If the user is relaxed, the reception desk may also request detailed information. If the user is feeling anxious, the reception desk may start with simple questions and gradually request more detailed information. This makes the input process more efficient by allowing the reception desk to prioritize input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0090] The reception system can prioritize displaying highly relevant sacred sites when the user inputs the sacred site they wish to visit, taking into account the user's geographical location. For example, the reception system can prioritize displaying sacred sites close to the user's current location. The reception system can also suggest highly relevant sacred sites based on the user's past visit history. The reception system can also suggest sacred sites to visit based on the user's current location and related event information. In this way, the reception system can prioritize displaying highly relevant sacred sites by taking into account the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into AI, which can then suggest the most suitable sacred sites.

[0091] The reception desk can analyze the user's social media activity when they input the pilgrimage site they wish to visit and suggest relevant sites. For example, the reception desk can suggest relevant sites based on information about accounts the user follows on social media. The reception desk can also suggest relevant sites based on content the user has shared on social media. The reception desk can also suggest relevant sites based on information about events the user has participated in on social media. In this way, the reception desk can suggest relevant sites by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then suggest the most suitable pilgrimage site.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is excited, the analysis unit may use an algorithm that provides results quickly. If the user is relaxed, the analysis unit may also use an algorithm that performs a detailed analysis. If the user is feeling anxious, the analysis unit may also use an algorithm that provides reassuring analysis results. This improves the accuracy of the analysis results by allowing the analysis unit to use an analysis algorithm that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the algorithm based on the emotions.

[0093] The analysis unit can select the optimal analysis method by referring to the user's past behavioral data during analysis. For example, the analysis unit can suggest the optimal pilgrimage route based on data of sacred sites the user has visited in the past. The analysis unit can also suggest the optimal accommodation based on the user's past accommodation selection history. The analysis unit can also analyze the user's past travel patterns and suggest the most efficient route. In this way, the analysis unit can select the optimal analysis method by referring to the user's past behavioral data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's behavioral data into AI, and the AI ​​can select the optimal analysis method.

[0094] The analysis department can customize the analysis results based on the user's current lifestyle. For example, if the user is busy with work, the analysis department can suggest a pilgrimage site that can be visited in a short time. If the user is on vacation, the analysis department can also suggest accommodations where they can stay for an extended period. If the user is traveling with family, the analysis department can also suggest family-friendly tourist spots. In this way, the analysis department can provide suggestions that meet the user's needs by offering analysis results based on the user's current lifestyle. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the user's lifestyle data into AI, which can then provide the optimal analysis results.

[0095] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will prioritize providing the most important information. If the user is relaxed, the analysis unit may also provide detailed information. If the user is feeling anxious, the analysis unit may also prioritize providing reassuring information. In this way, the analysis unit can prioritize providing information that is important to the user by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then prioritize based on the emotions.

[0096] The analysis unit can select the optimal analysis method by considering the user's geographical location information during analysis. For example, the analysis unit may prioritize analyzing sacred sites close to the user's current location. The analysis unit can also analyze highly relevant sacred sites based on the user's past visit history. The analysis unit can also analyze sacred sites that the user should visit based on event information related to the user's current location. In this way, the analysis unit can select the optimal analysis method by considering the user's geographical location information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information into AI, and the AI ​​can select the optimal analysis method.

[0097] The analysis department can optimize the analysis results by analyzing the user's social media activity during the analysis process. For example, the analysis department can analyze relevant sacred sites based on information about accounts that the user follows on social media. The analysis department can also analyze relevant sacred sites based on content that the user has shared on social media. The analysis department can also analyze relevant sacred sites based on information about events that the user has participated in on social media. In this way, the analysis department can provide optimal analysis results by analyzing the user's social media activity. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the user's social media data into AI, which can then provide optimal analysis results.

[0098] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is excited, the suggestion unit can present colorful and visually appealing suggestions. If the user is relaxed, the suggestion unit can present suggestions in a calm tone. If the user is feeling anxious, the suggestion unit can present suggestions that provide a sense of security. In this way, the suggestion unit improves user satisfaction by providing suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way it presents suggestions based on the emotion.

[0099] The suggestion unit can select the optimal suggestion method by referring to the user's past selection history when making a suggestion. For example, the suggestion unit can suggest the optimal accommodation based on the user's past selection data for accommodations. The suggestion unit can also suggest the optimal pilgrimage route based on the user's past selection history for pilgrimage routes. The suggestion unit can also analyze the user's past selection history and select the most efficient suggestion method. In this way, the suggestion unit can select the optimal suggestion method by referring to the user's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's selection history data into AI, and the AI ​​can select the optimal suggestion method.

[0100] The suggestion function can customize its suggestions based on the user's current interests and preferences. For example, it can suggest relevant locations based on information about anime and manga the user has recently searched for. It can also suggest relevant locations based on information about events and forums the user has attended. It can also suggest relevant locations based on information from newsletters and blogs the user subscribes to. This allows the suggestion function to provide suggestions tailored to the user's current interests and preferences, thereby improving user satisfaction. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input user interest data into AI, which can then customize the most suitable suggestions.

[0101] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize providing the most important information. If the user is relaxed, the suggestion unit may also provide detailed information. If the user is feeling anxious, the suggestion unit may also prioritize providing reassuring information. In this way, the suggestion unit can prioritize providing information that is important to the user by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then prioritize based on the emotions.

[0102] The suggestion unit can select the optimal suggestion method when making suggestions, taking into account the user's geographical location. For example, the suggestion unit may prioritize suggesting sacred sites close to the user's current location. The suggestion unit can also suggest highly relevant sacred sites based on the user's past visit history. The suggestion unit can also suggest sacred sites to visit based on the user's current location and related event information. In this way, the suggestion unit can select the optimal suggestion method by taking into account the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into AI, which can then select the optimal suggestion method.

[0103] The suggestion department can optimize its suggestions by analyzing the user's social media activity. For example, it can suggest relevant sacred sites based on information about accounts the user follows on social media. It can also suggest relevant sacred sites based on content the user has shared on social media. It can also suggest relevant sacred sites based on information about events the user has participated in on social media. In this way, the suggestion department can provide optimal suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input the user's social media data into AI, which can then provide optimal suggestions.

[0104] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is excited, the information delivery unit can provide colorful and visually appealing information. If the user is relaxed, the information delivery unit can also provide information in a calm tone. If the user is feeling anxious, the information delivery unit can also provide reassuring information. In this way, the information delivery unit improves user satisfaction by providing information delivery methods that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input user emotion data into a generative AI, and the generative AI can adjust the method of information delivery based on the emotions.

[0105] The information provision unit can provide the most relevant information by referring to the user's past information browsing history when providing information. For example, the information provision unit can provide relevant information based on information about sacred sites that the user has previously viewed. The information provision unit can also provide information about new sacred sites that the user might be interested in, based on the user's past information browsing history. The information provision unit can also analyze the user's past information browsing history and provide the most relevant information. In this way, the information provision unit can provide the most relevant information by referring to the user's past information browsing history. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's browsing history data into AI, and the AI ​​can provide the most relevant information.

[0106] The information provider can customize the information content based on the user's current interests and preferences when providing information. For example, the information provider can provide information on related pilgrimage sites based on information about anime and manga that the user has recently searched for. The information provider can also provide information on related pilgrimage sites based on information about events and forums that the user has attended. The information provider can also provide information on related pilgrimage sites based on information from newsletters and blogs that the user subscribes to. This allows the information provider to improve user satisfaction by providing information content based on the user's current interests and preferences. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user interest data into AI, which can then customize the information content to be optimal.

[0107] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is in a hurry, the information delivery unit will prioritize providing the most important information. If the user is relaxed, the information delivery unit may also provide detailed information. If the user is feeling anxious, the information delivery unit may also prioritize providing reassuring information. In this way, the information delivery unit can prioritize providing information that is important to the user by determining the priority of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input user emotion data into a generative AI, and the generative AI can determine priorities based on the emotions.

[0108] The information provision unit can provide optimal information by considering the user's geographical location when providing information. For example, the information provision unit can prioritize providing information about sacred sites close to the user's current location. The information provision unit can also provide information about highly relevant sacred sites based on the user's past visit history. The information provision unit can also provide information about sacred sites to visit based on the user's current location and related event information. In this way, the information provision unit can provide optimal information by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's geographical location information into AI, and the AI ​​can provide optimal information.

[0109] The information provision department can optimize the information content by analyzing the user's social media activity when providing information. For example, the information provision department can provide information on relevant sacred sites based on information about accounts that the user follows on social media. The information provision department can also provide information on relevant sacred sites based on content that the user has shared on social media. The information provision department can also provide information on relevant sacred sites based on information about events that the user has participated in on social media. In this way, the information provision department can provide optimal information content by analyzing the user's social media activity. Some or all of the above processing in the information provision department may be performed using AI or not. For example, the information provision department can input the user's social media data into AI, and the AI ​​can provide optimal information content.

[0110] The interaction unit can estimate the user's emotions and adjust its interaction methods based on those emotions. For example, if the user is excited, the interaction unit can provide an interface that facilitates lively interaction. If the user is relaxed, the interaction unit can also facilitate interaction in a calm tone. If the user is feeling anxious, the interaction unit can provide a reassuring way of interacting. In this way, the interaction unit improves user satisfaction by providing interaction methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input user emotion data into a generative AI, which can then adjust its interaction methods based on the emotion.

[0111] The interaction unit can provide the most suitable interaction method by referring to the user's past interaction history during interaction. For example, the interaction unit can provide relevant interaction methods based on information about events and forums the user has previously participated in. The interaction unit can also provide new interaction methods that might be of interest to the user based on their past interaction history. The interaction unit can also analyze the user's past interaction history and provide the most relevant interaction method. In this way, the interaction unit can provide the most suitable interaction method by referring to the user's past interaction history. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the user's interaction history data into AI, and the AI ​​can provide the most suitable interaction method.

[0112] The interaction function can customize interaction content based on the user's current interests and preferences. For example, the interaction function can provide relevant interaction methods based on information about anime and manga the user has recently searched for. It can also provide relevant interaction methods based on information about events and forums the user has participated in. It can also provide relevant interaction methods based on information about newsletters and blogs the user subscribes to. This improves user satisfaction by providing interaction content based on the user's current interests and preferences. Some or all of the above processing in the interaction function may be performed using AI or not. For example, the interaction function can input user interest data into AI, which can then customize the most suitable interaction content.

[0113] The interaction unit can estimate the user's emotions and prioritize interaction content based on those emotions. For example, if the user is in a hurry, the interaction unit will prioritize providing the most important interaction content. If the user is relaxed, the interaction unit can also provide detailed interaction content. If the user is feeling anxious, the interaction unit can also prioritize providing reassuring interaction content. In this way, the interaction unit can prioritize providing information that is important to the user by prioritizing interaction content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input user emotion data into a generative AI, which can then prioritize based on the emotions.

[0114] The interaction unit can provide the most suitable interaction method by considering the user's geographical location during interaction. For example, the interaction unit can prioritize providing events and forums close to the user's current location. The interaction unit can also provide highly relevant interaction methods based on the user's past visit history. The interaction unit can also provide interaction methods that the user should visit based on the user's current location and relevant event information. In this way, the interaction unit can provide the most suitable interaction method by considering the user's geographical location. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the user's geographical location information into AI, and the AI ​​can provide the most suitable interaction method.

[0115] The interaction unit can analyze a user's social media activity during interaction to optimize the content of the interaction. For example, the interaction unit can provide relevant interaction methods based on information about accounts the user follows on social media. The interaction unit can also provide relevant interaction methods based on content the user has shared on social media. The interaction unit can also provide relevant interaction methods based on information about events the user has participated in on social media. In this way, the interaction unit can provide optimal interaction content by analyzing the user's social media activity. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the user's social media data into AI, and the AI ​​can provide optimal interaction content.

[0116] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is excited, the data collection unit may use a method to collect data quickly. If the user is relaxed, the data collection unit may also use a method to collect detailed data. If the user is feeling anxious, the data collection unit may also use a data collection method that provides a sense of security. This improves the accuracy of data collection by providing a data collection method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the data collection method based on the emotions.

[0117] The data collection unit can provide the optimal collection method by referring to the user's past data collection history when collecting data. For example, the data collection unit can prioritize collecting relevant data based on data the user has collected in the past. The data collection unit can also collect new data that is likely to be of interest to the user based on their past data collection history. The data collection unit can also analyze the user's past data collection history and collect the most relevant data. In this way, the data collection unit can provide the optimal collection method by referring to the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's data collection history into AI, and the AI ​​can provide the optimal collection method.

[0118] The data collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated emotions. For example, if the user is in a hurry, the data collection unit will prioritize collecting the most important data. If the user is relaxed, the data collection unit can also collect detailed data. If the user is feeling anxious, the data collection unit can also prioritize collecting data that provides a sense of security. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine priorities based on the emotions.

[0119] The data collection unit can collect optimal data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize collecting data that is close to the user's current location. The data collection unit can also collect highly relevant data based on the user's past visit history. The data collection unit can also collect data that the user should visit based on event information related to the user's current location. In this way, the data collection unit can collect optimal data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can collect optimal data.

[0120] The reservation system can estimate the user's emotions and adjust the reservation method based on those emotions. For example, if the user is excited, the reservation system can provide a way to complete the reservation quickly. If the user is relaxed, the reservation system can also provide detailed reservation options. If the user is feeling anxious, the reservation system can provide a reassuring reservation method. This improves the accuracy of reservations by providing reservation methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input user emotion data into a generative AI, which can then adjust the reservation method based on the emotion.

[0121] The reservation department can provide the optimal reservation method by referring to the user's past reservation history at the time of booking. For example, the reservation department can prioritize suggesting relevant accommodations based on data of accommodations the user has previously booked. The reservation department can also suggest new accommodations that the user might be interested in based on their past reservation history. The reservation department can also analyze the user's past reservation history and suggest the most relevant accommodations. In this way, the reservation department can provide the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's reservation history data into AI, and the AI ​​can provide the optimal reservation method.

[0122] The reservation system can estimate the user's emotions and prioritize reservations based on those emotions. For example, if the user is in a hurry, the system will prioritize the most important reservations. If the user is relaxed, the system can also provide more detailed reservations. If the user is feeling anxious, the system can prioritize reservations that provide reassurance. In this way, the system can prioritize important reservations by determining the priority of reservations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input user emotion data into a generative AI, which can then determine priorities based on those emotions.

[0123] The reservation department can provide the optimal reservation method by considering the user's geographical location information at the time of booking. For example, the reservation department can prioritize suggesting accommodations close to the user's current location. The reservation department can also suggest highly relevant accommodations based on the user's past visit history. The reservation department can also suggest accommodations to visit based on the user's current location and related event information. In this way, the reservation department can provide the optimal reservation method by considering the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI or not. For example, the reservation department can input the user's geographical location information into AI, and the AI ​​can provide the optimal reservation method.

[0124] The photo-sharing unit can estimate the user's emotions and adjust the photo-sharing method based on the estimated emotions. For example, if the user is excited, the photo-sharing unit can provide a colorful and visually appealing photo-sharing interface. If the user is relaxed, the photo-sharing unit can also facilitate photo-sharing in a calm tone. If the user is feeling anxious, the photo-sharing unit can provide a reassuring photo-sharing method. This improves the accuracy of photo-sharing by providing a photo-sharing method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the photo-sharing unit may be performed using AI or not. For example, the photo-sharing unit can input user emotion data into a generative AI, which can then adjust the photo-sharing method based on the emotions.

[0125] The photo sharing unit can provide the optimal sharing method by referring to the user's past photo sharing history when sharing photos. For example, the photo sharing unit can prioritize sharing related photos based on data of photos the user has shared in the past. The photo sharing unit can also share new photos that are likely to interest the user based on their past photo sharing history. The photo sharing unit can also analyze the user's past photo sharing history and share the most relevant photos. In this way, the photo sharing unit can provide the optimal sharing method by referring to the user's past photo sharing history. Some or all of the above processing in the photo sharing unit may be performed using AI or not. For example, the photo sharing unit can input the user's photo sharing history into AI, and the AI ​​can provide the optimal sharing method.

[0126] The photo sharing unit can estimate the user's emotions and prioritize the content of photo sharing based on the estimated emotions. For example, if the user is in a hurry, the photo sharing unit will prioritize sharing the most important photos. If the user is relaxed, the photo sharing unit may also share detailed photos. If the user is feeling anxious, the photo sharing unit may also prioritize sharing photos that provide a sense of security. In this way, the photo sharing unit can prioritize sharing important photos by determining the priority of photo sharing content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the photo sharing unit may be performed using AI or not. For example, the photo sharing unit can input user emotion data into a generative AI, which can then determine priorities based on the emotions.

[0127] The photo sharing unit can share the most suitable photos by considering the user's geographical location information when sharing photos. For example, the photo sharing unit can prioritize sharing photos taken near the user's current location. The photo sharing unit can also share highly relevant photos based on the user's past visit history. The photo sharing unit can also share photos taken at places the user should visit based on event information related to the user's current location. In this way, the photo sharing unit can share the most suitable photos by considering the user's geographical location information. Some or all of the above processing in the photo sharing unit may be performed using AI or not. For example, the photo sharing unit can input the user's geographical location information into AI, and the AI ​​can share the most suitable photos.

[0128] The interaction facilitator can estimate the user's emotions and adjust the interaction facilitator based on the estimated emotions. For example, if the user is excited, the interaction facilitator can provide an interface that facilitates active interaction. If the user is relaxed, the interaction facilitator can also facilitate interaction in a calm tone. If the user is feeling anxious, the interaction facilitator can also provide an interaction facilitator that provides a sense of security. This improves the accuracy of interaction facilitator by providing an interaction facilitator that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction facilitator may be performed using AI or not. For example, the interaction facilitator can input user emotion data into a generative AI, which can then adjust the interaction facilitator based on the emotion.

[0129] The interaction promotion unit can provide the most suitable interaction promotion method by referring to the user's past interaction history when promoting interaction. For example, the interaction promotion unit can provide relevant interaction promotion methods based on information about events and forums the user has previously participated in. The interaction promotion unit can also provide new interaction promotion methods that the user might be interested in, based on the user's past interaction history. The interaction promotion unit can also analyze the user's past interaction history and provide the most relevant interaction promotion method. In this way, the interaction promotion unit can provide the most suitable interaction promotion method by referring to the user's past interaction history. Some or all of the above processing in the interaction promotion unit may be performed using AI or not. For example, the interaction promotion unit can input the user's interaction history data into AI, and the AI ​​can provide the most suitable interaction promotion method.

[0130] The interaction facilitator can estimate the user's emotions and prioritize interaction content based on those emotions. For example, if the user is in a hurry, the interaction facilitator will prioritize providing the most important interaction content. If the user is relaxed, the interaction facilitator can also provide detailed interaction content. If the user is feeling anxious, the interaction facilitator can also prioritize providing reassuring interaction content. In this way, the interaction facilitator can prioritize important interaction content by determining the priority of interaction content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction facilitator may be performed using AI or not. For example, the interaction facilitator can input user emotion data into a generative AI, which can then prioritize based on the emotion.

[0131] The interaction promotion unit can provide the most suitable interaction promotion method when promoting interaction, taking into account the user's geographical location information. For example, the interaction promotion unit can prioritize providing events and forums close to the user's current location. The interaction promotion unit can also provide highly relevant interaction promotion methods based on the user's past visit history. The interaction promotion unit can also provide interaction promotion methods that the user should visit based on the user's current location and relevant event information. In this way, the interaction promotion unit can provide the most suitable interaction promotion method by taking into account the user's geographical location information. Some or all of the above processing in the interaction promotion unit may be performed using AI or not. For example, the interaction promotion unit can input the user's geographical location information into AI, and the AI ​​can provide the most suitable interaction promotion method.

[0132] The interaction promotion unit can analyze a user's social media activity and optimize the content of interactions during the interaction promotion process. For example, the interaction promotion unit can provide relevant interaction methods based on information about accounts that a user follows on social media. The interaction promotion unit can also provide relevant interaction methods based on content that a user has shared on social media. The interaction promotion unit can also provide relevant interaction methods based on information about events that a user has participated in on social media. In this way, the interaction promotion unit can provide optimal interaction content by analyzing the user's social media activity. Some or all of the above processing in the interaction promotion unit may be performed using AI or not. For example, the interaction promotion unit can input the user's social media data into AI, and the AI ​​can provide optimal interaction content.

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

[0134] The pilgrimage platform can estimate the user's emotions and adjust the suggested pilgrimage sites based on those emotions. For example, if the user is excited, it can suggest active tourist spots and events. If the user is relaxed, it can suggest quiet places and relaxing accommodations. If the user is feeling anxious, it can suggest guided tours and support services that provide reassurance. In this way, the pilgrimage platform improves user satisfaction by providing destination suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's emotion data into the generative AI, which can then adjust the suggestions based on the emotion.

[0135] The pilgrimage platform can analyze a user's past visit history and suggest the most suitable destinations. For example, it can suggest new related pilgrimage sites based on data of sites the user has visited in the past. It can also suggest similar accommodations based on data of facilities the user has stayed at in the past. It can also analyze a user's past travel patterns and suggest the most efficient route. In this way, the pilgrimage platform improves user convenience by providing optimal suggestions based on the user's past visit history. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's visit history data into AI, which can then make optimal suggestions.

[0136] The pilgrimage platform can customize destination suggestions based on the user's current interests. For example, it can suggest relevant pilgrimage sites based on information about anime and manga the user has recently searched for. It can also suggest relevant pilgrimage sites based on information about events and forums the user has attended. It can also suggest relevant pilgrimage sites based on information from newsletters and blogs the user subscribes to. In this way, the pilgrimage platform improves user satisfaction by providing suggestions based on the user's current interests. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user interest data into AI, which can then make optimal suggestions.

[0137] The pilgrimage platform can suggest destinations considering the user's geographical location. For example, it can prioritize suggesting pilgrimage sites close to the user's current location. It can also suggest highly relevant pilgrimage sites based on the user's past visit history. It can also suggest pilgrimage sites to visit based on the user's current location and related event information. In this way, the pilgrimage platform can suggest the optimal destination by considering the user's geographical location. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's geographical location information into AI, which can then make the optimal suggestion.

[0138] The pilgrimage platform can analyze a user's social media activity and suggest relevant pilgrimage sites. For example, it can suggest relevant pilgrimage sites based on information about accounts the user follows on social media. It can also suggest relevant pilgrimage sites based on content the user shares on social media. It can also suggest relevant pilgrimage sites based on information about events the user has participated in on social media. In this way, the pilgrimage platform can provide optimal suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's social media data into AI, which can then make optimal suggestions.

[0139] The pilgrimage platform can estimate the user's emotions and prioritize suggested destinations based on those emotions. For example, if the user is in a hurry, the most important information can be provided first. If the user is relaxed, more detailed information can be provided. If the user is feeling anxious, information that provides reassurance can be provided first. In this way, the pilgrimage platform can prioritize information that is important to the user by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into a generative AI, which can then determine priorities based on those emotions.

[0140] The pilgrimage platform can estimate the user's emotions and adjust the way information is provided based on those emotions. For example, if the user is excited, it can provide colorful and visually appealing information. If the user is relaxed, it can provide information in a calm tone. If the user is feeling anxious, it can provide reassuring information. In this way, the pilgrimage platform improves user satisfaction by providing information in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision section may be performed using AI or not. For example, the information provision section can input the user's emotion data into the generative AI, which can then adjust the way information is provided based on the emotion.

[0141] The pilgrimage platform can estimate the user's emotions and adjust the interaction method based on those emotions. For example, if the user is excited, it can provide an interface that facilitates lively interaction. If the user is relaxed, it can also facilitate interaction in a calm tone. If the user is feeling anxious, it can also provide an interaction method that provides reassurance. In this way, the pilgrimage platform improves user satisfaction by providing interaction methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction section may be performed using AI or not. For example, the interaction section can input the user's emotion data into a generative AI, which can then adjust the interaction method based on the emotion.

[0142] The pilgrimage platform can estimate the user's emotions and adjust its data collection methods based on those emotions. For example, if the user is excited, it can use a method to collect data quickly. If the user is relaxed, it can use a method to collect detailed data. If the user is anxious, it can use a data collection method that provides a sense of security. This improves the accuracy of data collection by providing a data collection method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI, which can then adjust its data collection method based on the emotion.

[0143] The pilgrimage platform can estimate the user's emotions and adjust the booking method based on those emotions. For example, if the user is excited, it can provide a way to complete the booking quickly. If the user is relaxed, it can also provide detailed booking options. If the user is feeling anxious, it can provide a reassuring booking method. This improves the accuracy of bookings by providing booking methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking section may be performed using AI or not. For example, the booking section can input user emotion data into a generative AI, which can then adjust the booking method based on the emotion.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The reception desk receives input from the user about the sacred site they wish to visit. For example, a user could input, "I want to visit the steps of the shrine from the final scene of a specific movie." The reception desk then sends the information entered by the user to the AI. Step 2: The analysis department analyzes the information received by the reception department and proposes the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. For example, the analysis department analyzes data on sacred sites and accommodations the user has visited in the past to select the most suitable route and accommodation for the user. Step 3: The suggestion section provides information on pilgrimage routes and accommodations suggested by the analysis section. For example, the suggestion section displays detailed information on the optimal route and accommodations to the user. Step 4: The information department provides information specifically for pilgrimages to sacred sites. For example, they provide information on tourist spots to visit, events, local restaurants, and more. Step 5: The interaction section facilitates interaction among fans. For example, users can share photos of places they have visited and interact with other fans.

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0148] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, information provision unit, and communication unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user about the sacred site they wish to visit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and behavioral data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal pilgrimage route and accommodation. The information provision unit is implemented by the control unit 46A of the smart device 14 and provides information specifically for pilgrimage to sacred sites. The communication unit is implemented by the control unit 46A of the smart device 14 and facilitates interaction among fans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0151] As shown in Figure 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, information provision unit, and communication unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts input from the user regarding the sacred site they wish to visit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and behavioral data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal pilgrimage route and accommodation. The information provision unit is implemented by the control unit 46A of the smart glasses 214 and provides information specifically for pilgrimage to sacred sites. The communication unit is implemented by the control unit 46A of the smart glasses 214 and facilitates interaction among fans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0167] As shown in Figure 5, the data processing system 310 includes a 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, information provision unit, and communication unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts input from the user regarding the sacred site they wish to visit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and behavioral data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal pilgrimage route and accommodation. The information provision unit is implemented by the control unit 46A of the headset terminal 314 and provides information specifically for pilgrimage to sacred sites. The communication unit is implemented by the control unit 46A of the headset terminal 314 and facilitates interaction among fans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0183] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0185] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0186] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0188] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0189] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0190] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0191] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0192] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0193] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0198] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, information provision unit, and communication unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user about the sacred site they wish to visit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and behavioral data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal pilgrimage route and accommodation. The information provision unit is implemented by the control unit 46A of the robot 414 and provides information specifically for pilgrimage to sacred sites. The communication unit is implemented by the control unit 46A of the robot 414 and facilitates interaction among fans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0199] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0200] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0201] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0202] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0203] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0204] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has 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 ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0206] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0207] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to 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] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0210] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0211] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0212] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0213] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0214] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0215] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0216] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0217] (Note 1) A reception desk that accepts entries for the sacred sites you wish to visit, The analysis department analyzes the information received by the reception department and proposes the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. The Proposal Department provides information on pilgrimage routes and accommodations proposed by the aforementioned Analysis Department, An information service department that provides information specifically for pilgrimages to sacred sites, It includes a community section to promote interaction among fans. A system characterized by the following features. (Note 2) It includes a data collection unit that collects user preferences and behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a reservation department that handles the booking of suggested routes and accommodations. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a photo-sharing section where users can share photos of sacred sites they have visited. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a fan interaction department to facilitate interaction with other fans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for the sacred sites they want to visit based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and provide the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter a sacred site they wish to visit, the system will suggest input options based on their current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When you enter the sacred site you wish to visit, the system prioritizes displaying the most relevant sites based on your geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When you enter the sacred site you want to visit, the system analyzes your social media activity and suggests related sacred sites. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, the optimal analysis method is selected by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, the results are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, the optimal analysis method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, we analyze users' social media activity to optimize the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, the system selects the most suitable proposal method by referring to the user's past selection history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, customize the proposal content based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, the optimal proposal method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity to optimize the proposal content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information provision unit, When providing information, we refer to the user's past information browsing history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned information provision unit, When providing information, customize the content based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned information provision unit, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information provision unit, When providing information, we analyze users' social media activity to optimize the content of the information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned AC unit is It estimates the user's emotions and adjusts the method of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned AC unit is During interactions, the system provides the most suitable interaction method by referring to the user's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned AC unit is During interactions, the content of the interaction is customized based on the user's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned AC unit is It estimates the user's emotions and prioritizes interaction content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned AC unit is During interactions, the system provides the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned AC unit is During interactions, the system analyzes users' social media activity to optimize the content of those interactions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned data acquisition unit is When collecting data, the system provides the optimal collection method by referring to the user's past data collection history. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned data acquisition unit is We estimate user sentiment and prioritize data collection based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned data acquisition unit is When collecting data, the system takes into account the user's geographical location to select the most optimal data. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned reservation section is, When a reservation is made, the system provides the optimal reservation method by referring to the user's past reservation history. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned reservation section is, The system estimates the user's emotions and prioritizes bookings based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned reservation section is, When making a reservation, we provide the optimal reservation method by taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned photo sharing unit is It estimates the user's emotions and adjusts the photo sharing method based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned photo sharing unit is When sharing photos, the system refers to the user's past photo sharing history to provide the optimal sharing method. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned photo sharing unit is It estimates the user's emotions and prioritizes the content of photo sharing based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned photo sharing unit is When sharing photos, the system takes the user's geographical location into consideration to share the most suitable photos. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned Exchange Promotion Department, It estimates the user's emotions and adjusts the interaction facilitation methods based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 49) The aforementioned Exchange Promotion Department, When promoting communication, refer to the user's past communication history to provide an optimal communication promotion method The system according to Supplementary Note 5, characterized in that (Supplementary Note 50) The communication promotion unit estimates the user's emotion and determines the priority order of communication promotion content based on the estimated user emotion The system according to Supplementary Note 5, characterized in that (Supplementary Note 51) The communication promotion unit When promoting communication, consider the user's geographical location information to provide an optimal communication promotion method The system according to Supplementary Note 5, characterized in that (Supplementary Note 52) The communication promotion unit When promoting communication, analyze the user's social media activities to optimize the communication content The system according to Supplementary Note 5, characterized in that

Explanation of Reference Signs

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts entries for the sacred sites you wish to visit, The analysis department analyzes the information received by the reception department and proposes the optimal pilgrimage route and accommodation based on the user's preferences and behavioral data. The Proposal Department provides information on pilgrimage routes and accommodations proposed by the aforementioned Analysis Department, An information service department that provides information specifically for pilgrimages to sacred sites, It includes a community section to promote interaction among fans. A system characterized by the following features.

2. It includes a data collection unit that collects user preferences and behavioral data. The system according to feature 1.

3. It has a reservation department that handles the booking of suggested routes and accommodations. The system according to feature 1.

4. It features a photo-sharing section where users can share photos of sacred sites they have visited. The system according to feature 1.

5. It has a fan interaction department to facilitate interaction with other fans. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for the sacred sites they want to visit based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and provide the optimal input interface. The system according to feature 1.

8. The aforementioned reception unit is When users enter a sacred site they wish to visit, the system will suggest input options based on their current interests and preferences. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When you enter the sacred site you wish to visit, the system prioritizes displaying the most relevant sites based on your geographical location. The system according to feature 1.

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