system

Abstract

The system according to this embodiment aims to recommend the optimal routes and accommodations for fans of anime, manga, and games when they go on pilgrimages to real-world locations, and to promote interaction among fans. [Solution] The system according to the embodiment comprises a learning unit, a recommendation unit, an information provision unit, and an interaction unit. The learning unit learns the user's preferences and behavioral patterns. The recommendation unit recommends the optimal route and accommodation based on the information learned by the learning unit. The information provision unit provides information specifically for pilgrimage to sacred sites. The interaction unit facilitates interaction among fans.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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, there is a problem that it is difficult for fans of anime, manga, and games to find an optimal route and accommodation facilities when making a pilgrimage to a sacred place, and a function for promoting communication among fans is not sufficiently provided.

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a recommendation unit, an information provision unit, and an interaction unit. The learning unit learns the user's preferences and behavioral patterns. The recommendation unit recommends the optimal route and accommodation based on the information learned by the learning unit. The information provision unit provides information specifically for pilgrimage to sacred sites. The interaction unit facilitates interaction among fans. [Effects of the Invention]

[0007] The system according to this embodiment can recommend optimal routes and accommodations for fans of anime, manga, and games when they go on pilgrimages to real-life locations, 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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 site navigation agent system 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 pilgrimage sites of their works. When a user inputs the pilgrimage sites they wish to visit, the AI ​​learns the user's preferences and behavioral patterns and recommends the optimal travel plan and accommodation. It also provides functions to provide information specifically tailored to pilgrimage sites and to promote interaction among fans. For example, if a user inputs "I want to visit specific pilgrimage sites," the AI ​​will suggest the optimal route and accommodation, and a community function to promote interaction with other fans is also available. This system is expected to improve visitor satisfaction, streamline pilgrimage visits, and contribute to the local economy. Specifically, the AI ​​recommends the optimal route and accommodation based on the user's past behavior and preferences, and provides a function to promote interaction with other users. This makes planning pilgrimages easier and allows users to enjoy interacting with other fans. Thus, the pilgrimage site navigation agent system can recommend the optimal route and accommodation based on the user's preferences and behavioral patterns, and promote information specifically tailored to pilgrimage sites and interaction among fans.

[0029] The pilgrimage navigation agent system according to this embodiment comprises a learning unit, a recommendation unit, an information provision unit, and an interaction unit. The learning unit learns the user's preferences and behavioral patterns. For example, the learning unit analyzes the user's past travel history and trends in visited destinations to understand the user's preferences. The learning unit can also learn the user's preferred type of accommodation and food preferences. For example, the learning unit collects data on tourist spots visited and accommodations used by the user in the past to identify the user's preferences. The recommendation unit recommends the optimal route and accommodation based on the information learned by the learning unit. For example, the recommendation unit proposes the optimal travel plan based on the user's preferences and behavioral patterns. The recommendation unit can also recommend the optimal accommodation according to the user's means of transportation and budget. For example, the recommendation unit proposes a route that efficiently visits the tourist spots the user likes and recommends accommodation that fits the budget. The information provision unit provides information specifically for pilgrimage. For example, the information provision unit provides information on places to visit and events. The information provision unit can also provide information on the history and culture related to the pilgrimage sites. For example, the information provision section provides detailed information about the sacred sites that the user plans to visit, offering helpful information for their visit. The interaction section has functions to promote interaction among fans. For example, the interaction section provides online forums and chat functions. The interaction section can also provide information about offline events and promote interaction among fans. For example, the interaction section provides community functions that allow users to interact with other fans, providing a place to share the enjoyment of pilgrimage. As a result, the pilgrimage navigation agent system according to this embodiment can recommend the optimal route and accommodation based on the user's preferences and behavioral patterns, and can promote the provision of information specifically for pilgrimage and interaction among fans.

[0030] The learning unit learns the user's preferences and behavioral patterns. Specifically, the learning unit analyzes the user's past travel history and destination trends in detail to understand their preferences. For example, it collects data on tourist spots the user has visited in the past to identify the types of places they prefer. It also analyzes data on accommodations the user has stayed at to learn their preferred types of accommodations and budget ranges. Furthermore, regarding the user's food preferences, it collects data on restaurants they have visited and dishes they have eaten to understand their preferred types of cuisine and dining styles. This allows the learning unit to understand the user's detailed preferences and behavioral patterns, providing the foundational data for making optimal suggestions to individual users. The learning unit can continuously update this data to adapt to changes in user preferences and behavioral patterns. For example, it collects data from visits to new travel destinations and stays at new accommodations to reflect the user's latest preferences. This allows the learning unit to always learn user preferences based on the latest information and make more accurate suggestions. In addition, the learning unit can collect user feedback to improve the accuracy of its learning algorithms. For example, users can rate suggested routes and accommodations, and this feedback is used to improve the learning algorithm and incorporate it into future suggestions. This allows the learning unit to accurately understand user preferences and behavioral patterns, providing the foundational data necessary to make optimal suggestions for each individual user.

[0031] The recommendation department recommends the optimal route and accommodation based on information learned by the learning department. Specifically, the recommendation department proposes the best travel plan based on the user's preferences and behavioral patterns. For example, it may suggest a route that efficiently visits the user's favorite tourist spots and recommend accommodation that fits their budget. The recommendation department can also recommend the best accommodation based on the user's mode of transportation and budget. For example, it may recommend accommodation with ample parking for users traveling by car and accommodation with good access for users using public transport. Furthermore, the recommendation department can propose travel plans based on specific themes, depending on the user's travel purpose and interests. For example, it may suggest a route that visits historical tourist spots for users interested in history and a route that includes restaurants where users can enjoy local specialties for users interested in gourmet food. In addition, the recommendation department can suggest the best route based on the user's travel schedule and length of stay. For example, it may suggest an efficient route that visits tourist spots for short trips and a route that allows for a more relaxed schedule for longer trips. In this way, the recommendation department can recommend the best route and accommodation based on the user's preferences and behavioral patterns, thereby improving the user's travel experience. Furthermore, the recommendation system can collect user feedback and improve the accuracy of its recommendation algorithms. For example, users can rate suggested routes and accommodations, and this feedback can be used to improve the recommendation algorithms and incorporate it into future suggestions. This ensures that the recommendation system can always provide users with the most up-to-date information and the best possible recommendations.

[0032] The Information Department provides information specifically tailored for pilgrimages to sacred sites. Specifically, it provides information on places to visit and events. For example, it provides detailed information about sacred sites that users plan to visit, offering helpful information for their visit. The Information Department can also provide information about the history and culture associated with sacred sites. For example, it provides information about the historical background and cultural significance of sacred sites, enabling users to understand the importance of the place when they visit. The Information Department can also provide information on events related to pilgrimages. For example, it provides information on festivals and special events held at sacred sites, enabling users to participate in them. Furthermore, the Information Department can provide the latest news and topics related to pilgrimages. For example, it provides information on the latest research findings and new tourist spots related to sacred sites, ensuring users stay up-to-date. In this way, the Information Department can provide users with information tailored to pilgrimages, improving their visit experience. In addition, the Information Department can collect user feedback and continuously improve the accuracy and content of the information it provides. For example, users can rate the information provided, and the content of the information provided will be improved based on that rating and reflected in future provision. This allows the information provision department to always provide users with the most up-to-date information.

[0033] The Community Section has functions to promote interaction among fans. Specifically, the Community Section provides online forums and chat functions. For example, it provides community functions that allow users to interact with other fans and provide a place to share the enjoyment of pilgrimage. The Community Section can also promote interaction among fans by providing information on offline events. For example, it provides information on offline events related to pilgrimage and allows users to participate in those events. Furthermore, the Community Section can also provide a platform where users can share their pilgrimage experiences. For example, it provides a function that allows users to post photos and impressions of the pilgrimage sites they have visited and share information with other users. In this way, the Community Section can promote interaction among users and provide a place to share the enjoyment of pilgrimage. In addition, the Community Section can collect user feedback and continuously improve the accuracy and content of the functions it provides. For example, users can rate the functions provided, and the functions will be improved based on that rating and reflected in the next version. In this way, the Community Section can always provide users with the best functions based on the latest information.

[0034] The learning unit can learn the user's past behavior and preferences. For example, the learning unit can analyze the user's past travel history to understand their visiting trends. The learning unit can also learn the user's preferred types of accommodation and food preferences. For example, the learning unit can collect data on tourist spots the user has visited and accommodations they have used in the past to identify the user's preferences. By learning the user's past behavior and preferences, more accurate recommendations become possible. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past behavior data into a generating AI and have the generating AI identify the user's preferences.

[0035] The recommendation unit can recommend the optimal route and accommodation based on the information learned by the learning unit. For example, the recommendation unit can propose the optimal travel plan based on the user's preferences and behavioral patterns. The recommendation unit can also recommend the optimal accommodation according to the user's mode of transportation and budget. For example, the recommendation unit can propose a route that efficiently visits the user's favorite tourist spots and recommend accommodation that fits the budget. This improves user satisfaction by recommending the optimal route and accommodation based on learned information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the information learned by the learning unit into a generating AI and have the generating AI perform the recommendation of the optimal route and accommodation.

[0036] The information provision department can provide information specifically tailored for pilgrimages to sacred sites. For example, it can provide information on places to visit and events. It can also provide information about the history and culture related to the sacred sites. For example, it can provide detailed information about the sacred sites that the user plans to visit and provide information that will be useful during the visit. By providing information specifically tailored for pilgrimages to sacred sites, the user's pilgrimage will be more fulfilling. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision department can input information about sacred sites into a generative AI and have the generative AI perform the information provision.

[0037] The interaction section can have functions to promote interaction among fans. For example, it can provide online forums and chat functions. It can also provide information on offline events and promote interaction among fans. For example, the interaction section can provide community functions that allow users to interact with other fans and provide a place to share the enjoyment of pilgrimage to sacred sites. This strengthens the connections between users by promoting interaction among fans. Some or all of the above processing in the interaction section may be performed using, for example, a generative AI, or not using a generative AI. For example, the interaction section can input user interaction data into a generative AI and have the generative AI perform the task of promoting interaction.

[0038] The learning unit can optimize its learning algorithm by referring to the user's past travel history during the learning process. For example, the learning unit can analyze the frequency of visits to sacred sites the user has visited in the past and prioritize learning the frequently visited locations. The learning unit can also refer to the user's past ratings of accommodations and include highly-rated facilities in the learning data. Furthermore, the learning unit can consider the season and weather conditions the user has visited in the past to learn the optimal time to visit. This improves the accuracy of the learning algorithm by referring to the user's past travel history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's past travel history data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0039] The learning unit can add dynamic learning capabilities to reflect changes in the user's interests in real time during the learning process. For example, if a user becomes interested in a new anime or manga, the learning unit can add locations related to that work to its learning data. Furthermore, if a user shows interest in a particular genre, the learning unit can include locations and events related to that genre in its learning data. In addition, the learning unit can update the learning data in real time when the user's interests change, reflecting the latest information. This allows the learning unit to provide the most up-to-date information by reflecting changes in the user's interests in real time. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user interest change data into a generative AI and have the generative AI execute the dynamic learning capabilities.

[0040] The learning unit can analyze the user's social media activity during training and reflect it in the training data. For example, the learning unit can analyze posts about visits to sacred sites shared by the user on social media and add relevant information to the training data. The learning unit can also reflect information about accounts the user follows and groups the user participates in in the training data. Furthermore, the learning unit can include events and places the user has shown interest in on social media in the training data. This improves the accuracy of the training data by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the reflection of it in the training data.

[0041] The learning unit can optimize the training data by considering the user's geographical location information during training. For example, the learning unit can prioritize including sacred sites close to the user's current location in the training data. The learning unit can also reflect information about areas frequently visited by the user in the training data. Furthermore, the learning unit can include the optimal visiting route in the training data based on the user's geographical location. This improves the accuracy of the training data by considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location information data into a generative AI and have the generative AI perform the optimization of the training data.

[0042] The recommendation system can recommend the optimal route and accommodation by referring to the user's past travel history. For example, the recommendation system can analyze the frequency of visits to sacred sites the user has visited in the past and prioritize recommending frequently visited locations. It can also refer to the ratings of accommodations the user has used in the past and recommend highly-rated facilities. Furthermore, the recommendation system can consider the season and weather the user has visited in the past and recommend the optimal time to visit. In this way, by referring to the user's past travel history, the system can recommend the optimal route and accommodation. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the user's past travel history data into a generative AI and have the generative AI perform the recommendation of the optimal route and accommodation.

[0043] The recommendation system can add a dynamic recommendation function to reflect changes in the user's interests in real time. For example, if a user starts to take an interest in a new anime or manga, the recommendation system can recommend places related to that work. It can also recommend places and events related to a specific genre if the user shows interest in that genre. Furthermore, the recommendation system can update recommendations in real time when the user's interests change, reflecting the latest information. This allows the system to provide up-to-date information by reflecting changes in the user's interests in real time. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input user interest change data into a generative AI and have the generative AI execute the dynamic recommendation function.

[0044] The recommendation system can analyze a user's social media activity and reflect it in its recommendations. For example, it can analyze posts about pilgrimages shared by users on social media and add relevant information to its recommendations. It can also reflect information about accounts a user follows and groups they participate in. Furthermore, it can include events and places a user has shown interest in on social media in its recommendations. This improves the accuracy of recommendations by analyzing the user's social media activity. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input user social media activity data into a generative AI and have the generative AI perform the task of reflecting it in the recommendations.

[0045] The recommendation system can recommend the optimal route and accommodation by considering the user's geographical location. For example, the recommendation system can prioritize recommending sacred sites close to the user's current location. The recommendation system can also incorporate information about areas the user frequently visits into its recommendations. Furthermore, the recommendation system can recommend the optimal visiting route based on the user's geographical location. In this way, the recommendation system can recommend the optimal route and accommodation by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation system can input the user's geographical location data into a generative AI and have the generative AI perform the recommendation of the optimal route and accommodation.

[0046] The information provision unit can provide optimal information by referring to the user's past travel history when providing information. For example, the information provision unit can analyze the frequency of visits to sacred sites that the user has visited in the past and prioritize providing information on frequently visited locations. The information provision unit can also refer to the user's past ratings of accommodations and provide information on highly-rated facilities. Furthermore, the information provision unit can consider the season and weather when the user has visited in the past and provide information on the optimal time to visit. In this way, optimal information can be provided by referring to the user's past travel history. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input the user's past travel history data into a generative AI and have the generative AI perform the task of providing optimal information.

[0047] The information provision unit can add a dynamic information provision function to reflect changes in the user's interests in real time when providing information. For example, if a user starts to take an interest in a new anime or manga, the information provision unit can provide information about sacred sites related to that work. The information provision unit can also provide information about sacred sites and events related to a particular genre if the user shows an interest in that genre. Furthermore, the information provision unit can update the information in real time when the user's interests change, providing the latest information. This allows the information provision unit to provide the latest information by reflecting changes in the user's interests in real time. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision unit can input data on changes in the user's interests into a generative AI and have the generative AI execute the dynamic information provision function.

[0048] The information provision department can analyze the user's social media activity and reflect it in the information provided. For example, the information provision department can analyze posts about visits to sacred sites shared by users on social media and provide relevant information. The information provision department can also reflect information about accounts that users follow and groups they participate in in the information provided. Furthermore, the information provision department can provide information about events and places that users have shown interest in on social media. This improves the accuracy of the information provided by analyzing the user's social media activity. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision department can input the user's social media activity data into a generative AI and have the generative AI perform the task of reflecting it in the information provided.

[0049] 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 reflect information about areas frequently visited by the user in the content provided. Furthermore, the information provision unit can provide information on the optimal visiting route based on the user's geographical location. In this way, optimal information can be provided by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing optimal information.

[0050] The interaction unit can provide the optimal interaction method by referring to the user's past interaction history during interaction. For example, the interaction unit can refer to the user's past participation history in events and communities and suggest relevant interaction methods. The interaction unit can also prioritize providing interaction methods that the user has preferred in the past. Furthermore, the interaction unit can suggest the optimal timing for interaction based on the user's past interaction history. In this way, the optimal interaction method can be provided by referring to the user's past interaction history. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the interaction unit can input the user's past interaction history data into a generative AI and have the generative AI perform the task of providing the optimal interaction method.

[0051] The interaction unit can add dynamic interaction features to reflect changes in the user's interests in real time during interactions. For example, if a user starts to show interest in a new anime or manga, the interaction unit can suggest interaction events related to that work. The interaction unit can also suggest communities related to a specific genre if the user shows interest in that genre. Furthermore, the interaction unit can update interaction methods in real time when the user's interests change, providing the latest information. This allows for the provision of up-to-date information by reflecting changes in the user's interests in real time. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or not. For example, the interaction unit can input user interest change data into a generative AI and have the generative AI execute the dynamic interaction features.

[0052] The interaction unit can analyze a user's social media activity during interaction and reflect it in the interaction content. For example, the interaction unit can analyze posts about interaction events that a user has shared on social media and provide relevant information. It can also reflect information about accounts that a user follows and groups that they participate in in the interaction content. Furthermore, the interaction unit can provide information about events and communities that a user has shown interest in on social media. This improves the accuracy of the 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, for example, a generative AI, or not using a generative AI. For example, the interaction unit can input user social media activity data into a generative AI and have the generative AI perform the reflection of that data in the interaction content.

[0053] The interaction unit can provide the optimal interaction method during interaction, taking into account the user's geographical location information. For example, the interaction unit can prioritize suggesting interaction events close to the user's current location. The interaction unit can also incorporate information about areas the user frequently visits into the interaction content. Furthermore, the interaction unit can provide the optimal interaction method based on the user's geographical location. This allows the interaction unit to provide the optimal interaction method by considering the user's geographical location information. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interaction unit can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing the optimal interaction method.

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

[0055] The learning unit can not only learn user preferences and behavioral patterns, but also optimize the learning data by considering the user's health condition. For example, if a user is linked to a health management app, the learning unit can refer to that data and suggest a travel plan tailored to the user's physical condition. For instance, if a user tires easily, the learning data can include plans with shorter travel distances and more rest time. Furthermore, if a user has specific dietary restrictions, the learning data can include accommodations and restaurants that accommodate those restrictions. Additionally, if a user has allergies, the system can provide a safer travel plan by taking that information into account. In this way, optimizing the learning data based on the user's health condition can provide a safer and more comfortable travel experience.

[0056] The recommendation system not only suggests optimal routes and accommodations based on user preferences and behavioral patterns, but can also tailor recommendations according to the user's travel purpose. For example, if a user's purpose is relaxation, it can recommend accommodations in quiet environments and relaxation facilities. If a user wants to enjoy activities, it can recommend active events and adventure spots. Furthermore, if a user values ​​cultural experiences, it can recommend spots where they can experience local culture and history. By tailoring recommendations according to the user's travel purpose, it can provide a more satisfying travel experience.

[0057] The information provision department not only provides information specifically for pilgrimage to sacred sites, but can also adjust the depth of information according to the user's interests. For example, if a user has a strong interest in a particular anime or manga, it can provide detailed information related to that work. Also, if a user is interested in history and culture, it can provide information about the historical background and cultural significance related to the sacred site. Furthermore, if a user values ​​event information, it can provide detailed information on the latest events and how to participate. In this way, by adjusting the depth of information according to the user's interests, it becomes possible to provide more comprehensive information.

[0058] The interaction department not only facilitates interaction among fans but can also suggest interaction methods tailored to each user's style. For example, if a user prefers online interaction, online forums and chat functions can be enhanced. If a user prefers offline interaction, information about offline events can be prioritized. Furthermore, if a user prefers one-on-one interaction, individual interaction opportunities can be provided. In this way, by suggesting interaction methods according to each user's style, a more appropriate interaction experience can be provided.

[0059] The learning unit can analyze users' social media activity and reflect it in the training data. For example, it can analyze posts about visits to sacred sites that users have shared on social media and add relevant information to the training data. The learning unit can also reflect information about accounts that users follow and groups they participate in in the training data. Furthermore, the learning unit can include events and places that users have shown interest in on social media in the training data. This improves the accuracy of the training data by analyzing users' social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The learning unit learns the user's preferences and behavioral patterns. For example, it analyzes the user's past travel history and trends in destinations to understand their preferences. It can also learn the type of accommodation and food preferences the user prefers. Step 2: The recommendation unit recommends the optimal route and accommodation based on the information learned by the learning unit. For example, it proposes the optimal travel plan based on the user's preferences and behavioral patterns, and recommends the optimal accommodation according to the user's mode of transportation and budget. Step 3: The information department provides information specifically for pilgrimages to sacred sites. For example, it provides information on places to visit, events, and the history and culture related to the sacred sites. Step 4: The interaction section promotes interaction among fans. For example, it facilitates interaction among fans by providing online forums and chat functions, and by providing information on offline events.

[0062] (Example of form 2) The pilgrimage site navigation agent system 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 pilgrimage sites of their works. When a user inputs the pilgrimage sites they wish to visit, the AI ​​learns the user's preferences and behavioral patterns and recommends the optimal travel plan and accommodation. It also provides functions to provide information specifically tailored to pilgrimage sites and to promote interaction among fans. For example, if a user inputs "I want to visit specific pilgrimage sites," the AI ​​will suggest the optimal route and accommodation, and a community function to promote interaction with other fans is also available. This system is expected to improve visitor satisfaction, streamline pilgrimage visits, and contribute to the local economy. Specifically, the AI ​​recommends the optimal route and accommodation based on the user's past behavior and preferences, and provides a function to promote interaction with other users. This makes planning pilgrimages easier and allows users to enjoy interacting with other fans. Thus, the pilgrimage site navigation agent system can recommend the optimal route and accommodation based on the user's preferences and behavioral patterns, and promote information specifically tailored to pilgrimage sites and interaction among fans.

[0063] The pilgrimage navigation agent system according to this embodiment comprises a learning unit, a recommendation unit, an information provision unit, and an interaction unit. The learning unit learns the user's preferences and behavioral patterns. For example, the learning unit analyzes the user's past travel history and trends in visited destinations to understand the user's preferences. The learning unit can also learn the user's preferred type of accommodation and food preferences. For example, the learning unit collects data on tourist spots visited and accommodations used by the user in the past to identify the user's preferences. The recommendation unit recommends the optimal route and accommodation based on the information learned by the learning unit. For example, the recommendation unit proposes the optimal travel plan based on the user's preferences and behavioral patterns. The recommendation unit can also recommend the optimal accommodation according to the user's means of transportation and budget. For example, the recommendation unit proposes a route that efficiently visits the tourist spots the user likes and recommends accommodation that fits the budget. The information provision unit provides information specifically for pilgrimage. For example, the information provision unit provides information on places to visit and events. The information provision unit can also provide information on the history and culture related to the pilgrimage sites. For example, the information provision section provides detailed information about the sacred sites that the user plans to visit, offering helpful information for their visit. The interaction section has functions to promote interaction among fans. For example, the interaction section provides online forums and chat functions. The interaction section can also provide information about offline events and promote interaction among fans. For example, the interaction section provides community functions that allow users to interact with other fans, providing a place to share the enjoyment of pilgrimage. As a result, the pilgrimage navigation agent system according to this embodiment can recommend the optimal route and accommodation based on the user's preferences and behavioral patterns, and can promote the provision of information specifically for pilgrimage and interaction among fans.

[0064] The learning unit learns the user's preferences and behavioral patterns. Specifically, the learning unit analyzes the user's past travel history and destination trends in detail to understand their preferences. For example, it collects data on tourist spots the user has visited in the past to identify the types of places they prefer. It also analyzes data on accommodations the user has stayed at to learn their preferred types of accommodations and budget ranges. Furthermore, regarding the user's food preferences, it collects data on restaurants they have visited and dishes they have eaten to understand their preferred types of cuisine and dining styles. This allows the learning unit to understand the user's detailed preferences and behavioral patterns, providing the foundational data for making optimal suggestions to individual users. The learning unit can continuously update this data to adapt to changes in user preferences and behavioral patterns. For example, it collects data from visits to new travel destinations and stays at new accommodations to reflect the user's latest preferences. This allows the learning unit to always learn user preferences based on the latest information and make more accurate suggestions. In addition, the learning unit can collect user feedback to improve the accuracy of its learning algorithms. For example, users can rate suggested routes and accommodations, and this feedback is used to improve the learning algorithm and incorporate it into future suggestions. This allows the learning unit to accurately understand user preferences and behavioral patterns, providing the foundational data necessary to make optimal suggestions for each individual user.

[0065] The recommendation department recommends the optimal route and accommodation based on information learned by the learning department. Specifically, the recommendation department proposes the best travel plan based on the user's preferences and behavioral patterns. For example, it may suggest a route that efficiently visits the user's favorite tourist spots and recommend accommodation that fits their budget. The recommendation department can also recommend the best accommodation based on the user's mode of transportation and budget. For example, it may recommend accommodation with ample parking for users traveling by car and accommodation with good access for users using public transport. Furthermore, the recommendation department can propose travel plans based on specific themes, depending on the user's travel purpose and interests. For example, it may suggest a route that visits historical tourist spots for users interested in history and a route that includes restaurants where users can enjoy local specialties for users interested in gourmet food. In addition, the recommendation department can suggest the best route based on the user's travel schedule and length of stay. For example, it may suggest an efficient route that visits tourist spots for short trips and a route that allows for a more relaxed schedule for longer trips. In this way, the recommendation department can recommend the best route and accommodation based on the user's preferences and behavioral patterns, thereby improving the user's travel experience. Furthermore, the recommendation system can collect user feedback and improve the accuracy of its recommendation algorithms. For example, users can rate suggested routes and accommodations, and this feedback can be used to improve the recommendation algorithms and incorporate it into future suggestions. This ensures that the recommendation system can always provide users with the most up-to-date information and the best possible recommendations.

[0066] The Information Department provides information specifically tailored for pilgrimages to sacred sites. Specifically, it provides information on places to visit and events. For example, it provides detailed information about sacred sites that users plan to visit, offering helpful information for their visit. The Information Department can also provide information about the history and culture associated with sacred sites. For example, it provides information about the historical background and cultural significance of sacred sites, enabling users to understand the importance of the place when they visit. The Information Department can also provide information on events related to pilgrimages. For example, it provides information on festivals and special events held at sacred sites, enabling users to participate in them. Furthermore, the Information Department can provide the latest news and topics related to pilgrimages. For example, it provides information on the latest research findings and new tourist spots related to sacred sites, ensuring users stay up-to-date. In this way, the Information Department can provide users with information tailored to pilgrimages, improving their visit experience. In addition, the Information Department can collect user feedback and continuously improve the accuracy and content of the information it provides. For example, users can rate the information provided, and the content of the information provided will be improved based on that rating and reflected in future provision. This allows the information provision department to always provide users with the most up-to-date information.

[0067] The Community Section has functions to promote interaction among fans. Specifically, the Community Section provides online forums and chat functions. For example, it provides community functions that allow users to interact with other fans and provide a place to share the enjoyment of pilgrimage. The Community Section can also promote interaction among fans by providing information on offline events. For example, it provides information on offline events related to pilgrimage and allows users to participate in those events. Furthermore, the Community Section can also provide a platform where users can share their pilgrimage experiences. For example, it provides a function that allows users to post photos and impressions of the pilgrimage sites they have visited and share information with other users. In this way, the Community Section can promote interaction among users and provide a place to share the enjoyment of pilgrimage. In addition, the Community Section can collect user feedback and continuously improve the accuracy and content of the functions it provides. For example, users can rate the functions provided, and the functions will be improved based on that rating and reflected in the next version. In this way, the Community Section can always provide users with the best functions based on the latest information.

[0068] The learning unit can learn the user's past behavior and preferences. For example, the learning unit can analyze the user's past travel history to understand their visiting trends. The learning unit can also learn the user's preferred types of accommodation and food preferences. For example, the learning unit can collect data on tourist spots the user has visited and accommodations they have used in the past to identify the user's preferences. By learning the user's past behavior and preferences, more accurate recommendations become possible. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past behavior data into a generating AI and have the generating AI identify the user's preferences.

[0069] The recommendation unit can recommend the optimal route and accommodation based on the information learned by the learning unit. For example, the recommendation unit can propose the optimal travel plan based on the user's preferences and behavioral patterns. The recommendation unit can also recommend the optimal accommodation according to the user's mode of transportation and budget. For example, the recommendation unit can propose a route that efficiently visits the user's favorite tourist spots and recommend accommodation that fits the budget. This improves user satisfaction by recommending the optimal route and accommodation based on learned information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the information learned by the learning unit into a generating AI and have the generating AI perform the recommendation of the optimal route and accommodation.

[0070] The information provision department can provide information specifically tailored for pilgrimages to sacred sites. For example, it can provide information on places to visit and events. It can also provide information about the history and culture related to the sacred sites. For example, it can provide detailed information about the sacred sites that the user plans to visit and provide information that will be useful during the visit. By providing information specifically tailored for pilgrimages to sacred sites, the user's pilgrimage will be more fulfilling. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision department can input information about sacred sites into a generative AI and have the generative AI perform the information provision.

[0071] The interaction section can have functions to promote interaction among fans. For example, it can provide online forums and chat functions. It can also provide information on offline events and promote interaction among fans. For example, the interaction section can provide community functions that allow users to interact with other fans and provide a place to share the enjoyment of pilgrimage to sacred sites. This strengthens the connections between users by promoting interaction among fans. Some or all of the above processing in the interaction section may be performed using, for example, a generative AI, or not using a generative AI. For example, the interaction section can input user interaction data into a generative AI and have the generative AI perform the task of promoting interaction.

[0072] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is excited, the learning unit can prioritize including places that particularly moved the user among past sacred sites in its training data. If the user is depressed, the learning unit can also include relaxing accommodations and healing spots in its training data. Furthermore, if the user has neutral emotions, the learning unit can include balanced travel plans in its training data. This allows for the provision of more appropriate information by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0073] The learning unit can optimize its learning algorithm by referring to the user's past travel history during the learning process. For example, the learning unit can analyze the frequency of visits to sacred sites the user has visited in the past and prioritize learning the frequently visited locations. The learning unit can also refer to the user's past ratings of accommodations and include highly-rated facilities in the learning data. Furthermore, the learning unit can consider the season and weather conditions the user has visited in the past to learn the optimal time to visit. This improves the accuracy of the learning algorithm by referring to the user's past travel history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's past travel history data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0074] The learning unit can add dynamic learning capabilities to reflect changes in the user's interests in real time during the learning process. For example, if a user becomes interested in a new anime or manga, the learning unit can add locations related to that work to its learning data. Furthermore, if a user shows interest in a particular genre, the learning unit can include locations and events related to that genre in its learning data. In addition, the learning unit can update the learning data in real time when the user's interests change, reflecting the latest information. This allows the learning unit to provide the most up-to-date information by reflecting changes in the user's interests in real time. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user interest change data into a generative AI and have the generative AI execute the dynamic learning capabilities.

[0075] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is excited, the learning unit can increase the learning frequency to quickly provide the latest information. Conversely, if the user is depressed, the learning unit can decrease the learning frequency to reduce the burden of information provision. Furthermore, if the user has neutral emotions, the learning unit can update information at a normal learning frequency. This allows for the provision of more appropriate information by adjusting the learning frequency based on 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-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0076] The learning unit can analyze the user's social media activity during training and reflect it in the training data. For example, the learning unit can analyze posts about visits to sacred sites shared by the user on social media and add relevant information to the training data. The learning unit can also reflect information about accounts the user follows and groups the user participates in in the training data. Furthermore, the learning unit can include events and places the user has shown interest in on social media in the training data. This improves the accuracy of the training data by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the reflection of it in the training data.

[0077] The learning unit can optimize the training data by considering the user's geographical location information during training. For example, the learning unit can prioritize including sacred sites close to the user's current location in the training data. The learning unit can also reflect information about areas frequently visited by the user in the training data. Furthermore, the learning unit can include the optimal visiting route in the training data based on the user's geographical location. This improves the accuracy of the training data by considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location information data into a generative AI and have the generative AI perform the optimization of the training data.

[0078] The recommendation system can estimate the user's emotions and adjust recommendations based on those emotions. For example, if the user is excited, the recommendation system might recommend active events or popular pilgrimage sites. If the user is depressed, it might recommend relaxing accommodations or healing spots. Furthermore, if the user is emotionally neutral, it might recommend a balanced travel plan. By adjusting recommendations based on the user's emotions, more appropriate recommendations can be made. 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the recommendations.

[0079] The recommendation system can recommend the optimal route and accommodation by referring to the user's past travel history. For example, the recommendation system can analyze the frequency of visits to sacred sites the user has visited in the past and prioritize recommending frequently visited locations. It can also refer to the ratings of accommodations the user has used in the past and recommend highly-rated facilities. Furthermore, the recommendation system can consider the season and weather the user has visited in the past and recommend the optimal time to visit. In this way, by referring to the user's past travel history, the system can recommend the optimal route and accommodation. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the user's past travel history data into a generative AI and have the generative AI perform the recommendation of the optimal route and accommodation.

[0080] The recommendation system can add a dynamic recommendation function to reflect changes in the user's interests in real time. For example, if a user starts to take an interest in a new anime or manga, the recommendation system can recommend places related to that work. It can also recommend places and events related to a specific genre if the user shows interest in that genre. Furthermore, the recommendation system can update recommendations in real time when the user's interests change, reflecting the latest information. This allows the system to provide up-to-date information by reflecting changes in the user's interests in real time. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input user interest change data into a generative AI and have the generative AI execute the dynamic recommendation function.

[0081] The recommendation system can estimate the user's emotions and determine recommendation priorities based on those emotions. For example, if the user is excited, the recommendation system will prioritize recommending active events and popular pilgrimage sites. If the user is depressed, the recommendation system can prioritize recommending relaxing accommodations and healing spots. Furthermore, if the user is emotionally neutral, the recommendation system can prioritize recommending balanced travel plans. This allows for more appropriate recommendations by prioritizing recommendations based on 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI determine recommendation priorities.

[0082] The recommendation system can analyze a user's social media activity and reflect it in its recommendations. For example, it can analyze posts about pilgrimages shared by users on social media and add relevant information to its recommendations. It can also reflect information about accounts a user follows and groups they participate in. Furthermore, it can include events and places a user has shown interest in on social media in its recommendations. This improves the accuracy of recommendations by analyzing the user's social media activity. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input user social media activity data into a generative AI and have the generative AI perform the task of reflecting it in the recommendations.

[0083] The recommendation system can recommend the optimal route and accommodation by considering the user's geographical location. For example, the recommendation system can prioritize recommending sacred sites close to the user's current location. The recommendation system can also incorporate information about areas the user frequently visits into its recommendations. Furthermore, the recommendation system can recommend the optimal visiting route based on the user's geographical location. In this way, the recommendation system can recommend the optimal route and accommodation by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation system can input the user's geographical location data into a generative AI and have the generative AI perform the recommendation of the optimal route and accommodation.

[0084] The information provision unit can estimate the user's emotions and adjust the method of information provision based on the estimated emotions. For example, if the user is excited, the information provision unit may prioritize providing information on active events and popular pilgrimage sites. Similarly, if the user is depressed, the information provision unit may prioritize providing information on relaxing accommodations and healing spots. Furthermore, if the user is emotionally neutral, the information provision unit may provide balanced information. This allows for the provision of more appropriate information by adjusting the method of information provision based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information provision unit may be performed using AI or not. For example, the information provision unit can input user emotion data into a generative AI and have the generative AI adjust the method of information provision.

[0085] The information provision unit can provide optimal information by referring to the user's past travel history when providing information. For example, the information provision unit can analyze the frequency of visits to sacred sites that the user has visited in the past and prioritize providing information on frequently visited locations. The information provision unit can also refer to the user's past ratings of accommodations and provide information on highly-rated facilities. Furthermore, the information provision unit can consider the season and weather when the user has visited in the past and provide information on the optimal time to visit. In this way, optimal information can be provided by referring to the user's past travel history. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input the user's past travel history data into a generative AI and have the generative AI perform the task of providing optimal information.

[0086] The information provision unit can add a dynamic information provision function to reflect changes in the user's interests in real time when providing information. For example, if a user starts to take an interest in a new anime or manga, the information provision unit can provide information about sacred sites related to that work. The information provision unit can also provide information about sacred sites and events related to a particular genre if the user shows an interest in that genre. Furthermore, the information provision unit can update the information in real time when the user's interests change, providing the latest information. This allows the information provision unit to provide the latest information by reflecting changes in the user's interests in real time. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision unit can input data on changes in the user's interests into a generative AI and have the generative AI execute the dynamic information provision function.

[0087] The information provision unit can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is excited, the information provision unit can prioritize providing information on active events and popular pilgrimage sites. If the user is depressed, the information provision unit can also prioritize providing information on relaxing accommodations and healing spots. Furthermore, if the user is emotionally neutral, the information provision unit can provide balanced information. This allows for the provision of more appropriate information by prioritizing information provision based on 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 unit may be performed using AI, or not. For example, the information provision unit can input user emotion data into a generative AI and have the generative AI determine the priority of information provision.

[0088] The information provision department can analyze the user's social media activity and reflect it in the information provided. For example, the information provision department can analyze posts about visits to sacred sites shared by users on social media and provide relevant information. The information provision department can also reflect information about accounts that users follow and groups they participate in in the information provided. Furthermore, the information provision department can provide information about events and places that users have shown interest in on social media. This improves the accuracy of the information provided by analyzing the user's social media activity. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision department can input the user's social media activity data into a generative AI and have the generative AI perform the task of reflecting it in the information provided.

[0089] 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 reflect information about areas frequently visited by the user in the content provided. Furthermore, the information provision unit can provide information on the optimal visiting route based on the user's geographical location. In this way, optimal information can be provided by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing optimal information.

[0090] The interaction unit can estimate the user's emotions and adjust the method of interaction based on the estimated emotions. For example, if the user is excited, the interaction unit can suggest events that promote active interaction with other fans. If the user is depressed, the interaction unit can also suggest relaxing interaction methods or healing communities. Furthermore, if the user is feeling neutral, the interaction unit can suggest balanced interaction methods. This allows for more appropriate interaction by adjusting the method of interaction based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 unit may be performed using AI or not using AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI adjust the method of interaction.

[0091] The interaction unit can provide the optimal interaction method by referring to the user's past interaction history during interaction. For example, the interaction unit can refer to the user's past participation history in events and communities and suggest relevant interaction methods. The interaction unit can also prioritize providing interaction methods that the user has preferred in the past. Furthermore, the interaction unit can suggest the optimal timing for interaction based on the user's past interaction history. In this way, the optimal interaction method can be provided by referring to the user's past interaction history. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the interaction unit can input the user's past interaction history data into a generative AI and have the generative AI perform the task of providing the optimal interaction method.

[0092] The interaction unit can add dynamic interaction features to reflect changes in the user's interests in real time during interactions. For example, if a user starts to show interest in a new anime or manga, the interaction unit can suggest interaction events related to that work. The interaction unit can also suggest communities related to a specific genre if the user shows interest in that genre. Furthermore, the interaction unit can update interaction methods in real time when the user's interests change, providing the latest information. This allows for the provision of up-to-date information by reflecting changes in the user's interests in real time. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or not. For example, the interaction unit can input user interest change data into a generative AI and have the generative AI execute the dynamic interaction features.

[0093] The interaction unit can estimate the user's emotions and determine the priority of interactions based on those emotions. For example, if the user is excited, the interaction unit will prioritize suggesting active events and popular communities. If the user is depressed, the interaction unit can also prioritize suggesting relaxing interactions and comforting communities. Furthermore, if the user is emotionally neutral, the interaction unit can prioritize suggesting balanced interactions. This allows for more appropriate interactions by prioritizing interactions based on 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 and have the generative AI determine the priority of interactions.

[0094] The interaction unit can analyze a user's social media activity during interaction and reflect it in the interaction content. For example, the interaction unit can analyze posts about interaction events that a user has shared on social media and provide relevant information. It can also reflect information about accounts that a user follows and groups that they participate in in the interaction content. Furthermore, the interaction unit can provide information about events and communities that a user has shown interest in on social media. This improves the accuracy of the 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, for example, a generative AI, or not using a generative AI. For example, the interaction unit can input user social media activity data into a generative AI and have the generative AI perform the reflection of that data in the interaction content.

[0095] The interaction unit can provide the optimal interaction method during interaction, taking into account the user's geographical location information. For example, the interaction unit can prioritize suggesting interaction events close to the user's current location. The interaction unit can also incorporate information about areas the user frequently visits into the interaction content. Furthermore, the interaction unit can provide the optimal interaction method based on the user's geographical location. This allows the interaction unit to provide the optimal interaction method by considering the user's geographical location information. Some or all of the above processing in the interaction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interaction unit can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing the optimal interaction method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The learning unit can not only learn user preferences and behavioral patterns, but also optimize the learning data by considering the user's health condition. For example, if a user is linked to a health management app, the learning unit can refer to that data and suggest a travel plan tailored to the user's physical condition. For instance, if a user tires easily, the learning data can include plans with shorter travel distances and more rest time. Furthermore, if a user has specific dietary restrictions, the learning data can include accommodations and restaurants that accommodate those restrictions. Additionally, if a user has allergies, the system can provide a safer travel plan by taking that information into account. In this way, optimizing the learning data based on the user's health condition can provide a safer and more comfortable travel experience.

[0098] The recommendation system not only suggests optimal routes and accommodations based on user preferences and behavioral patterns, but can also tailor recommendations according to the user's travel purpose. For example, if a user's purpose is relaxation, it can recommend accommodations in quiet environments and relaxation facilities. If a user wants to enjoy activities, it can recommend active events and adventure spots. Furthermore, if a user values ​​cultural experiences, it can recommend spots where they can experience local culture and history. By tailoring recommendations according to the user's travel purpose, it can provide a more satisfying travel experience.

[0099] The information provision department not only provides information specifically for pilgrimage to sacred sites, but can also adjust the depth of information according to the user's interests. For example, if a user has a strong interest in a particular anime or manga, it can provide detailed information related to that work. Also, if a user is interested in history and culture, it can provide information about the historical background and cultural significance related to the sacred site. Furthermore, if a user values ​​event information, it can provide detailed information on the latest events and how to participate. In this way, by adjusting the depth of information according to the user's interests, it becomes possible to provide more comprehensive information.

[0100] The interaction department not only facilitates interaction among fans but can also suggest interaction methods tailored to each user's style. For example, if a user prefers online interaction, online forums and chat functions can be enhanced. If a user prefers offline interaction, information about offline events can be prioritized. Furthermore, if a user prefers one-on-one interaction, individual interaction opportunities can be provided. In this way, by suggesting interaction methods according to each user's style, a more appropriate interaction experience can be provided.

[0101] The learning unit can estimate the user's emotions and adjust the priority of the training data based on those emotions. For example, if the user is excited, it can prioritize including places that particularly moved them among the sacred sites they have visited in the past in its training data. If the user is depressed, it can also include relaxing accommodations and healing spots in its training data. Furthermore, if the user is emotionally neutral, it can include balanced travel plans in its training data. By adjusting the priority of the training data based on the user's emotions, it can provide more relevant information.

[0102] The recommendation system can estimate the user's emotions and adjust recommendations based on those emotions. For example, if the user is excited, it can recommend active events or popular pilgrimage sites. If the user is depressed, it can recommend relaxing accommodations or healing spots. Furthermore, if the user is emotionally neutral, it can recommend a balanced travel plan. By adjusting recommendations based on the user's emotions, it becomes possible to provide more appropriate recommendations.

[0103] The information provision department can estimate the user's emotions and adjust the method of information provision based on those estimates. For example, if the user is excited, it can prioritize providing information on active events and popular pilgrimage sites. If the user is depressed, it can prioritize providing information on relaxing accommodations and healing spots. Furthermore, if the user is emotionally neutral, it can provide balanced information. In this way, by adjusting the method of information provision based on the user's emotions, more appropriate information can be provided.

[0104] The interaction function can estimate the user's emotions and adjust the interaction method based on those emotions. For example, if the user is excited, it can suggest events that promote active interaction with other fans. If the user is depressed, the interaction function can also suggest relaxing interaction methods or healing communities. Furthermore, if the user is feeling neutral, the interaction function can suggest balanced interaction methods. By adjusting the interaction method based on the user's emotions, more appropriate interactions become possible.

[0105] The interaction function can estimate the user's emotions and prioritize interactions based on those emotions. For example, if the user is excited, it will prioritize suggesting active events and popular communities. If the user is depressed, it can prioritize suggesting relaxing interactions and comforting communities. Furthermore, if the user is emotionally neutral, it can prioritize suggesting balanced interactions. This allows for more appropriate interactions by prioritizing interactions based on the user's emotions.

[0106] The learning unit can analyze users' social media activity and reflect it in the training data. For example, it can analyze posts about visits to sacred sites that users have shared on social media and add relevant information to the training data. The learning unit can also reflect information about accounts that users follow and groups they participate in in the training data. Furthermore, the learning unit can include events and places that users have shown interest in on social media in the training data. This improves the accuracy of the training data by analyzing users' social media activity.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The learning unit learns the user's preferences and behavioral patterns. For example, it analyzes the user's past travel history and trends in destinations to understand their preferences. It can also learn the type of accommodation and food preferences the user prefers. Step 2: The recommendation unit recommends the optimal route and accommodation based on the information learned by the learning unit. For example, it proposes the optimal travel plan based on the user's preferences and behavioral patterns, and recommends the optimal accommodation according to the user's mode of transportation and budget. Step 3: The information department provides information specifically for pilgrimages to sacred sites. For example, it provides information on places to visit, events, and the history and culture related to the sacred sites. Step 4: The interaction section promotes interaction among fans. For example, it facilitates interaction among fans by providing online forums and chat functions, and by providing information on offline events.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the learning unit, recommendation unit, information provision unit, and interaction unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the learning unit learns the user's preferences and behavioral patterns using the control unit 46A of the smart device 14. The recommendation unit recommends the optimal route and accommodation using the specific processing unit 290 of the data processing unit 12. The information provision unit provides information specifically for pilgrimage using the output device 40 of the smart device 14. The interaction unit facilitates interaction among fans via the communication I / F 44 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the learning unit, recommendation unit, information provision unit, and interaction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit learns the user's preferences and behavioral patterns using the control unit 46A of the smart glasses 214. The recommendation unit recommends the optimal route and accommodation using the identification processing unit 290 of the data processing unit 12. The information provision unit provides information specifically for pilgrimage using the speaker 240 of the smart glasses 214. The interaction unit facilitates interaction among fans via the communication I / F 44 of the smart glasses 214. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the learning unit, recommendation unit, information provision unit, and interaction unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit learns the user's preferences and behavioral patterns using the control unit 46A of the headset terminal 314. The recommendation unit recommends the optimal route and accommodation using the specific processing unit 290 of the data processing unit 12. The information provision unit provides information specifically for pilgrimage using the display 343 of the headset terminal 314. The interaction unit facilitates interaction among fans via the communication I / F 44 of the headset terminal 314. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the learning unit, recommendation unit, information provision unit, and interaction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the learning unit learns the user's preferences and behavioral patterns using the control unit 46A of the robot 414. The recommendation unit recommends the optimal route and accommodation using the specific processing unit 290 of the data processing unit 12. The information provision unit provides information specifically for pilgrimage using the speaker 240 of the robot 414. The interaction unit facilitates interaction among fans via the communication I / F 44 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A learning unit that learns user preferences and behavioral patterns, Based on the information learned by the aforementioned learning unit, the recommendation unit recommends the optimal route and accommodation, 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) The aforementioned learning unit, Learn the user's past behavior and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recommendation department, Based on the information learned by the aforementioned learning unit, the optimal route and accommodation are recommended. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information provision unit, Providing information specifically for pilgrimage to sacred sites. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned AC unit is Features that promote interaction among fans The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During training, the learning algorithm is optimized by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During the learning process, we will add a dynamic learning feature that reflects changes in the user's interests in real time. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, the system analyzes users' social media activity and incorporates it into the training data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During training, the training data is optimized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, It estimates the user's emotions and adjusts recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, When making recommendations, the system refers to the user's past travel history to suggest the best route and accommodation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, We will add a dynamic recommendation feature to reflect changes in user interests in real time during the recommendation process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, When making recommendations, we analyze the user's social media activity and reflect it in the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, the system takes the user's geographical location into consideration to suggest the best route and accommodation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, When providing information, we refer to the user's past travel history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, We will add a dynamic information delivery function to reflect changes in user interests in real time when providing information. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned information provision unit, When providing information, we analyze users' social media activity and reflect it in the content of the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) 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 25) 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 26) The aforementioned AC unit is Add a dynamic interaction feature to reflect changes in user interests in real time during interactions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned AC unit is It estimates the user's emotions and determines the priority of interactions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned AC unit is During interactions, the system analyzes users' social media activity and reflects this in the content of the interactions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned AC unit is During interactions, the system provides the optimal interaction method by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns user preferences and behavioral patterns, Based on the information learned by the aforementioned learning unit, the recommendation unit recommends the optimal route and accommodation, 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. The aforementioned learning unit, Learn the user's past behavior and preferences. The system according to feature 1.

3. The aforementioned recommendation department, Based on the information learned by the aforementioned learning unit, the optimal route and accommodation are recommended. The system according to feature 1.

4. The aforementioned information provision unit, Providing information specifically for pilgrimage to sacred sites. The system according to feature 1.

5. The aforementioned AC unit is Features that promote interaction among fans The system according to feature 1.

6. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

7. The aforementioned learning unit, During training, the learning algorithm is optimized by referencing the user's past travel history. The system according to feature 1.

8. The aforementioned learning unit, During the learning process, we will add a dynamic learning feature that reflects changes in the user's interests in real time. The system according to feature 1.

9. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system according to feature 1.

10. The aforementioned learning unit, During training, the system analyzes users' social media activity and incorporates it into the training data. The system according to feature 1.

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